Biomass Resource Mapping in Vietnam FINAL REPORT ON BIOMASS ATLAS FOR VIETNAM AUGUST 2018 This report was prepared by Full Advantage, Simosol, Institute of Energy and Enerteam, under contract to The World Bank. It is one of several outputs from the biomass resource mapping component of the activity “Renewable Energy Resource Mapping and Geospatial Planning – Vietnam” [Project ID: P145513]. This activity is funded and supported by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank, under a global initiative on Renewable Energy Resource Mapping. Further details on the initiative can be obtained from the ESMAP website. This document is an interim output from the above-mentioned project. Users are strongly advised to exercise caution when utilizing the information and data contained, as this has not been subject to full peer review. The final, validated, peer reviewed output from this project will be the Vietnam Biomass Atlas, which will be published once the project is completed. Copyright © 2018 International Bank for Reconstruction and Development / THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org This work is a product of the consultants listed, and not of World Bank staff. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work and accept no responsibility for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: +1-202-522-2625; e-mail: pubrights@worldbank.org. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above, or to esmap@worldbank.org. BIOMASS RESOURCE MAPPING IN VIETNAM FINAL REPORT ON BIOMASS ATLAS FOR VIETNAM Prepared by: Full Advantage Co., Ltd. (FA), Thailand (Lead Consultant) Simosol Oy and partners from Finland Institute of Energy, Vietnam Energy Conservation Research and Development Center, Vietnam Date: 31 August 2018 Page 1 Country: Vietnam Project title and ID: Renewable Energy Resource Mapping: Biomass [Phases 1-3] - Vietnam Project ID: P145513 Implementing agency: The World Bank (Vietnam) in close coordination with the General Department of Energy (GDE) under the Ministry of Industry and Trade (MOIT) of Vietnam The Consultant Consortium: Full Advantage Co., Ltd., Thailand (Lead Consultant) Dr. Ludovic Lacrosse, Team Leader/Biomass Expert Dr. Tran Quang Cu, Training & Field Survey Monitoring Coordinator Mr. Bienvenido Anatan, Project Coordinator Ms. Anongnuch Tabklam, Administrative Support Simosol Oy, Finland Dr. Jussi Rasinmäki, Remote Sensing/GIS Expert Dr. Antti Mäkinen, Geospatial Energy Planning Expert Dr. Jussi Kollin, IT / Database Expert Dr. Jussi-Pekka Aittola, Biomass to Energy Conversion Planning Expert VTT Technical Research Center of Finland Mr. Heikki Astola, Remote Sensing Expert Dr. Yrjo Rauste, Radar Remote Sensing Expert MHG Systems, Finland Mr. Seppo Huurinainen, Biomass Field Survey Expert Wiltrain Oy, Finland Mr. Jorma Meronen, Biomass/Biogas/W2E Expert PITCO Pvt., Ltd., Pakistan Mr. Qazi Sabir, Field Biomass Survey Expert Institute of Energy, Vietnam Mr. Nguyen Duc Cuong, Local Project Coordinator Mr. Vu Ngoc Duc, Local Biomass Expert Ms. Dang Huong Giang, Local Event and Field Survey Monitoring Expert Energy Conservation Research and Development Center, Vietnam Mr. Tiet Vinh Phuc, Local Project Coordinator Dr. Phan Hieu Hien, Local Biomass Expert Ms. Tran Thi Yen Phuong, Local Event and Field Survey Monitoring Expert Date of report: 31 August 2018 Page 2 TABLE OF CONTENTS 1. Executive Summary.......................................................................................................... 8 2. Introduction..................................................................................................................... 10 3. Project Outputs and Deliverables ................................................................................. 11 3.1 Expected Outputs of the Project .......................................................................................................... 11 3.2 Summary of Achievements vs Expected Outputs ............................................................................. 11 4. Vietnam Biomass Atlas .................................................................................................. 14 4.1 Crop Biomass Feedstock Potential....................................................................................................... 14 4.2 Greenfield Power Plant Potential.......................................................................................................... 27 4.3 Electricity Generation Potential at Biomass Producing Sites ......................................................... 32 4.3.1 Sugar Mills.......................................................................................................................................... 32 4.3.2 Rice Mills ............................................................................................................................................ 38 4.3.3 MSW Landfills ................................................................................................................................... 43 4.3.4 Livestock Farms ............................................................................................................................... 47 4.3.5 Wood Processing Mills................................................................................................................... 50 5. Conclusions and Recommendations ............................................................................. 53 5.1 Conclusions ................................................................................................................................................ 53 5.2 Recommendations .................................................................................................................................... 54 6. Annexes ........................................................................................................................... 56 Annex 1: Biomass Resource Mapping Methodology ............................................................................... 56 Annex 2: Electricity generation potential at the surveyed biomass producing sites ....................... 81 Annex 3: Biomass Atlas Components ........................................................................................................ 96 3.1 Survey Data .......................................................................................................................................... 96 3.2 Land Use Classification ...................................................................................................................... 96 3.3 Biomass Feedstock Data ................................................................................................................... 96 3.4 Power Plant Analysis Data ................................................................................................................ 97 3.5 Greenfield site suitability analysis data........................................................................................... 98 3.6 Biomass Atlas training data ............................................................................................................... 99 Annex 4: Instructions to the Vietnam Biomass Atlas Usage .............................................................. 100 Annex 5: Instructions to the Vietnam Biomass Atlas Maintenance .................................................. 126 Page 3 LIST OF TABLES Table 1: Summary of Achievements vs Expected Outputs ............................................................................. 12 Table 2: Residue to crop ratios used for the atlas ........................................................................................... 17 Table 3: Lower heating values of different biomass residues ......................................................................... 18 Table 4: Country-level annual theoretical potential of crop harvesting residues ..................................... 18 Table 5: Country-level annual theoretical potential of crop processing residues .................................... 19 Table 6: Technical potential of crop harvesting residues based on their existing uses ........................... 21 Table 7: Technical potential of crop harvesting residues based on their existing uses and farmers' willingness to sell ...................................................................................................................................................... 21 Table 8. The mean annual potential with 95% confidence interval for different types of crop residues for the sampled 504 districts ................................................................................................................................. 24 Table 9: Analyzed combinations of power plant technologies and capacities............................................ 28 Table 10: Summarized results of the analysis of the 40 surveyed sugar mills ............................................ 35 Table 11: Summarized results of the analysis of the 54 surveyed rice mills ............................................... 41 Table 12: Summarized results of the analysis of the 38 surveyed MSW landfills ...................................... 45 Table 13: Summarized results of the analysis of the 67 surveyed livestock farms .................................... 48 Table 14: Summarized results of the analysis of the 40 surveyed wood processing mills ...................... 50 Table 15: List of meetings and workshops conducted..................................................................................... 56 Table 16: Summary of number of districts surveyed, farmers interviewed and datasets accepted ...... 59 Table 17: Summary of the accepted datasets by industrial sector ................................................................ 63 Table 18: The date ranges for 24 Sentinel-1 image sets used in land use classification........................... 66 Table 19: The 52 land use classes actually used in the classification ............................................................ 69 Table 20: Electricity generation potential at the surveyed sugar mills (the milling season 2016-17) ... 82 Table 21: Electricity generation potential at the surveyed rice mills (the milling season 2016-17)...... 84 Table 22: Electricity generation potential at the surveyed landfills .............................................................. 87 Table 23: Electricity generation potential at the surveyed livestock farms ................................................ 89 Table 24: Electricity generation potential at the surveyed wood processing mills ................................... 93 Table 25: Links for access to the results of survey data ................................................................................. 96 Table 26: Links for access to the results of land use classification ............................................................... 96 Table 27: Links for accessing the maps and datasets for the theoretical potential of crop harvesting residues ....................................................................................................................................................................... 96 Table 28: Links for access to the maps and datasets of the technical potential of crop harvesting residues ....................................................................................................................................................................... 97 Table 29: Links for access to the results of site suitability analysis of sugar mills ..................................... 97 Table 30: Links for access to the results of site suitability analysis of rice mills ....................................... 97 Table 31: Links for access to the results of site suitability analysis of MSW landfills ............................... 98 Table 32: Links for access to the results of site suitability analysis of livestock farms ............................ 98 Table 33: Links for access to the results of site suitability analysis of wood processing mills ............... 98 Table 34: Links for access to the results of site suitability analysis .............................................................. 98 Table 35: Links for access to the Biomass Atlas training data ....................................................................... 99 Table 36: Requirements for training on Biomass Atlas Usage .................................................................... 100 Table 37: Requirements for generating the Biomass Atlas with the Biomass Atlas model ................. 127 Page 4 LIST OF FIGURES Figure 1: Agro-Ecological Zones of mainland Vietnam .................................................................................... 15 Figure 2: Theoretical potential of crop residues, including both harvesting and processing residues for all crops ................................................................................................................................................................ 20 Figure 3: Technical potential of crop residues based on the existing uses of crop harvesting residues ....................................................................................................................................................................................... 22 Figure 4: Technical potential of crop residues based on the existing uses and farmers' willingness to sell crop harvesting residues .................................................................................................................................. 23 Figure 5. Distribution of the 504 districts targeted by the field survey ...................................................... 26 Figure 6: Site suitability indicator map for 3 MW power plants with grate steam boiler ....................... 29 Figure 7: Site suitability indicator map for 15 MW power plants with BFB steam boiler ....................... 30 Figure 8: Site suitability indicator map for 25 MW power plants with CFB steam boiler ...................... 31 Figure 9: Map of potential high-pressure cogeneration plants at the 40 surveyed sugar mills .............. 37 Figure 10: Map of potential power plants at the 54 surveyed rice mills in Vietnam ................................ 42 Figure 11: Map of potential power plants at the 38 surveyed MSW landfills in Vietnam ........................ 46 Figure 12: Map of potential power plants at the 67 surveyed livestock farms in Vietnam ..................... 49 Figure 13: Map of potential power plants at the 40 surveyed wood processing mills in Vietnam ........ 52 Figure 14: A reference field sample that was included into land use classification reference sample data set ........................................................................................................................................................................ 58 Figure 15: An example of a rejected reference field sample due to having been recorded in the middle of a road leaving uncertainty for the actual location of the field. .................................................... 58 Figure 16: Locations of farms with collected datasets accepted ................................................................... 61 Figure 17: Map of the surveyed industrial sites ................................................................................................. 64 Figure 18: 24 Sentinel-1 image tile sets used in the analysis........................................................................... 66 Figure 19: Land cover classification areas used in the analysis ...................................................................... 67 Figure 20: MONRE land use dataset for the 33 southern provinces showing non-agricultural areas used as classification mask. ..................................................................................................................................... 70 Figure 21: The land use classification result in the Central Highlands. The number of validation samples for the class is in brackets....................................................................................................................... 71 Figure 22: The land use classification result in the South-East AEZ ............................................................. 72 Figure 23: The land use classification accuracy results for six northernmost regions ............................. 73 Figure 24: The land use classification accuracy results for seven southernmost regions ....................... 74 Figure 25: Components of the first atlas and harvest residue feedstock available at farm level ........... 75 Figure 26: Steps to create the industrial scale power generation potential atlas ..................................... 76 Figure 27: The modeling principle for the different site suitability factors. ................................................ 77 Figure 28: Road and watercourse network data used in the analysis .......................................................... 78 Figure 29: Grid stations, and the computed grid station distance index..................................................... 79 Page 5 LIST OF ACRONYMS AEZ Agro-Ecological Zone AHAV Animal Husbandry Association of Vietnam CTU Can Tho University DARD Department of Agriculture and Rural Development (of the provinces) DOIT Department of Industry and Trade (of provinces) DONRE Department of Natural Resources and Environment (of provinces) ENERTEAM Energy Conservation Research and Development Center (Vietnam) ESA European Space Agency ESMAP Energy Sector Management Assistance Program FA Full Advantage Co., Ltd. (Thailand) FAO Food and Agriculture Organization GDE General Department of Energy (under MOIT) GIZ Gesellschaft fur Internationale Zusammenarbeit (Germany) GIS Geographic Information System GOV Government of Vietnam HUST Hanoi University of Science and Technology IE Institute of Energy (Vietnam) M&E Monitoring and Evaluation MOIT Ministry of Industry and Trade MONRE Ministry of Natural Resources and Environment MSW Municipal Solid Waste NFIS National Forest Inventory and Statistics NLU Nong Lam University NPDP National Power Development Plan PCF Power Capacity Factor PITCO PITCO Private Limited (Pakistan) RE Renewable Energy REDP Renewable Energy Development Project RERM Renewable Energy Resource Mapping SAR Synthetic Aperture Radar S-1 Sentinel-1 SNV SNV Netherland Development Organization TOR Terms of Reference USTH University of Science and Technology of Hanoi VDA Vietnam Diary Association VFU Vietnam Forestry University VNUA Vietnam National University of Agriculture VSSA Vietnam Sugar and Sugarcane Association WB World Bank Page 6 UNITS MW Megawatt GW Gigawatt MWh Megawatt-hour GWh Gigawatt-hour MJ Megajoule GJ Gigajoule TJ Terajoule MWhth Megawatt-hour thermal GWhth Gigawatt-hour thermal kg kilogram m meter km kilometer ha hectare Page 7 1. EXECUTIVE SUMMARY The present report is the Final Report on the Biomass Resource Assessment Study for Vietnam. The report summaries the achievements of the study and presents the Biomass Atlas for Vietnam as its final product. Overall Achievements All the expected outputs of the study were achieved as per the TOR. of the Study The outputs/deliverables of the project can be accessed at https://esmap.org/re_mapping_vietnam. Biomass Atlas for The Biomass Atlas consists of raw survey data, the atlas datasets and Vietnam the maps. The Biomass Atlas contains two sections: the first one related to biomass feedstock availability and the second one related to the potential use of the biomass feedstock for energy generation. The residues of 18 crops were included in the Biomass Atlas. The crop residues are divided into two categories: crop harvesting residues and crop processing residues. Crop harvesting residues are generated in the field during crop harvesting activities while crop processing residues are produced during crop processing operations at agro-industrial sites. Both theoretical and technical potentials of crop residues were assessed. The theoretical biomass feedstock potential was estimated at about 59.89 million tonnes/year with an energy potential of 768,853 TJ/year (213,570 GWhth/year) for crop harvesting residues and 20.86 million tonnes/year with an energy potential of 245,094 TJ/year (68,082 GWhth/year) for crop processing residues. Based on the existing uses of the residues, the technical potential of crop harvesting residues was estimated at about 15.22 million tonnes/year with an energy potential of 195,773 TJ/year (54,381 GWhth/year). If the farmers' willingness to sell their biomass residues is taken into consideration, the technical potential of crop harvesting residues decreases to about 7.95 million tonnes/year with an energy potential of 101,068 TJ/year (28,075 GWhth/year). The analysis of the electricity generation potential at the biomass producing sites shows that bagasse offers the highest potential via their use as fuel in cogeneration plants. The total power capacity output of the cogeneration plants using bagasse generated from the 40 existing sugar mills in 2016-2017 milling season in Vietnam is estimated at about 600 MW. Municipal Solid Wastes (MSW) can also be used in large-scale grid-connected power plants with a combined installed power capacity of 130 MW. However, rice husk, wood residues and livestock manure seem to offer a limited energy potential which is limited to captive power plants that generate electricity to cover the power requirements of the rice mills, wood processing mills or livestock farms. It should be noted that the analysis does not cover Page 8 all the existing MSW landfills, rice mills, wood processing mills and livestock farms in Vietnam due to limited resources for carrying out an exhaustive survey. The potential for greenfield power plants using crop harvesting residues was assessed based on their site suitability indicators. These site suitability indicators take into account the feedstock sourcing area size, the transport network density in the region, and the distance to a grid. A high site suitability value indicates a good site for a potential power plant, whereas a low value indicates a poor location. The site suitability maps were produced for 18 different combinations of energy conversion technologies and power plant capacities. Information During the process of the study, several seminars, workshops and Dissemination and trainings were conducted to present the study objectives or to Capacity Building disseminate the study results to the local stakeholders and to build their capacity in usage and maintenance of the Biomass Atlas. Seven (7) multi-stakeholder seminars and workshops were conducted. These events attracted a total of 178 participants. In addition, several individual meetings with local institutions and companies were organized during the missions of the consultants to Vietnam. Key Lessons Learned The key lessons learned can be summarized as follows: • The field survey and collection of the data is a hard, time- consuming exercise. It needs to be well planned and excellently coordinated; • The use of universities specialized in agriculture (i.e. NLU and VNUA) was key to the success of the field survey; • Comprehensive training of enumerators is essential. Each enumerator should conduct at least 5 test surveys to make sure that he/she is familiar with the Survey App on smartphone, questionnaires and develops interview skills. • For remote areas where people do not speak Kinh language, a translator is needed. • The involvement of local agriculture officers in the field surveys was essential to facilitate the contact with farmers; • Good knowledge of the biomass producers and consumers by the local consultants is necessary to facilitate the industrial surveys; • A well-designed and continuous data validation process helped the international consultants (Simosol and FA) to immediately check and correct any erroneous data; • The constructive feedback received from local stakeholders during the seminars/workshops was essential to finalize the production of a most appropriate Biomass Atlas for Vietnam. Page 9 2. INTRODUCTION According to the "National Power Development Plan (NPDP) for the period of 2011-2020 with an outlook to 2030" (referred to as PDP VII)1, the Government of Vietnam (GOV) has set a national target for increasing the total amount of power generation and import from about 19,500 MW in 2010 to 75,000 MW and 146,800 MW by 2020 and 2030, respectively. The total electricity generation and import is expected to be 330 billion kWh in 2020 and 695 billion kWh in 2030. The amount of electricity generated from renewable energy (RE) sources would be around 42 billion kWh in 2030, accounting for 6% of the total amount of electricity generation and import. The revised NPDP (PDP VII-revised) promulgated by the Prime Minister of Vietnam in 20162 has reduced the total electricity demand projection for 2030 from 695 to 572 TWh/year. However, the target for electricity generation from RE sources (excluding large-scale and pumped-storage hydropower plants) was increased from 42 TWh/year to around 61 TWh/year, making up 10.7% of the total electricity generated and imported in 2030. Solar power will account for 3.3% of the total electricity generation and import, followed by small hydropower (3.2%), wind power (2.1%), and biomass and MSW (2.1%). In order to attain such ambitious targets, the GOV has endeavored to exploit various sources of power generation and supply: fossil fuels (coal and gas), hydropower, nuclear power, RE and imported power. As Vietnam has a huge potential of RE resources, the GOV has set a goal to increase the total installed power capacity of RE sources from around 2,400 MW in 2015 to 23,350 MW in 2030. The installed power capacity is expected to be 6,000 MW for wind power, 12,000 MW for solar power, 3,350 MW for small hydro power (with a capacity below 30 MW), and 2,000 MW for biomass (including MSW) by 2030.3 The Ministry of Industry and Trade (MOIT) is implementing the Renewable Energy Development Project (REDP) funded by the World Bank. The objective of the REDP is to increase the supply of electricity to the national grid from RE sources on a commercially, environmentally and socially sustainable basis. The REDP has three components: (i) Investment Implementation; (ii) Regulatory Development and (iii) Project Pipeline Development. MOIT is implementing several technical assistance activities to strengthen the capacity of government agencies and stakeholders for exploiting the sizable RE resources of Vietnam. In addition to studies on supporting mechanisms for development of RE and cumulative impact assessment for cascade hydropower projects, MOIT has requested the assistance of the World Bank for a Renewable Energy Resource Mapping (RERM) project, with funding from the Energy Sector Management Assistance Program (ESMAP), a global knowledge and technical assistance program administered by the WB and supported by eleven bilateral donors. The development objective of this project is to increase the output and diversity of renewable electricity generation in Vietnam. The outcome objective is to improve the awareness of the government and the private sector of the resource potential for biomass, small hydropower, and wind, and providing the government with a spatial planning framework to guide commercial investments. 1 Decision 37/2011/QD-TTg dated 14 June 2011 of the Prime Minister of Vietnam 2 Decision 428/QD-TTg dated 18 March 2016 of the Prime Minister of Vietnam 3 Vietnam: Energy sector assessment, strategy, and road map. Asian Development Bank, December 2015. Page 10 Under this RERM project, the World Bank has contracted a Consortium of consultants led by Full Advantage Co., Ltd. (FA) to develop a Biomass Atlas for Vietnam (hereafter called “FA Consortium”). The FA Consortium involves several Finnish companies led by Simosol Oy, and two local partners: the Institute of Energy (IE) and the Energy Conservation Research and Development Center (ENERTEAM). The Vietnam National University of Agriculture (VNUA) and Nong Lam University (NLU) were contracted by MOIT to conduct the field survey and data collection on crop and industrial biomass residues. The overall objective of this biomass resource mapping project is to support the sustainable expansion of electricity generation from biomass by providing the national government, provincial authorities and commercial developers in Vietnam with an improved understanding of the location and potential of biomass resources. The specific objective is to support RE mapping and geospatial planning for biomass resources in Vietnam. The project consists of three phases: • Phase 1: Project inception, team building, data source identification, preparation of terms of reference (TOR) for field survey and data collection, and implementation planning; • Phase 2: Data collection/analysis and creation of draft Biomass Atlas; • Phase 3: Production and publication of a validated Final Biomass Atlas for Vietnam. 3. PROJECT OUTPUTS AND DELIVERABLES 3.1 Expected Outputs of the Project According to the Terms of Reference (TOR), the expected outputs of the project include: Phase 1: • Conduct of inception meetings, identification and assessment of existing data sources needed for the project, and team building; • Development of an Implementation Plan for Phase 2; • Preparation of the TOR for field survey and data collection to be conducted by the local contractors hired by MOIT; • Conduct of Phase 1 Workshop Phase 2: • Conduct of remote data collection and analysis; • Conduct of a training workshop on field survey and data collection; • Support and validation of the data collected by the local survey contractors; • Acquisition of GIS data of other driving components • Conduct of data analysis and development of draft biomass atlas; • Conduct of stakeholder data validation workshop. Phase 3: • Production of final Biomass Atlas for Vietnam; • Conduct of workshops to disseminate the Biomass Atlas; • Conduct of trainings for local stakeholders in using and updating the Biomass Atlas. 3.2 Summary of Achievements vs Expected Outputs The expected outputs and the summary of achievements of the project are presented in Table 1. Page 11 Table 1: Summary of Achievements vs Expected Outputs Activity Expected outputs Achievements PHASE 1: Conduct of inception meetings, • Inception meetings conducted • A kick-off meeting with WB and MOIT was held in Hanoi on 2 Jun 2015. Twelve (12) participants identification and assessment of • Existing data sources identified attended the meeting. existing data sources needed for and assessed • An inception meeting was conducted in Hanoi on 3 Jun 2015. Twenty one (21) participants the project, and team building • Local counterparts identified, attended the meeting. and their capacity assessed • Site visits to a sugar mill in Hau Giang province and a rice mill in Can Tho City. • Inception Report prepared and • Twelve (12) existing studies and publications were obtained and reviewed. The reviews were submitted reported in the Inception Report • Several local stakeholders (GIZ, SNV, NLU, VNUA, HUST, VFU, USTH, CTU, etc.) were contacted to obtain existing information on the biomass mapping exercises in Vietnam. • The Inception Report was developed and submitted on 27 Jun 2015. Development of an • Implementation Plan prepared • Implementation Plan for Phase 2 was developed and submitted on 15 Oct 2015. Implementation Plan for Phase 2 and submitted Preparation of the TOR for field • TOR prepared and submitted • TOR for nationwide field survey and data collection were prepared, submitted to and approved survey and data collection to be by WB and MOIT on 9 Oct 2015. conducted by the local contractors hired by MOIT Conduct of Phase 1 Workshop • Phase I Workshop conducted • A workshop was held in Hanoi on 16-17 Sep 2015 to present the outputs and results of Phase 1 of the project. PHASE 2: Remote data collection and • Remote data collected and • Satellite images were acquired from Sentinel-1 and were analyzed to produce the raw biomass analysis analyzed cluster images for field observation and inspection. • A field inventory plan was developed. Training on data collection for • Training on field survey and • MHG Biomass Manager was developed. enumerators data collection conducted • Required smartphone applications for navigation, data entry and data transfers were acquired. • Training on field survey and data collection was conducted on 28-29 September 2016 for 32 participants from Nong Lam University (NLU) and Vietnam National University of Agriculture (VNUA). Page 12 Data collection and validation • Field surveys conducted, and • Field surveys were conducted, and the data on crop biomass residues were collected in ALL 63 data collected (by the local cities/provinces of Vietnam with 21,212 farmers interviewed. Collected data on crop biomass consultants hired by MOIT) residues were validated, and 19,985 datasets were accepted. • Collected data validated by FA • Field surveys on industrial biomass residues were conducted in Vietnam. 261 datasets from seven Consortium industrial sectors (including 40 sugar mills, 54 rice mills, 38 MSW landfills, 67 livestock farms, 40 wood processing mills, 16 brick-making factories and 6 pulp and paper mills) were collected and validated. Acquisition of GIS data of other • GIS data of other driving • Power grid sub-station locations (digitized at the World Bank from a paper map), driving components components (road network, OpenStreetMap road and waterway network data were acquired. power T&D network, etc.) acquired and verified. Data analysis and development • A comprehensive database • The collected data were processed and integrated into a comprehensive database. of draft biomass atlas necessary for biomass • Draft biomass resource maps were produced. resource mapping, including raw data files elaborated • Draft biomass resource maps developed Conduct of stakeholder data • A stakeholder data validation • A stakeholder data validation workshop was conducted on 15 November 2017. Twenty four (24) validation workshop workshop conducted participants attended the workshop. PHASE 3: Production of final Biomass • Final Biomass Atlas for • The final Biomass Atlas for Vietnam including associated GIS files and datasets was produced. Atlas for Vietnam Vietnam including associated GIS files and datasets produced Conduct of workshops to • Dissemination and training • Biomass Atlas Dissemination Workshops and Training Workshops on Biomass Atlas Usage were disseminate the Biomass Atlas workshops conducted conducted in Hanoi (15 Aug 2018) and Ho Chi Minh City (17 Aug 2018). They were attended by and organize training on its a total of 61 participants (31 in Hanoi and 30 in Ho Chi Minh City). A training workshop on the usage and maintenance Biomass Atlas Maintenance was also conducted in Hanoi on 15 Aug 2018. Page 13 4. VIETNAM BIOMASS ATLAS Based on the Implementation Plan approved by the WB in March 2015, five types of biomass resources are included in the Biomass Atlas for Vietnam: • Crop harvesting residues; • Crop processing residues; • Livestock residue; • Municipal Solid Waste (MSW), and • Wood processing residues The Biomass Atlas for Vietnam has two main components: the maps and datasets. The maps are derived from the atlas datasets and each visually illustrates one specific aspect of the biomass-based energy production potential in Vietnam. The datasets contain the full results of the mapping project and can be used in numerical analysis with a GIS program. It should be noted that the Biomass Atlas and its associated datasets provide information on the potential and suitability of biomass-based power generation in Vietnam from the technical feedstock availability and from an infrastructure point of view. For each concrete project to be developed in the future, its economic and financial viability as well as an optimal biomass supply chain should be assessed during the project feasibility study. The mapping methodology is described in Annex 1. The maps and main datasets are introduced in the following sections, while the full set of datasets is provided in Annex 3. Training materials directed to familiarize novice GIS users with the use and update of the Biomass Atlas data using GIS software is included in Annexes 4 and 5. 4.1 Crop Biomass Feedstock Potential The theoretical crop biomass feedstock potential is based on the total amount of crop production. The crop residues are divided into two categories: crop harvesting residues and crop processing residues. Crop harvesting residues are generated in the field during crop harvesting activities while crop processing residues are produced during crop processing operations at agro-industrial sites. For Vietnam, the crop residues of 18 crops were included in the Biomass Atlas. Estimates of both crop residue categories were generated individually for each Agro-Ecological Zone (AEZ) of Vietnam. The delineation of AEZs is based on similarities in environmental attributes such as temperature, rainfall, soil characteristics and topography. These attributes essentially determine the types of crops, as well as their growth period and productivity. The amount of crop production was estimated using two main information sources: the land use classification based on the Sentinel-1 satellite images, and the district level crop yields obtained from the field survey. The land use classification was carried on for each of the 20 m x 20 m pixel covering Vietnam in the Sentinel-1 images. The crop harvesting residues were aggregated for the atlas to 1 km x 1 km pixels based on cropping season information in the 20 m x 20 m land use classification. Page 14 Figure 1: Agro-Ecological Zones of mainland Vietnam The annual production of the crop type j in the land pixel i is calculated using the formula: Pij = Aij * CYij [1] Where: Pij = annual production of the crop type j in the land pixel i, in tonnes/year Aij = combined cultivation area of the crop type j in the land pixel i (1 km x 1 km) over the cropping seasons, in ha CYij = crop yield of the crop type j in the land pixel i, in tonnes/ha/cropping season The district level crop yields based on the field survey executed within the project are used for calculating the crop production. Page 15 The crop production was converted to the annual theoretical production of crop residues by using the conversion factors (residue-to-crop ratios) and the formula: CRijk,theo = Pij * RCRjk [2] The theoretical amounts of firewood generated from pruning the perennial industrial crops (cashew nut, rubber, tea, coffee and pepper) and fruit crops (grape, mango, orange, mandarin, longan, litchi and rambutan) are calculated using the formula: CRijk,theo = Aij * RCRjk [3] Where: CRijk,theo = annual theoretical production of crop residue type k produced from the crop type j in the land pixel i, in tonnes/year RCRjk = average residue-to-crop ratio of the crop residue type k, in tonne/tonne of crop type j produced or tonne/ha.year of crop type j cultivated in the land pixel i (see Table 2) The annual theoretical production of the crop residue type k from the crop type j for the whole country (CRjk,theo) is calculated using the formula: CRjk,theo = ∑ ,ℎ [4] The annual technical production of the crop residue type k from the crop type j in the land pixel i (CRijk,tech) of the crop harvesting residues was derived from its annual theoretical production (CRijk,to) by excluding the existing uses of the residues based on the field survey results. CRijk,tech = CRijk,theo - CRijk,uses [5] During the field survey, the following existing uses of crop harvesting residues were recorded: animal fodder, domestic burning (cooking), selling to biomass supplier, selling to industry, organic fertilizer or open field burning. Only the crop harvesting residues that would have been burnt at the fields were included in the technical feedstock potential. The annual technical production of the crop residue type k from the crop type j for the whole country (CRjk,tech) is calculated using the formula: CRjk,tech = ∑ ,ℎ [6] The theoretical and technical energy potentials of the crop residue type k can be calculated by multiplying the annual production of the crop residue by its LHV. The type of crops, the type of crop residues, their RCR and the LHV of the residues are provided in Table 2 and 3. Page 16 Table 2: Residue to crop ratios used for the atlas Type of Type of crop residue RCRjk, (used Unit RCR range crop j k in this study) Crop biomass residue (after harvesting) Paddy Rice straw t/t of paddy 1.00 0.33 – 2.15 Sugarcane Sugarcane trash t/t of sugarcane (stem) 0.10 0.05 – 0.30 Maize Maize trash t/t of maize (grain) 2.20 1.00 – 3.77 Peanut Peanut straw t/t of peanut (in-shell) 2.00 2.00 – 2.30 Cassava Cassava stalks t/t of cassava (root) 0.30 0.06 – 0.30 Soybean Soybean straw t/t of soybean (grain) 0.30 in/a Sweet potato Sweet potato straw t/t of sweet potato (root) 0.30 in/a Cotton Cotton stalks t/t of cotton harvested 3.40 2.76 – 4.25 Cashew nut Firewood t/ha.year 0.70 in/a Rubber Firewood t/ha.year 0.50 in/a Coffee Firewood t/ha.year 0.70 in/a Tea Firewood t/ha.year 0.50 in/a Pepper Firewood t/ha.year 0.50 in/a Coconut Firewood t/ha.year 6.50 in/a Grape Firewood t/ha.year 0.50 in/a Mango Firewood t/ha.year 0.50 in/a Orange Firewood t/ha.year 0.50 in/a Mandarin Firewood t/ha.year 0.50 in/a Longan Firewood t/ha.year 0.50 in/a Litchi Firewood t/ha.year 0.50 in/a Rambutan Firewood t/ha.year 0.50 in/a Agro-industrial biomass residues (after crop processing) Paddy Rice husk t/t of paddy 0.20 0.15 – 0.36 Maize Corn cobs t/t of maize (grain) 0.30 0.20 – 0.50 Maize Maize shells (husks) t/t of maize (grain) 0.20 0.20 – 0.40 Sugarcane Sugarcane bagasse t/t of sugarcane (stem) 0.30 0.14 – 0.40 Peanut Peanut shells t/t of peanut (in-shell) 0.30 0.30 – 0.48 Cassava Cassava peels t/t of cassava (root) 0.12 0.10 – 0.15 Cashew nut Cashew nut shells t/t of cashew nut (in-shell) 0.60 0.50 – 0.70 Coffee Coffee husk t/t of coffee bean 0.40 0.21 – 0.46 Coconut Coconut husk t/t of coconut fruit 0.30 0.30 – 0.53 Coconut Coconut shells t/t of coconut fruit 0.15 0.12 – 0.15 Wood processing residues Wood logs Wood edges, slabs, etc. t/t of wood logs 0.78 0.62 – 0.83 Wood logs Sawdust t/t of wood logs 0.22 0.17 – 0.38 Notes: in/a: Information is not available; Firewood refers to the tree bark, leaves and branches, shrubs, etc. from pruning the perennial industrial crops (cashew nut, rubber, tea, coffee, pepper and coconut), fruit crops (grape, mango, orange, mandarin, longan, litchi and rambutan). The RCRs are country-specific values for Vietnam which were obtained from the field surveys as well as from studies conducted by various local and international institutions (IE, ENERTEAM, GIZ, SNV, ADB). The RCR values used in this study mainly come from the report on the “Strategy and Master Plan for Renewable Energy Development in Vietnam up to 2020 with an outlook to 2030” prepared by IE for the Ministry of Industry and Trade in 2011. It should be noted that, for most of residue types, the range of RCR values used in this study fall within the range of values used in FAO’s Bioenergy and Food Security (BEFS) Rapid Appraisal Tool for crop residues assessment. Page 17 Table 3: Lower heating values of different biomass residues Moisture content LHV LHV Type of crop j Type of crop residue k of residues (%) (MJ/kg) (MWhth/tonne) Crop biomass residue (after harvesting) Paddy Rice straw 12.0 12.60 3.50 Sugarcane Sugarcane trash 25.0 12.50 3.47 Maize Maize trash 16.0 12.50 3.47 Peanut Peanut straw 15.0 15.00 4.17 Cassava Cassava stalks 15.0 17.00 4.72 Soybean Soybean straw 15.0 12.40 3.44 Cotton Cotton stalks 12.5 15.00 4.17 Various Firewood 30.0 12.20 3.39 Agro-industrial biomass residues (after crop processing) Paddy Rice husk 10.5 13.00 3.61 Maize Corn cobs 17.6 14.10 3.92 Maize Maize shells (husks) 16.0 12.50 3.47 Sugarcane Sugarcane bagasse 50.0 7.50 2.08 Peanut Peanut shells 9.0 16.40 4.56 Cassava Cassava peels 40.0 8.40 2.33 Cashew nut Cashew nut shells 10.4 17.80 4.94 Coffee Coffee husk 11.0 16.70 4.64 Coconut Coconut husk 9.0 12.90 3.58 Coconut Coconut shells 10.0 16.90 4.69 Wood processing residues Wood logs Wood edges, slabs, etc. 20.0 14.30 3.97 Wood logs Sawdust 30.0 12.20 3.96 The moisture content of “as-received” biomass residues was obtained from the studies conducted by various local and international institutions (IE, ENERTEAM, GIZ, SNV, ADB). The LHVs used in this study are country-specific for Vietnam which mainly come from the draft report on the “Strategy and Master Plan for Renewable Energy Development in Vietnam up to 2020 with an outlook to 2030”. In case the country-specific LHV values for certain types of biomass residues are not available, they will be calculated based on the global-average LHV values of moisture-free biomass residues and moisture content of as-received biomass residues. The annual calculated theoretical potentials of crop harvesting residues and crop processing residues are presented in Tables 4 and 5, respectively. Table 4: Country-level annual theoretical potential of crop harvesting residues Type of crop j Annual production Energy potential of residues Type of of residues residues k TJ/year GWhth/year (tonnes) Paddy Rice straw 35,766,728 450,661 125,184 Maize Maize trash 16,147,141 201,839 56,066 Sugarcane Sugarcane trash 1,842,331 23,029 6,397 Peanut Peanut straw 1,040,245 15,604 4,334 Soybean Soybean straw 13,293 165 46 Cassava Cassava stalks 3,246,617 55,192 15,331 Perennial crops Firewood 1,831,488 22,344 6,207 Fruit crops Firewood 1,548 19 5 Total 59,889,391 768,853 213,570 Page 18 Table 5: Country-level annual theoretical potential of crop processing residues Annual production Energy potential of residues Type of crop j Type of residues k of residues (tonnes) TJ/year GWhth/year Paddy Rice husk 7,153,346 92,993 25,832 Maize Corn cobs 2,201,883 31,047 8,624 Maize Maize shells (husk) 1,467,922 18,349 5,097 Sugarcane Sugarcane bagasse 5,526,992 41,452 11,515 Cassava Cassava peels 1,298,647 10,909 3,030 Coffee Coffee husk 1,558,343 26,024 7,229 Coconut Coconut husk 922,116 11,895 3,304 Peanut Peanut shells 156,037 2,559 711 Cashew nut Cashew nut shells 116,507 2,074 576 Coconut Coconut shells 461,058 7,792 2,164 Total 20,862,851 245,094 68,082 Figure 2 illustrates the theoretical feedstock potential of crop residues over the map of Vietnam. While this map shows the potential for the total amount of generated biomass residues, the Biomass Atlas's GIS datasets contain a more detailed description of the potential, broken down by the type of the crop residue, as well as the location down to the 1 km x 1 km resolution. The map contains both the crop harvesting residues and processing residues. The location as far as the processing residues are concerned is not accurate, as these residues are not produced at the site of cultivation but rather at the site of industrial processing of the crop. Therefore, for processing residues, the location indicated is a proxy location pinpointing the site of original biomass production. The links to access the Biomass Atlas map and GIS datasets for the theoretical potential of crop residues are provided in Annex 3. Page 19 Figure 2: Theoretical potential of crop residues, including both harvesting and processing residues for all crops Page 20 The technical crop feedstock potential of the crop residues was derived from the theoretical feedstock potential by excluding the existing use of the harvesting residues based on the field survey results. During the field survey, the following uses of crop harvesting residues were recorded: animal fodder, domestic burning (cooking), selling to biomass supplier, selling to industry, organic fertilizer or open field burning. Only the crop harvesting residues that would have been burning at the fields were included in the technical feedstock potential. Table 6 and Figure 3 present the technical potential of the crop harvesting residues based on their existing uses. Table 6: Technical potential of crop harvesting residues based on their existing uses Annual technical Energy potential of residues Type of crop Type of potential of residues j residues k TJ/year GWhth/year (tonnes) Paddy Rice straw 8,714,689 109,805 30,501 Maize Maize trash 3,763,194 47,040 13,067 Sugarcane Sugarcane trash 1,270,724 15,884 4,412 Peanut Peanut straw 169,631 2,544 707 Soybean Soybean straw 1,500 19 5 Cassava Cassava stalks 959,601 16,313 4,531 Perennial crops Firewood 341,315 4,164 1,157 Fruit crops Firewood 350 4 1 Total 15,221,004 195,773 54,381 Another aspect affecting the availability of the crop harvesting residues for power generation is the willingness of the farmers to participate in the biomass feedstock supply chain (i.e. to sell their biomass residues to the market). This aspect was also covered in the survey and was aggregated to the district level from the individual surveys by weighing the farmer responses. Figure 4 presents the technical feedstock potential of the crop residues based on the existing uses and the farmers' willingness to sell the crop harvesting residues. Table 7 lists the technical potential for crop harvesting residues after taking into account both the existing uses and the willingness to sell. Table 7: Technical potential of crop harvesting residues based on their existing uses and farmers' willingness to sell Annual technical Energy potential of residues Type of crop Type of potential of residues j residues k TJ/year GWhth/year (1000' tonnes) Paddy Rice straw 4,903,776 61,788 17,163 Maize Maize trash 2,387,979 29,850 8,292 Sugarcane Sugarcane trash 300,966 3,762 1,045 Peanut Peanut straw 143,991 2,160 600 Soybean Soybean straw 159 2 1 Cassava Cassava stalks 187,230 3,183 884 Perennial crops Firewood 26,455 323 90 Fruit crops Firewood 2 0 0 Total 7,950,558 101,068 28,075 Page 21 Figure 3: Technical potential of crop residues based on the existing uses of crop harvesting residues Note: the color scale was changed compared to the theoretical potential map [Background map: Microsoft® Bing™ Maps] Page 22 Figure 4: Technical potential of crop residues based on the existing uses and farmers' willingness to sell crop harvesting residues Note: the color scale was changed compared to the theoretical potential map Page 23 The links for access to the Biomass Atlas map and GIS datasets for the technical potential of crop residues are provided in Annex 3. The crop processing residues are included in the maps of feedstock potential presented in Figures 2 to 4, but as noted, they are generated at the agro-industrial sites, not in the field. Therefore, for the two most important crop processing residues, bagasse and rice husk, their real technical potential for energy generation at the agro-industrial sites, i.e., sugar and rice mills, is analyzed and presented in section 4.3. Table 8 contains the confidence intervals for the yearly production of different crop residues based on the 504 surveyed districts. It should be noted that these figures cover only the parts of the country shown in Figure 5, not the whole country. However, the upper and lower confidence interval bounds can be used to get an indication of same bounds for the whole country. Table 8. The mean annual potential with 95% confidence interval for different types of crop residues for the sampled 504 districts Mean annual potential Type of residues Feedstock type with a 95% +/- confidence interval (1000' t/yr) Cassava, peel Theoretical 1,299 150 Theoretical 3,247 374 Cassava, stalk Technical, based on residue use 960 17 Technical, based on residue use and farmers' willingness to sell 187 4 Coconut, husk Theoretical 922 53 Coconut, shell Theoretical 461 27 Theoretical 928 0 Coconut, firewood Technical, based on residue use 261 71 Technical, based on residue use and farmers' willingness to sell 25 29 Coffee, husk Theoretical 1,558 6 Theoretical 450 0 Coffee, firewood Technical, based on residue use 9 8 Technical, based on residue use and farmers' willingness to sell 0 1 Cashew nut, shell Theoretical 117 0 Theoretical 51 0 Cashew nut, Technical, based on residue use 4 2 firewood Technical, based on residue use and farmers' willingness to sell 0 0 Theoretical 1 0 Litchi, firewood Technical, based on residue use 0 0 Technical, based on residue use and farmers' willingness to sell 0 0 Theoretical 1 0 Longan, firewood Technical, based on residue use 0 0 Technical, based on residue use and farmers' willingness to sell 0 0 Maize, cob Theoretical 2,202 87 Maize, shell Theoretical 1,468 58 Theoretical 16,147 635 Maize, trash Technical, based on residue use 3,763 179 Technical, based on residue use and farmers' willingness to sell 2,388 155 Theoretical 0 0 Mandarin, Technical, based on residue use 0 0 firewood Technical, based on residue use and farmers' willingness to sell 0 0 Page 24 Theoretical 0 0 Orange, firewood Technical, based on residue use 0 0 Technical, based on residue use and farmers' willingness to sell 0 0 Theoretical 1,040 51 Peanut, straw Technical, based on residue use 170 30 Technical, based on residue use and farmers' willingness to sell 144 15 Peanut, shell Theoretical 156 8 Theoretical 7 0 Pepper, firewood Technical, based on residue use 3 1 Technical, based on residue use and farmers' willingness to sell 1 0 Theoretical 0 0 Rambutan, Technical, based on residue use 0 0 firewood Technical, based on residue use and farmers' willingness to sell 0 0 Rice, husk Theoretical 7,153 277 Theoretical 35,767 1,383 Rice, straw Technical, based on residue use 8,715 292 Technical, based on residue use and farmers' willingness to sell 4,904 202 Theoretical 394 0 Rubber, firewood Technical, based on residue use 64 16 Technical, based on residue use and farmers' willingness to sell 1 0 Theoretical 13 0 Soybean, straw Technical, based on residue use 2 0 Technical, based on residue use and farmers' willingness to sell 0 0 Sugarcane, bagasse Theoretical 5,527 340 Theoretical 1,842 113 Sugarcane, trash Technical, based on residue use 1,271 3 Technical, based on residue use and farmers' willingness to sell 301 1 Theoretical 0 0 Tea, firewood Technical, based on residue use 0 0 Technical, based on residue use and farmers' willingness to sell 0 0 Page 25 Figure 5. Distribution of the 504 districts targeted by the field survey (shown in green on the map) Page 26 4.2 Greenfield Power Plant Potential This part of the Biomass Atlas consists of site suitability indicator maps for greenfield power plants using crop harvesting residue feedstock. A high site suitability value indicates a good site for a potential power plant, whereas a low value indicates a poor location. The process of analysis of the greenfield power plant potential is as follows: (1) Calculating the relative fuel sourcing distance: Ifs = (Df - Dss) / Df [7] Ifm = (Df - Dsm) / Df [8] Where, Ifs = relative sourcing distance for the most abundant single feedstock, (0 to 1) Ifm = relative sourcing distance for the most abundant feedstock, and auxiliary feedstock suitable for mixing with it, (0 to 1) Dss = sourcing distance for the most abundant single feedstock, given the capacity of the power plant, km Dsm = sourcing distance for multiple feedstock, given the most abundant single feedstock and the capacity of the power plant, km Df = maximum feedstock distance, 50 km (2) Calculating the relative transport network density: It = DEd / DEm [9] Where, It = relative transport network density, (0 to 1) DEd = transport network density for the district the site is located in, km/km² DEm = maximum transport network density across all districts, km/km² (3) Calculating the relative grid station connection distance: Ig = (Dgm - Dg) / Dgm [10] Where, Ig = relative grid station connection distance, (0 to 1) Dgs = grid substation connection distance for the site, km Dgm = maximum grid substation distance cut-off, 100 km (4) Calculating the site-suitability index: ... SIsf = 100 * (0.6 * Isf + 0.3 * Ig + 0.1 * It) [11] Page 27 SImf = 100 * (0.6 * Imf + 0.3 * Ig + 0.1 * Ir) [12] Where, SIsf = site-suitability index based on single fuel feedstock, (0 to 100) SImf = site-suitability index based on multi-fuel feedstock, (0 to 100) This part of the Biomass Atlas consists of site suitability indicator maps for greenfield power plants using crop harvesting residue feedstock. A high site suitability value indicates a good site for a potential power plant, whereas a low value indicates a poor location. This site suitability indicator takes into account the feedstock sourcing area size, the road network density in the region, and the distance to a grid power station. The first two factors serve as proxies for site-dependent operational costs, and the third one as site dependent investment cost proxy. The site suitability indicator map can be used by the potential project developers/investors for initially screening the locations for greenfield biomass-based power plant. In order to select the best site, more detailed investigation and assessment of biomass residues availability and their supply chains should be conducted during the project feasibility study phase. Each of the indicator components get values between 0 and 100, scaled linearly between the worst and best values for the component in the dataset. This means that a component gets value 100 for smallest sourcing area, shortest direct distance to a grid power station and the highest road network density in the whole dataset, and vice versa for value 0. The three components are then combined so that the site suitability indicator also gets values between 0 and 100, where 100 indicate a site where all the three factors are optimal. The weights used in combining the components were 0.6 for feedstock sourcing area, 0.3 for grid power station distance and 0.1 for road network density. The maximum direct sourcing distance allowed was 50 km. The maximum feedstock sourcing area, and hence distance, is determined by both the power plant capacity and the technology used. The power plant modeling includes a compatibility matrix between different crop residues and technology & capacity combinations. Other factors included in the model are feedstock pre- processing and storage. The site suitability indicator value was computed for 18 different combinations of energy conversion technologies and power plant capacities as shown in Table 9. Table 9: Analyzed combinations of power plant technologies and capacities Technology Power plant capacity (MW) Grate combustion steam boiler + steam turbine 3, 8 and 15 Bubbling fluidized bed combustion steam boiler + steam turbine 8, 15, 25, 50 and 100 Circulating fluidized bed combustion steam boiler + steam turbine 15, 25, 50 and 100 Gasifier + syngas engine/turbine 0.5 and 1.5 Anaerobic digester + biogas engine/turbine 0.5,1.5, 3 and 8 Each combination can be illustrated with a map. Figures 6, 7 and 8 illustrate the site suitability indicator maps for various power plant technologies with different gross power capacities. Page 28 The links for access to the results of site suitability analysis are provided in Annex 3. Figure 6: Site suitability indicator map for 3 MW power plants with grate steam boiler Note: Red color indicating high potential and blue color potential approaching zero [Background map: Google® Google Streets™] Page 29 Figure 7: Site suitability indicator map for 15 MW power plants with BFB steam boiler Note: Red color indicating high potential and blue color potential approaching zero [Background map: Google® Google Streets™] Page 30 Figure 8: Site suitability indicator map for 25 MW power plants with CFB steam boiler Note: Red color indicating high potential and blue color potential approaching zero [Background map: Google® Google Streets™] Page 31 4.3 Electricity Generation Potential at Biomass Producing Sites An analysis of agro-industrial sites covered by the industrial survey was conducted with the aim of evaluating the potential of each site for implementing a biomass-based power or cogeneration plant. 4.3.1 Sugar Mills All 40 existing sugar mills in Vietnam with a total design sugarcane crushing capacity of 165,750 TCD were surveyed. In the last crushing season 2016-17, about 17.1 million tonnes of sugarcane were processed in these sugar mills with an average operating time of 2,990 hours (around 125 days). Based on the industrial survey, a total of 5.1 million tonnes/year of bagasse was generated in the 40 surveyed sugar mills. About 98.6% of this amount of bagasse is used as fuel in cogeneration plants to produce electricity and low-pressure process steam for covering the energy demand of the sugar mills. Most existing cogeneration plants are equipped with low-pressure steam boilers and back- pressure steam turbines working between 18 and 38 bar. Only eight mills have already installed a high-pressure (65 or 98 bar) steam boilers. The total installed power capacity for the 40 surveyed cogeneration plants was at 522 MW. However, only around 158 MW with an electricity amount of 397.1 GWh/year were sold to the grid. Electricity consumption of the surveyed sugar mills varied from 30 to 48 kWh/tonne of sugarcane crushed (36.7 kWh/tonne in average). The process steam consumption was between 450 to 660 kg/tonne of sugarcane crushed (555 kg/tonne in average). There is a large technical potential for implementing new high-pressure cogeneration plants using bagasse at the sugar mills. The potential is calculated based on the assumption that all existing low pressure back-pressure steam turbine-based cogeneration systems would be converted into high- pressure systems using extraction condensing steam turbines. It should be noted that the use of new extraction condensing steam turbine allows the high-pressure cogeneration system to run during the off-milling season by utilizing additional biomass feedstock sourced from the vicinity of the sugar mill. Although the investment costs to convert the existing low-pressure to high-pressure cogeneration systems are high4, the implementation of high-pressure cogeneration systems should be considered as a priority for sugar mills in order to optimize the use of bagasse for power generation. Sticking to old, inefficient and polluting low-pressure systems should not be an option anymore. The process of analysis of new high-pressure cogeneration systems at sugar mills is as follows: (1) Calculating the energy input from bagasse (GWhth/year): For each sugar mill, the energy input from bagasse to the new high-pressure cogeneration plant is calculated based on the annual amount of bagasse produced at the sugar mill and the LHV of bagasse. The data on bagasse production was obtained from the industrial survey. ENIbg = (Pbg * LHVbg) / 1000 [13] 4 Based on the Consultant’s experience in the region, the investment costs are USD 0.9 - 1.2 million/MW of installed power capacity for the capacity range of 15 - 35 MW. For the small-scale systems (<10 MW), the investment costs may reach USD 1.5 million/MW. Page 32 Where, ENIbg = energy input from bagasse, in GWhth/year Pbg = bagasse production, in tonnes/year LHVbg = lower heating value of bagaase (7.5 MJ/kg or ~2.083 MWhth/tonne) 1000 = conversion factor from MWhth to GWhth (2) Calculating the total energy output from a cogeneration plant in case only bagasse is used: An overall cogeneration efficiency of 75% was conservatively assumed for the cogeneration system at each sugar mill. Based on this assumption and the energy input from bagasse, the total energy output (i.e., steam thermal energy for process and electricity generation) from a cogeneration plant can be calculated. ENObg = ENIbg * OEEcp [14] Where, ENObg = total energy output from bagasse-based cogeneration plant, in GWh/year ENIbg = energy input from bagasse, in GWhth/year OEEcp = overall energy efficiency of bagasse-based cogeneration plant, in % (3) Calculating the process steam consumed by the sugar mill (GWhth/year): In order to calculate the process steam consumption (in thermal energy unit) of the sugar mill, it was assumed that low pressure steam at 2.5 bar and 130oC is used for the sugar mill. PSEsm = SPSsm * SCsm * hps / 1000 [15] Where, PSEsm = process steam thermal energy consumed by the sugar mill, in GWhth/year SPSsm = specific process steam consumed by the sugar mill, in tonne of steam/tonne of sugarcane processed SCsm = amount of sugarcane processed by the sugar mill, in tonne/year hps = enthalpy of the process steam, in MWhth/tonne (enthalpy of process steam at 2.5 bar and 130oC is 2721.9 kJ/kg (~ 0.756 MWhth/tonne) 1000 = conversion factor from MWhth to GWhth (4) Calculating the gross electricity output of the cogeneration plant in case only bagasse is used: The gross electricity output of the cogeneration plant is calculated by subtracting the process steam thermal energy consumed by the sugar mill from its total energy output. EGbg = ENObg - PSEsm [16] Where, Page 33 EGbg = gross electricity output of the cogeneration plant in case only bagasse is used, in GWh/year (5) Calculating the gross power output of the cogeneration plant in case only bagasse is used: The gross power output of the cogeneration plant is calculated based on the calculated gross electricity output (EGbg) and the operating time of the sugar mill (obtained from the industrial survey). PGbg = EGbg * 1000 / OPTsm [17] Where, PGbg = gross power output of the cogeneration plant, in MW OPTsm = operating time of the sugar mill, in hours/year 1000 = conversion factor from GWh to MWh (6) Calculating the net electricity output in case only bagasse is used: This value is calculated from the gross electricity generation and an assumed parasitic load (electricity own-consumption) of the cogeneration plant. ENbg = EGbg * (100% - EOCcp) [18] Where, ENbg = net electricity output of the cogeneration plant in case only bagasse is used, in GWh/year EOCcp = electricity own-consumption of the cogeneration plant, in % of the gross electricity output. This value was assumed based on the gross power output of the cogeneration plant. (7) Calculating electricity export to the grid (GWh/year) in case only bagasse is used: The amount of electricity exported to the grid is calculated by subtracting the electricity consumed by the sugar mill from the net electricity output of the cogeneration plant. The electricity consumption of the sugar mill is calculated based on the assumption that 30 kWh of electricity is required for processing one tonne of sugarcane. EXbg = ENbg – PECsm [19] Where, EXbg = electricity export to the grid in case only bagasse is used, in GWh/year PECsm = electricity consumption by the sugar mill, in GWh/year. This value is calculated based on the amount of sugarcane processed in a year (SCsm) and the specific electricity consumption of the sugar mill (kWh/tonne of sugarcane processed). These data are obtained from the industrial survey. (8) Calculating the energy input from additional biomass feedstock for year-round operation of the cogeneration plant: Based on the results of the industrial survey, the operating time of the sugar Page 34 mills during the milling season 2016-17 varied from 1,490 to 5,630 hours/year (equivalent to an annual Plant Capacity Factor (PCF) of 17.0 - 64.3%). Assuming that the annual PCF of the cogeneration plant increases to 85%, the annual PCF of the cogeneration plant running on additional biomass feedstock during off-milling season is calculated at 68.0 - 20.7%. Based on these PCF values and the rated gross power capacity defined in step (5), the gross electricity generation of the cogeneration plant running on additional feedstock can be calculated. (9) Calculating the electricity export to the grid from the cogeneration plant running on additional biomass feedstock during off-milling season: The net electricity output (i.e., electricity exported to the grid) of the cogeneration plant running on additional biomass feedstock is calculated from the gross electricity generation defined in step (7) and the assumed parasitic load (electricity own- consumption) of the cogeneration plant. (10) Calculating the amount of additional biomass feedstock and its sourcing area: Then, the amount of energy input from additional biomass feedstock is calculated using an assumed value of 25% for electrical efficiency of the cogeneration plant running in pure-power generation mode. For the additional biomass feedstock, the fuel deterioration during storage for a period of six months was taken into account. Based on the required amount of the energy input from additional biomass feedstock and the technical potential of suitable crop harvesting residues in the vicinity of the sugar mill, the amount of additional biomass feedstock (tonnes/year) and the sourcing area will be calculated. In other words, the sourcing area for additional biomass feedstock (km²/GWh) takes into account the real distribution of the crop fields and crop residues within the vicinity of the sugar mill. The additional biomass feedstock sourcing area matches the best-case scenario, which is able to source all of the technically available crop harvesting residues suitable for the cogeneration plant from the immediate neighborhood of the sugar mill. Therefore, it helps ranking the sugar mills in terms of the ease of sourcing the additional biomass feedstock. The results of the sugar mills analysis show that the new high-pressure cogeneration plants at 40 sugar mills could have a combined power capacity output of around 600 MW during the crushing season of 2016-17. In order to run these cogeneration plants at an annual PCF of 85% (7,446 hours/year), around 3.413 million tonnes/year of additional biomass feedstock are needed. A total amount of about 958.3 GWh/year of electricity could be exported to the grid if only bagasse is used during the milling season, which is about 2.4 times higher than the total power capacity of all 40 existing low-pressure cogeneration plants. In case both bagasse and additional biomass feedstock are used as fuels for the cogeneration plants all year round, about 3,363 GWh/year of electricity could be exported. Table 10 summarizes the results of the analysis of the 40 surveyed sugar mills. The detailed analysis results for each sugar mill are provided in Annex 2. Table 10: Summarized results of the analysis of the 40 surveyed sugar mills Description Unit Value Number of sugar mills surveyed 40 Total design sugarcane crushing capacity t/day 165,750 Total sugarcane crushing capacity during the season 2016-17 t/day 137,460 Total sugarcane processed t/year 17,127,181 Total bagasse generated t/year 5,142,220 Total additional biomass feedstock sourced t/year 3,412,876 Page 35 Power generation technology used High pressure Total gross power capacity output MW 600 Total gross electricity generated GWh/year 4,467.6 Total net electricity output GWh/year 4,007.2 Total electricity consumption by the sugar mills GWh/year 644.2 Total electricity exported to the grid, of which: GWh/year 3,363.0 From bagasse only (during the milling season) GWh/year 958.3 From additional feedstock (during the off-milling season) GWh/year 2,404.8 The map of potential high-pressure cogeneration plants at the 40 surveyed sugar mills in Vietnam is shown in Figure 9. In this figure, the size of the circle is proportional to the potential power plant capacity output (ranging from 2.4 MW to 55 MW), and the color of the circle relates to the sourcing area for additional biomass feedstock (km2 for each additional GWh required). The sourcing areas range from dark red at 0.13-0.57 km²/GWh to dark blue at 1.92-2.36 km²/GWh, and other color hues in between those two ranges. The links for access to the survey results, the map and datasets for the sugar mills analysis are provided in Annex 3. Page 36 Figure 9: Map of potential high-pressure cogeneration plants at the 40 surveyed sugar mills Note: The area of the circle denotes the power plant capacity, and the color the relative ranking of sourcing area for additional feedstock: blue hues–larger sourcing area per GWh, red hues–smaller sourcing area per GWh [Background map: Google® Google Streets™] Page 37 4.3.2 Rice Mills It must be mentioned that under the framework of this project, only 54 rice mills were surveyed during the industrial survey. The total amount of paddy milled in these 54 surveyed rice mills was 4.3 million tonnes/year which accounted for only 9.8% of total amount of paddy produced in Vietnam in 2016 (43.69 million tonnes). The total amount of rice husk generated from these rice mills was about 869,797 tonnes/year of which 249,755 tonnes/year (28.7% of the total) was used by the rice mills for in-house purposes (i.e., drying paddy, producing rice husk pellets, etc.). The surplus amount of rice husk (620,042 tonnes/year) was being sold out. If this amount would be used for power generation by the rice mills, it could support around 66.4 MW of power capacity, i.e. an average of around 1.2 MW per mill. These rice husk-based power plants could generate about 465.6 GWh/year of electricity. Based on the industrial survey, the electricity consumption of the surveyed rice mills varied from 25 to 45 kWh/tonne of paddy milled with an average value of 31.2 kWh/tonne. During the 2016-17 milling season, a total of 129.6 GWh/year of electricity was consumed by the 54 surveyed rice mills. As the power generation potential (1.2 MW per mill) is too low to attract investors, the use of additional biomass feedstock was considered in the analysis of the power generation potential in order to increase the capacity of each power plant to be able to export power to the grid. It was assumed that the steam boiler of the power plant would be run on rice husk or on other locally sourced biomass feedstock, or on a mixture of them. The minimum fixed power plant capacity of 3 MW is assumed for the power plants at all 54 surveyed rice mills. The additional biomass feedstock was calculated in order to assure an annual plant capacity factor of 80% for all power plants. As for the sugar mills, the analysis results for each rice mill contain the sourcing area (km²/GWh) for the additional biomass feedstock needed to operate the power plant, with the sourcing area matching the best-case sourcing scenario. The process of analysis of potential power plants at rice mills is as follows: (1) Calculating the energy input from rice husk: The energy input from rice husk is calculated based on the rice husk production of each rice mill (obtained from the industrial survey) and its LHV. This value varies between rice mills. ENIrh = (Prh * LHVrh) / 1000 [20] Where, ENIrh = energy input from rice husk, in GWhth/year Prh = rice husk production, in tonnes/year LHVrh = lower heating value of rice husk (13.0 MJ/kg or ~3.611 MWhth/tonne) 1000 = conversion factor from MWhth to GWhth (2) Calculating the gross electricity output of the power plant in case only rice husk is used: As most of rice husk power plants in Vietnam will have a small or medium size, the medium pressure (40-50 bar) grate steam boiler with fully condensing steam turbine system can be used. With these Page 38 assumptions, the gross electrical efficiency of the power plant will be 20.8%. The gross electricity output of each power plant using only rice husk is calculated using the formula: EGrh = ENIrh * EEpp [21] Where, EGrh = gross electricity output of the power plant in case only rice husk is used, in GWh/year EEpp = electrical efficiency of the power plant, in % (20.8%) (3) Calculating the gross power output of the power plant in case only rice husk is used: The gross power output of the power plant is calculated based on the calculated gross electricity output (EGrh) and an assumed annual PCF of 80%. PGrh = (EGrh * 1000) / (8760 * PCF) [22] Where, PGrh = gross power output of the power plant in case only rice husk is used, in MW 1000 = conversion factor from GWh to MWh 8760 = number of hours in a year The calculated gross power output of the power plant for some rice mills may be less than 3 MW due to insufficient rice husk production. For such rice mills, as mentioned above, a minimum fixed power plant capacity of 3 MW will be assumed, and the use of additional biomass feedstock considered in the analysis of the electricity generation potential. (4) Calculating the net electricity output of the power plant: This value is calculated from the gross electricity output and an assumed parasitic load (electricity own-consumption) of the power plant. ENpp = EGpp * (100% - EOCpp) [23] Where, ENpp = net electricity output of the power plant, in GWh/year EGpp = gross electricity output of the power plant, in GWh/year EOCpp = electricity own-consumption of the power plant, in % of the gross electricity output. This value was assumed based on the gross power output of the power plant. For a 3 MW power plant, the gross electricity output of the power plant running at 80% annual PCF is calculated at 21.024 GWh/year and the net electricity output is calculated at 17.870 GWh/year. Page 39 (5) Calculating electricity export: The amount of electricity exported to the grid is calculated by subtracting the electricity consumed by the rice mill from the net electricity output of the power plant. EXpp = ENpp – PECrm [24] Where, EXpp = electricity export to the grid, in GWh/year PECrm = electricity consumption by the rice mill, in GWh/year. This value is calculated based on the amount of paddy milled in a year (tonnes/year) and the specific electricity consumption of the rice mill (kWh/tonne of paddy milled). These data are obtained from the industrial survey. (6) Calculating the energy input from biomass fuel required for a 3 MW power plant: As mentioned above, in case the calculated gross power output of the power plant running on rice husk is less than 3 MW, a minimum fixed power plant capacity of 3 MW will be assumed. The energy input from biomass fuels (rice husk and other additional biomass feedstock) required for a 3 MW power plant is calculated using the formula: ENIbf = (PGpp/1000) * (8760 * PCF) / EEpp [25] Where, ENIbf = energy input from biomass fuel required for a 3 MW power plant, in GWhth/year PGpp = gross power output of the power plant (3 MW) 1000 = conversion factor from MW to GW PCF = annual plant capacity factor of the power plant (80%) The energy input from biomass fuel required for a 3 MW power plant running at 80% annual PCF is calculated at 101.1 GWhth/year. (7) Calculating the energy input from additional biomass feedstock (GWhth/year): Energy input from additional feedstock is calculated by subtracting the amount of energy input from rice husk (ENIrh) from the total amount of energy input required for the power plant (ENIbf). For the additional feedstock, the effect of fuel deterioration during the storage period of six months was also taken into account. (8) Calculating the amount of additional biomass feedstock (tonnes/year) and its sourcing area (km²/GWh): This is done based on the amount of energy input calculated in step (7) and the technical potential of suitable crop harvesting residues. It takes into account the real distribution of fields and crops, but assuming 100% sourcing ability of the available feedstock. According to the General Statistics Office of Vietnam, a total of 43.69 million tonnes of paddy were produced in 2016. Based on an average rice husk to paddy ratio of 20%, the total amount of rice husk generated in 2016 could be estimated at around 8.738 million tonnes. With a LHV of rice husk of 13.0 MJ/kg, this amount of rice husk would represent an energy potential of about 113,594 TJ Page 40 (31,554 GWhth). If 100% of this amount of rice husk would have been collected and used for power generation, the potential power capacity output would be about 937 MW, based on an average electrical efficiency of 20.8% and an annual PCF of 80%. The results of the rice mills analysis show that, the calculated combined power capacity output of the potential power plants at 54 surveyed rice mills is 188.5 MW. A calculated amount of biomass fuel of 1.863 million tonnes/year is required of which 0.620 million tonnes/year come from rice husk and 1.243 million tonnes/year from additional biomass feedstock. These potential power plants could export about 1,002 GWh/year of electricity to the grid. Table 11 summarizes the results of the analysis of the 54 surveyed rice mills. The detailed analysis results for each rice mill are provided in Annex 2. Table 11: Summarized results of the analysis of the 54 surveyed rice mills Description Unit Value Number of rice mills surveyed 54 Total paddy milling capacity t/day 30,909 Total paddy processed t/year 4,292,484 Total rice husk generated t/year 869,797 Total rice husk used by the rice mills t/year 249,755 Total surplus rice husk (used for power generation) t/year 620,042 Total additional biomass feedstock sourced t/year 1,243,489 Power generation technology used Medium pressure Total gross power capacity output MW 188.5 Total gross electricity generated GWh/year 1,320.9 Total net electricity output GWh/year 1,132.1 Total electricity consumption by the rice mills GWh/year 129.6 Total electricity exported to the grid, of which: GWh/year 1,002.5 From rice husk only GWh/year 284.5 From additional feedstock GWh/year 718.0 The map of potential power plants at the 54 surveyed rice mills in Vietnam is provided in Figure 10. The bigger the circle, the higher the capacity of the power plant (ranging from 3 MW to 9 MW). The red circles show “zero” sourcing area for the additional feedstock (i.e., no additional feedstock needed), the dark blue circles indicate sourcing area ranging from 0.36 to 0.47 km2/GWh while other colors represent intermediate sourcing areas. The links for access to the survey results, the map and datasets for the rice mills analysis are provided in Annex 3. Page 41 Figure 10: Map of potential power plants at the 54 surveyed rice mills in Vietnam Note: The color indicates the ranking of the sourcing area for additional feedstock: blue hues – larger sourcing area per GWh, red hues – smaller sourcing area, white – in between [Background map: Google® Google Streets™] Page 42 4.3.3 MSW Landfills There is a potential use of MSW for energy generation at the landfills in Vietnam. The two technologies which are most frequently used for generating electricity and/or heat from MSW include: • direct combustion of organic materials of MSW in an incinerator/steam boiler to produce high pressure steam. Then, the steam is used in a steam turbo-generator to generate electricity or both electricity and heat; • anaerobic digestion of the biodegradable fraction of MSW to produce biogas which is used in a gas engine or turbine system to generate electricity or both electricity and heat. The technology of using incinerator/steam boiler to convert MSW to energy is a relatively old technology. The electrical efficiency of this technology is low (typically, 14-28%) due to the low efficiency of the incinerator/steam boiler burning MSW with high moisture content, i.e. with low LHV. Another problem associated with direct combustion of MSW to generate electricity is the potential for pollutants (such as nitrogen oxides, sulphur dioxide, heavy metals and dioxins) to enter the atmosphere with the flue gases from the incinerator/boiler. In this study, the anaerobic digestion of MSW is proposed as it has a higher electrical efficiency and a lower environmental impact than the direct combustion technology. The following steps were used for analyzing the potential power plants installed at the landfills based on anaerobic digester combined with gas engine/turbine systems: (1) Calculating the annual biogas production for each landfill: Based on the amount and percentage of organic matter of MSW disposed at each landfill (obtained from the industrial survey), the annual biogas production is calculated by using an average biogas production rate of 120 m3/tonne of organic MSW as-received basis. BGPmsw = MSW * ORG * BGRmsw [26] Where, BGPmsw = amount of biogas produced, in m3/year MSW = amount of MSW dumped at the landfill, in tonnes/year ORG = organic matter of MSW, in % of MSW dumped BGRmsw = average biogas production rate, in m3/tonne of organic MSW (2) Calculating the energy input from biogas to the gas engine/turbine: This value is calculated based on the annual biogas production and biogas LHV. Assuming that biogas produced from MSW consists of 60% of methane, the biogas LHV was calculated at 21.54 MJ/m3. ENIbio,msw = BGPmsw * (LHVbio,msw / 3600) / 1000 [27] Where, ENIbio,msw = energy input from biogas to the gas engine/turbine, in GWhth/year Page 43 LHVbio,msw = low heating value of biogas, in MJ/m3 (21.54 MJ/m3) 3600 = conversion factor from MJ to MWhth 1000 = conversion factor from MWhth to GWhth (3) Calculating the gross electricity output from the power plant: The gross electricity output was calculated based on an assumed gross electrical efficiency of 40% for biogas engine-based technology and 30% for biogas turbine-based technology. EGmsw = ENIbio,msw * EEpp [28] Where, EGmsw = gross electricity output of the power plant running on biogas produced from MSW, in GWh/year EEpp = electrical efficiency of the biogas-based power plant, in % (4) Calculating the rated gross power capacity: The rated gross power capacity of each power plant was calculated based on its gross electricity output and annual PCF. PGmsw = (EGmsw * 1000) / (8760 * PCFmsw) [29] Where, PGmsw = gross power output of the power plant running on biogas produced from MSW, in MW PCFmsw = annual plant capacity factor of the power plant, in %. This value was assumed at 90% for all power plants 1000 = conversion factor from GWh to MWh 8760 = number of hours in a year (5) Calculating the electricity export: The amount of electricity export was calculated based on an average parasitic load (electricity own-consumption) of the power plant. ENmsw = EGmsw * (100% - EOCpp,msw) [30] Where, ENmsw = net electricity output (electricity export) of the power plant, in GWh/year EGmsw = gross electricity output of the power plant, in GWh/year EOCpp,msw = electricity own-consumption of the power plant, in % of the gross electricity output. This value was assumed at 5% for all power plants. According to the Vietnam Status of Environment Report 2016, the amount of MSW generated in Vietnam in 2015 was 38,000 t/day. Based on a growth rate of 12% per year, the amount of MSW generated in 2017 can be estimated at 48,000 t/day (17,520,000 t/yr). With an average organic content of 60%, the amount of organic matter generated was 10,512,000 t/yr. If this amount would have been collected for biogas production, the amount of biogas produced would be around 1,261 Page 44 million m3/yr. The theoretical potential power capacity that could be generated from this amount of biogas would be 383 MW. However, the industrial survey covered only 38 landfills in Vietnam. The combined amount of MSW collected at these landfills is around 20,140 t/day (around 42% of amount of MSW generated in 2017 in Vietnam). Some amount of MSW (1,910 t/day) is being recycled, incinerated or used for fertilizer production, and the remaining amount of around 18,230 t/day is currently dumped at these 38 landfills. This amount of MSW generates around 9,829 t/day of organic MSW. If this organic MSW could be used for electricity generation, around 131 MW of gross power capacity could be generated in the anaerobic digester-based power plants. Table 12 summarizes the results of analyzing the 38 surveyed MSW landfills. The detailed analysis results for each landfill are provided in Annex 2. Table 12: Summarized results of the analysis of the 38 surveyed MSW landfills Description Unit Value Number of MSW landfills surveyed 38 Total MSW collected t/day 20,144 Total MSW dumped t/day 18,231 Total organic MSW generated t/day 9,829 Total annual biogas production million Nm3/yr 430.5 Power generation technology used Biogas engine Total gross power capacity output MW 131 Total gross electricity generated GWh/year 1,029.8 Total net electricity output GWh/year 978.3 Total electricity consumption by the MSW landfills GWh/year Data were not available Total electricity exported to the grid GWh/year 978.3 The map of potential power plants at the surveyed MSW landfills in Vietnam is provided in Figure 11. The bigger the circle, the higher the capacity of the power plant (ranging from 0.02 MW to 33 MW). The links for access to the survey results, the map and datasets for MSW landfills analysis are provided in Annex 3. Page 45 Figure 11: Map of potential power plants at the 38 surveyed MSW landfills in Vietnam Note: Bigger area of the circle denotes higher capacity [Background map: Google® Google Streets™] Page 46 4.3.4 Livestock Farms The following steps were used for analyzing the potential power plants installed at the livestock farms, based on an anaerobic digester combined with gas engine or turbine systems: (1) Calculating the annual biogas production for each livestock farm (m3/year): The annual biogas production is calculated based on the amount of manure available at each livestock farm (obtained from the industrial survey) and the average biogas production rate (m 3/t of manure on an as received basis). Based on the industrial survey, the biogas production rate is 30 m3/t for cow manure, and 40-57 m3/t for pig manure. (2) Calculating the energy input from biogas to the gas engine/turbine (GWhth/year): This value is calculated based on the annual biogas production and biogas LHV. Assuming that biogas produced from livestock manure consists of 65% of methane, the biogas LHV was calculated at 23.33 MJ/m3. (3) Calculating the gross electricity output from the power plant (GWh/year): The gross electricity output was calculated based on an assumed gross electrical efficiency of 40% for biogas engine-based technology and 30% for biogas turbine-based technology. (4) Calculating the rated gross power capacity (MW): The rated gross power capacity of each power plant was calculated based on its gross electricity output and an annual PCF of 90%. (5) Calculating the electricity export (GWh/year): The amount of electricity exported to the grid was calculated based on an average parasitic load of 5% assumed for all power plants. According the General Statistics Office of Vietnam, the major livestock population in Vietnam was estimated at around 29.08 million pig-heads and 5.50 million cattle-heads in 2016. With an average manure production rate of 1.76 kg/day/head for pigs and 14.34 kg/day/head for cattle (from the industrial survey), the amount of manure generated in 2016 was about 18.68 million tonnes of pig- manure and 28.79 million tonnes of cattle-manure. If these amounts of manure could be collected and used for biogas production, the amount of biogas produced would be about 2 billion m 3. As the livestock sector in Vietnam is concentrated in small individual household farms, the number of large- scale biogas plants which can be equipped with electricity generation is limited. Assuming that only 15% of manure from pig farms and 5% of manure from cattle farms could be used for biogas production5, the amount of biogas produced would be about 156 million m3/year. The potential for electricity generation from that amount of biogas would be around 405 GWh/year with a total installed power capacity of around 50 MW. The industrial survey covered only 67 livestock farms in Vietnam, including 21 cow farms and 46 pig farms. A total of around 343,770 animals are raised in these 67 livestock farms of which 111,610 are cows and 232,160 are pigs. The combined amount of manure collected at these farms is around 2,010 t/day. Around 16.0% of the collected manure (322 t/day) is being sold out and 57.3% (1,150 5 Based on WB’s report “An Overview of Agricultural Pollution in Vietnam: The Livestock Sector” published in 2017, the volume of manure discharged from household smallholders accounted for 84.5% for pig farms and 96.7% for cattle farms. Page 47 t/day) are used for producing dried/composted fertilizer for sales or for in-farm use (18 farms are using all 100% of their manure for these purposes). The remaining amount of 536 t/day (165,617 t/year) of manure available at 49 livestock farms is currently used for production of biogas. The produced biogas is currently used for cooking purpose or flared. Only five of the surveyed farms are using biogas for power generation with a combined installed power capacity of around 0.5 MW. If 536 t/day of available manure are used for biogas production, a total of 7.41 million m3 of biogas could be produced and around 2.4 MW of gross power capacity could be generated in the anaerobic digester-based power plants. Table 13 summarizes the results of the analysis of the 67 surveyed livestock farms. The detailed analysis results for each farm are provided in Annex 2. Table 13: Summarized results of the analysis of the 67 surveyed livestock farms Description Unit Value Number of livestock farms surveyed 67 Total manure collected t/day 2,010 Total manure sold out and used for fertilizer production t/day 1,474 Total manure used for biogas production t/day 536 Total volume of biogas produced million Nm3/year 7.41 Power generation technology used Biogas engine Total gross power capacity output MW 2.4 Total gross electricity generated GWh/year 19.21 Total net electricity output GWh/year 18.25 Total electricity consumption by the livestock farms GWh/year Data were not available Total electricity exported to the grid GWh/year 18.25 The map of potential power plants at the surveyed livestock farms in Vietnam is provided in Figure 12. The bigger the circle is, the higher the capacity of the power plant (from 1 to 576 kW). The links for access to the survey results, the map and datasets for livestock farms analysis are provided in Annex 3. Page 48 Figure 12: Map of potential power plants at the 67 surveyed livestock farms in Vietnam [Background map: Google® Google Streets™] Page 49 4.3.5 Wood Processing Mills The industrial survey covered 40 wood processing mills of various types (15 saw mills, 10 furniture manufacturing plants, 10 combined sawmilling and furniture manufacturing factories, 3 plywood production plants and 2 MDF manufacturing mills). The total amount of input wood (logs and sawn wood) processed in these mills in 2017 was around 386,970 tonnes/year. The total amount of wood residues (sawdust, edges and slabs) generated from these surveyed wood processing mills was about 101,630 tonnes/year of which 51,100 tonnes/year (50.3% of the total) was used by the mills for in- house purposes (mainly for wood drying). The surplus amount of wood residues (50,530 tonnes/year) was being sold. If this amount would be used for power generation by the wood processing mills, it could support around 5.4 MW of gross power capacity. As the power generation potential (0.13 MW per mill) is too low to attract investors, the use of additional biomass feedstock was considered in the analysis of the power generation potential in order to increase the capacity of each power plant to be able to export power to the grid. It was assumed that the steam boiler of the power plant would be run on wood residues or on other locally sourced biomass feedstock, or on a mixture of them. The minimum fixed power plant capacity of 3 MW is assumed for the power plants at all 40 surveyed wood processing mills. The additional biomass feedstock was calculated in order to assure an annual plant capacity factor of 80% for all power plants. The methodology used for analyzing the electricity generation potential of the power plants at the wood processing mills is similar to that used for the case of rice mills. The results of the wood processing mills analysis show that, if the surplus amount of wood residues (50,530 tonnes/year) could be used for power generation, the calculated combined power capacity output of the potential power plants at 40 surveyed rice mills is around 120 MW. A calculated amount of biomass fuel of about 1.51 million tonnes/year is required of which 0.05 million tonnes/year come from wood residues and 1.46 million tonnes/year from additional biomass feedstock. These potential power plants could export about 715 GWh/year of electricity to the grid. Table 14 summarizes the results of the analysis of the 40 surveyed wood processing mills. The detailed analysis results for each mill are provided in Annex 2. Table 14: Summarized results of the analysis of the 40 surveyed wood processing mills Description Unit Value Number of wood processing mills surveyed 40 Total input wood processing capacity t/day 2,820 Total input wood processed in the last year 2017 t/year 386,974 Total wood residue generated t/year 101,628 Total wood residue used by the wood processing mills t/year 51,096 Total surplus wood residue (used for power generation) t/year 50,532 Total additional biomass feedstock sourced t/year 1,459,208 Power generation technology used Medium pressure Total gross power capacity output MW 120 Total gross electricity generated GWh/year 841.0 Total net electricity output GWh/year 714.8 Total electricity consumption by the wood processing mills GWh/year Data were not available Total electricity exported to the grid, of which: GWh/year 714.8 From wood residues only GWh/year 32.2 From additional feedstock GWh/year 682.6 Page 50 The map of potential power plants at the 40 surveyed wood processing mills in Vietnam is provided in Figure 13. The power capacity output is 3 MW for all power plants. The red circles show smaller sourcing area for the additional biomass feedstock, the dark blue circles indicate larger sourcing area. The links for access to the survey results, the map and datasets for wood processing mills analysis are provided in Annex 3. Page 51 Figure 13: Map of potential power plants at the 40 surveyed wood processing mills in Vietnam [Background map: Google® Google Streets™] Page 52 5. CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions This report presents the final product of the assignment, i.e. the Biomass Atlas for Vietnam. More details on interim outputs as well as the detailed approach and methodology used for developing the Biomass Atlas for Vietnam can be found in separate reports, which are available at https://esmap.org/re_mapping_vietnam. They are: • the Inception Report, • the Implementation Plan, • the Report on Training Workshop on Field Survey and Data Collection in Vietnam, • the Report on Data Validation Workshop, • the Report on Phase 2 Implementation and Revised Work Plan for Phase 3. The Biomass Atlas presents both theoretical and technical potentials of crop residues. Crop residues of 18 crops were included in the Biomass Atlas. The theoretical potential of crop harvesting residues was estimated at around 59.89 million tonnes/year with an equivalent energy potential of 768,853 TJ/year (213,570 GWhth/year). 58.6% of the total energy potential come from rice straw, 26.3% from maize trash, 7.2% from cassava stalk, 3.0% from sugarcane trash, 2.9% from firewood generated from pruning the perennial crops, and 2.1% from other types of residues (peanut straw, soybean straw and firewood from pruning fruit trees). The theoretical generation potential of crop processing residues was estimated at 20.86 million tonnes/year with an equivalent energy potential of 245,094 TJ/year (68,082 GWhth/year). Rice husk accounts for 37.9% of this energy potential, followed by bagasse with 16.9%, corn cobs with 12.7%, coffee husk with 10.6%, maize husk with 7.5%, coconut husk with 4.9%, cassava peels with 4.5%, coconut shells with 3.2%, peanut shells with 1.0% and cashew nut shells with 0.8%. Based on the existing uses of the residues by the farmers, the technical potential of crop harvesting residues was estimated at about 15.22 million tonnes/year with an equivalent energy potential of 195,773 TJ/year (54,381 GWhth/year). Rice straw accounts for 56.1% of this energy potential, followed by maize trash with 24.0%, cassava stalk with 8.3%, sugarcane trash with 8.1%, firewood from pruning the perennial crops with 2.1% and peanut straw with 3.0%. It can be seen from these percentages that large amounts of rice straw, maize trash, peanut straw, cassava stalk and firewood are being used by the farmers (in forms of cooking fuel, animal fodder and fertilizer) or sold to industries. In case the farmers' willingness to sell their biomass residues is taken into account, the technical potential of crop harvesting residues decreases to about 7.95 million tonnes/year with an equivalent energy potential of 101,068 TJ/year (28,075 GWhth/year). Rice straw accounts for a majority of this energy potential with 61.1%, followed by maize trash with 29.5%, sugarcane trash with 3.7%, cassava stalk with 3.1%. Other types of residues (peanut straw, soybean straw and firewood) account for only 2.5%. Page 53 The Biomass Atlas for Vietnam also presents the potential for implementing power plants at the biomass producing sites (such as sugar mills, rice mills, wood processing mills, MSW landfills and livestock farms) as well as the potential for greenfield power plants using crop harvesting residue feedstock. The analysis showed that bagasse offers the highest potential as fuel for cogeneration plants at the existing sugar mills of Vietnam. It shows that new high-pressure cogeneration plants at 40 sugar mills could have a combined power capacity output of 600 MW, based on a total amount of generated bagasse of about 5.1 million tonnes/year in the 2016-2017 milling season. These potential cogeneration plants could export about 958 GWh/year if only bagasse is used or 3,363 GWh/year if around 3.4 million tonnes/year of additional biomass feedstock are used as fuel for the cogeneration plants. MSW can also be used for large-scale grid-connected power plants. With a total MSW amount of around 20,140 tonnes/day generated at the 38 surveyed landfills, around 131 MW of gross power capacity could be generated based on the anaerobic digester-based power generating technology. These potential MSW-based power plants could export about 978.3 GWh/year to the grid. However, rice husk, wood processing residues and livestock manure offer a rather limited energy potential which is limited to captive power plants aiming at generating electricity for covering the power requirements of the mills/farms. It should be noted that the analysis does not include all the existing MSW landfills, rice mills, wood processing mills and livestock farms in Vietnam due to limited resources for carrying out an exhaustive survey. The potential for greenfield power plants using crop harvesting residues was assessed based on their site suitability indicators. This site suitability indicator takes into account the feedstock sourcing area size, the road network density in the region, and the distance to the grid. A high site suitability value indicates a good site for a potential power plant, whereas a low value indicates a poor location. The site suitability maps were produced for 18 different combinations of energy conversion technologies and power plant capacities. 5.2 Recommendations The crop-level accuracy of the land use classification needs to be taken into account when evaluating a single site feasibility. Particularly crops cultivated in small home-gardens or orchards having sizes close to or below the land use mapping resolution of 20 m x 20 m are not well covered in the results. Except for the sugar sector, broader survey of industrial sites should be conducted to complete the database for the key industrial sectors with high potential of biomass residues. These sectors include rice mills, wood processing mills, MSW landfills and livestock farms. The Biomass Atlas for Vietnam shall be broadly disseminated via WB, MOIT and other channels. The stakeholders who have participated in the training on Biomass Atlas usage and maintenance shall share their knowledge and skills with other local stakeholders. Page 54 Plans shall be made to secure funds for a regular updating of the Biomass Atlas by the persons who have been trained. NLU, VNUA and other universities shall use the project study methodologies and outputs as training materials for building the capacity of their students. Page 55 6. ANNEXES Annex 1: Biomass Resource Mapping Methodology 1.1 End User Interaction During the process of project implementation, the consulting consortium maintained a close interaction with the key local stakeholders who are the potential end-users of the Biomass Atlas of Vietnam. This interaction helped the consulting consortium not only to update the local stakeholders on the progress of the project implementation, but also to receive their feedback on the project. Seven (7) multi-stakeholder meetings and workshops were conducted. These events attracted a total of 178 participants (see Table 15). In addition, several individual meetings with local institutions and companies were also organized during the missions of the consultants to Vietnam. The details of these seminars, workshops and meetings were reported in separate reports which are available at https://esmap.org/re_mapping_vietnam. Table 15: List of meetings and workshops conducted No. of No. Name of event Location Date/Time Participants 1. Kick-off meeting Hanoi 2 Jun 2015 12 2. Inception meeting Hanoi 3 Jun 2015 21 3. Phase I Workshop Hanoi 16-17 Sep 2015 28 4. Training Workshop on Field Survey HCMC (Nong Lam 28-29 Sep 2016 32 and Data Collection University) 5. Stakeholder Data Validation Workshop Hanoi 17 Nov 2017 24 6. Final Biomass Atlas Dissemination Hanoi 15 Aug 2018 31 Workshop and Training Workshop on Biomass Atlas Usage and Maintenance 7. Final Biomass Atlas Dissemination HCMC 17 Aug 2018 30 Workshop and Training Workshop on Biomass Atlas Usage Total 178 1.2 Mapping Methodology The mapping methodology for the Biomass Atlas consisted of four distinct components: crop biomass field survey, industrial survey, satellite image analysis and bioenergy potential modeling. Each of these components is described in the following sections. 1.2.1 Crop Biomass Field Survey As agreed with WB and MOIT during the Inception Mission, while the field survey sampling will cover the whole country, more samples will be collected in areas with abundant biomass. The resulting Biomass Atlas will provide more details on those areas and fewer details on areas with scarce biomass resource. Page 56 No field survey was conducted for forested areas. Instead the dataset that was planned to be used for the baseline biomass information is the output of the National Forest Inventory (NFIS) project executed by the forest authority VNFOREST in 2014-2016 as an open data sharing policy is being implemented at the FORMIS II project in VNFOREST. However, to date the open data-sharing policy has not been put into practice, and access to the NFIS data has not been possible. Field survey on the crop biomass residues The field survey for crop biomass residue production was conducted by VNUA (based in Hanoi) and NLU (based in Ho Chi Minh City) (hereinafter referred to as "the Local Consultants") after being contracted by MOIT. The FA Consortium was responsible for monitoring and validating the said survey. The field survey was a person-to-person interview by the survey team with the farmers. It was executed with mobile phones using the MHG Mobile Application (a proprietary-software developed by the Consortium partner - MHG Systems Oy Ltd.). The phone application was used to record the responses of the interviewed farmer, indicate the location of the interview, and take geo- referenced photos of the surveyed crop fields. The detailed assignment of the Local Consultants and the procedure of conducting the field survey were described in the TOR for the Local Consultants, which were developed during Phase 1 of the project. The field survey on the crop biomass residues was carried out in 63 provinces and cities of Vietnam. A total number of 18,900 interviews was proposed (calculated as 63 provinces/cities x 5 main crops x 60 interviews/province). The exact number and locations of the survey/interviews (i.e., district and commune) per province or city were defined by the Local Consultants based on its preliminary discussion with the Department of Agriculture and Rural Development (DARD) of each province/city and were submitted for approval to MOIT by the Local Consultants before executing the survey. The FA Consortium developed a complete crop biomass survey form (see section 4.1). The Local Consultants were trained by the FA Consortium on how to fill in the survey form using smartphones during the Training Workshop on Field Survey and Data Collection organized at NLU on September 28-29, 2016. Validation of Crop Biomass Survey During the field interviews, Simosol downloaded the interview data from the MHG Mobile server, validated the data for logical consistency against agreed validation rules, and reported any invalid data back to the survey teams for checking as daily Excel survey data files, which highlighted the invalid data. The survey teams compiled the corrected daily Excel files into weekly files and sent those directly to Simosol for validation. Once the weekly file passed the validation rules, it was considered the final survey result file for that week. Another verification executed at Simosol was for the "reference field" of the survey questionnaire. The interviewed farmer selected a single field for which the crops cultivated between Nov 2015 and Nov 2016 were recorded and a photograph of the field was taken. If the crop information recorded and the photograph taken did not match, or the location of the photograph could not reliably be assigned to a field identifiable from a very high-resolution satellite image, the reference field was excluded from the land use classification reference dataset. Figure 14 shows a sample of an accepted Page 57 reference field, and also demonstrates how the area to include into the reference sample was digitized during the validation process. Figure 14: A reference field sample that was included into land use classification reference sample data set (The yellow dots and white line delineate the area that was used as the reference sample for this field) Figure 15 gives an example of a reference field that was excluded from the reference data set. Figure 15: An example of a rejected reference field sample due to having been recorded in the middle of a road leaving uncertainty for the actual location of the field. Page 58 Results of Crop Biomass Survey VNUA completed their part of the crop biomass survey on December 8, 2016 and NLU on June 22, 2017. A total of 21,179 farmers in 514 districts were interviewed of which 13,835 interviews were conducted by NLU and 7,344 interviews by VNUA. After the validation, a total of 19,950 datasets were accepted of which 13,516 datasets were collected by NLU and 6,434 datasets were collected by VNUA. The number of surveyed districts, the number of farmers interviewed, and the number of datasets accepted for each of the provinces are presented in Table 16. Table 16: Summary of number of districts surveyed, farmers interviewed and datasets accepted Number of Number of Number of No. Province districts farmers datasets surveyed interviewed accepted 1 An Giang 8 515 515 2 Ba Ria-Vung Tau 5 213 213 3 Bac Giang 10 272 272 4 Bac Kan 4 110 74 5 Bac Lieu 4 292 269 6 Bac Ninh 8 83 83 7 Ben Tre 4 275 277 8 Binh Dinh 7 297 297 9 Binh Duong 7 369 373 10 Binh Phuoc 8 867 773 11 Binh Thuan 7 731 727 12 Ca Mau 4 273 274 13 Can Tho city 3 146 146 14 Cao Bang 12 238 172 15 Da Nang City 1 14 14 16 Dak Lak 12 999 987 17 Dak Nong 6 611 613 18 Dien Bien 7 279 280 19 Dong Nai 11 534 537 20 Dong Thap 6 489 489 21 Gia Lai 14 1,281 1,126 22 Ha Giang 11 387 309 23 Ha Nam 5 84 84 24 Ha Noi City 5 59 59 25 Ha Tay (now, Ha Noi City) 12 217 217 26 Ha Tinh 11 265 239 27 Hai Duong 12 171 171 28 Hai Phong City 2 96 96 29 Hau Giang 4 250 250 30 Ho Chi Minh City 6 132 125 31 Hoa Binh 11 255 201 32 Hung Yen 10 101 101 33 Khanh Hoa 5 174 278 34 Kien Giang 9 856 856 35 Kon Tum 8 401 402 36 Lai Chau 6 193 173 37 Lam Dong 10 726 583 38 Lang Son 11 210 210 Page 59 39 Lao Cai 9 166 163 40 Long An 11 588 588 41 Nam Dinh 10 212 212 42 Nghe An 18 576 520 43 Ninh Binh 7 151 116 44 Ninh Thuan 4 148 148 45 Phu Tho 12 202 202 46 Phu Yen 6 245 245 47 Quang Binh 7 198 153 48 Quang Nam 8 224 224 49 Quang Ngai 5 268 268 50 Quang Ninh 9 93 94 51 Quang Tri 8 185 181 52 Soc Trang 7 405 405 53 Son La 11 634 541 54 Tay Ninh 9 625 625 55 Thai Binh 8 174 174 56 Thai Nguyen 7 401 210 57 Thanh Hoa 22 594 492 58 Thua Thien - Hue 9 114 114 59 Tien Giang 7 412 412 60 Tra Vinh 5 294 294 61 Tuyen Quang 6 188 160 62 Vinh Long 5 204 205 63 Vinh Phuc 9 140 105 64 Yen Bai 9 273 234 Total 514 21,179 19,950 Page 60 Figure 16: Locations of farms with collected datasets accepted Page 61 1.2.2 Industrial Biomass Survey With the support from FA Consortium, the local consultants (NLU and VNUA) have developed a methodology for the industrial biomass survey, including the following six steps: • Identification and preparation of a list of industries to be surveyed; • Sending of the survey questionnaires to the identified industries; • Compilation and analysis/validation of the data received; • Selection of sites for on-site visits; • Conducting site visits to the selected industries; • Preparation of report on industrial biomass survey. The local consultants identified the major industries to be surveyed through the list of industries provided by provincial authorities (e.g. DARDs, DOITs, DONREs), by the relevant industry associations (e.g. VSSA, AHAV, VDA), and from other publicly available sources. The background information on the industrial sites (such as name of industrial site, detailed address, email and telephone number of contact person, size of the industrial site, etc.) were also collected. The survey questionnaires developed by FA Consortium (those were used during the training workshop on field survey and data collection) were sent to the identified industries for collection of relevant data. The local consultants used follow-up calls and e-mails to explain to the industries the purpose of the survey as well as the details of the survey questionnaire. The data received from the survey questionnaires were compiled and analyzed by the local consultants. Then, they were sent to FA Consortium for preliminary validation. Based on the analysis of the feedback from industries, and on the results of the preliminary data validation by FA Consortium, the local consultants selected sites for on-site visits to confirm the data provided in the complete questionnaire and, if needed, to collect additional information. Validation of Industrial Biomass Survey FA Consortium carried out the validation of the data collected through industrial biomass survey by the local consultants. The preliminary validation was conducted for the preliminary datasets obtained through the survey questionnaires, and the final validation was done for the final datasets collected after on-site visits. The local consultants compiled the final datasets in an Excel file and sent it to FA Consortium for validation. The following key data/aspects were checked: • GIS coordinates of the surveyed sites (using Google map); • Residue-to-Crop Ratio (RCR); • Characteristics of the biomass residues (e.g. moisture content, LHV); • Completeness and consistency of the data. The checked datasets with errors/missing information highlighted were sent back to the local consultants. They re-contacted the contact persons at the surveyed industries to verify the incorrect Page 62 data and/or to complete the missing information. The validated and verified data were eventually sent back to FA Consortium. That process continued until all datasets were finally accepted. Results of Industrial Biomass Survey A total of 261 datasets from seven industrial sectors were validated and accepted, of which 112 datasets were collected by NLU, 123 datasets collected by VNUA and 26 datasets were computed by FA Consortium from its database. Summary of the accepted datasets by industrial sector is given in Table 17. Table 17: Summary of the accepted datasets by industrial sector Datasets Datasets Datasets collected by Sector collected by collected by Total FA NLU VNUA Consortium Sugar mills 3 22 15 40 Rice mills 46 8 0 54 MSW landfills 4 28 6 38 Livestock farms 14 48 5 67 Wood processing mills 29 11 0 40 Brick-making factories 16 0 0 16 Pulp and paper mills 0 6 0 6 Total 112 123 26 261 The map of surveyed industrial sites is presented in Figure 17. Page 63 Figure 17: Map of the surveyed industrial sites Page 64 1.2.3 Satellite image analysis The purpose of the satellite image analysis was to produce a land use classification of agricultural fields for the whole country. Together with the crop yield and residue information collected during the field survey, the land use classification forms the basis for estimating the localized biomass feedstock potential from agricultural production. Gathering of satellite images The first step in the satellite image analysis was gathering the satellite images. Due to prevailing cloudiness over the country, synthetic aperture radar (SAR) images with 20 m x 20 m ground resolution from the Sentinel-1 (S-1) satellite by the European Space Agency (ESA) were used. The benefit of SAR images is that the radar signal is an active signal sent by the satellite, and this signal penetrates the cloud cover to reach the ground. This increases the availability of images over any given period considerably, compared to the optical satellite images, especially during the rainy season. The S-1 images were downloaded from the Copernicus Open Access Hub6. Twenty-four (24) image datasets covering Vietnam and distributed over a timespan of 12 months were used to do the classification at the level of identifying, the cultivated crops for each of the cropping seasons within a year. Figure 18 shows the coverage of the first Sentinel-1 image dataset over the map of Vietnam. Table 18 lists the exact data ranges of the 24 Sentinel-1 image datasets. In total 913 Sentinel-1 images were used in the crop level land use classification. As can be seen in Figure 18, there is a region in Northern Vietnam that was not covered by Sentinel-I images for the time period in question. The same applies also for some of the islands close to the mainland Vietnam. 6 https://scihub.copernicus.eu/ Page 65 Figure 18: 24 Sentinel-1 image tile sets used in the analysis (The numbering relates to the relative path of the Sentinel-1 data) Table 18: The date ranges for 24 Sentinel-1 image sets used in land use classification Set 1 2015-11-02 2015-11-28 Set 2 2015-11-18 2015-12-14 Set 3 2015-12-02 2015-12-28 Set 4 2015-12-19 2016-01-14 Set 5 2016-01-02 2016-01-28 Set 6 2016-01-19 2016-02-14 Set 7 2016-02-02 2016-02-28 Set 8 2016-02-17 2016-03-14 Set 9 2016-03-02 2016-03-28 Set 10 2016-03-19 2016-04-14 Set 11 2016-04-02 2016-04-28 Set 12 2016-04-18 2016-05-14 Set 13 2016-05-02 2016-05-28 Set 14 2016-05-19 2016-06-14 Set 15 2016-06-02 2016-06-28 Page 66 Set 16 2016-06-18 2016-07-14 Set 17 2016-07-02 2016-07-28 Set 18 2016-07-19 2016-08-14 Set 19 2016-08-02 2016-08-28 Set 20 2016-08-19 2016-09-14 Set 21 2016-09-02 2016-09-28 Set 22 2016-09-18 2016-10-14 Set 23 2016-10-02 2016-10-28 Set 24 2016-10-19 2016-11-14 For the land use classification, Vietnam was divided into 11 distinctive geographical areas according to agro ecological zones (AEZ) and orbital direction of satellite images (see north/east indication in Figure 19). Figure 19: Land cover classification areas used in the analysis Page 67 Analysis of satellite images The satellite image processing consisted of 6 stages described below. 1. Image unpacking: the unpacking stage extracts the satellite image data and its metadata from the distribution archive downloaded from Scihub Copernicus Open Access Hub. 2. Image processing: The Sentinel Application Platform (SNAP) was used to process the original Sentinel-1 data to 20m x 20m pixel size. Radiometric corrections, terrain-flattening and terrain- corrections were also applied using the SNAP software. 3. Edge masking: Sentinel-1 image characteristically have degraded data quality towards the vertical sides of the image. Edge masks of at least 5 km in width were used to erase incorrect data from sides of each satellite image. 4. Image mosaicking and stacking: Once quality of the images was assured, the images were mosaicked and stacked together to form a time-series that covers the whole country. Separate seamless images cover each AEZ separately in each 24 points-of-time, spread evenly over the analysis date range. 5. Ground reference data processing: Individual field observations recorded with the MHG Mobile software during the field survey were processed into AEZ specific reference observations. A separate quality control was executed at this stage selecting only those samples that clearly represented a field pixel. In total, 80% field crop references were used for classification, and 20% for the classification result validation. Additionally, 5418 reference samples for the other than agricultural land use classes were generated using very high-resolution imagery available online. Random image samples, covering roughly 300 m x 300 m at sub one-meter resolution, were generated. On each of these, a clear sample of one of the other land use classes (forest, urban, bush, water, bare area) was assigned to a polygon location using visual interpretation of the image content. 6. Land cover classification: The information in the 24 image time-series mosaic and ground reference samples was used to produce a land cover/land use classification for each of the 20 m x 20 m pixels using a Random Forest classifier separately for each land cover classification area. After the pixel-wise Random Forest classification, the land cover class predictions were further fine-tuned by using the confusion matrix information (see below) and the pixel neighborhood. This means that the final classification was done by changing the classification to the second most likely class from the most likely class, if the second most class was abundant in the 400 m x 400 m context around the pixel, the most likely class was rare in the same context, and the likelihood difference between the most likely and second most likely class was small. The land cover classes used in the classification were derived from the field survey information by analyzing existing combinations of crops. These, along with the non-agricultural classes used are listed in Table 19. Furthermore, for 33 southern provinces, a MONRE land use classification dataset was also available (Figure 20). This dataset was used as an analysis area mask, meaning that only the areas belonging to agricultural production areas were classified for these provinces. Page 68 Table 19: The 52 land use classes actually used in the classification Crop classes rubber-rubber-rubber fallow-fallow-rice rice-fallow-rice rice-fallow-fallow pepper-pepper-pepper rice-waterlogged-fallow rice-rice-fallow rice-rice-other other-other-other rice-rice-soybean rice-rice-rice rambutan-rambutan-rambutan cashew_nut-cashew_nut-cashew_nut mango-mango-mango sugarcane-sugarcane-sugarcane orange-orange-orange fallow-rice-rice rice-rice-waterlogged cassava-cassava-cassava longan-longan-longan fallow-cassava-cassava mandarin-mandarin-mandarin coffee-coffee-coffee coconut-coconut-coconut maize-maize-maize maize-rice-fallow fallow-fallow-maize fallow-fallow-cassava tea-tea-tea rice-rice-sweet_potato cassava-fallow-cassava rice-rice-sugarcane fallow-rice-fallow waterlogged-rice-rice maize-rice-rice sugarcane-fallow-fallow fallow-maize-rice peanut-maize-fallow litchi-litchi-litchi fallow-fallow-sugarcane fallow-litchi-fallow cassava-maize-fallow fallow-maize-maize fallow-maize-fallow other-rice-rice sweet_potato-rice-rice rice-rice-maize Other classes water bare_area forest bush urban Page 69 Figure 20: MONRE land use dataset for the 33 southern provinces showing non-agricultural areas used as classification mask. Figure 21 shows the classification accuracy for the Central Highlands agro-ecological zone. Overall, the classification accuracy is very good, highlighted by the dark blue diagonal in the chart. The values (i.e. the different shades of blue) in the chart show the proportion of the validation samples that have been predicted to be of some class. For example, all 2006 samples of rubber-rubber-rubber have been predicted to be rubber-rubber-rubber, but the class other-other-other has been predicted to be either other-other-other, sugarcane-sugarcane-sugarcane or cassava-cassava-cassava, and the different shades of blue give an indication of how these predictions are distributed across these three classes (most are predicted correctly to be other-other-other). Page 70 Figure 21: The land use classification result in the Central Highlands. The number of validation samples for the class is in brackets However, for some of the other AEZs the classification accuracy was not as high. The classification confusion matrix in Figure 22 highlights three problematic areas for the classification; (i) overrepresentation of the most common land use classes in the expense of the less prominent classes (other classes classified as rubber trees, the most prominent crop class), (ii) misclassification between permanent, woody crops (i.e., rubber, cashew nut, coffee, forest, mango, etc.), and (iii) misclassification between the "other" classes and crop classes (cassava and maize classified as “bush” to a small extent). Page 71 Figure 22: The land use classification result in the South-East AEZ When utilizing the Biomass Atlas results, the user is strongly advised to take the land use classification accuracy results into account. The full set of the classification confusion matrices containing this information is provided in Figures 23 and 24. When interpreting the matrices, one should also take into account the classes between which the possible misclassification has happened (e.g. for the purpose of evaluating the rice crop residue feedstock availability, a misclassification between two different rice classes is likely to be of no practical relevance). The confusion matrices were created before masking the analysis areas with the MONRE dataset for those provinces it was available. Page 72 Figure 23: The land use classification accuracy results for six northernmost regions Page 73 Figure 24: The land use classification accuracy results for seven southernmost regions Page 74 1.2.4 Biomass feedstock potential modeling of the Biomass Atlas The first stage of development of the Vietnam Biomass Atlas is illustrated in Figure 25. The first step of the analysis was the creation of the land cover classification. The details for this step are given in section 1.2.3 of this Annex. Figure 25: Components of the first atlas and harvest residue feedstock available at farm level The field survey data were used to derive the harvest biomass residue feedstock map from the land cover classification map. The results for the survey were aggregated at district level. For each crop being analyzed, the crop yield and current utilization patterns for the harvest residues were aggregated from single responses to district level average values, as well as their standard deviation and min-max values. The utilization patterns modeling used the answers to these survey questions: • Residue use; used as fertilizer in the field, % • Residue use; sold to biomass trader, % • Residue use; other, % • Residue use; left in field for burning, % • Residue use; directly sold to industry, % • Residue use; as fuel in household, % • Residue use; animal fodder, % In addition, a set of questions about attitudes towards participation in a commercial feedstock supply chain was asked. In the analysis, only the portion currently left in field for burning was considered to be available as feedstock for power generation. Page 75 The crop yield values from the survey responses were converted to different types of crop biomass residues based on residue-to-crop-ratios (see Table 2). Then, the biomass potential map was produced for each of the crop biomass residues. These maps show the amount of the crop harvesting residues available at the farms (see Figures 2, 3 and 4). At the finest level of detail, these maps are at 20x20 m resolution, but for usability, they are further aggregated to 1,000 m x1,000 m resolution, which will reduce the file size to a usable level. The second step of the data analysis and atlas creation is illustrated in Figure 26. Figure 26: Steps to create the industrial scale power generation potential atlas The output of the second step is a series of maps highlighting areas of high potential for industrial scale power generation. Each map is for a specific energy conversion technology and plant size. The maps are created based on a concept of a suitability index: the higher the index value, the higher the potential of the site for industrial power generation with the given technology. The set of analyzed technologies and plant sizes is: • Grate furnace type steam boiler + steam turbine: 3 MW, 8 MW, 15 MW. • Bubbling Fluidized Bed Combustion steam boiler + steam turbine: 8 MW, 15 MW, 25 MW, 50 MW, 100 MW. • Circulating Fluidized Bed Combustion steam boiler + steam turbine: 15 MW, 25 MW, 50 MW, 100 MW. • Gasifier + syngas engine/turbine: 0.5 MW, 1.5 MW. • Anaerobic digester + biogas engine: 0.5 MW, 1.5 MW, 3 MW, 8 MW Page 76 The site suitability indicator maps were produced for 18 different combinations of energy conservation technologies and power plant capacities (see Figures 6, 7 and 8 as examples). To derive these maps, the harvesting biomass residue feedstock maps are used to model the distance from which the feedstock must be sourced to the power plant. In order to derive the distance, the applicability of each feedstock for the given technology was graded with a scoring system: 0 – not suitable, 1 – acceptable, 2 – recommended. The feedstock sourcing distance-modeling principle is illustrated in Figure 27. The same principle applies also to the other factors used in modeling the site suitability index. First a 1,000 x 1,000 m grid is spanned over the whole country. Then for each grid cell (one marked in the figure with "X") the minimum distance from which the total feedstock amount needed to operate the power plant can be sourced is computed using the feedstock map. This is done for the recommended feedstock for the plant that is most abundantly available: the shorter the sourcing distance, the higher the site suitability index value for the grid cell under analysis. Figure 27: The modeling principle for the different site suitability factors. However, before computing the site suitability for single fuel availability, the direct, "as-the-crow- flies" minimum sourcing distance is converted to an approximation of the real sourcing distance with the help of road network density in the area. The road network density, shown in Figure 28, was estimated based on road network data downloaded from the OpenStreetMap7. 7 http://wiki.openstreetmap.org/wiki/Downloading_data Page 77 Figure 28: Road and watercourse network data used in the analysis Page 78 Next to the single fuel sourcing distance index, the second index to be computed for creating the final site-suitability indices, was the multi-fuel sourcing index. Again, the feedstock suitability was modeled, but now conditional on the primary feedstock, and its minimum share of the feedstock mix. Based on this grading, we computed a multi-fuel sourcing distance index similarly to the single fuel sourcing distance index. Similar distance-based index was computed for distance to the nearest power transmission network grid station (Figure 29) Figure 29: Grid stations, and the computed grid station distance index. (Darker green indicating higher index values, 50 km maximum distance for the grid connection) Page 79 Finally, three distance indices (fuel sourcing, grid station connection and road network density) were combined as a single site-suitability index by weighing the individual indices (0.6 for feedstock procurement distance, 0.3 for grid connection distance and 0.1 for road network density). As there are two indices for the fuel sourcing distance, separate indices were computed for single-fuel and multi-fuel sourcing. The result is the final site-suitability index for each analyzed technology and power plant size combination (see Figure 6 as an example). The site-suitability index is useful for greenfield projects looking for the most relevant places for an investment for further analysis. Feasibility analyses for power plant installation on an existing industrial site, can utilize the components of the atlas data that are used to derive the site-suitability index map. How to do this in practice was the focus of the training workshops on Biomass Atlas usage and maintenance. Page 80 Annex 2: Electricity generation potential at the surveyed biomass producing sites This Annex presents the key results of analysis of the electricity generation potential at the 239 surveyed biomass-generating industrial sites, including: 1. 40 sugar mills 2. 54 rice mills 3. 38 MSW landfills 4. 67 livestock farms 5. 40 wood processing mills Page 81 Table 20: Electricity generation potential at the surveyed sugar mills (the milling season 2016-17) (ranked by Gross Power Output) Electricity Electricity export Gross Additional Biomass Bagasse export (use of Bagasse power biomass feedstock available (use of bagasse No. Province District Sugar mill production capacity feedstock sourcing for cogen bagasse and (t/yr) output sourced area plant (t/yr) only) additional (MW) (t/yr) (km2/GWh) (GWh/yr) biomass) (GWh/yr) 1 Nghe An Quy Hop Nghe An Sugar Mill 324,800 323,200 55.0 105.64 336.51 306,887.7 0.323 An Khe 2 Gia Lai An Khe Sugar Mill 450,000 450,000 54.4 93.93 321.25 341,537.9 0.259 Township Cam Ranh KSC Khanh Hoa Sugar 3 Khanh Hoa 530,000 530,000 47.9 99.19 251.12 213,019.2 0.606 Township Mill Thanh Thanh Cong-Tay 4 Tay Ninh Tan Chau 288,432 288,432 45.1 76.05 275.98 263,838.0 0.557 Ninh Sugar Mill Ayun Pa Sugar Mill (Gia 5 Gia Lai Ayun Pa Lai Sugarcane 181,000 181,000 41.0 81.60 262.74 269,844.7 0.249 Thermoelectricity JSC) KCP Son Hoa Sugar 6 Phu Yen Son Hoa 332,000 332,000 28.9 43.51 144.81 153,272.3 0.898 Mill Tuyen 7 Ham Yen Tuyen Quang Sugar Mill 135,000 135,000 26.2 45.54 163.34 160,842.5 0.379 Quang 8 Khanh Hoa Ninh Hoa Ninh Hoa Sugar Mill 200,000 200,000 20.6 36.22 112.80 114,057.5 0.760 9 Nghe An Tan Ky Song Con Sugar Mill 116,000 116,000 19.3 32.75 114.06 109,172.1 0.708 10 Long An Ben Luc NIVL Sugar Mill 124,390 124,390 18.6 23.83 109.41 115,572.6 0.673 11 Phu Yen Son Hoa Van Phat Sugar Mill 184,000 180,000 17.7 28.51 89.89 94,126.1 1.188 12 Dak Lak Ea Kar The 333 Sugar Mill 126,000 100,800 15.2 23.72 85.98 93,934.2 0.925 Tay Ninh Bien Hoa-Tay Ninh 13 Tay Ninh 111,843 111,843 13.4 22.34 76.13 72,569.3 0.602 Township Sugar Mill Thach 14 Thanh Hoa Viet-Dai Sugar Mill 151,306 150,759 13.0 4.26 64.79 83,289.1 0.238 Thanh Quang 15 Duc Pho Pho Phong Sugar Mill 69,300 69,300 12.7 19.67 75.78 85,883.6 2.711 Ngai 16 Thanh Hoa Tho Xuan Lam Son Sugar Mill 186,160 186,160 11.7 5.05 55.74 69,773.6 0.284 17 Dak Lak Ea Sup Dak Lak Sugar Mill 77,500 62,500 10.9 16.55 62.65 69,832.8 1.581 Page 82 Soc Trang 18 Soc Trang Soc Trang Sugar Mill 120,160 120,160 10.0 23.02 52.45 39,778.3 1.101 Township Bien Hoa-Tri An Sugar 19 Dong Nai Vinh Cuu 72,943 72,943 9.7 13.57 53.99 55,759.4 1.764 Mill Vi Thanh 20 Hau Giang Vi Thanh Sugar Mill 134,974 134,974 9.6 12.80 44.80 44,190.9 0.440 Township 21 Tra Vinh Tra Cu Tra Vinh Sugar Mill 102,000 100,980 9.1 13.47 46.71 45,909.4 1.297 22 Binh Dinh Tay Son Binh Dinh Sugar Mill 73,300 73,300 8.9 4.58 46.07 65,144.3 1.039 Kon Tum 23 Kon Tum Kon Tum Sugar Mill 85,000 85,000 8.7 26.84 47.15 30,665.7 2.773 Township 24 Ben Tre Chau Thanh Ben Tre Sugar Mill 82,310 82,310 8.6 14.90 45.59 42,386.0 1.032 Tuyen 25 Son Duong Son Duong Sugar Mill 76,000 76,000 7.9 7.35 41.90 48,885.4 0.917 Quang 26 Dong Nai Dinh Quan La Nga Sugar Mill 65,550 65,550 7.5 11.87 40.51 39,509.0 1.506 27 Hau Giang Long My Long My Phat Sugar Mill 31,700 31,700 7.5 5.74 44.80 53,900.4 0.360 28 Long An Duc Hoa Hiep Hoa Sugar Mill 53,020 53,020 7.4 9.60 41.93 44,650.4 0.980 29 Hau Giang Phung Hiep Phung Hiep Sugar Mill 155,043 155,043 6.7 4.62 22.27 24,748.7 1.246 30 Son La Mai Son Son La Sugar Mill 108,000 108,000 6.5 6.22 28.13 30,970.8 4.858 31 Phu Yen Tuy Hoa Tuy Hoa Sugar Mill 73,000 73,000 6.0 7.68 26.81 30,082.8 2.254 32 Cao Bang Phuc Hoa Cao Bang Sugar Mill 42,000 30,000 5.5 3.42 29.97 37,949.2 0.828 33 Thanh Hoa Nong Cong Nong Cong Sugar Mill 56,000 56,000 5.1 6.36 26.97 28,730.6 0.384 34 Tay Ninh Tan Chau Nuoc Trong Sugar Mill 59,910 59,910 3.7 9.80 15.56 8,230.8 5.318 Thoi Binh-Ca Mau 35 Ca Mau Thoi Binh 23,040 23,040 3.7 3.77 20.46 23,846.9 1.833 Sugar Mill 36 Kien Giang Giong Rieng Kien Giang Sugar Mill 19,660 19,660 3.7 3.21 20.91 25,275.4 0.432 Ham Thuan 37 Binh Thuan MK-Vietnam Sugar Mill 24,000 24,000 3.5 3.93 18.95 21,455.1 0.905 Bac P.Rang- Ninh 38 T.Cham Phan Rang Sugar Mill 56,879 37,230 3.4 2.41 13.56 15,920.5 0.686 Thuan Township 39 Nghe An Anh Son Song Lam Sugar Mill 20,000 20,000 3.2 2.54 17.99 22,138.3 1.980 Hoa Binh 40 Hoa Binh Hoa Binh Sugar Mill 20,000 20,000 2.4 2.19 12.54 15,294.7 2.052 Township Total 5,142,220 5,063,204 600.0 958.3 3,363.0 3,412,876 Page 83 Table 21: Electricity generation potential at the surveyed rice mills (the milling season 2016-17) (ranked by Gross Power Output and Additional Biomass Feedstock Sourcing Area) Electricity Rice husk Gross Electricity Additional Biomas export (use of Rice husk available power export (use biomass feedstock rice husk and No. Province District Rice mill generation for power capacity of rice husk feedstock sourcing additional (t/yr) generation output only) sourced area biomass) (t/yr) (MW) (GWh/yr) (t/yr) (km2/GWh) (GWh/yr) Thanh Phat Limited 1 Long An Thanh Hoa 120,000 84,000 9.0 37.51 37.51 0.0 0.00 Liability Company Phat Ngan (Tu Le) 2 Ben Tre Mo Cay 108,000 75,600 8.1 33.75 33.75 0.0 0.00 Private Enterprise Bui Tien Dat Private 3 Long An Thanh Hoa 108,000 75,600 8.1 33.75 33.75 0.0 0.00 Enterprise Vu Hoang Private 4 Tien Giang Cai Lay 60,000 60,000 6.4 30.65 30.65 0.0 0.00 Enterprise Tan Long II Rice 5 Tien Giang Cai Lay 60,000 60,000 6.4 30.65 30.65 0.0 0.00 Grinding Machine Tan Long Private 6 Tien Giang Cai Lay 60,000 60,000 6.4 30.65 30.65 0.0 0.00 Enterprise Duc Bao Ngoc Private 7 Long An Thanh Hoa 40,000 28,000 3.0 11.87 11.87 0.0 0.00 Enterprise Tan Long III Rice 8 Tien Giang Cai Lay 5,400 5,400 3.0 2.55 16.97 24,738.3 0.146 Grinding Machine Nam Nhan Private 9 Tien Giang Cai Lay 5,500 5,500 3.0 2.69 17.05 24,621.5 0.159 Enterprise Binh Minh Private 10 Tien Giang Cai Lay 3,100 3,100 3.0 1.50 17.39 27,237.3 0.178 Enterprise Van Loi II Rice Grinding 11 Tien Giang Cai Lay 3,600 3,600 3.0 1.76 17.33 26,690.8 0.179 Machine Hoa Mai Private 12 Tien Giang Cai Lay 3,420 3,420 3.0 1.61 17.30 26,887.7 0.179 Enterprise Tai Loc Tai Private 13 Tien Giang Cai Be 7,220 5,000 3.0 2.01 16.69 25,189.8 0.205 Enterprise Tan Vinh Private 14 Tien Giang Cai Be 7,260 5,000 3.0 2.09 16.77 25,185.9 0.208 Enterprise Vu Nhung Private 15 Tien Giang Cai Be 6,400 4,500 3.0 1.91 16.91 25,733.4 0.208 Enterprise Page 84 Cao Lanh Vo Thi Be Tu Private 16 Dong Thap 440 440 3.0 0.23 17.82 30,191.4 0.215 City Enterprise Son Lam Private 17 Tien Giang Cai Be 3,200 2,300 3.0 0.96 17.36 28,145.0 0.221 Enterprise Nam Khoi Private 18 Tien Giang Cai Be 4,100 3,200 3.0 1.31 17.14 27,152.5 0.230 Enterprise Minh Tam Private 19 Tien Giang Cai Be 2,800 2,000 3.0 0.86 17.45 28,466.3 0.230 Enterprise Duong Loan Rice 20 Hung Yen An Thi 306 306 3.0 0.13 17.80 30,504.8 0.245 Milling Enterprise Loan Binh Private 21 Tien Giang Cai Be 10,500 7,000 3.0 2.81 16.21 22,983.6 0.246 Enterprise Song Thanh Private 22 Tien Giang Cai Be 11,200 7,500 3.0 2.99 16.07 22,436.4 0.246 Enterprise Phuoc Thanh Private 23 Tien Giang Cai Be 6,000 5,200 3.0 2.43 16.98 24,953.7 0.246 Enterprise Hai On Private 24 Tien Giang Cai Be 19,200 16,000 3.0 7.36 15.02 13,135.1 0.258 Enterprise Cong Thanh Private 25 Tien Giang Cai Lay 44,000 24,000 3.0 8.72 11.27 4,377.5 0.276 Enterprise Rice Mill of Hung Cuc 26 Thai Binh Dong Hung 7,200 2,200 3.0 0.00 16.25 28,287.0 0.277 Co., Ltd. 27 Tien Giang Cai Lay Le Ngoc An Machine 5,400 0 3.0 0.00 17.39 30,575.4 0.311 Hung Thinh Private 28 Dong Thap Lap Vo 23,000 23,000 3.0 11.98 15.17 5,476.8 0.320 Enterprise Xuan Thu Rice 29 Long An Moc Hoa 2,400 1,680 3.0 0.71 17.51 28,832.6 0.321 Grinding Factory Chin Bien Private 30 Long An Moc Hoa 900 600 3.0 0.25 17.74 30,015.6 0.323 Enterprise My Chau Private 31 Long An Moc Hoa 5,000 3,500 3.0 1.42 17.06 26,838.6 0.332 Enterprise Cao Lanh Van Buu Private 32 Dong Thap 3,060 3,060 3.0 1.60 17.52 27,321.8 0.342 City Enterprise Nam Dinh Rice Mill of Huong 33 Nam Dinh 4,000 0 3.0 0.00 17.24 30,770.3 0.463 City Giang Trading JSC Cauke Food Processing 34 Tra Vinh Cau Ke 2,000 0 3.0 0.00 17.57 30,382.6 0.537 Factory 35 Dong Thap Chau Duc Lap Private 10,000 10,000 3.0 5.13 16.62 19,702.8 0.582 Page 85 Thanh Enterprise Tan An Tam Trang Private 36 Long An 3,500 0 3.0 0.00 17.36 30,295.4 0.584 Township Enterprise Rice Mill of Truong Vi 37 Nam Dinh Giao Thuy 3,000 600 3.0 0.00 17.33 30,048.1 0.587 Co., Ltd. Dai Hiep Thanh 38 Long An Tan Tru 1,460 1,168 3.0 0.53 17.65 29,038.6 0.632 company limited 39 Long An Tan Tru Thinh Vuong II Co. Ltd. 1,890 0 3.0 0.00 17.60 30,302.7 0.632 40 Long An Tan Tru Thinh Vuong I Co., Ltd 1,890 0 3.0 0.00 17.60 30,302.7 0.632 Phuoc Loi Private 41 Long An Tan Tru 7,300 0 3.0 0.00 16.97 30,280.1 0.641 Enterprise Tan An Binh Phuong - Tan Tru 42 Long An 2,190 2,000 3.0 0.98 17.57 28,172.5 0.642 Township Co., Ltd Thanh Ba Rice Milling 43 Hai Phong Vinh Bao 403 403 3.0 0.14 17.76 30,232.7 0.644 Enterprise Pham Thi Cuc Private 44 Long An Ben Luc 12,400 2,400 3.0 0.00 16.07 27,809.8 0.690 Enterprise Ly Lieu Rice Milling 45 Nam Dinh Giao Thuy 1,192 1,192 3.0 0.61 17.72 29,378.4 0.777 Enterprise Tri Mai Private 46 Long An Thu Thua 14,987 0 3.0 0.00 15.77 30,402.2 0.783 Enterprise 47 Long An Thu Thua Thinh Phat Co., Ltd 16,790 0 3.0 0.00 15.41 30,402.2 0.783 Ngoc Phuong Nam 48 Long An Thu Thua 17,000 3,000 3.0 0.00 15.47 27,161.9 0.839 company limited Tan An Cong Thanh 2 Private 49 Long An 10,950 9,950 3.0 4.77 16.29 19,452.9 0.985 Township Enterprise Ut Dung Private 50 Long An Thanh Hoa 2,400 1,680 3.0 0.71 17.51 28,797.4 1.026 Enterprise Van Phat Private 51 Long An Thanh Hoa 2,400 1,680 3.0 0.71 17.51 28,797.4 1.026 Enterprise Hoai Dung Private 52 Long An Thanh Hoa 7,200 5,040 3.0 2.14 16.79 25,000.6 1.034 Enterprise 53 Bac Giang Hiep Hoa Hiep Hoa Rice Mill 66 50 3.0 0.02 17.86 30,670.0 1.206 Dong Hoi Hoang Thi Chau Rice 54 Quang Binh 173 173 3.0 0.07 17.83 30,108.7 4.518 City Milling Enterprise Total 869,797 620,042 188.4 284.5 1,002.5 1,243,489 Page 86 Table 22: Electricity generation potential at the surveyed landfills (ranked by Gross Power Output) MSW MSW Organic Annual Rated Name of Landfill Gross Electricity collected dumped MSW biogas gross (Waste electricity export to No. Province District (tonne/day (tonne/day (tonne/day production power Management output the grid on wet on wet on wet (Million capacity Company) (GWh/year) (GWh/year) basis) basis) basis) m3/year) (MW) Ho Chi 1 Binh Chanh Da Phuoc Landfill 5,000.0 5,000.0 2,500.0 109,500,000 261.92 33.2 248.83 Minh City Nam Son Solid Waste 2 Hanoi City Soc Son 4,521.0 4,499.2 2,249.6 98,532,480 235.69 29.9 223.91 Treatment Complex Ho Chi 3 Cu Chi Tam Tan Landfill 2,000.0 1,500.0 750.0 32,850,000 78.58 10.0 74.65 Minh City Da Nang 4 Lien Chieu Khanh Son Landfill 900.0 885.0 619.5 27,134,100 64.90 8.2 61.66 City Hai Phong Trang Cat Solid Waste 5 Hai An 1,000.0 550.0 330.0 14,454,000 34.57 4.4 32.85 City Treatment Complex 6 Dong Nai Vinh Cuu Sonadezi Landfill 650.0 650.0 325.0 14,235,000 34.05 4.3 32.35 Bau Can Domestic and 7 Dong Nai Long Thanh Industrial Solid Waste 528.9 528.9 264.5 11,582,910 27.71 3.5 26.32 Treatment Complex Nghi Yen Solid Waste 8 Nghe An Nghi Loc 514.0 514.0 257.0 11,256,600 26.93 3.4 25.58 Treatment Complex Hai Phong 9 Hai An Dinh Vu Landfill 1,000.0 400.0 244.0 10,687,200 25.56 3.2 24.29 City Thai Thai Nguyen Da Mai-Tan Cuong 10 220.0 220.0 187.0 8,190,600 19.59 2.5 18.61 Nguyen City Landfill Buon Ma 11 Dak Lak Cu Ebur Landfill 246.0 230.0 184.0 8,059,200 19.28 2.4 18.31 Thuot City Tam Xuan 2 Solid 12 Quang Nam Nui Thanh Waste Treatment and 252.0 252.0 176.4 7,726,320 18.48 2.3 17.56 Landfill 13 Thanh Hoa Don Son Dong Nam Landfill 280.0 252.0 168.8 7,395,192 17.69 2.2 16.80 Dai Hiep Solid Waste 14 Quang Nam Dai Loc 209.0 209.0 146.3 6,407,940 15.33 1.9 14.56 Treatment and Landfill 15 Nam Dinh My Loc Loc Hoa Landfill 200.0 200.0 140.0 6,132,000 14.67 1.9 13.93 Ninh Binh Solid Waste 16 Ninh Binh Tam Diep 210.0 195.0 117.0 5,124,600 12.26 1.6 11.65 Treatment Plant Page 87 Can Tho 17 Co Do Co Do Landfill 300.0 225.0 112.5 4,927,500 11.79 1.5 11.20 City Phuoc An Solid Waste 18 Dong Nai Nhon Trach 200.0 200.0 100.0 4,380,000 10.48 1.3 9.95 Treatment Complex 19 Long An Thanh Hoa Tam Sinh Nghia Landfiil 200.0 200.0 100.0 4,380,000 10.48 1.3 9.95 20 Hanoi City Son Tay Xuan Son Landfill 248.0 248.0 99.2 4,344,960 10.39 1.3 9.87 Bac Giang 21 Bac Giang Da Mai Landfill 120.0 120.0 84.0 3,679,200 8.80 1.1 8.36 City 22 Thanh Hoa Sam Son Sam Son Landfill 118.0 118.0 79.1 3,462,828 8.28 1.1 7.87 Nui Thoong Solid 23 Hanoi City Chuong My Waste Treatment 160.0 160.0 78.4 3,433,920 8.21 1.0 7.80 Complex Long Xuyen 24 An Giang Binh Duc Landfill 150.0 150.0 75.0 3,285,000 7.86 1.0 7.46 City Hung Yen Hung Yen Solid Waste 25 Hung Yen 75.0 75.0 67.5 2,956,500 7.07 0.9 6.72 City Treatment Complex Tuyen 26 Yen Son Nhu Khe Landfill 97.0 96.8 64.8 2,839,226 6.79 0.9 6.45 Quang Tan Hung Solid Waste 27 Tay Ninh Tan Chau 150.0 120.0 60.0 2,628,000 6.29 0.8 5.97 Treatment Plant 28 Tien Giang Tan Phuoc Tan Lap Landfill 120.0 120.0 60.0 2,628,000 6.29 0.8 5.97 Tam Nghia Solid Waste 29 Quang Nam Nui Thanh 62.0 62.0 43.4 1,900,920 4.55 0.6 4.32 Treatment and Landfill 30 Khanh Hoa Ninh Hoa Hon Ro Sanitary Landfill 60.0 60.0 42.0 1,839,600 4.40 0.6 4.18 31 Bac Giang Viet Yen Doi Ong Mat Landfill 57.5 45.0 31.5 1,379,700 3.30 0.4 3.14 32 Ha Nam Thanh Lien Dam Gai Landfill 100.0 30.0 21.0 919,800 2.20 0.3 2.09 33 Khanh Hoa Ninh Hoa Hon Ro Sanitary Landfill 20.0 20.0 14.0 613,200 1.47 0.2 1.39 34 Hanoi City Gia Lam Kieu Ky Landfill 40.0 40.0 14.0 613,200 1.47 0.2 1.39 35 Dak Lak Ea Kar Ea Kar Landfill 15.0 15.0 13.5 591,300 1.41 0.2 1.34 Cu Jut Solid Waste 36 Dak Nong Cu Jut 16.0 11.5 8.6 377,775 0.90 0.1 0.86 Landfill 37 Bac Ninh Que Vo Phu Lang Landfill 30.0 30.0 1.5 65,700 0.16 0.02 0.15 Luong Son Solid Waste 38 Hoa Binh Luong Son 75.0 0.0 0.0 0 0.00 0.00 0.00 Treatment Complex Total 20,144.4 18,231.4 9,829.1 430,514,471 1,029.8 130.5 978.3 Page 88 Table 23: Electricity generation potential at the surveyed livestock farms (ranked by Gross Power Output) Manure Manure Number Annual Gross Gross Electricity Type collected available for of biogas electricity power export to No. Province District Name of Dairy Farm of (t/day on biogas animals production output capacity the grid farm as-received production raised (m3/yr) (MWh/yr) (kW) (MWh/yr) basis) (t/day) Tay Ninh Dairy Farm 1 Tay Ninh Ben Cau Cow 8,000 160.00 160.00 1,752,000 4,541.2 576.0 4,314.1 (Vinamilk JSC) Cow Farm of Huy Long 2 Long An Duc Hue Cow 5,000 100.00 100.00 1,095,000 2,838.2 360.0 2,696.3 An Co., Ltd. Binh Nguyen Duc Thang Pig 3 Phu Giao Pig 20,000 40.00 40.00 730,000 1,892.2 240.0 1,797.6 Duong Farm Binh Loc Ninh 1-2 Pig Farm 4 Loc Ninh Pig 14,400 36.00 36.00 657,000 1,702.9 216.0 1,617.8 Phuoc (Loc Phat Co., Ltd.) Trang Phu Son Livestock 5 Dong Nai Pig 22,000 57.00 30.00 624,150 1,617.8 205.2 1,536.9 Bom Breeding Company Dairy Farm of the Moc 6 Son La Moc Chau Chau Dairy Cattle Cow 23,698 350.00 30.00 328,500 851.5 108.0 808.9 Breeding JSC Huong 7 Ha Tinh Ha Tinh Dairy Farm Cow 2,000 27.00 17.00 186,150 482.5 61.2 458.4 Son Hanoi Chuong Pig Farm of Mrs. Dang 8 Pig 3,000 10.96 10.96 182,500 473.0 60.0 449.4 City My Thi Nam Phuong Tam Diep Pig Farm of 9 Ninh Binh Tam Diep the Thuy Phuong Swine Pig 6,000 11.50 9.70 177,025 458.8 58.2 435.9 Research Center Ho Chi 10 Cu Chi Gia Phat Pig Farm Pig 2,600 9.10 9.10 166,075 430.5 54.6 408.9 Minh City Hanoi Chuong Pig Farm of Mr. Nguyen 11 Pig 2,500 9.13 9.13 164,250 425.7 54.0 404.4 City My Van Tien Hanoi Chuong Pig Farm of Mr. Nguyen 12 Pig 2,000 7.42 7.42 135,050 350.0 44.4 332.5 City My Viet Do Hanoi Chuong Pig Farm of Mr. Luu 13 Pig 2,000 7.31 7.31 120,450 312.2 39.6 296.6 City My Huu Quyen Binh 14 Loc Ninh Dong Thanh 2 Pig Farm Pig 10,630 20.35 6.11 111,325 288.6 36.6 274.1 Phuoc 15 Binh Dinh Van Canh Thanh Phu Pig Farm Pig 2,500 9.50 6.50 94,900 246.0 31.2 233.7 Page 89 16 Binh Dinh Phu Cat Nhat Vinh Pig Farm Pig 2,200 6.00 6.00 91,250 236.5 30.0 224.7 Hiep Anh Xuan Limited-Liability One- 17 Gia Lai Dak Po Pig 4,000 5.00 5.00 73,000 189.2 24.0 179.8 member Trade Services Company DOLICO Suoi Cao Pig 18 Dong Nai Xuan Loc Pig 2,400 6.00 4.00 73,000 189.2 24.0 179.8 Farm Thai 19 Dong Hy Phuc Thinh Pig Farm Pig 4,000 4.00 4.00 73,000 189.2 24.0 179.8 Nguyen 20 Bac Giang Viet Yen Pig Farm of Mr. Tho Pig 2,000 3.60 3.60 65,700 170.3 21.6 161.8 Pig Farm of Mr. Chu 21 Bac Giang Viet Yen Pig 1,500 3.30 3.30 60,225 156.1 19.8 148.3 The Van Long Nguyen Tan Hau Pig 22 Dong Nai Pig 1,200 3.00 3.00 54,750 141.9 18.0 134.8 Thanh Farm 23 Binh Dinh An Nhon Thai Nguyen Pig Farm Pig 1,500 3.50 3.00 43,800 113.5 14.4 107.9 Thuan Nguyen Duc Lua Pig 24 Bac Ninh Pig 1,500 2.00 2.00 36,500 94.6 12.0 89.9 Thanh Farm Hanoi Pig Farm of Mr. Nguyen 25 Ung Hoa Pig 16,300 30.00 2.00 36,500 94.6 12.0 89.9 City Van Thanh 26 Binh Dinh Hoa An Huy Tuyet Pig Farm Pig 600 3.00 2.00 32,850 85.1 10.8 80.9 Viet Hung Breeding 27 Thai Binh Hung Ha Cow 7,000 80.00 2.00 21,900 56.8 7.2 53.9 One-Member Co., Ltd. Thanh 28 Tho Xuan Thanh Hoa Dairy Farm Cow 1,600 22.00 2.00 21,900 56.8 7.2 53.9 Hoa Phat Dat Livestock 29 Vinh Phuc Phuc Yen Pig 2,000 2.25 1.20 21,900 56.8 7.2 53.9 Farm Yen Bai Pig Farm of the Dam 30 Yen Bai Pig 2,100 3.00 1.00 18,250 47.3 6.0 44.9 City Mo Co., Ltd. DABACO Nucleus 31 Bac Ninh Tien Du Pig 30,000 22.00 1.00 18,250 47.3 6.0 44.9 Breeding Pig Co., Ltd. Don Da Lat Dairy Farm No. 32 Lam Dong Cow 1,000 10.00 1.40 15,330 39.7 5.0 37.7 Duong 1 of Vinamilk 33 Dong Nai Bien Hoa Tam Phuoc Pig Farm Pig 500 1.20 0.70 14,600 37.8 4.8 36.0 Hanoi Pig Farm of Mr. Nguyen 34 My Duc Pig 5,000 7.50 0.70 12,775 33.1 4.2 31.5 City Van The Hai Kim Hoang Van Thuan Pig 35 Pig 1,600 0.90 0.65 11,863 30.7 3.9 29.2 Duong Thanh Farm 36 Tuyen Yen Son Tuyen Quang Dairy Cow 1,976 33.00 1.50 10,950 28.4 3.6 27.0 Page 90 Quang Farm of Vinamilk Pig Farm of Mr. Nguyen 37 Ha Nam Kim Bang Pig 1,800 2.90 0.60 10,950 28.4 3.6 27.0 Van Che Hanoi Pig Farm of Mr. Quach 38 Ung Hoa Pig 2,000 3.00 0.50 9,125 23.7 3.0 22.5 City Thanh Toan 39 Bac Giang Viet Yen Thanh Oanh Pig Farm Pig 3,000 2.50 0.50 9,125 23.7 3.0 22.5 Hanoi 40 Dong Anh Hong Nhien Pig Farm Pig 4,000 4.00 2.00 7,300 18.9 2.4 18.0 City Hanoi 41 Dong Anh Mr. Khanh Pig Farm Pig 2,000 2.50 1.00 6,570 17.0 2.2 16.2 City Pig Farm of Mr. Pham 42 Hung Yen My Hao Pig 1,200 1.60 0.30 5,475 14.2 1.8 13.5 Khac Bo Pig Farm of Mr. Nhiem 43 Bac Giang Yen Dung Pig 1,000 2.30 0.30 5,475 14.2 1.8 13.5 (CP Vietnam) Pig Farm of Mr. Chu 44 Bac Giang Viet Yen Pig 500 0.45 0.25 4,563 11.8 1.5 11.2 Van Oanh Don Da Lat Dairy Farm No. 45 Lam Dong Cow 400 4.00 0.40 4,380 11.4 1.4 10.8 Duong 2 of Vinamilk Thai Tan 46 Tran Thi Mai Pig Farm Pig 1,200 0.82 0.22 4,015 10.4 1.3 9.9 Nguyen Cuong 47 Bac Giang Viet Yen Hung An Pig Farm Pig 1,200 1.20 0.20 3,650 9.5 1.2 9.0 Livestock Farm of Mrs. 48 Son La Moc Chau Cow 86 1.12 0.23 3,139 8.1 1.0 7.7 Nguyen Thi Chi 49 Thai Binh Thai Thuy Le Van Khoa Pig Farm Pig 1,700 1.00 0.17 3,103 8.0 1.0 7.6 Mang Gia Lai Livestock Joint 50 Gia Lai Cow 7,000 180.00 0.00 0 0.0 0.0 0.0 Yang Stock Company Tay Nguyen Dairy 51 Gia Lai Chuprong Products Joint Stock Cow 5,000 75.00 0.00 0 0.0 0.0 0.0 Company Nguyen Van Thanh 52 Gia Lai Dak Po Pig 1,200 2.40 0.00 0 0.0 0.0 0.0 Super-lean Pig Farm Nguyen Van Thi Pig 53 Binh Dinh Phu Cat Pig 1,700 4.50 0.00 0 0.0 0.0 0.0 Farm Pig Farm of Masan 54 Nghe An Quy Hop Pig 2,000 3.60 0.00 0 0.0 0.0 0.0 Nutri Co., Ltd. Pleiku Dairy Farm of Gia Lai 55 Gia Lai Cow 2,900 37.70 0.00 0 0.0 0.0 0.0 City Livestock JSC 56 Thanh Nhu Nhu Thanh Dairy Farm Cow 2,000 16.00 0.00 0 0.0 0.0 0.0 Page 91 Hoa Thanh Dairy Farm of Thong Thanh 57 Yen Dinh Nhat Dairy One- Cow 2,447 27.00 0.00 0 0.0 0.0 0.0 Hoa Member Co., Ltd. Nucleus Breeding Pig 58 Dak Nong Cu Jut Center of Green Farm Pig 35,000 45.00 0.00 0 0.0 0.0 0.0 Asia Co. Don Dairy Farm of Da Lat 59 Lam Dong Cow 1,000 15.00 0.00 0 0.0 0.0 0.0 Duong Milk JSC Nghia Dairy Farm of the TH 60 Nghe An Cow 14,900 180.00 0.00 0 0.0 0.0 0.0 Dan True Milk (Cluster 1) Nghia Dairy Farm of the TH 61 Nghe An Cow 13,600 157.70 0.00 0 0.0 0.0 0.0 Dan True Milk (Cluster 2) Nghia Dairy Farm of the TH 62 Nghe An Cow 6,000 64.80 0.00 0 0.0 0.0 0.0 Dan True Milk (Cluster 3) Thanh Cow Farm of the Anh 63 Ngoc Lac Cow 1,000 10.00 0.00 0 0.0 0.0 0.0 Hoa Minh Giang Co., Ltd. Thuan Nguyen Van Hung Pig 64 Bac Ninh Pig 2,000 2.50 0.00 0 0.0 0.0 0.0 Thanh Farm Thanh Ba Thuoc Livestock 65 Ba Thuoc Cow 5,000 50.00 0.00 0 0.0 0.0 0.0 Hoa Breeding JSC Nucleus Breeding Pig Thanh 66 Phu Tho Farm of Tien Hai Co., Pig 2,031 1.52 0.00 0 0.0 0.0 0.0 Thuy Ltd. Hanoi Pig Farm of Mr. Dao 67 Ung Hoa Pig 2,600 3.12 0.00 0 0.0 0.0 0.0 City Van Quyet Total 343,768 2,009.8 536.0 7,409,538 19,205.3 2,436 18,245.4 Page 92 Table 24: Electricity generation potential at the surveyed wood processing mills (ranked by Gross Power Output and Additional Biomass Feedstock Sourcing Area) Electricity Electricity Wood Gross export export (use Additional Wood residue Feedstock power (use of of wood biomass Name of Wood resdiue available sourcing No. Province District capacity wood residue & feedstock Processing Mill generation for power area output residue additional sourced (t/yr) generation (km2/GWh) (MW) only) biomass) (t/yr) (t/yr (GWh/yr) (GWh/yr) 1 Bac Giang Luc Nam An Lam Co., Ltd. 3,532.0 0.0 3.0 0.00 17.87 30,757.7 0.698 Sam Lanh Wood 2 Ha Nam Duy Tien Manufacturing and 986.0 0.0 3.0 0.00 17.87 30,814.2 0.752 Processing Mill 3 Hanoi City Gia Lam Hai Nam Co., Ltd. 460.0 460.0 3.0 0.29 17.87 30,475.3 0.945 Dai Phat Hoan Hao Private 4 Dong Nai Bien Hoa 300.0 250.0 3.0 0.16 17.87 34,480.9 1.125 Enterprise Toan Gia Private Enterprise 5 Dong Nai Bien Hoa 260.0 200.0 3.0 0.13 17.87 34,543.1 1.125 - Factories II 6 Dong Nai Bien Hoa Duc Long Private Enterprise 450.0 380.0 3.0 0.24 17.87 34,319.4 1.125 Truong Nga Private 7 Dong Nai Bien Hoa 460.0 400.0 3.0 0.26 17.87 34,294.6 1.125 Enterprise Hoang De Private 8 Dong Nai Bien Hoa 750.0 670.0 3.0 0.43 17.87 33,959.1 1.125 Enterprise Thu Trinh Private 9 Dong Nai Bien Hoa 300.0 240.0 3.0 0.15 17.87 34,514.0 1.131 Enterprise Huy Hoang Phat Trading 10 Dong Nai Bien Hoa Service PTE Wood 470.0 430.0 3.0 0.27 17.87 34,277.8 1.131 Processing Nhu Y Ngoc Wood Private 11 Dong Nai Bien Hoa 3,500.0 3,000.0 3.0 1.91 17.87 31,047.9 1.178 Enterprise Minh Nguyet (Moonlight) 12 Dong Nai Bien Hoa Co., LTD - Wood 2,200.0 2,000.0 3.0 1.28 17.87 32,289.8 1.178 Processing Tan Phu Hoa Private 13 Dong Nai Bien Hoa 400.0 300.0 3.0 0.19 17.87 34,401.1 1.178 Enterprise 14 Dong Nai Bien Hoa Cong Lap Wood Processing 250.0 190.0 3.0 0.12 17.87 34,537.8 1.178 Dang Long Co., LTD 15 Dong Nai Bien Hoa 3,500.0 3,000.0 3.0 1.91 17.87 31,047.9 1.178 Processing Export Wooden Page 93 Product Thien An Thinh Trading 16 Dong Nai Bien Hoa Service PTE Wood 400.0 350.0 3.0 0.22 17.87 34,360.3 1.185 Processing 17 Dong Nai Bien Hoa Thai An Private Enterprise 250.0 220.0 3.0 0.14 17.87 34,521.9 1.185 Bao Lan One-Member Co., 18 Bac Giang Yen The 2,718.0 2,718.0 3.0 1.73 17.87 27,729.6 1.196 Ltd. Binh Cong ty TNHH Che bien 19 Di An 1,000.0 900.0 3.0 0.57 17.87 30,733.9 1.319 Duong Lam san Binh An Thai Thai 20 Nguyen Viet Bac Plywood JSC 1,872.0 0.0 3.0 0.00 17.87 30,680.0 1.381 Nguyen City Binh Cong ty TNHH Do go 21 Di An 550.0 480.0 3.0 0.31 17.87 31,208.8 1.383 Duong Nguyen Thanh Binh Cong ty TN MTV Phu Gia 22 Di An 7,500.0 6,000.0 3.0 3.83 17.87 24,938.8 1.457 Duong Loc Phu Thanh Phat 23 Dong Nai Bien Hoa Manufacturing and Export 1,000.0 800.0 3.0 0.51 17.87 34,257.9 1.463 Co., Ltd. Vuong Thien Nhat One 24 Dong Nai Bien Hoa 1,050.0 900.0 3.0 0.57 17.87 34,131.9 1.463 Member Co., Ltd Glee Wood Processing 25 Phu Tho Lam Thao 400.0 400.0 3.0 0.26 17.87 30,753.2 1.497 Company Binh Cong ty TNHH SX-KD-TM- 26 Thuan An 540.0 375.0 3.0 0.24 17.87 33,431.1 1.504 Duong DV Thien Phat Binh Cong ty TNHH SX-TM-DV 27 Thuan An 506.0 333.0 3.0 0.21 17.87 33,393.6 1.560 Duong Hiep Sanh Binh Cong ty TNHH SX-TM-DV 28 Thuan An 350.0 300.0 3.0 0.19 17.87 33,505.2 1.579 Duong Hung Loc Phat Binh Cong ty TNHH Go Lien 29 Tan Uyen 1,800.0 1,500.0 3.0 0.96 17.87 32,361.2 1.675 Duong Phat Binh Cong ty TNHH Do go Lap 30 Tan Uyen 15,000.0 11,000.0 3.0 7.02 17.87 20,759.6 1.675 Duong Dat Binh Chon MDF VRG DONGWHA 31 15,000.0 0.0 3.0 0.00 17.87 40,557.5 1.683 Phuoc Thanh Wood JSC Hoang Anh Gia Lai Wood 32 Gia Lai Pleiku 9,270.0 270.0 3.0 0.17 17.87 61,225.1 2.238 Joint Stock Company 33 Gia Lai Pleiku Cong ty TNHH MTV Noi 3,000.0 1,560.0 3.0 1.00 17.87 59,254.2 2.310 Page 94 That SESAN Quang Minh Long Trading and 34 Cam Pha 124.8 15.6 3.0 0.01 17.87 30,940.2 2.396 Ninh Service JSC 35 Gia Lai Pleiku Hiep Loi Co., Ltd. 840.0 420.0 3.0 0.27 17.87 63,639.2 2.446 Chu Tam Phuc Gia Lai Trading 36 Gia Lai 9,900.0 8,100.0 3.0 5.17 17.87 43,351.7 2.461 Prong Co., Ltd. 37 Gia Lai Pleiku Cong ty TNHH 30/4 Gia Lai 2,159.0 952.0 3.0 0.61 17.87 63,151.9 2.654 MDF Veneer Manufacturing 38 Dak Nong Dak Song 630.0 630.0 3.0 0.40 17.87 44,580.0 4.404 Plant of MDF Bison JSC Quy PISICO Forestry Export 39 Binh Dinh 6,030.6 0.0 3.0 0.00 17.87 44,768.1 5.735 Nhon Processing Enterprise Quang Quang Nam MDF 40 Hiep Duc 1,920.0 788.0 3.0 0.50 17.87 45,212.2 9.507 Nam Manufacturing Mill Total 101,628.4 50,531.6 120.0 32.2 714.8 1,459,208 Page 95 Annex 3: Biomass Atlas Components The Vietnam Biomass Atlas consists of various maps and datasets. The links for access to these maps and datasets are provided in Tables 25 to 35. 3.1 Survey Data Table 25: Links for access to the results of survey data Atlas data Can be accessed at: GIS & other datasets: Grid power station data file: survey\grid_station\Gridstation_3405.shp file content description: survey\metadata\grid_station.txt Road network density Data aggregated to districts: file: feedstock\districts\district.shp file content description: feedstock\metadata\districts.txt District level aggregates of the field file: feedstock\districts\district.shp survey data file content description: feedstock\metadata\districts.txt Other datasets: Field survey interview data files: survey\field\ file content description: survey\metadata\field.txt 3.2 Land Use Classification Table 26: Links for access to the results of land use classification Atlas data Can be accessed at: Map GIS & other datasets: Land use classification file: vietnam_crop.ers file content description: metadata\land_use.txt 3.3 Biomass Feedstock Data Table 27: Links for accessing the maps and datasets for the theoretical potential of crop harvesting residues Atlas data Can be accessed at: Map GIS datasets: Theoretical feedstock potential (all file: feedstock\theoretical_feedstock_per_pixel_all_crops.tif crops) QGIS style: feedstock\styles\theoretical_fs_all_crops.qml file content description: feedstock\metadata\feedstock.txt Theoretical feedstock potential file: feedstock\theoretical_feedstock_per_pixel.tif (single crops) QGIS style: feedstock\styles\theoretical_feedstock_blue-red.qml file content description: feedstock\metadata\feedstock.txt District level crop residue yields (all file: feedstock\theoretical_fs_per_ha_and_district.tif crops) QGIS style: feedstock\styles\theoretical_feedstock_blue-red.qml file content description: feedstock\metadata\feedstock.txt Other datasets: Crop yield data from the survey file: feedstock\crop_yield.xlsx aggregated to the district level (min, mean, max yield) Page 96 Table 28: Links for access to the maps and datasets of the technical potential of crop harvesting residues Atlas data Can be accessed at: Map, based on existing use of biomass residues only Map, based on both existing use and farmers' willingness to sell GIS datasets: Technical feedstock potential, based on file: feedstock\technical_feedstock_per_pixel_residue.tif existing use only file content description: feedstock\metadata\feedstock.txt Technical feedstock potential, based on file: feedstock\technical_feedstock_per_pixel_willing.tif existing use and farmers’ willingness to file content description: feedstock\metadata\feedstock.txt sell District level data on existing use and file: feedstock\districts\district.shp willingness to sell biomass residues file content description: feedstock\metadata\districts.txt Other datasets: Feedstock summary by country and by file: feedstock\feedstock.xlsx district, including sampled district confidence intervals for yearly feedstock amounts 3.4 Power Plant Analysis Data Table 29: Links for access to the results of site suitability analysis of sugar mills Atlas data Can be accessed at: Map GIS & other datasets: Sugar mill analysis file: industrial\sugarmills\sugarmills.shp file content description: industrial\metadata\sugarmills.txt Other datasets: Mill analysis results without the map file: output\sugarmills.xlsx data Cogeneration model the analysis results file: data\technology\Tech Suitability Matrix_VN Biomass are based on, feedstock to conversion Mapping_2017-10-28_Final.xlsx technology suitability mapping Survey results file: data\industrial_survey\Industrial survey in Vietnam_18_06_15.xlsx Table 30: Links for access to the results of site suitability analysis of rice mills Atlas data Can be accessed at: Map GIS & other datasets: Rice mill analysis file: industrial\ricemills\ricemills.shp file content description: industrial\metadata\ricemills.txt Other datasets: Mill analysis results without the map file: output\ricemills.xlsx data Cogeneration model the analysis results file: data\technology\Tech Suitability Matrix_VN Biomass are based on, feedstock to conversion Mapping_2017-10-28_Final.xlsx technology suitability mapping Survey results file: data\industrial_survey\Industrial survey in Vietnam_18_06_15.xlsx Page 97 Table 31: Links for access to the results of site suitability analysis of MSW landfills Atlas data Can be accessed at: Map GIS & other datasets: MSW landfill analysis file: industrial\MSW\MSW.shp file content description: industrial\metadata\MSW.txt Other datasets: Survey results file: data\industrial_survey\Industrial survey in Vietnam_18_06_15.xlsx Table 32: Links for access to the results of site suitability analysis of livestock farms Atlas data Can be accessed at: Map GIS & other datasets: Livestock analysis file: industrial\livestock\livestock.shp file content description: industrial\metadata\livestock.txt Other datasets: Survey results file: data\industrial_survey\Industrial survey in Vietnam_18_06_15.xlsx Table 33: Links for access to the results of site suitability analysis of wood processing mills Atlas data Can be accessed at: Map GIS & other datasets: Wood processing analysis file: industrial\wood_processing\wood_processing.shp file content description: industrial\metadata\wood_processing.txt Other datasets: Survey results file: data\industrial_survey\Industrial survey in Vietnam_18_06_15.xlsx 3.5 Greenfield site suitability analysis data Table 34: Links for access to the results of site suitability analysis Atlas data Can be accessed at: Map GIS & other datasets: Site suitability indicator files: site_suitability\heatmap_combined_MIXED.tif site_suitability\heatmap_combined_SINGLE.tif site_suitability\heatmap_gs_distance.tif site_suitability\heatmap_r_mixed.tif site_suitability\heatmap_r_single.tif site_suitability\heatmap_rn_density.tif file content description: site_suitability\metadata\site_suitability.txt Grid power station data file: data\grid_station\Gridstation_3405.shp Transport network density file: data\roads\transport_network_3405.shp Other datasets: Energy conversion model the file: data\technology\Tech Suitability Matrix_VN Biomass analysis results are based on Mapping_2017-10-28_Final.xlsx Page 98 3.6 Biomass Atlas training data Table 35: Links for access to the Biomass Atlas training data Atlas data Can be accessed at: GIS & other datasets: Training dataset files: training\ Dataset usage Annex 4: Instructions to the Vietnam Biomass Atlas Usage Dataset maintenance Annex 5: Instructions to the Vietnam Biomass Atlas Maintenance Page 99 Annex 4: Instructions to the Vietnam Biomass Atlas Usage Set-up For these instructions you need two things, the QGIS software, and the training dataset: Table 36: Requirements for training on Biomass Atlas Usage Requirement Can be accessed at QGIS https://www.qgis.org Training dataset Included in the Biomass Atlas data package available at http://esmap.org/re_mapping_vietnam After downloading the training dataset zip-file, unzip it and make a note of the folder where you unzipped it. This is the folder you will find the exercise data referred to below. Task 1: Power Plant Investment Feasibility for a Sugar Mill Your task is to evaluate the feasibility of switching a sugar mill's power plant into year-round operation using a mixed feedstock from the current status of operating it only during the milling season. For this evaluation, you need to figure out from how far from the sugar mill you would need to source the additional feedstock for the off-season operation of the power plant. Let's use the An Khe Sugar Mill as an example. To answer this question, you need to: Subtask 1.1: Find out the steam turbine size the mill can have to run it on bagasse for the milling season plus two months Throughout this workshop manual, steps needed to take are documented in the tables like the one below, please follow the instructions in the tables step-by-step, and keep coming back to this manual for the instructions. Files needed power_plant_model.xlsx In Excel/ 1) With Excel, open power_plant_model.xlsx. It is located in the folder where unzipped the OpenOffice workshop.zip file you downloaded above. The file is within the workshop folder. 2) Go to the "sugarmills" sheet. Find the yearly bagasse production & bagasse-to-sugarcane ratio for the An Khe Sugar Mill. 2) Enter that values in the red cell in the "MW Cogen-Sugar" sheet: 3) Take a note of the value at the yellow cell of the sheet It will tell you the "size" of the power plant you use for finding out the sourcing distance for the additional feedstock needed to extend the operation of the power plant year round. Page 100 Subtask 1.2: Now you need to find the An Khe Sugar Mill from the Atlas maps. You start by putting the sugar mills on the map in QGIS. Open up QGIS, and then: Files \industrial\sugarmills\sugarmills.shp needed feedstock\districts\districts_3405.shp In QGIS 1) Add the sugarmills as a new vector layer to QGIS Note: As the screenshots were taken on Linux & QGIS 3.2, they will look different to what you might see on Windows or with a different version of QGIS. => => Page 101 2) To add some context to the map, add another new vector layer to QGIS. Repeat the steps above, and select districts_3405.shp from the folder where you unzipped the workshop data. Page 102 The problem now is that the sugar mills disappeared! (Note: the colour the district polygons have, will be random) You can make the districts disappear too, by unchecking the checkbox in front of them in the "Layers Panel" list: Page 103 3) But let's make the district polygons transparent, so that we can see both the location of the sugar mills and the district borders on the map. Right-click on the districts layer in the "Layer Panel", and choose Properties (alternatively, you can just double click on the layer name) Page 104 => => Click OK at the bottom right corner of the dialog Page 105 Page 106 Subtask 1.3: Find the An Khe Sugar Mill site in QGIS In QGIS 1) Open up the attribute table for the sugarmills layer. Right click on the layer name in the Layer Panel: => Page 107 => => Press Enter and select the sugar mill (tick the box) & press zoom map to selected rows tool. Page 108 (zoom map to selected row) You can close the Attribute table - sugarmills window now. 2) Zoom the map closer to the An Khe Sugar Mill site => Page 109 => Page 110 Subtask 1.4: Find the sourcing distance for the additional feedstock needed for the An Khe Sugar Mill site Files heatmap_r_mixed.tif needed In QGIS 1) Add the heatmap_r_mixed.tif raster layer to QGIS => => Your map will most likely turn to black 2) Move the heatmap_r_mixed layer to the bottom of the Layer Panel list by pressing & holding left mouse button and dragging heatmap_r_mixed layer to bottom Page 111 => You should now see the yellow dot for the An Khe sugar mill site: 3) Let's make the heatmap_r_mixed raster look a bit nicer. Right-click the heatmap_r_mixed, choose Properties => Page 112 => Select style_heatmap_r_mixed.qml and click Open => Click OK to close the Layer Properties dialogue 4) Find the exact sourcing distance value for the An Khe sugar mill site First make sure you're still looking at the right sugar mill Page 113 => Click on the yellow dot on the map with info tool (which then turns the dot red) If you need to, you can pan and zoom the map with these tools: => Now change the active layer to the heatmap_r_mixed layer, and again click on the An Khe sugar mill site dot. => In the Identify Results panel you now have the sourcing distances for different types and size categories of power plants given as the sourcing area radius in km. The radius is the direct "as crow flies" distance, not the road transport distance. The band number interpretations are: Band Power plant Horizontal grate combustion steam boiler + steam turbine (GC) 1 3 MW 2 8 MW 3 15 MW Page 114 Bubbling fluidized bed combustion steam boiler + steam turbine 4 8 MW 5 15 MW 6 25 MW 7 50 MW 8 100 MW Circulating fluidized bed combustion steam boiler + steam turbine 9 15 MW 10 25 MW 11 50 MW 12 100 MW Gasifier + syngas engine/turbine 13 0.5 MW 14 1.5 MW Anaerobic digester + biogas engine/turbine 15 0.5 MW 16 1.5 MW 17 3 MW 18 8 MW Pick the values closest to the capacity you defined at the beginning with the Excel sheet (the yellow cell on the sheet). Extrapolate or interpolate the sourcing distance value for the capacity you got from Excel. This number will tell you from how far from the sugar mill you'd need to purchase all the available field harvest residue to run the power plant all year. "All available" is here defined to mean harvest residue currently being burned on the fields by the farmers that are willing to participate in a commercial supply chain for power generation. Task 2: Identifying and Evaluating a Greenfield Investment Opportunity Your task is to find a potential site for a power plant that uses harvest residues collected from fields, and evaluate how much harvest residue, and of what kind is available within a 15 km radius from that site. To answer the question, you need to Subtask 2.1: Open the site index raster that is part of the Atlas, and decide on the site you want to analyse Files heatmap_combined_MIXED.tif needed In QGIS 1) Open the heatmap_combined_MIXED.tif raster in QGIS the same way you opened heatmap_r_mixed.tif in the previous exercise, see step 4 above. Apply the style style_heatmap_combined_MIXED.qml on the layer, again see step 4 for instructions The end result should look like this: Page 115 Locate a place that has high site index values, indicated by red colour, and preferably does not have sugar mills right next to it (to avoid competition for harvest residues). Subtask 2.2: Next we mark that location with a point on the map and create the 15 km radius sourcing area around it. In QGIS 1) Create a new vector layer on the map => Page 116 => If right coordinate system (In our case EPSG:3405) cannot be found from the drop menu in Geometry type section, then look for it from the icon on right side of the menu, => and type 3405 to filter field, => press OK => give the new layer a name, and save it => You will have a new layer on the Layers Panel Page 117 => Check that also the QGIS project you are working with has the same coordinate system as the newly added layer: 2) Add a new point to the new layer to the place you picked for analysis While your new layer is highlighted on the Layer's Panel, click Toggle Editing tool Page 118 If you don't see that toolbar button, right click over an empty spot on the toolbar, and select: => Zoom the map to the place where you want to add the point, and then: => click on the place on the map you selected for analysis, give an ID for the new point (e.g. 1) and then, click here again, and save the changes when prompted: 3) Create a 15 km radius buffer around the site marked with the point Page 119 => Qgis will automatically open my_site_buffer.shp with layer name Buffered Page 120 Subtask 2.3: Next you calculate how much and what type of harvest residue is available from that 15 km radius circle. Files technical_feedstock_per_pixel_willing.tif needed power_plant_model.xlsx In QGIS 1) Add the rasters technical_feedstock_per_pixel_willing.tif to your map. See instructions for step 4 for the previous exercise for adding raster layers to the map. Note: since these rasters are 28 band rasters, each band containing the available biomass for each pixel, there is no obvious way of styling the layer for display. If you want, you can show the values for a single band (i.e. single harvest residue) for example by right clicking the layer name in the Layers Panel, and then: 2) For each band that has the biomass for a field based harvest residue, calculate the total amount Page 121 within the 15 km radius circle. The bands in the raster are: Band Feedstock, short Feedstock, long 1 RicStr Rice straw 2 MaiTra Maize straw 3 SugTra Sugar trash 4 PeaStr Peanut straw 5 SoyStr Soy straw 6 CasSta Cassava stalks 7 CshWoo Cashew wood 8 RubWoo Rubber wood 9 CofWoo Coffee wood 10 TeaWoo Tea wood 11 PepWoo Pepper wood 12 CocWoo Coconut wood 13 MngWoo Mango wood 14 OraWoo Orange wood 15 ManWoo Mandarin wood 16 LonWoo Longan wood 17 LitWoo Litchi wood 18 RamWoo Rambutan wood 19 RicHus Rice husk 20 MaiCob Maize cobs 21 MaiShe Maize shells 22 SugBag Sugarcane bagasse 23 PeaShe Peanut shells 24 CasPee Cassava peel 25 CasShe Cashew nut shells 26 CofHus Coffee husk 27 CocHus Coconut husk 28 CocShe Coconut shells => For this task you will use the Zonal statistics tool of QGIS from Toolbox in Processing tab, Page 122 In case you are using some version of Qgis not having zonal statistics as ready installed tool you can easily add it as a plugin by, => and typing zonal statistics to search bar. => Once Zonal Statistics tool is opened Page 123 => After running the Zonal Statistics like this, you will have the raster statistics in the buffered point layer attributes. You can use the Identify features-tool to have a look at the numbers. Select the Identify Features tool and while the Buffered layer is selected in the Layers Panel, click on the 15 km circle. The Identify Results panel will show statistics for that area based on the technical_feedstock_per_pixel_willing.tif raster, You will want the _sum and _count values; _sum is the sum of pixel values for band 1 for the raster, i.e. tonnes of rice straw available. The _count value is the number of pixels from which this amount comes from. The size of a single pixel is 1000 m x 1000 m. You can check that value to validate that the sum is for the area under the circle. Write down the sum in the appropriate green cell in the "MW Power" sheet of the power_plant_model.xlsx Page 124 Repeat this step for all the relevant bands as well. After that, you have the your power plant model primed with feedstock data, and can see for example the gross power capacity of the power plant, (below shown with fuel contribution of rice straw only): Of course it's not realistic to assume that you can source 100% of the available feedstock, but now you are able to create a baseline, and can start playing with the sourcing assumptions. Page 125 Annex 5: Instructions to the Vietnam Biomass Atlas Maintenance UPDATING OF THE BIOMASS ATLAS DATA The purpose of this document is to introduce the structure and the parameterization of the Biomass Atlas Model so that in the event of updates to some of the input data for the Biomass Atlas, new versions of the Atlas datasets can be generated. The exercises in this chapter rely on a sample of the original Atlas data to keep the runtime for the exercises at a reasonable level. Setting up remote Biomass Atlas environment using SSH client (PuTTy) These instructions have been written by bearing in mind that the Biomass Atlas model is executed on a remote Linux server accessed using an SSH client on a Windows desktop. If you are running the whole exercise locally on a Windows desktop, you can skip these remote access instructions. In order to install PuTTy SHH client needed for remote access, go to http://www.putty.org and follow the "Download PuTTy link" and from there, download the "putty.exe" to your computer. A detailed PuTTy documentation can be found from http://the.earth.li/~sgtatham/putty/0.66/htmldoc/ To sign in to the remote server, write the server address in the text field under “Host Name (or IP address). Click “Open”. The first time you log on the server you are shown a pop-up “PuTTY Security Alert”; click “Yes”. More detailed instructions on logging in to remote server using PuTTY can be found here: http://the.earth.li/~sgtatham/putty/0.66/htmldoc/Chapter2.html#gs Set-up The Biomass Atlas model, used to generate the Biomass Atlas datasets, is implemented with the Python programming language. It also relies on several Python modules that need to be installed together with Python. Page 126 We begin the atlas setup by preparing a Python environment that has required Python modules for Biomass Atlas model. To keep this environment separate from other Python environments on the same system we encapsulate our working environment using a virtual environment. To be able to generate a virtual environment in our Biomass Atlas main directory, we need to install virtualenv & virtualenvwrapper, by running commands: $ pip install virtualenv and $ pip install virtualenvwrapper. See more details on how to use virtualenvwrapper on https://virtualenvwrapper.readthedocs.io/en/latest/ Now let’s create a virtual environment called “atlas”. Then with “atlas” activated, we are going to install required modules and run the actual tasks. With virtualenvwrapper properly set up, execute the command: $ mkvirtualenv atlas After executing that command your terminal prompt should start with the string: (atlas) That is an indication that you have now successfully created and activated the virtual environment and we can start installing required modules. Table 33 lists the required modules, and the easiest way to get them installed on Linux. On Windows, a good source for installation files of the needed modules is http://www.lfd.uci.edu/~gohlke/pythonlibs/. Table 37: Requirements for generating the Biomass Atlas with the Biomass Atlas model Requirement Can be accessed at Python 2.7 https://www.python.org/downloads/ pip https://pip.pypa.io/en/stable/installing/ Python modules, installed with pip: Execute on command line rasterio pip install rasterio shapely pip install shapely fiona pip install fiona xlrd pip install xlrd xlsxwriter pip install xlsxwriter rtree pip install rtree numpy pip install numpy pyproj pip install pyproj affine pip install affine scipy pip install scipy futures pip install futures Biomass Atlas Model Included in the the Biomass Atlas data package available at http://esmap.org/re_mapping_vietnam see folder atlas_model Training dataset Included in the the Biomass Atlas data package available at http://esmap.org/re_mapping_vietnam see folder training After downloading the Biomass Atlas data package, unzip it and make a note of the folder where you did the unzipping. This is the folder you will find the model and the exercise data referred to below. Page 127 Also, if you start a new SSH session to the server, make sure you activate the “atlas” v irtual environment before executing the commands listed in the task instructions. Task 1: Changing How the Atlas Is Generated Overview of the Biomass Atlas model The Biomass Atlas Model consists of two main scripts: feedstock.py module and heatmap.py module (located in /atlas_model/src directory). Both of the modules are controlled by a number of settings in constants.py file, which is located in /atlas_model/src/utils directory. The steps in running the whole Biomass Atlas model are: 1. Set the run parameters by editing the constants.py file 2. Run the feedstock.py module 3. Run the heatmap.py module Detailed instructions for doing this are below. Before running the Biomass Atlas, you must change the current working directory to the Biomass Atlas main directory, by running a command: $ cd /atlas_model Here is the folder in which you unzipped the model and training data. Note that these instructions are written for Linux, so you need to adapt them for Windows (e.g. \ instead of / as the directory separator in path names). Above the "$" marks the "command prompt" in your PuTTy window, i.e. you're meant to type the text following the $-sign and press Enter/Return key. On Windows this would be your Command Line window. Modifying Biomass Atlas settings in the constants.py In this example, we will be running the Biomass Atlas model for only a small subset of the whole country, as running the model for the whole of Vietnam would take a considerable time. To do this, we need to set the model land use classification to layer named An_Khe_55km.ers (land use classification within 55 km of An Khe sugar mill) by modifying the constants.py file. The area is shown in the left image as the bluish colored area in central Vietnam. Note that this is also how you would assign a completely new land use classification to the Atlas. The constants.py module can be modified using nano editor by running the following command (here we assume that you successfully changed your working directory to /atlas_model in the previous step above): $ nano src/utils/constants.py (on Windows, use e.g. Notepad to open the file) Page 128 After starting the nano editor, your screen should look like this: Subtask 1.1: Changing the model inputs - land use classification file Using the nano editor, find a row that contains a text LANDUSE_CLASSIFICATION. You can do this either by moving down using arrows, or by searching for the text using "Where Is" command (press ctrl + w). After finding the correct row, change the row contents into the following: LANDUSE_CLASSIFICATION = os.path.join('..’, 'training’, 'An_Khe_55km.ers') After the change, the constants.py should look like this in nano editor. NB: `..´ is used to move one level up in the directory hierarchy, i.e. to the directory Page 129 Save the changes (press ctrl + o), and now the land use classification will be read from a An_Khe_55km.ers instead of the mosaic that combines a large number of Sentinel images to cover the whole Vietnam. Subtask 1.2: Changing the model parameters - maximum biomass sourcing distance Before running the Biomass Atlas model, we should still edit some of the input parameters of the Biomass Atlas model. In this example we will modify the maximum biomass sourcing distance. By default, the maximum sourcing distance is 50 km, but you should change it to 25 km. This means that the maximum allowed distance, from which biomass can be transported to a power plant, will be 25 km. To modify the maximum sourcing distance, search for the text MAX_DISTANCE from the constants.py using the nano editor and change its value to 25. After the edit, the constants.py should look like this. Subtask 1.3: Changing the model outputs - result heatmap file names Another thing we want to change, are the names of the output files that will be generated when running the Biomass Atlas model. The names of the output files, and other model outputs are also defined in the constants.py file. The Biomass Atlas model will generate a number of raster files during the processing, but the most relevant outputs are the so-called combined heatmaps, which represent the potential for biomass power plants of different types and capacities. In order to compare model outputs with different input parameters, we want to save the outputs from separate model runs with separate names. Using the nano editor, search for the text PATH_TO_COMBINED_HM_SINGLE from the constants.py file. Change the name of the file to heatmap_combined_SINGLE_run1.tif. Do the same for the PATH_TO_COMBINED_HM_MIXED and change the output file name to heatmap_combined_MIXED_run1.tif. The "SINGLE" refers to heatmap for a single fuel power plant and "MIXED" refers to a heatmap for a mixed fuel power plant. After these changes, the constants.py file should look like this: Page 130 Now, make sure you save your edits (ctrl + o), exit the nano editor (ctrl + x) and we're ready to run the model! Subtask 1.4: Running the Biomass Atlas model The Biomass Atlas model should be run in two steps: first the feedstock.py module and then the heatmap.py module. Run the feedstock.py module with the following command: $ python src/feedstock.py The server logs will show messages about the execution of the model, and in case anything goes wrong, the error messages. If you did the edits in the previous steps following the instructions, then there should be no error messages. After the feedstock.py module has been run successfully, the next step is to run the heatmap.py module. This is done with the following command: $ python src/heatmap.py Running the heatmap.py module will take a while, as it will run a spatial analysis for the biomass potential for 18 power plant type and capacity combinations. The heatmap.py module will generate the power plant potential heatmaps and after the model run has ended, we're ready to analyse the results. Subtask 1.5: Re-running the Biomass Atlas model with alternative parameters In this example, we want to run the model twice with different parameters in order to see how the parameters affect the model outputs. In subtask 1.2, we changed the maximum sourcing distance to 25 km. Now, edit the constants.py and change the maximum sourcing distance back to 50 km. In subtask 1.3, we change the output filenames by adding "_run1" to the end of the output heatmap file names. For the model re-run, we want to change the output file names so that we will have two alternative sets of result files to compare. Follow the instructions of subtask 1.3, but now the filenames, so that you change the "_run1" into "_run2". After finishing the above edits, save your changes (ctrl + o) and exit nano editor (ctrl + x). Then, re- run the heatmap.py model. Notice that you don't need to re-run the feedstock.py again as you didn't change any parameters that affect the feedstock.py module. Page 131 Task 2: Checking the Results with QGIS. Did the Atlas Change? Subtask 2.1: Loading the results from remote server to desktop computer In order to view the Biomass Atlas model results, you need to first load the model outputs (the raster files) from the remote server to your desktop computer. You can download files from the server using a program called PSCP, which you can download from the same web page as PuTTy. PSCP download: http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html Copy the downloaded pscp.exe to the folder of where you want to copy the files. For these instructions, we assume the desired location for downloading the heatmaps is “C:\Atlas”, so the pscp.exe should be saved to the folder “C:\Atlas”. PSCP is a command-line tool and should be run from the command-line prompt. To start the prompt, click the Windows icon at the bottom of the screen, and type “cmd” and press Enter in the “Search for programs and files” text box at the bottom of the menu. In the prompt, change to the folder by typing C: (and Enter) followed by cd \Atlas Again, assuming that you want to work in the C:\Atlas folder. Change this according to the folder in which you want to have the data. To copy the output files to C:\Atlas use the following command, but replace the user name and the server name with those you used when logging on with PuTTY to run Atlas: pscp @:/atlas_model/output/tt_cap/heatmap_combined_*.tif . Make sure to include the last point in the command. Page 132 After pressing Enter, the program will ask for the same password it did when logging in with PuTTY. If everything worked OK, you should have now downloaded all four combined heatmaps you just generated in the two model runs. heatmap_combined_SINGLE_run1.tif heatmap_combined_MIXED_run1.tif heatmap_combined_SINGLE_run2.tif heatmap_combined_MIXED_run2.tif To copy only one single file, replace “heatmap_combined_*.tif” with the name of the desired file. Subtask 2.2: Loading the heatmaps into QGIS After downloading the generated heatmaps to your desktop computer, you can view them in QGIS. To do this, first start QGIS and create a new project. Next, add the rasters to QGIS by selecting "Layer" from the top menu, select "Add Layer" and finally "Add Raster Layer...". Page 133 From the file selector, select the four combined heatmaps you generated and downloaded from the server: heatmap_combined_SINGLE_run1.tif, heatmap_combined_MIXED_run1.tif, heatmap_combined_SINGLE_run2.tif and heatmap_combined_MIXED_run2.tif. After loading the heatmaps to QGIS, each of the heatmaps should show as a separate layer in the layer listing on the left side of your QGIS application (see the following image). Page 134 Let’s check the layers with the info tool more closely. Left click one of the visible heatmaps with info tool and it should give you a selection of layers which you can take look of more closely. You can first select heatmap_combined_MIXED_run1 (the one we see here as golden brown). Info tool should give you information on all the separate bands off heatmap_combined_MIXED_run1 layer, Page 135 Now under the Identify Results panel on the right side, you see different suitability values between 0 to 100 for different power plants described earlier. No data values in some bands indicate that there is not enough feedstock available within the maximum sourcing distance to support year-round operation of the power plant designated at that band. As an example, no data value for Band 07 seems to mean that 50 MW Bubbling fluidized bed combustion steam boiler + steam turbine combo is not feasible at the location for whole year operation. If we click again the visible heatmaps and select heatmap_combined_SINGLE_run1 layer for closer look, we realize that there are no data values at clicked location. It means that with 25 km procurement distance (run1) and by only using a single feedstock type, we are not able to supply any type of power plant the whole year round. And if we check the info for heatmap_combined_SINGLE_run2, we notice that the situation is the same even with 50 km procurement distance. We can confirm this by visualizing only heatmap_combined_SINGLE_run1 or run2 layer. Blank view (even when heatmap_combined_SINGLE_run1 is checked) indicates that it is not feasible to run any of the power plant types the whole year round using single fuel, even with 50 km procurement distance. Page 136 Subtask 2.3: Setting the layer style of the heatmaps in QGIS The visual representation of the layers in QGIS are controlled by layer style. In order to compare the alternative heatmaps, we want to set their visual properties to represent the potential for biomass- based power plants. To do this, first select the heatmap_combined_MIXED_run1 layer, press the right button of your mouse and select "Properties" from the pop-up menu (see the following image). In the layer properties, select "Style", from there set Render type as "Singleband pseudocolor", set the Band as "Band 01", select green color map, set minimum value to 0 and maximum to 100 and click "Classify" button. After this, the layer style settings should look similar to the following image. Make sure that your layer style settings are ok, then click "Apply" and "OK". Page 137 The above steps changed the style of one of the two layers visible. Next, you should copy the same style for the other layer. This can be done by selecting the layer that you just modified, opening the pop-up menu by right clicking, and selecting "Style" and "Copy style". Then, select one of the other three layers and do the same steps, except that in the end, select "Paste style". Subtask 2.4: Comparing the layers from alternative runs After setting the styles to same, you can start comparing the heatmaps from the alternative model runs. In the image left, you can see the heatmaps for single stock power plants from runs 1 and 2 (25 km and 50 km maximum sourcing distance). The raster band 1 is for 3 MW horizontal grate combustion steam boiler + steam turbine (GC). So, in the current settings, the raster displays the potential, or goodness, of each 1 x 1 km cell for a 3 MW HGC power plant, so that dark green means high potential and light green low potential. The white areas are outside the maximum sourcing distance. Page 138 Task 3: Changing the weights of different factors affecting the heatmap Besides the maximum sourcing distance, there are also other factors that affect what the site suitability index heatmaps end up looking like. These are "nearest grid station distance factor" and "road network density factor". Find out where these are in constants.py, change them so that the weight of the sourcing distance is 80%, the weight for the grid station distance is 20%, and the road network distance has no weight at all (0%). Rename the output rasters from having the "_run2" to have "_run3" ending. Compare the results of this model run to the previous ones in QGIS. FINAL WORDS ABOUT UPDATING THE ATLAS The key to successful Atlas maintenance is understanding the different settings in atlas_model/src/utils/constants.py The file is documented with comments outlining the purpose of each setting. When new input data for atlas generation is available, of particular interest are the settings in section # PATHS TO MODEL INPUTS, i.e. the new data should be entered into the files pointed to by the settings in that section of constants.py For help with troubleshooting, please contact Simosol Oy at info@simosol.fi Page 139