SOLAR RESOURCE AND PHOTOVOLTAIC POTENTIAL OF INDONESIA May 2017 This report was prepared by Solargis, under contract to The World Bank. It is one of several outputs from the solar resource mapping component of the activity Energy Resource Mapping and Geospatial Planning Indonesia [Project ID: P145273]. 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 report 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 validation using ground measurement data or peer review. The final output from this project will be a validated Indonesia Solar Atlas, which will be published once the project is completed. To obtain additional maps and information on solar resources globally, please visit: http://globalsolaratlas.info Copyright © 2017 THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org The World Bank, comprising the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA), is the commissioning agent and copyright holder for this publication. However, 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. 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World Bank Group, Global ESMAP Initiative Renewable Energy Resource Mapping and Geospatial Planning – Indonesia Project ID: P7180885 Solar Resource and Photovoltaic Power Potential of Indonesia May 2017 This report has been prepared by Marcel Suri, Tomas Cebecauer, Nada Suriova, Branislav Schnierer, Artur Skoczek, Juraj Betak, Veronika Madlenakova and Daniel Chrkavy from Solargis All maps in this report are prepared by Solargis Solargis s.r.o., Pionierska 15, 831 02 Bratislava, Slovakia Reference No. (Solargis): 170-09/2017 http://solargis.com Solargis is ISO 9001:2008 certified company for quality management World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 4 of 86 Table of contents Table of contents ............................................................................................................................................... 4 Acronyms ........................................................................................................................................................... 5 Glossary ............................................................................................................................................................. 7 Executive summary ............................................................................................................................................ 9 1 Introduction ............................................................................................................................................... 11 1.1 Inventory of previous solar resource projects ............................................................................................ 11 1.2 Evaluation of the existing data and studies ................................................................................................ 12 1.3 Objectives ...................................................................................................................................................... 13 2 Solargis methods and data ......................................................................................................................... 14 2.1 Solar resource data ...................................................................................................................................... 14 2.3 Solar power systems: technical options and energy simulation ............................................................... 29 3 Solar resource and PV potential of Indonesia ............................................................................................. 34 3.1 Geography ..................................................................................................................................................... 34 3.2 Air temperature at 2 metres ......................................................................................................................... 46 3.3 Global Horizontal Irradiation ........................................................................................................................ 49 3.4 Direct Normal Irradiation .............................................................................................................................. 54 3.5 Global Tilted Irradiation ................................................................................................................................ 57 3.6 Photovoltaic power potential ....................................................................................................................... 62 3.7 Solar climate ................................................................................................................................................. 67 3.8 Evaluation ...................................................................................................................................................... 70 4 Priority areas for meteorological stations .................................................................................................. 73 4.1 Localisation criteria ...................................................................................................................................... 73 4.2 Areas suitable for solar meteorological stations ....................................................................................... 73 5 Solargis data delivery for Indonesia ........................................................................................................... 76 5.1 Spatial data products ................................................................................................................................... 76 5.2 Project in QGIS format .................................................................................................................................. 79 5.3 Digital maps .................................................................................................................................................. 79 6 List of maps ............................................................................................................................................... 81 7 List of figures ............................................................................................................................................ 82 8 List of tables .............................................................................................................................................. 83 9 References ................................................................................................................................................ 84 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 5 of 86 Acronyms AC Alternating current AERONET The AERONET (AErosol RObotic NETwork) is a ground-based remote sensing network dedicated to measure atmospheric aerosol properties. It provides a long- term database of aerosol optical, microphysical and radiative parameters. AOD Aerosol Optical Depth at 670 nm. This is one of atmospheric parameters derived from MACC database and used in Solargis. It has important impact on accuracy of solar calculations in arid zones. CFSR Climate Forecast System Reanalysis. The meteorological model operated by the US service NOAA. CFSv2 The Climate Forecast System Version 2. CFSv2 meteorological models operated by the US service NOAA (Operational extension of Climate Forecast System Reanalysis, CFSR). CPV Concentrated Photovoltaic systems, which uses optics such as lenses or curved mirrors to concentrate a large amount of sunlight onto a small area of photovoltaic cells to generate electricity. CSP Concentrated solar power systems, which use mirrors or lenses to concentrate a large amount of sunlight onto a small area, where it is converted to heat for heat engine connected to an electrical power generator. DC Direct current DIF Diffuse Horizontal Irradiation, if integrated solar energy is assumed. Diffuse Horizontal Irradiance, if solar power values are discussed. DNI Direct Normal Irradiation, if integrated solar energy is assumed. Direct Normal Irradiance, if solar power values are discussed. ECMWF European Centre for Medium-Range Weather Forecasts is independent intergovernmental organisation supported by 34 states, which provide operational medium- and extended-range forecasts and a computing facility for scientific research. ESMAP Energy Sector Management Assistance Program, a multi-donor trust fund administered by The World Bank EUMETSAT European Organisation for the Exploitation of Meteorological Satellites, intergovernmental organisation for establishing, maintaining and exploit European systems of operational meteorological satellites GFS Global Forecast System. The meteorological model operated by the US service NOAA. GHI Global Horizontal Irradiation, if integrated solar energy is assumed. Global Horizontal Irradiance, if solar power values are discussed. GIS Geographical Information System GTI Global Tilted (in-plane) Irradiation, if integrated solar energy is assumed. Global World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 6 of 86 Tilted Irradiance, if solar power values are discussed. KSI Kolmogorov–Smirnov Index, a statistical index for comparing of functions or samples MACC Monitoring Atmospheric Composition and Climate – meteorological model operated by the European service ECMWF (European Centre for Medium-Range Weather Forecasts) Meteosat IODC Meteosat satellite operated by EUMETSAT organization. IODC: Indian Ocean Data Coverage MERRA Modern-Era Retrospective Analysis for Research and Applications, a NASA reanalysis for the satellite era using an Earth observing systems MTSAT 2 Multifunctional Transport Satellite operated by Japan Meteorological Agency (JMA), also known as Himawari 7, positioned at 145° East Himawari 8 Geostationary weather satellite operated by the Japan Meteorological Agency (JMA) NASA National Aeronautics and Space Administration organization NOAA NCEP National Oceanic and Atmospheric Administration, National Centre for Environmental Prediction NOAA ISD NOAA Integrated Surface Database with meteorological data measured by ground- based measurement stations NOCT The Nominal Operating Cell Temperature, is defined as the temperature reached by open circuited cells in a module under the defined conditions: Irradiance on cell 2 surface = 800 W/m , Air Temperature = 20°C, Wind Velocity = 1 m/s and mounted with open back side. PV Photovoltaic PVOUT Photovoltaic electricity output calculated from solar resource and air temperature time series. RSR Rotating Shadowband Radiometer SOLIS Solar Irradiance Scheme, Solar clear-sky model for convert meteorological satellite images into radiation data SRTM Shuttle Radar Topography Mission, a service collecting topographic data of Earth's land surfaces STC Standard Test Conditions, used for module performance rating to ensure the same measurement conditions: irradiance of 1,000 W/m², solar spectrum of AM 1.5 and module temperature at 25°C. TEMP Air Temperature at 2 metres UV Ultraviolet radiation World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 7 of 86 Glossary AC power output Power output measured at the distribution grid at a connection point. of a PV power plant Aerosols Small solid or liquid particles suspended in air, for example desert sand or soil particles, sea salts, burning biomass, pollen, industrial and traffic pollution. All-sky irradiance The amount of solar radiation reaching the Earth's surface is mainly determined by Earth-Sun geometry (the position of a point on the Earth's surface relative to the Sun which is determined by latitude, the time of year and the time of day) and the atmospheric conditions (the level of cloud cover and the optical transparency of atmosphere). All-sky irradiance is computed with all factors considered Bias Represents systematic deviation (over- or underestimation) and it is determined by systematic or seasonal issues in cloud identification algorithms, coarse resolution and regional imperfections of atmospheric data (aerosols, water vapour), terrain, sun position, satellite viewing angle, microclimate effects, high mountains, etc. Clear-sky irradiance The clear sky irradiance is calculated similarly to all-sky irradiance but without considering the impact of cloud cover. Fixed-mounted modules Photovoltaic modules assembled on fixed bearing structure in a defined tilt to the horizontal plane and oriented in fixed azimuth. Frequency of data (30- Period of aggregation of solar data that can be obtained from the Solargis database. minute, hourly, daily, monthly, yearly) Installed DC capacity Total sum of nominal power (label values) of all modules installed on photovoltaic power plant. Long-term average Average value of selected parameter (GHI, DNI, etc.) based on multiyear historical time series. Long-term averages provide a basic overview of solar resource availability and its seasonal variability. P50 value Best estimate or median value represents 50% probability of exceedance. For annual and monthly solar irradiation summaries, it is close to average, since multiyear distribution of solar radiation resembles normal distribution. P90 value Conservative estimate, assuming 90% probability of exceedance (with the 90% probability the value should be exceeded). When assuming normal distribution, the P90 value is also a lower boundary of the 80% probability of occurrence. P90 value can be calculated by subtracting uncertainty from the P50 value. In this report, we apply a simplified assumption of normal distribution of yearly values. PV electricity production AC power output of a PV power plant expressed as percentage of installed DC capacity. Root Mean Square Represents spread of deviations given by random discrepancies between measured Deviation (RMSD) and modelled data and is calculated according to this formula: 3 ' 2 '45 ' ()*+,-). − (0.)1). = On the modelling side, this could be low accuracy of cloud estimate (e.g. intermediate clouds), under/over estimation of atmospheric input data, terrain, World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 8 of 86 microclimate and other effects, which are not captured by the model. Part of this discrepancy is natural - as satellite monitors large area (of approx. 3 x 4 km), while sensor sees only micro area of approx. 1 sq. centimetre. On the measurement side, the discrepancy may be determined by accuracy/quality and errors of the instrument, pollution of the detector, misalignment, data loggers, insufficient quality control, etc. 2 Solar irradiance Solar power (instantaneous energy) falling on a unit area per unit time [W/m ]. Solar resource or solar radiation is used when considering both irradiance and irradiation. 2 Solar irradiation Amount of solar energy falling on a unit area over a stated time interval [Wh/m or 2 kWh/m ]. Spatial grid resolution In digital cartography, the term applies to the minimum size of the grid cell or in the other words minimal size of the pixels in the digital map World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 9 of 86 Executive summary This report presents results of the solar resource mapping and photovoltaic power potential evaluation, as a part of a technical assistance, implemented by the World Bank, for the renewable energy development in Indonesia. The study has two objectives: • Improve the awareness and knowledge of resources for solar energy technologies by producing a comprehensive countrywide data set and maps based on satellite and meteorological modelling. This report evaluates key solar climate features, and geographic and time variability of solar power potential in the country. The outcomes are supported by explanation of the methodology and evaluation of the data uncertainty. • Provide support information for installation of meteorological stations in Indonesia by identifying and evaluating the most feasible areas. The data used in this report are based on the satellite and meteorological models. Due to insufficient number of high accuracy solar measurements in Indonesia, the data and the outcomes published in this study have higher uncertainty, compared to other regions. The uncertainty of the model outputs, in Indonesia, can be reduced by acquiring high quality measurements at several meteorological stations with specialised solar-measuring equipment. Satellite-based and meteorological models are used for computing solar resource and meteorological data that are suitable for production of high-resolution maps and for use in Geographical Information Systems (GIS). A set of primary data parameters is discussed in the study. They are relevant for evaluation of energy yield and performance of the solar power plants, especially based on the use of photovoltaic technology: • Global Horizontal Irradiation (GHI), Diffuse horizontal Irradiation (DIF), Global Tilted Irradiation (GTI) and Direct Normal Irradiation (DNI) • Air temperature at 2 metres above ground • Photovoltaic power potential (PVOUT). The deliverables are designed to support effective development of solar energy strategies and projects in their first stages. This phase delivers data computed by Solargis model without considering regional measurements. The model outcomes are delivered in two formats: • Raster GIS data for the whole territory of the Republic of the Union of Indonesia, representing long-term monthly and yearly average values. This data layers are accompanied by geographical data layers in raster and vector format. • Digital maps for high resolution poster printing and in medium resolution format The report also provides an indicative analysis of regions and areas where deployment of solar measuring stations could take place. These stations would contribute to the validation of the models and reducing the data uncertainty. Once at least one year of measurements is available the data can be used for adaptation of solar and meteorological models and for detailed analysis of solar climate at representative sites. This report addresses the following topics: Chapter 1 evaluates existing data sets and studies and sets the current work into the broader perspective. Solar radiation basics and principles of photovoltaic power potential calculation are described in Chapter 2. Chapters 2.1 and 2.2. describe measuring and modelling approaches for developing reliable solar and meteorological data including information about the data uncertainty. Chapter 2.3 explains the relevance of solar resource and meteorological data for deployment of solar power technologies. An emphasis is given to photovoltaic (PV) technology, which has high potential for developing utility-scale projects close to larger load centres, as well as for deployment of off-grid, hybrid systems and mini-grids for electrification of small communities. Rooftop PV systems are considered as good option for covering electricity consumption at any place. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 10 of 86 Chapter 3 presents analysis and evaluation of geographical, meteorological and solar resource data in Indonesia. Eight representative sites are selected to show potential regional geographical differences in the country through tables and graphs. Chapter 3.1 introduces support geographical data that influence deployment strategies and performance of solar power plants. Chapters 3.2 to 3.5 summarize geographical differences and seasonal variability of solar resource in Indonesia. Chapter 3.6 presents PV power generation potential of the country. The theoretical specific PV electricity output is calculated from the most commonly used PV technology: fixed system with crystalline-silicon (c-Si) PV modules optimally tilted and oriented towards the Equator. Chapter 3.7 delineates solar climate zones that are relevant for deployment of solar monitoring stations and PV power systems. Chapter 3.8 summarizes analytical information and brings conclusions. In Chapter 4 the solar resource and meteorological conditions are evaluated in the context of possible deployment of solar meteorological stations for validation of the models, excluding areas that are not suitable for solar meteorological stations in Indonesia. Chapter 5 provide an inventory of the technical features of the delivered data products. This report, supported by the GIS data and maps, serves as an input for knowledge-based decisions targeting development of solar power in Indonesia. The outcomes show very good potential for exploitation of solar resources in Indonesia, indicating good opportunities for deployment of all type of photovoltaic applications. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 11 of 86 1 Introduction Solar electricity offers a unique opportunity to achieve long-term sustainability goals, such as development of modern economy, healthy and educated society, clean environment, and improvement of geopolitical stability. Solar power plants exploit local solar energy resources; they do not require heavy support infrastructure, they are scalable, and supply or improve electricity services. Important feature of solar electricity is that it is accessible also in remote locations, without access to electricity, thus providing development opportunities anywhere. While solar resources are fuel to solar power plants, the local geographical and climate conditions determine the efficiency of their operation. Free fuel makes solar technology attractive; however effective investment and technical decisions require detailed, accurate and validated solar and meteorological data. Accurate data are also needed for the cost-effective operation of solar power plant. High quality solar resource and meteorological data can be obtained by satellite-based meteorological models and by instruments installed at the meteorological stations. 1.1 Inventory of previous solar resource projects Several solar resource assessment initiatives are documented below, as publications and online data resources. The works show steady growing interest and different stages of development of solar resource assessment and energy modelling in the region. Review and analysis of the global and diffuse solar radiations in Jakarta, Indonesia (1985) The publication prepared by Mr. Parangtopo et al. from the Institute of Material Science and Energy, Faculty of Science, University of Indonesia, brings analysis based on the total solar energy radiation and sunshine duration data from the period 1965-1979 measured by Indonesian Institute of Meteorology and Geophysics. As a result, the diffuse solar radiation, clearness index histogram, space insolation distributions, daily, monthly and yearly diagrams are presented [1]. Solar radiation data for Indonesia (1992) The publication by Mr. Graham Morrison from School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia, processed data measured in years 1969-1976 by Meteorological and Geophysics Centre, Jakarta, into a computer compatible form. As a result, irradiation conditions in Indonesia are presented in a map and compared to neighbouring countries [2]. Estimation of global solar radiation in the Indonesian climate region (2000) The publication by Mr. Sugiyatno Halawa from the Research and Development Centre for Applied Physics in Bandung, Indonesia, presents monthly average daily global solar radiation correlation applicable to the Indonesian climatic region. The correlation developed is based on the meteorological data collected from seven meteorological stations and evolves from the Sayigh’s “Universal Formula” with a slight modification to suit the Indonesian climatic conditions [3]. Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system (2012) The publication prepared by Mr. Meita Rumbayan, Mr. Asifujiang Abudureyimu, and Mr. Ken Nagasaka from Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Japan, World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 12 of 86 determine the theoretical potential of solar irradiation in Indonesia by using artificial neural networks method and visualize the solar irradiation by province as solar map for the entire of Indonesia [4]. The results are presented as monthly solar radiation maps of 30 provinces of Indonesia. SWERA GIS data and maps Monthly averages are available at SWERA web site as resource from NASA Surface meteorology and Solar Energy Release 6.0 Data Set (Jan 2008). The data consists of 22-year monthly & annual average (from July 1983 to June 2005), with resolution of approx. 110 km. The dataset is accessible from online source http://en.openei.org/datasets/dataset/solar-monthly-and-annual-average-global-horizontal-irradiance-gis-data- at-one-degree-resolution-of PVGIS online tool The tool is accessible from http://re.jrc.ec.europa.eu/pvgis/. The database is based on Meteosat satellite data calculation [5] and offers solar resource long-term monthly averages. The database is not validated in the region and it covers only Western part of Indonesia. 1.2 Evaluation of the existing data and studies The previously developed solar and meteorological data sets (See Chapter 1.1) do not fulfil the requirements for accuracy and reliability needed for large-scale commercial development. The main features that differentiate Solargis database from the above-mentioned data sets (NASA, SWERA, etc.): • The models are based on new and advanced algorithms, validated at different climate zones • Use of modern and systematically updated input data for the models: satellite, atmospheric and meteorological • Database has global coverage at very high resolution • Historical subhourly time series data is updated in real time • Data can be used for project development but also for monitoring and forecasting • Data is systematically validated and quality controlled • There is customer support and supporting consultancy services The new data set from Solargis focuses on a supply of data and services for development and financing of large-scale solar power plants, worldwide, and in Indonesia. The main objective is to systematically supply reliable, validated and high-resolution data to solar industry with low uncertainty and systematic quality control. Production of accurate solar and meteorological data requires elaborated models, outputs of which cover extensive territories at high level of detail. The long record of historical data should be produced by such models for representative characterization of the solar climate that is critical in the phase of project development and energy yield assessment. The solar and meteorological models should also be able to update the data in real time to support monitoring and forecasting of solar power plants. At the country level, modern solar measuring stations are used for accuracy enhancement of such models. They are also needed for developing understanding of the regional and local patterns of weather. This knowledge helps to reduce risks and to improve performance of solar systems. A combination of the model data with modern solar and meteorological measurements is used to support solar energy development in all stages of its lifecycle. Solar meteorological stations are typically installed at the sites where large solar power plants are developed. Such stations stay on the site, over the project lifetime, and provide data for monitoring, performance evaluation and forecasting of solar power. For improved accuracy and reliability of the measurements, it is a good practice to combine local measurements with solar and meteorological models. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 13 of 86 High accuracy solar resource and meteorological data are needed for development and operation of commercial solar power plants. Typically, detailed data, describing the local climate, is needed for a site of interest. Existing meteorological stations are geographically dispersed, and they typically do not operate high-accuracy solar sensors. In majority of cases, high accuracy solar measurements for a site of interest are not available. Therefore, a procedure how solar and meteorological data is used in a project development is following: • In the first stage, for energy yield and performance assessment of the power plants, the data from solar and meteorological models are used. This data has higher uncertainty as it has not been validated by local measurements. • When the site is secured and the project development starts, a solar meteorological station is installed. The high accuracy meteorological equipment is used to collect local data for an initial period of at least one year. Such measurements are than used for site adaptation of solar models, and for delivering higher accuracy data that is used for energy yield assessment, financial calculations and for optimising the technical design. • After solar power plant starts the operation, the solar and meteorological measurements are used in combination with models for monitoring, performance evaluation and forecasting of solar power. At large power plants, solar measurements are collected over their all lifetime. To summarize, the solar and meteorological data is used for the following tasks related to solar power generation: 1. Country-level evaluation and strategical assessment 2. Prospection, selection of candidate sites for future power plants, and prefeasibility analysis 3. Project evaluation, solar and energy yield assessment, technical design and financing 4. Monitoring and performance assessment of solar power plants and forecasting of solar power 5. Quality control of solar measurements. This report addresses the first topic, from the list above. 1.3 Objectives This report offers a country-level evaluation of the solar resources, geographical conditions and photovoltaic power generation potential of Indonesia. The study also describes methods, and outcomes of solar resource mapping. This analysis is based on the use of the Solargis model data. The solar model was validated by measurements only available in a wider region with similar geographical conditions. Because of limited validation one must expect higher uncertainty of the model outputs. To reduce this uncertainty and to improve understanding of the solar climate at local scale, it is proposed to install solar meteorological stations with high accuracy instruments. This report evaluates suitable areas for deployment of this type of solar meteorological stations. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 14 of 86 2 Solargis methods and data 2.1 Solar resource data 2.1.1 Introduction Solar resource (physical term solar radiation) is fuel to solar energy systems. The solar radiation available for solar energy systems at the ground level depends on processes in the atmosphere. This leads to a high spatial and temporal variability at the Earth’s surface. The interactions of extra-terrestrial solar radiation with the Earth’s atmosphere, surface and objects are divided into four groups: 1. Solar geometry, trajectory around the sun and Earth's rotation (declination, latitude, solar angle) 2. Atmospheric attenuation (scattering and absorption) by: 2.1 Atmospheric gases (air molecules, ozone, NO2, CO2 and O2) 2.2 Solid and liquid particles (aerosols) and water vapour 2.3 Clouds (condensed water or ice crystals) 3. Topography (elevation, surface inclination and orientation, horizon) 4. Shadows, reflections from surface or local obstacles (trees, buildings, etc.) and re-diffusion by atmosphere. The atmosphere attenuates solar radiation selectively: some wavelengths are associated with high attenuation (e.g. UV) and others with a good transmission. Solar radiation called "short wavelength" (in practice, 300 to 4000 nm) is of main interest to solar power technology and is used as a reference. The component that is neither reflected nor scattered, and which directly reaches the surface, is called direct radiation; this is the component that produces shadows. Component scattered by the atmosphere, and which reaches the ground is called diffuse radiation. Small part of the radiation reflected by the surface and reaching an inclined plane is called the reflected radiation. These three components together create global radiation. A proportion of individual components at any time are given by Sun position and by the actual state of atmosphere – mainly occurrence of clouds, air pollution and humidity. According to the generally adopted terminology, in solar radiation two terms are distinguished: 2 • Solar irradiance indicates power (instant energy) per second incident on a surface of 1 m (unit: W/ 2 m ). 2 2 • Solar irradiation, expressed in MJ/ m or Wh/m it indicates the amount of incident solar energy per unit area during a lapse of time (hour, day, month, etc.). Often, the term irradiance is used by the authors of numerous publications in both cases, which can be sometimes confusing. In solar energy applications, the following three solar resources are relevant: • Direct Normal Irradiation/Irradiance (DNI): it is the direct solar radiation from the solar disk and the region closest to the sun (circumsolar disk of 5° centred on the sun). DNI is the component that is important to concentrating solar collectors used in Concentrating Solar Power (CSP) and high- performance cells in Concentrating Photovoltaic (CPV) technologies. • Global Horizontal Irradiation/Irradiance (GHI): sum of direct and diffuse radiation received on a horizontal plane. GHI is a reference radiation for the comparison of climatic zones; it is also the essential parameter for calculation of radiation on a flat plate collector. • Global Tilted Irradiation/Irradiance (GTI) or total radiation received on a surface with defined tilt and World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 15 of 86 azimuth, fixed or sun-tracking. This is the sum of the scattered radiation, direct and reflected. A term Plan of Array (POA) irradiation//irradiance is also used. In the case of photovoltaic (PV) applications, GTI can be occasionally affected by shading from surrounding terrain or objects, and GTI is then composed only from diffuse and reflected components. This happens usually for sun at low angles over the horizon. Solar radiation data can be acquired by two complementary approaches: 1. Ground-mounted sensors are good in providing high frequency and accurate data (for well-maintained, high accuracy measuring equipment) for a specific location. 2. Satellite-based models provide data with lower frequency of measurement, but representing long history over lager areas. Satellite-models are not capable of producing instantaneous values at the same accuracy as ground sensors, but can provide robust aggregated values. This Chapter summarizes approaches for measuring and computing these parameters, and the main sources of uncertainty. Methods for combination of data acquired by these two complementary approaches with the aim to get maximum from their benefits were developed. The most effective approach is to correlate multiyear satellite time series with data measured locally over short periods of time (at least one year) to reduce uncertainty and achieve more reliable long term estimates. 2.1.2 Solar radiation measurements Global irradiance for horizontal and tilted plane is most often measured by (i) pyranometers using thermocouple junction or (ii) silicon photodiode cells. Diffuse irradiance is measured with the same sensors as global irradiance, except that the sun is obscured with a sun-tracking disk or rotating shadow band to block the direct component. Direct Normal Irradiance is commonly measured by pyrheliometers, where the instrument always aims directly at the sun using a continuously sun tracking mechanism. A variety of instruments exists, with different properties and achievable accuracy of measurements (Table 2.1). Due to required accuracy in the solar industry, it is recommended to measure solar radiation with the highest- accuracy instruments: • Secondary standard pyranometers for Global Horizontal Irradiation (GHI) and (with shading ball/disc on a tracker) also for Diffuse Horizontal Irradiation (DIF) • First class pyrheliometer for Direct Normal Irradiation (DNI). This instrumentation is more expensive, and it is also more susceptible to soiling, thus it is more demanding for maintenance. However, if professional cleaning and operation are rigorously followed, the measuring set-up works reliably, delivering data with the lowest possible uncertainty. Accuracy of Global Horizontal irradiance, measured with a thermopile pyranometer, is affected by two sources of error: the thermal imbalance problem and the cosine error of the sensor, resulting in a minimum uncertainty (for the most accurate sensor) of daily sums at about ±2%. Direct Normal Irradiance, if measured by pyrheliometers, may be measured at daily uncertainty of about ±1% for a freshly calibrated high-accuracy pyrheliometer under ideal conditions. This uncertainty can more than double in case of rapid fluctuations of radiation, when using older instruments, or after prolonged exposure to challenging weather. Table 2.1: Theoretically-achievable uncertainty of pyranometers at 95% confidence level ISO 9060 class Hourly totals Daily totals Secondary standard ±3% ±2% First class ±8% ±5% Second class ±20% ±10% World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 16 of 86 Global and diffuse components can also be measured by a Rotating Shadowband Radiometer (RSR) or by an integrated pyranometer such Sunshine Pyranometer (e.g. by SPN1). In such a case, DNI is calculated from global and diffuse irradiance. Rotating Shadowband Radiometer (RSR) instruments can be installed as an alternative to the above-mentioned instruments, if measurements take place in a more challenging and remote environment with limited possibilities for cleaning and maintenance. However, if RSR is to be used, it is proposed to add one redundant measurement using a thermopile-type instrument for crosschecking the accuracy of RSR measurements. The photodiodes and RSR devices are also affected by cosine error and temperature. Empirical functions are used to correct the raw data, but theoretical daily uncertainty is around ±4% to 5%. These instruments have narrower spectral sensitivity, thus operating these instruments in very different environmental conditions from those used for calibration may lead to increased uncertainty. Utilization of the state-of-the-art instruments does not alone guarantee good results. The lowest possible uncertainties of solar measurements are essential for accurate determination of the solar resource. Uncertainty of measurements in outdoor conditions is always higher than the one declared in the technical specifications of the instrument (Table 2.1). The uncertainty may dramatically increase in extreme operating conditions and in cases of limited or insufficient maintenance. Solar radiation measurements are not only subject to errors, as regards the instant values. The radiometric response of the instruments also undergoes seasonal variability and longterm drift. Without careful maintenance, periodical check-up and calibration, the measured values can significantly differ from the “true” ones. Rigorous on-site maintenance is crucial for sustainable quality of the longterm measuring campaign. Not only regular care of instruments is necessary, but also documenting all changes in instrumentation, calibration, cleaning and variations of the instruments’ behaviour. Measuring solar radiation is sensitive to imperfections and errors, which result in visible and hidden anomalies in the output data. The errors may be introduced by measurement equipment, system setup or operation-related problems. Errors in data can severely affect derived data products and subsequent analyses; a thorough quality check is needed prior the data use. 2.1.3 Solargis satellite-based model Numerical models using satellite and atmospheric data have become a standard for calculating solar resource time series and maps. The same models are also used for real-time data delivery for system monitoring and solar resource forecasting. Data from reliable operational solar models are routinely used by solar industry. In this study, we applied a model developed and operated by the company Solargis. This model operationally calculates a high-resolution solar resource data and other meteorological parameters. Its geographical extent covers most of the land surface between 60º North and 55º South latitudes. A comprehensive overview of the Solargis model was made available in several publications [6, 7, 8, 9]. The related uncertainty and requirements for bankability are discussed in [10, 11]. In Solargis approach, solar irradiance is calculated in 5 steps: 1. Calculation of clear-sky irradiance, assuming all atmospheric effects except of clouds, 2. Calculation of cloud properties from satellite data, 3. Integration of clear-sky irradiance and cloud effects and calculation of global horizontal irradiance (GHI) 4. Calculation of direct normal irradiance (DNI) from GHI and clear-sky irradiance. 5. Calculation of global tilted irradiance (GTI) from GHI and DNI. Models used in individual calculation steps are parameterized by a set of inputs characterizing the cloud properties, state of the atmosphere and terrain conditions. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 17 of 86 The clear-sky irradiance is calculated by the simplified SOLIS model [12]. This model allows fast calculation of clear-sky irradiance from the set of input parameters. Sun position is deterministic parameter, and it is described by the algorithms with satisfactory accuracy. Stochastic variability of clear-sky atmospheric conditions is determined by changing concentrations of atmospheric constituents, namely aerosols, water vapour and ozone. Global atmospheric data, representing these constituents, are routinely calculated by world meteorology data centres: • In Solargis, the new generation aerosol data set representing Atmospheric Optical Depth (AOD) is used. The core data set is from MACC-II/CAMS project (ECMWF) [13, 14]. An important feature of this data set is that it captures daily variability of aerosols and allows simulating more precisely the events with extreme atmospheric load of aerosol particles. Thus, it reduces uncertainty of instantaneous estimates of GHI and especially DNI, and it allows for improved statistical distribution of irradiance values [15, 16]. For years 1999 to 2002, data from the MERRA-2 model (NASA) [17] homogenized with MACC- II/CAMS model are used. The Solargis calculation accuracy of the clear-sky irradiance is especially sensitive to the information on aerosols. • Water vapour is also highly variable in space and time, but it has lower impact on the values of solar radiation, compared to aerosols. The daily GFS and CFSR values (NOAA NCEP) are used in Solargis, thus representing the daily variability from 1999 to the present [18, 19, 20]. • Ozone absorbs solar radiation at wavelengths shorter than 0.3 µm, thus having negligible influence on the broadband solar radiation. Map 2.1: Coverage of solar geostationary satellite data in Indonesia The clouds are the most influencing factor, modulating clear-sky irradiance. Effect of clouds is calculated from the satellite data in the form of cloud index (cloud transmittance). The cloud index is derived by relating irradiance recorded by the satellite in several spectral channels and surface albedo to the cloud optical properties. In this study, a data from the Meteosat MFG IODC (Eumetsat), MTSAT and Himawari 8 satellite (JMA) satellites are used. Data from Meteosat IODC mission is available for a period from 1999 till the last day in a time step of 30 minutes. Data from the Pacific satellite mission is available from 2007 to 2015 (MTSAT) in a time step of 30 minutes, and for a period 2016 onwards in a time step of 10 minutes (Map 2.1). In Solargis, the modified calculation scheme by Cano has been adopted to retrieve cloud optical properties from the satellite data [21]. A number of improvements have been introduced to better cope with specific situations such as snow, ice, or high albedo areas (arid zones and deserts), and with complex terrain. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 18 of 86 To calculate Global Horizontal Irradiance (GHI) for all atmospheric and cloud conditions, the clear-sky global horizontal irradiance is coupled with the cloud index. From GHI, other solar irradiance components (direct, diffuse and reflected) are calculated. Direct Normal Irradiance (DNI) is calculated by the modified Dirindex model [22]. Diffuse horizontal irradiance is derived from GHI and DNI according to the following equation: DIF = GHI - DNI * Cos Z (1) Where Z is the zenith angle between the solar position and the Earth’s surface. Calculation of Global Tilted Irradiance (GTI) from GHI deals with direct and diffuse components separately. While calculation of the direct component is straightforward, estimation of diffuse irradiance for a tilted surface is more complex, and is affected by limited information about shading effects and albedo of nearby objects. For converting diffuse horizontal irradiance for a tilted surface, the Perez diffuse transposition model is used [23]. The reflected component is also approximated considering that knowledge of local conditions is limited. Model for simulation of terrain effects (elevation and shading) based on high-resolution elevation and horizon data is used in the standard Solargis methodology [24]. Model by Ruiz Arias is used to achieve enhanced spatial representation – from the resolution of satellite (several km) to the resolution of digital terrain model. Solargis model version 2.1 has been used for computing the data. Table 2.2 summarizes technical parameters of the model inputs and of the primary outputs. Table 2.2: Input data used in the Solargis solar radiation model and related GHI and DNI outputs Inputs into the Solargis Source Time Original Approx. grid model of input data representation time step resolution Cloud index Meteosat IODC 1999 to date 30 minutes 2.7 to 4.6 km (EUMETSAT)* MTSAT (JMA) 2007 to 2015 30 minutes 4.0 to 8.0 km Himawari 8 (JMA) ** 2016 to date 10 minutes 2.0 to 5.0 km Atmospheric optical depth MACC/CAMS 2003 to date 3 hours 75 km and 125 km (aerosols) (ECMWF) MERRA-2 (NASA) 1999 to 2002 1 hour 50 km Water vapour CFSR/GFS 1999 to date 1 hour 35 and 55 km (NOAA) Elevation and horizon SRTM-3 - - 250 m (SRTM) Solargis primary data - 1999 to 2016*** 30 minutes 250 m outputs (GHI and DNI) supplied for Myanmar * Data from IODC is used on Sumatra only ** Data from MTSAT and Himawari-8 in all territories except for Sumatra *** Solargis operational model provides the data updates in real time. The deliverables within this project cover complete calendar years from 1999 (Meteosat IODC) and 2007 (MTSAT) till 2015 (see Chapter 5.1) The achievable uncertainty of the solar irradiance derived from the satellite-based models depends on performance and limits of partial models as well as on the quality and the model inputs (mostly aerosols and satellite data). The modelling of solar irradiance in variable geographical conditions of Indonesia is challenging and several features influencing final data uncertainty should be considered: 1. Aerosols: there is a limited knowledge available on aerosols (pollution, burning biomass, forest fires etc.). Some of satellite based aerosol datasets are inherent part of AOD databases used in Solargis model, but local sources of pollution may not be accurately recorded in Solargis model inputs. 2. Regional effect of extreme events such as forest fires and volcano eruptions, which may significantly increase amount of pollutants (reducing solar radiation) in the atmosphere. Modelling of such features is difficult as it is usually represented in the input aerosol databases with insufficient accuracy. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 19 of 86 Moreover, the knowledge on effects of such extreme situations is low and not adequately represented in traditional solar radiation models. 3. Persistent cloudy weather and monsoons: for this type of weather conditions the identification accuracy of cloud properties (their effect on amount of irradiance at ground level) from satellite data is in general lower. 4. Specific satellite data features. Indonesia is located between two geostationary satellite missions: IODC (Meteosat MFG) and Pacific (MTSAT and Himawari). In both satellite missions, Indonesia is located at the edge of satellite images, where the identification of clouds has higher uncertainty. Moreover, data from the older satellite platforms (e.g. MTSAT) have lower positional accuracy (images may be slightly shifted). Even though the Solargis model uses advanced methods to correct image geometry, the identification of clouds has higher uncertainty in a contrast areas such as coastlines and ocean. 5. High mountains are very challenging for solar radiation modelling. The main problem is the identification of clouds using satellite images. Presence of terrain shading makes the cloud identification erroneous, and the clouds and snow have similar reflectivity in visible wavelengths. The use of satellite data acquired in the infrared spectrum helps in the cloud identification, but the resulting uncertainty remains higher. 2.1.4 Measured vs. satellite data – adaptation of solar model For qualified solar resource assessment, it is important to understand characteristics of ground measurements and satellite-modelled data (Table 2.3). In general, top-quality and well-maintained instruments provide data with lower uncertainty than the satellite model. However, such data are rarely available for the required location and extrapolation of measured information from nearby station is limited only to short distances. Moreover, the period of measurements is usually too short to describe longterm weather conditions. On the other hand, the satellite data can provide long climatic history − more than 18 (10) years in Indonesia − for any location, but may not accurately represent the micro-climatic conditions of a specific site. The ground measurements and satellite data complement each other and it is beneficial to correlate them and to adapt the satellite model for the specific site of interest. Site-adapted satellite model data provide long history time series with lower uncertainty. The model adaptation has two steps: 1. Identification of systematic differences between hourly satellite data and local measurements for the period when both data sets overlap; 2. Development of a correction method that is applied for the whole period represented by the satellite time series. The improvements of such site-adapted data depend on the quality and accuracy of measured and satellite data. In the most favourable cases, the resulting uncertainty is still slightly higher than uncertainty of ground measurements. In general, site adaptation of satellite data by local measurements will result in lower uncertainty under the following conditions: • At least one year of ground-measured data is available (preferably two years or more) to cover all seasons; • The solar measuring station is equipped with more than one instrument, allowing redundancy checks for GHI, DNI and DIF values; • Ground measurements are of high quality, which should be traceable in the cleaning, maintenance and calibration logs. • High quality satellite data are used, with good representation of irradiance variability, extreme situations and with consistent longterm quality. • Advanced site-adaptation methods are used, capable to address specific sources of satellite-ground data differences (e.g. correction of aerosols, reduction of cloud identification problems). Besides World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 20 of 86 reduced uncertainty of longterm estimate (lower bias), the model adaptation method should also improve random deviations (lower RMSD) and should provide more representative (sub) hourly values (lower KSI). The site-adaptation of the model data is typically performed for locations where large-scale solar power plants are planned. During the project development phase a meteorological station with high-accuracy instruments is installed on the site and operated for at least one year, to get good understanding of local conditions. Local measurements are then used for site-adaptation of satellite model. The site-adapted long-term data are crucial for local solar resource evaluation and for accurate simulation of performance of various solar energy technologies with low uncertainty. The ground measurements can be correlated with satellite data also at a regional level – within so called regional model adaptation, focused on broader territory rather than a single site. In the case of regional adaptation, the method aims to identify and reduce regional systematic deviations of the model typically driven by insufficient characterization of aerosols or specific cloud patterns. The regional adaptation requires measurements from several stations, which allows distinguishing the systematic model issues relevant to a large region, from the features related to a microclimate. The result of regional adaptation is improved solar resource database in the regional context with overall reduction of systematic errors. Table 2.3: Comparing solar data from solar measuring stations and from satellite models Data from solar measuring stations Data from satellite-based models Availability/ Available only for limited number of Data are available for any location within latitudes 60N and accessibility sites. Most often, data cover only 55S. Data cover long period, in Indonesia more than 18 recent years. (10) years. Original spatial Data represent the microclimate of a Satellite models represent area with complex spatial resolution site. resolution: clouds are mapped at approx. 2.0-8.0 km, aerosols at 50-125 km and water vapour at 34 km. Terrain can be modelled at spatial resolution of up to 90 metres. Methods for enhancement of spatial resolution are often used. Original time Seconds to minutes 10 minutes (Himawari) and 30 minutes (MTSAT and IODC) resolution in Southeast Asia, depending on the satellite platform Quality Data need to go through rigorous Quality control of the input data is necessary. Outputs are quality control, gap filling and cross- regularly validated. Under normal operation, the data have comparison. only few gaps, which are filled by intelligent algorithms. Stability Instruments need regular cleaning and If data are geometrically and radiometrically pre- control. Instruments, measuring processed, a complete history of data can be calculated practices, maintenance and calibration with one single set of algorithms. Data computed by an may change over time. Thus, regular operational satellite model may change slightly over time, calibration is needed. Longterm as the model and its input data evolve. Thus, regular stability is typically a challenge. reanalysis and temporal harmonization of inputs is used in state-of-the-art models. Uncertainty Uncertainty is related to the accuracy Uncertainty is given by the characteristics of the model, of the instruments, maintenance and resolution and accuracy of the input data. operation of the equipment, Uncertainty of models is higher than high quality local measurement practices, and quality measurements. The data may not exactly represent the control. local microclimate, but are usually stable and may show systematic deviation, which can be reduced by good quality local measurements (site-adaptation of the model). 2.1.5 Validation of solar radiation model data and uncertainty Accuracy of Solargis data has been compared with high-quality solar measurements at 220+ meteorological stations, distributed worldwide. Map 2.2 and Table 2.4 show selected validation sites in the broader region, World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 21 of 86 fulfilling the minimum requirements of measured data quality. Tables 2.5 and 2.6 show the Solargis model quality indicators for solar primary parameters: DNI and GHI. Table 2.4: Selected validation sites in the region Site name Country Latitude Longitude Elevation Source [º] [º] [m] Silpakorn Thailand 13.8188 100.0408 72 SOLARFLUX USM Penang* Malaysia 5.358 100.302 51 SOLARFLUX Bukit Kototabang Indonesia -0.2019 100.3181 864 GAW Palangkaraya* Indonesia -2.228 113.946 27 SOLARFLUX El Nido airport* Philippines 11.205 119.413 4 SOLARFLUX Ishigakijima Japan 24.3367 124.1633 11 BSRN Cocos (Keeling) Islands Australia -12.1892 96.8344 3 BSRN Momote Papua New -2.058 147.425 6 BSRN Guinea Darwin Australia -12.4239 130.8925 30 BOM Broome Australia -17.9475 122.2353 7 BOM * Less than one year of measurements Map 2.2: Solar radiation validation sites Comparison of the validation statistics, computed for the solar meteorological sites in the region, shows overall stability of the Solargis model and of the underlying input data. Locally increased bias was identified in some World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 22 of 86 sites, that may be effect of the specific local conditions (e.g. anthropogenic pollution, forest fires), limited accuracy of model and its input data as well as properties of ground measurements (short period of available data, lower accuracy of instruments). Table 2.5: Global Horizontal Irradiance – quality indicators in the region Global Horizontal Irradiance, GHI Bias Root Mean Square Deviation, RMSD 2 [W/m ] [%] Hourly [%] Daily [%] Monthly [%] Silpakorn -9 -1.9 23.6 12.2 5.0 USM Penang* 24 6.1 32.4 13.8 7.1 Bukit Kototabang 2 0.6 31.6 14.8 2.5 Palangkaraya* -20 -4.6 21.7 9.8 8.0 El Nido airport* -13 -3.1 26.4 10.7 5.7 Ishigakijima -5 -1.3 24.2 14.3 2.3 Cocos (Keeling) Islands -20 -4.1 18.3 8.4 4.9 Momote -13 -2.9 25.9 12.4 3.8 Darwin 11 2.1 18.3 8.5 2.9 Broome 1 0.1 11.7 6.0 2.1 * Less than one year of measurements Table 2.6: Direct Normal Irradiance – quality indicators in the region Direct Normal Irradiance, DNI Bias Root Mean Square Deviation, RMSD 2 [W/m ] [%] Hourly [%] Daily [%] Monthly [%] Bukit Kototabang 18.7 8.9 72.6 42.1 11.0 Ishigakijima 2.3 0.7 43.7 25.7 4.9 Cocos (Keeling) Islands -29 -7.1 38.4 20.6 8.9 Momote 5.3 1.5 49.2 24.1 5.2 Darwin 11.3 2.2 29.4 14.9 3.2 Broome 13.6 2.1 21.2 12.7 4.6 The ground measurement sites (Table 2.5, Map 2.2) are selected from the wider region and only two of them are in Indonesia (one of them has data available only for few months). The available ground measurements are not sufficient to analyse the model performance, rather they give only indication of model performance in a broader context. With assumption of the Solargis model performance stability over the lager territories with the similar geographical conditions, confirmed in other regions, we estimate the expected uncertainty of yearly solar radiation summaries of the Solargis data for Indonesia. In Indonesia, we recognise three main regions with different expected uncertainty (Map 2.3, Table 2.7). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 23 of 86 Table 2.7: Uncertainty of long-term yearly estimates for GHI, GTI and DNI values in Indonesia Global Horizontal Global Tilted Direct Normal Irradiation (GHI) Irradiation (GTI) Irradiation (DNI) I. Northern Java, southern Sulawesi, southern 5 to 6.5% 6 to 7.5% 10 to 18 % islands, low lands of New Guinea II. Eastern Sumatra, central part of Kalimantan, ±6 to 8% ±7 to 9% ±12 to 18% parts of New Guinea, Maluku III. High mountains, western coast of Sumatra ±7 to 10% ±8 to 11% ±14 to 22% In the data comparison exercise run for Europe and North Africa, Solargis has been identified as the best performing satellite-based solar database [25]. Map 2.3: Regions of solar resource uncertainty in Indonesia Source: SRTM-3 2.2 Meteorological data 2.2.1 Measured vs. modelled data – features and uncertainty Meteorological parameters are an important part of a solar energy project assessment as they determine the operating conditions and effectiveness of operation of solar power plants. The most important meteorological parameter for the operation of photovoltaic power plants is air temperature, which directly impact power production (energy yield is decreasing when temperature is increasing). Meteorological data can be collected by two approaches: (1) by measuring at meteorological sites and (2) computing by meteorological models. The requirements for the meteorological data for solar energy projects are: • Long and continuous record of data, preferably covering the same period as satellite-based solar resource data, • Data should reliably represent the local climate, World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 24 of 86 • Data should be accurate, quality-controlled and without gaps. The best option would be to have continuous measurements from high-accuracy sensors installed on the site in a meteorological station. However, except for sites where long-term meteorological observations are operated as part of national meteorological service or some other observation network, this option is typically not available. Even if some measurements are available, often the time series are incomplete or not reliable. Most often, the only alternative is to derive historical meteorological data from meteorological models. Several models are available; a good option is to use Climate Forecast System Reanalysis (CFSR) and its operational extension the Climate Forecast System Version 2 (CFSv2) models (source NOAA, NCEP, USA) covering long period of time with continuous data [19, 20]. The results of these models are implemented in Solargis. The ground measurements play an important role in the assessment of local climate conditions as they determine the efficiency of photovoltaic power production. The role of the measurements in solar energy development is twofold: • Measurements are used for validation and accuracy enhancement of historical data derived from the solar and meteorological models • The high frequency measurements (typically one-minute data) are used for improved understanding of the microclimate of the site, especially for capturing the extremes. Data inputs and method In this delivery, the air temperature, wind speed and relative humidity is derived from the meteorological models: CFSR and CFSv2 (Table 2.8). The original spatial resolution of the models is enhanced to 1 km for air temperature by spatial disaggregation and use of the Digital Elevation Model SRTM-3. Important note: the numerical weather model has lower spatial and temporal resolution compared to the solar resource data. Local microclimate of the site may deviate from the values derived from the numerical models. Table 2.8: Original source of Solargis meteorological data for Indonesia: models CFSR and CFSv2. Climate Forecast System Reanalysis Climate Forecast System (CFSR) (CFSv2) Period 1999 to 2010 2011 to date* Original spatial resolution 30 x 35 km 20 x 22 km Original time resolution 1 hour 1 hour * Solargis generates data updates from CFSv2 model in real time. The deliverables within this project cover complete calendar years from 1999 (compatible with Meteosat IODC region) and 2007 (compatible with MTSAT/Himawari satellite region) till 2016. Data from the two sources described above have their advantages and disadvantages (Table 2.9). Air temperature, wind speed and relative humidity retrieved from the meteorological models have lower spatial and temporal resolution compared to on-site meteorological measurements, and they have lower accuracy. Thus, modelled parameters may characterize only regional climate patterns rather than local microclimate; especially extreme values may be smoothed and not well represented. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 25 of 86 Table 2.9: Comparing data from meteorological stations and weather models Meteorological station data Data from meteorological models Availability/ Available only for selected sites. Data may Data are available for any location. Data cover long accessibility cover various periods of time period of time (decades) Original spatial Local measurement representing Regional simulation, representing regional weather resolution microclimate with all local weather patterns with relatively coarse grid resolution. occurrences Therefore, the local values may be smoothed, especially the extreme values. Original time From 1 minute to 1 hour 1 hour resolution Quality Data need to go through rigorous quality Only basic quality control is needed. No gaps. control, gap filling and cross-comparison. Relatively stable outputs if data processing is systematically controlled. Stability Sensors, measuring practices, maintenance In case of reanalysis, long history of data is and calibration may change over time. Thus, calculated with one single stable model. Data for long-term stability is often a challenge. operational forecast model may slightly change over time, as model development evolves Uncertainty Uncertainty is related to the quality and Uncertainty is given by the resolution and accuracy of maintenance of sensors and measurement the model. Uncertainty of meteorological models is practices, usually sufficient for solar energy higher than high quality local measurements. The applications. data may not exactly represent the local microclimate, but are usually sufficient for solar energy applications. 2.2.2 Validation of air temperature data The validation was carried out to compare the modelled data with measurements available at selected meteorological stations in Indonesia, available through NOAA ISD network (Table 2.10 and Map 2.4). In general, the data from the meteorological models represent larger area, and it may not be capable to represent accurately the microclimate, especially in mountains. The validation was made on larger number of power plants (109 stations in total) but only selected stations (14 stations) results are presented in the tabular form. Map 2.4: Position of meteorological stations considered in the validation. Validation results of stations depicted in red colour are presented in tables below World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 26 of 86 Table 2.10: Meteorological stations and time periods considered in the model validation Meteorological station Data source Validation Latitude* Longitude* Elevation* period [º] [º] [m a.s.l.] Mopah NOAA ISD 01/2008 – 12/2015 -8.5200 140.4180 3 Saumlaki NOAA ISD 01/2008 – 12/2015 -7.9830 131.3000 24 Pattimura NOAA ISD 01/2008 – 12/2015 -3.7100 128.0890 10 Wamena NOAA ISD 01/2008 – 12/2015 -4.1030 138.9570 1550 Mau Hau NOAA ISD 01/2008 – 12/2015 -9.6690 120.3020 12 Denpasar Ngurah Rai NOAA ISD 01/2008 – 12/2015 -8.7480 115.1670 4 Hasanuddin NOAA ISD 01/2008 – 12/2015 -5.0620 119.5540 14 Toli Toli Lalos NOAA ISD 01/2008 – 12/2015 1.0170 120.8000 2 Serang NOAA ISD 01/2008 – 12/2015 -6.1170 106.1330 40 Muaratewe Beringin NOAA ISD 01/2008 – 12/2015 -0.9500 114.9000 60 Paloh NOAA ISD 01/2008 – 12/2015 1.7670 109.3000 15 Fatmawati Soekarno NOAA ISD 01/2008 – 12/2015 -3.8640 102.3390 15 Sultan Syarif Kasim NOAA ISD 01/2008 – 12/2015 0.4610 101.4450 31 Malikus Saleh NOAA ISD 01/2008 – 12/2015 5.2270 96.9500 27 *Remark: the position of meteorological stations from the ISD network can be less accurate Air temperature is derived from both meteorological models and post-processed to the spatial resolution of 1 km by post-processing and disaggregation (Table 2.11). Considering spatial and time interpolation, for hourly values the deviation of the model to the ground observations can occasionally reach several degrees Celsius. Table 2.11: Air temperature at 2 m: accuracy indicators of the model outputs [ºC]. Meteorological CFSR and CFSv2 models station Bias RMSD hourly RMSD daily RMSD monthly Mopah -0.4 1.5 1.1 0.5 Saumlaki -0.2 1.4 0.9 0.3 Pattimura 0.6 2.0 1.0 0.6 Wamena -4.3 4.7 4.6 4.3 Mau Hau -0.8 2.0 1.4 0.9 Denpasar Ngurah Rai -0.6 1.4 0.9 0.6 Hasanuddin -0.8 1.9 1.2 0.9 Toli Toli Lalos -1.7 2.4 2.0 1.7 Serang -0.3 1.8 1.4 0.4 Muaratewe Beringin 0.0 2.8 2.4 0.5 Paloh -0.3 1.9 1.3 0.4 Fatmawati Soekarno -0.9 1.9 1.5 0.9 Sultan Syarif Kasim -1.4 2.7 2.3 1.5 Malikus Saleh 0.5 2.0 1.3 0.6 The modelled air temperature in Indonesia fits quite well the measured data, regarding average values. Daily temperature amplitude is often reduced for the stations located on the coast due to limited resolution of the meteorological model. For the same reason for the stations located in complex mountainous regions there can World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 27 of 86 be higher bias in the annual values. Wamena airport is an example of such a station exhibiting high negative bias, -4.3°C (model underestimates the air temperature). There are not many stations in the mountains, so the actual quality of the model data is difficult to estimate is such areas. For the stations located in a flat land the quality of model data is quite good with small negative annual bias between -1.5°C to 0°C (85 out of 109 stations). Average hourly RMSE data is low (2.2°C) due to small daily amplitude of temperature in Indonesia. 2.2.3 Validation of wind speed and relative humidity data Beside air temperature, the wind speed and relative humidity are the most important meteorological parameters having impact on PV energy production. Similar validation procedure was carried out for wind speed and humidity. The results for selected 14 stations are presented in Tables 2.12 and 2.13. It was found that quality of wind speed data is quite good with 42, 72 and 94 stations, respectively, out of analysed 109 having bias lower then ±0.5 m/s, ±1.0 m/s and ±1.5 m/s. Table 2.12: Wind speed at 10 m: accuracy indicators of the model outputs [m/s]. Meteorological CFSR and CFSv2 models station Bias RMSD hourly RMSD daily RMSD monthly Mopah 0.0 2.0 1.0 0.4 Saumlaki 1.3 2.3 1.8 1.4 Pattimura 1.5 2.5 2.2 1.7 Wamena -2.4 3.5 2.9 2.5 Mau Hau -1.0 2.1 1.5 1.1 Denpasar Ngurah Rai 0.0 1.6 1.0 0.3 Hasanuddin -0.1 1.6 0.6 0.3 Toli Toli Lalos 0.1 1.1 0.7 0.2 Serang -0.1 1.5 1.0 0.3 Muaratewe Beringin -0.9 1.5 1.5 0.9 Paloh 0.5 1.5 1.1 0.5 Fatmawati Soekarno -0.9 2.0 1.4 1.0 Sultan Syarif Kasim -1.8 2.2 2.1 1.8 Malikus Saleh 0.0 1.4 0.8 0.1 The climate of Indonesia is tropical. Relative humidity ranges between 60% and 100%. Meteorological model data fit quite well the measured values with 59 and 97 stations, respectively, out of analysed 109, having bias lower then ±5% and ±10%. This accuracy should be sufficient for solar energy applications. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 28 of 86 Table 2.13: Relative humidity at 2 m: accuracy indicators of the model outputs [%]. Meteorological CFSR and CFSv2 models station Bias RMSD hourly RMSD daily RMSD monthly Mopah -4 9 7 4 Saumlaki -6 9 8 6 Pattimura -9 12 10 9 Wamena 23 25 25 23 Mau Hau -2 10 7 4 Denpasar Ngurah Rai -5 9 6 5 Hasanuddin -6 12 9 7 Toli Toli Lalos 0 9 5 1 Serang -5 10 9 6 Muaratewe Beringin -8 16 15 9 Paloh -7 11 9 7 Fatmawati Soekarno -6 12 9 6 Sultan Syarif Kasim 4 13 11 5 Malikus Saleh -13 17 15 13 2.2.4 Expected uncertainty of meteorological model data model The meteorological parameters summarised in this report are derived from two similar numerical meteorological models covering periods from 1999 to 2010 (CFSR model) and 2011 to date (CFSv2). Taking into account the validation results, the uncertainty of the estimate for the main meteorological parameters is summarised in Table 2.14. It must be noted that despite relatively large number of meteorological stations used in this validation there are regions (like mountains, tropical rain forests) where there are no stations at all. As a result the validation in these regions ma not show full picture. Table 2.14: Expected uncertainty of modelled meteorological parameters in Indonesia. Unit Annual Monthly Hourly Air temperature at 2 m °C ±2.0 ±2.0 ±3.0 Wind speed at 10 m m/s ±1.0 ±1.0 ±2.5 Relative humidity at 2 m % ±5 ±5 ±10 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 29 of 86 2.3 Solar power systems: technical options and energy simulation 2.3.1 Photovoltaic systems Solar radiation is the most important parameter for PV power simulation, as it is fuel for solar power plants. The intensity of global irradiance received by tilted surface of PV modules (GTI) is calculated from two primary parameters stored in the Solargis database and delivered in this project: • Global Horizontal Irradiance (GHI) • Direct Normal Irradiance (DNI) There are two main types of solar energy technologies: photovoltaic (PV) and concentrating solar power (CSP). Photovoltaics exploits global horizontal or tilted irradiation, which is the sum of direct and diffuse components (see equation (1) in Chapter 2.1.3). To simulate power production from a PV system, global irradiance received by a flat surface of PV modules must be correctly calculated. Due to clouds, PV power generation reacts to changes of solar radiation in the matter of seconds or minutes (depending on the size of a module field), thus intermittency (short-term variability) of the PV power production is to be considered. Similarly, the effect of seasonal variability is important as well. PV technology will become an important part of energy mix in Indonesia. It is also an attractive option for small- scale provision of electricity, particularly in remote areas of eastern Indonesia. In many areas, local minigrids or off-grid systems are less costly solution than grid extension to the households remaining to be electrified [26]. Possible configurations range from small solar home systems, through local mini-grid systems (hybrid systems, where PV is support for diesel, micro-hydroelectric or home biogas generators) to ground mounted large scale grid connected systems in range of tens or hundreds of MWp [27]. For PV applications, several technical options are briefly described below. Two types of mounting of PV modules are considered: • Build in an open space, where PV modules are ground-mounted in a fixed position or on sun-trackers • Mounted on roofs or façades of buildings. Three types of a PV system are considered for Indonesia: • Grid-connected PV power plants • Mini-grid PV systems • Off-grid PV systems Most large-scale PV power plants are built in open space and have PV modules mounted at a fixed position. Fixed mounting structures (often mounted at an optimum tilt) offer a simple and efficient choice for implementing the PV power plants. A well-designed structure is robust and ensures long-life performance at low maintenance costs. Sun-tracking systems offer an alternative. Solar trackers adjust the orientation of the PV modules during a day to a more favourable position in relation to the sun, so the PV modules collect more solar radiation. Roof or façade mounted PV systems are typically small to medium size, i.e. ranging from hundreds of watts to hundreds of kilowatts. Modules can be mounted on roofs (flat or tilted), façades or can be directly integrated as part of a building structure. PV modules in these systems are often installed in a suboptimal position (deviating from the optimum angle), and this results in a lower performance, compared to open space systems. PV modules, which are mounted at very low tilt (less than 10 degrees), may be affected by higher surface pollution due to limited natural cleaning. Another reduction of PV power output is often determined by nearby shading structures. Trees, masts, neighbouring buildings, roof structures or self-shading of crystalline silicon modules especially have some influence on reduced PV system performance. The main characteristic of grid-connected systems is their geographic dispersion and connection into a distribution grid. Direct connection into grid also means that the inverter must provide support functions to the electrical grid, required by regulations (voltage, frequency, isolation check, etc.). For comparison, a utility scale World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 30 of 86 power plant has its own protection equipment, separated from the inverter and assembled typically on the high- voltage side. Inverters can reach higher efficiencies, and are required to have anti-islanding protection, which means that they work only if grid voltage is present (due to safety reasons). Other connection options, combined with batteries, are also used. Mini-grid PV systems provide small isolated distribution grid for local consumers, usually in remote areas. Typical size of installed PV systems is in the range of several hundreds of kWp. Mini-grid may be adapted to meet requirements of local needs, sometimes with several types of electricity generators (hybrid systems) and battery storage. This type of electrification gives prospects for development to remote and rural communities, because it is often the only economically-viable option for supply of electricity. Off-grid PV systems are small systems, not connected into distribution grid. They are usually equipped with energy storage (classic lead acid or modern-type batteries) and/or connected to diesel generators. Batteries are maintained through charge controllers for protection against overcharging or deep discharge. Depending on size and functionality of the off-grid PV system, it can work with AC (together with inverter) or DC voltage source. 2.3.2 Solar concentrating power systems Solar concentrating technologies can only exploit direct normal irradiance (DNI), as diffuse irradiance cannot be concentrated. Instant (short-term) variability of DNI is very high, especially in equatorial tropics. Advantage of solar thermal power plants, often denoted as Concentrating Solar Power technology, is that they have means to control short-term and daily variability due to the inertia of the whole system (solar field, heat transfer and storage), which in addition can be supported by fossil fuels. This type of technology is mentioned briefly below, for the sake of completeness, although it is not expected that solar concentrators are implemented in Indonesia. CSP (Concentrated Solar Power) technologies concentrate DNI onto a small area through mirrors or lenses. Focused light is then converted to heat which drives a heat engine like a steam turbine, connected to an electrical power generator. There are three main CSP systems: • Parabolic trough systems: U-shaped mirrors with oil-filled pipes running along their centre • Power tower systems: they include large number of flat mirrors tracking the sun and focusing irradiance onto a receiver in tower • Dish engine systems: they include stand-alone parabolic reflector that concentrates light onto a focal point with a Stirling engine to generate power. CSP generators are most effective in arid or semiarid countries with high intensities of DNI and low atmospheric pollution i.e. low diffuse irradiation (DIF). Another type of technology converting DNI into electricity is Concentrated Photovoltaic (CPV). This technology is based on the use of lenses or curved mirrors to concentrate sunlight onto a small area of high-efficiency PV cells. High concentration CPV has to use very precise solar trackers. The advantage of CPV over flat plate PV is a potential for cost reduction due to the smaller area of photovoltaic material. The necessity of sun tracking partially balances out the smaller price of semiconductor material used. CPV technology requires also more maintenance during the lifetime of the power plant. Power production from CPV may be more sensitive to changing weather conditions. The advantage of CPV over CSP is full scalability, similar to flat plate PV modules. There is regionally limited potential for CSP and CPV in Indonesia, and therefore this report focuses mainly on photovoltaic technology. Its potential is studied for a system with fixed-mounted PV modules, considered here as the mainstream technology. Installed capacity of a PV power plant is usually determined by the available space and options to maintain the stability of the power grid. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 31 of 86 2.3.3 Principles of photovoltaic power simulation PV energy simulation results, presented in Chapter 3.6, are based on software developed by Solargis. This Chapter summarizes key elements of the simulation chain. Table 2.15: Specification of Solargis database used in the PV calculation in this study Data inputs for PV simulation Global tilted irradiation (GTI) for optimum angle towards South (0° to 4°) or towards North (0° to 15°), derived from GHI and DNI; air temperature at 2 m (TEMP). Spatial grid resolution (approximate) Approx. 275 metres (9 arc-sec) Time resolution 30-minute Geographical extent (this study) Republic of Indonesia Period covered by data (this study) 01/1999 to 12/2016 The Solargis PV software is based on the implementation of scientifically proven methods [28 to 35] and uses Solargis time series of solar radiation and air temperature data on the input (Table 2.15). Data and model quality are checked using field tests and ground measurements. Figure 2.1: Simplified Solargis PV simulation chain World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 32 of 86 In PV energy simulation procedure, there are several energy losses occurring in the individual steps of energy conversion (Figure 2.1): 1. Losses due to terrain shading caused by far horizon. Shading of local features such as nearby building, structures or vegetation is not considered in the calculation, 2. Energy conversion in PV modules is reduced by Losses due to angular reflectivity, which depends on relative position of the sun and plane of the module and Temperature Losses, caused by performance of PV modules working outside of STC conditions defined in datasheets, 3. DC output of PV array is further reduced by Losses due to dirt, soiling or snow depending mainly on the environmental factors and module cleaning, Losses by inter-row shading caused by preceding rows of modules and Mismatch and DC cabling losses, which are given by slight differences between nominal power of each module and small losses on cable connections, 4. DC to AC energy conversion is performed by inverter. Efficiency of this conversion step is reduced by Inverter losses, given by inverter efficiency function. Further factors, reducing AC energy output, are Losses in AC cabling and Transformer losses (apply only for large–scale open space systems), 5. Availability. This empirical parameter quantifies electricity losses incurred by shutdown of a PV power plant due to maintenance or failures, including issues in the power grid. Availability of well operated PV system is approximately 99%. According to experience in many countries, the crystalline silicon PV modules show low performance degradation (reduction of conversion efficiency) over time. The rate of the performance degradation is higher at the beginning of the exposure, and then stabilizes at a lower level. Initial degradation may be close to value of 0.8% for the first year and 0.5% or less for the next years [32]. Degradation of PV modules is not considered in this study. Results of calculation of PV power potential for Indonesia are shown in Chapter 3.6. 2.3.4 Configuration of a reference PV system considered in this study Photovoltaic power production has been calculated using numerical models developed and implemented in- house by Solargis. As introduced in Chapter 2.1, 30-minute time series of solar radiation and air temperature, representing last 18 and 10 years, respectively are used as an input to the simulation. The models are developed based on the advanced algorithms, expert knowledge and recommendations given in [34] and tested using monitoring results from existing PV power plants. Table 2.17 summarize losses and related uncertainty throughout the PV computing chain. Table 2.16: Reference configuration - photovoltaic power plant with fixed-mounted PV modules Feature Description Configuration represents a typical PV power plant of 1 MW-peak or higher. All calculations are Nominal capacity scaled to 1 kWp, so that they can be easily multiplied for any installed capacity. Crystalline silicon modules with positive power tolerance. NOCT 46ºC and temperature Modules coefficient of the Pmax -0.45 %/K Inverters Central inverter with Euro efficiency 97.5% Fixed mounting structures facing towards equator with optimum tilt (the range from 0º to 15º). Mounting of PV modules Relative row spacing 2.5 (ratio of absolute spacing and table width) Transformer Medium voltage power transformer World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 33 of 86 Map 3.18 shows theoretical potential power production of a PV system installed with a typical technology configuration, for large-scale open space PV power plants. The technical parameters are described in Table 2.16. In this study, the reference configuration for the PV potential calculation is a PV system with crystalline-silicon (c-Si) modules mounted in a fixed position on a table facing towards equator and inclined at an angle close to optimum, i.e. at the angle at which the yearly sum of global tilted irradiation received by PV modules is maximized (0° to 4° on a northern hemisphere and 0° to 15° on a southern hemisphere, depending on a geographical region). The fixed-mounting of PV modules is very common and provides a robust solution with a minimum maintenance effort. Geographical differences in potential PV production are shown on the example of eight selected sites (see Chapters 3.1 and 3.6). Table 2.17: Yearly energy losses and related uncertainty in PV power simulation Simulation step Losses Uncertainty Notes [%] [± %] 1 Global Tilted Irradiation N/A 6.0 to 11.0 Annual Global Irradiation falling on the surface of (model estimate with terrain PV modules shading) 2 Module surface angular -2.7 to -3.4 1.0 Medium polluted surface of PV modules is reflectivity (numerical model) considered Temperature losses -1.5 to -13.0 3.5 Depends on the temperature and irradiance. NOCT (numerical model) of 46ºC is considered 3 Polluted surface of modules -3.5 1.5 Losses due to dirt, dust, soiling, snow and bird (empirical estimate) droppings Module inter-row shading -0.1 to -0.3 0.3 Partial shading of strings by modules from (model estimate) the preceding rows Mismatch between modules -0.5 0.5 Well-sorted modules and lower mismatch are (empirical estimate) considered. DC cable losses -2.0 1.5 This value can be calculated from the electrical (empirical estimate) design 4 Conversion in the inverter -2.5 0.5 Given by the Euro efficiency of the inverter, which (value from the technical data is considered at 97.5% sheet) AC cable losses -0.5 0.5 Standard AC connection is assumed (empirical estimate) Transformer losses -1.0 0.5 Standard transformer is assumed (empirical estimate) 5 Availability 0.0 0.0 A theoretical value of 100% technical availability is considered Range of cumulative losses -13.5 to -24.3 7.4 to 11.8 These values are indicative and do not consider and indicative uncertainty project specific features and performance degradation of a PV system over its lifetime PV electricity potential is calculated based on a set of assumptions shown in Tables 2.16 and 2.17. These assumptions are approximate, and they may differ in various parameters, in real projects. As can be seen, the uncertainty of solar resource is the highest element of energy simulation. The results presented in Chapter 3.6 do not consider performance degradation of PV modules due to aging. They also lack a detail: these results cannot be used for financial assumptions of any particular project. Detailed assessment of energy yield of a specific power plant is offered within a scope of site-specific bankable expert study. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 34 of 86 3 Solar resource and PV potential of Indonesia 3.1 Geography This report analyses solar and meteorological data for Indonesia that determine photovoltaic power production and influence its performance efficiency. We analyse also other geographical factors that influence development and operation of solar photovoltaic power plants. Indonesia is located in South Asia between latitudes 6° North and 11° South and longitudes 91° and 145° East. We demonstrate the variability of solar potential in two forms: • At the country level in the form of maps • In the form of graphs and tables for eight selected sites that represent the geographical variability of solar power production in Indonesia. While trying to address the geographical diversity, these sites are also chosen in regions where majority of PV systems will be very likely deployed. The position of selected sites is summarised in Table 3.1 and Map 3.1. All the data in tables and graphs, shown in this Chapter 3, relate to these eight sites. Table 3.1: Position of eight selected sites in Indonesia ID Site name Island Latitude Longitude Elevation [°] [°] [metres a.s.l.] 1 Binjai Sumatra 3.60127 98.50460 33 2 Jambi airport Sumatra -1.64575 103.65354 19 3 Jakarta, University of Indonesia Java -6.36808 106.82845 76 4 Pontianak airport Kalimantan -0.14988 109.40488 1 5 Surabaya Java -7.32250 112.68213 3 6 Kupang Timor -10.08692 123.86587 31 7 Manado airport Sulawesi 1.54684 124.92397 83 8 Jayapura Papua -2.67182 140.80187 6 Geographic information and maps bring additional value to the solar data. Geographical characteristics of the country, from regional to local scale may represent technical and environmental prerequisites, but also constraints for solar energy development. In this report, we collected the following data that have relevance to development of photovoltaic solar power plants, focusing on large (utility-scale) ground-mounted systems (rooftop systems have almost no limitations and can be installed anywhere): • Terrain: physical limitation for development (Maps 3.2 and 3.3) • Land cover: defines use of land for settlements and economic activities (Map 3.4) • Main road network: defining accessibility of sites (Map 3.5) • Protected areas may pose limits to the size of power plants and the related infrastructure (Map 3.6) • Population density (Map 3.7) • Forest fires (creating air pollution and haze) and volcanic eruptions (Map 3.8 and 3.9) • Rainfall (precipitation) has impact on cleaning of PV modules (Map 3.10) • Air temperature has impact on performance efficiency of PV (Map 3.11). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 35 of 86 Map 3.1: Position of eight selected representative sites in Indonesia. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 36 of 86 Eight sites are selected with an aim to have balanced perspective of solar power generation by respecting the geographical diversity and population distribution in Indonesia. To some extent, terrain elevation and slope (Maps 3.2 and 3.3) represents limiting factor for locating large-scale solar power facilities. High elevation and steep slopes (higher than approx. 7 to 10 degrees) pose extra challenges to the development of large scale PV. Small solar power installations (residential, off-grid stand- alone or mini-grids) in Indonesia can be installed in any populated area: they play crucial role for development especially in remote communities. Land cover map (Map 3.4) shows that the most of settlements and economic activities (industry, agriculture and transport) − that require access to electrical power − are developed in areas with elevation up to 1000 m a.s.l. with terrain slopes below 10 degrees. Settlements are dispersed in highlands and remote areas as well. Urbanisation and transport infrastructure is the most developed in Java and Sumatra islands, where also electricity demand is the highest (Map 3.5). The most densely populated areas coincide, to a large degree, with areas most suitable for solar investments (Map 3.7). Nature protection areas (Map 3.6) represent limitation for deployment of large scale solar power plants. They are dispersed across all the islands of Indonesia. However, having just a local impact, it is not difficult to manage planning and development with the knowledge of these constrains. Increasing economic activity has brought numerous challenges and changes to ecosystems. One of these challenges is deforestation. Deforestation, driven mainly by agricultural expansion is the main culprit for forest fires in Indonesia. These fires occur regularly during the dry season, mainly on the islands of Sumatra and Kalimantan (Borneo), and are the main driver behind increased aerosols in the atmosphere of the region. Regarding solar irradiance, aerosols could substantially affect solar irradiance. They hinder direct solar irradiation (DNI), while slightly increasing diffused horizontal irradiance. According to Global Forest Watch dataset of forest fires [39], in the period of 2001 to 2015 the year 2015 was particularly severe (Map 3.8). In Solargis data, particularly for Pontianak airport in West Kalimantan, we can see a drop in direct solar irradiation (DNI) by 5% in September 2015, when compared to the long-term average for this site (see Chapter 3.4). Indonesia is affected also by almost constant volcanic activity (Map 3.9), since it lies in the volcanic region called “The Ring of Fire” [41]. This activity should be also considered for evaluation of large scale PV. Volcanic activity is assessed by Volcanic Explosivity Index (VEI), mainly by the international community and by the United States Geographical Survey [41]. The frequency of volcanic eruptions in Southeast Asia is historically given as 95% of VEI 3 every 11 years and VEI 4 every 16 years. VEI 1 and above has the strength to effect local climate, while VEI above 3 to 4 can affect the whole region. Closer look in the data from the Global Volcanism program has given evidence for higher occurrence of explosions in Indonesia: there are eruptions somewhere in Indonesia of any intensity (VIE 0 to VIE 8) every two months, with the data showing 62 occurrences of VEI 2, 15 occurrences of VEI 3 and 3 of VEI 4 in the last 18 years. These explosions can last for years with varying intensity. The most recent explosions are: the 2010 Mount Merapi explosion (VEI 4), Mount Kelut in 2014 (VEI 4) and numerous explosion of Mount Soputan between 2011 and 2016 (all VEI 3). Ash cloud from these eruptions, depending on the distance from its source, can accumulate from few mm to few cm on the ground and any object, as well as the energy transmission infrastructure. This results in breakage of lines, outages and hindering the PV production of solar panels. Aerosols ejected from vents which reach significant height can disperse in the atmosphere, even stratosphere and travel hundreds and thousands of kilometres, lowering solar irradiance depending. Moreover, the SO2 (as well as other components) emitted into the atmosphere can lower local and even global temperature. Rainfall has importance from the view point of cleaning of the surface of PV modules. Excessive rainfall (especially high intensities) may pose a risk to development and operation of solar power plants (Map 3.10). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 37 of 86 Map 3.2: Terrain elevation above sea level. Source: SRTM-3 and Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 38 of 86 Map 3.3: Terrain slope. Source: SRTM-3 and Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 39 of 86 Map 3.4: Land cover. Source: GlobCover 2009 (© ESA 2010 and UCLouvain) World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 40 of 86 Map 3.5: Main roads and urban centres. Source: OpenStreetMap.org contributors; administrative boundaries by Cartography Unit, GSDPM (World Bank Group) World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 41 of 86 Map 3.6: Nature protection areas Source: OpenStreetMap.org contributors; adapted by Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 42 of 86 Map 3.7: Population density. Source: Gridded Population of the World (GPW) v.4 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 43 of 86 Map 3.8: Occurrence of forest fires Source: Global Forest Watch, World Resources Institute, Washington D.C. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 44 of 86 Map 3.9: Volcanic eruptions in Indonesia between 1999 and 2016 Source: Smithsonian Institution, Washington D.C. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 45 of 86 Map 3.10: Long-term yearly average sum of rainfall (precipitation). Source: Global Precipitation Climatology Centre (DWD) World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 46 of 86 3.2 Air temperature at 2 metres Map 3.11: Long-term yearly average of air temperature at 2 metres. Source: Models CFSR and CFSv2, NOAA, post-processed by Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 47 of 86 Air temperature determines the operating environment and performance efficiency of the solar power systems. Air temperature at 2 metres is used as one of inputs in the solar energy simulation models. In this report, data and maps showing the yearly and monthly average air temperature are discussed. Map 3.11 shows yearly average values. The long-term averages of air temperature are aggregated from the hourly data derived from the CFSR and CFSv2 meteorological models (see Chapter 2.2) and post-processed by Solargis. The aggregated data show good match with the validation sites at number of meteorological stations in Indonesia (Chapter 2.2.2). As regards the hourly model values, the extreme day and night values of air temperature especially in the mountains may be partially smoothed, and the models may not represent sufficiently extreme values of the local microclimate. In case of PV power plants, air temperature has a primary influence on the power conversion efficiency in the PV modules, and it also influences other components (inverters, transformers, etc.). Higher air temperature reduces power conversion efficiency of a PV power plant. Table 3.2 shows monthly characteristics of air temperature at eight selected sites; they represent statistics calculated over 24-hour diurnal cycle. Minimum and maximum air temperatures are calculated as the average of minimum and maximum values of temperature during each day (assuming full diurnal cycle - 24 hours), individually for each month. Monthly averages of minimum and maximum daily values show typical daily amplitude in each month (Figure 3.1). Table 3.2: Monthly averages and average minima and maxima of air-temperature at 2 m at 8 sites Temperature [°C] Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Min Min Min Min Min Min Min Min Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 22.8 22.9 23.2 23.5 24.2 24.8 25.0 24.8 January 26.5 25.4 26.6 26.3 27.5 27.8 26.7 27.1 32.0 29.5 31.2 31.0 33.0 32.7 29.1 30.5 22.9 22.7 23.2 23.4 24.2 24.3 24.8 24.6 February 27.0 25.6 26.3 26.6 27.2 27.6 26.7 27.1 33.1 30.2 30.4 32.2 32.2 32.4 29.2 30.8 23.4 23.0 23.4 23.5 24.3 23.5 25.0 24.8 March 27.6 26.0 26.8 26.8 27.6 27.5 26.9 27.2 34.1 31.0 31.5 32.5 33.1 33.2 29.4 30.8 23.6 23.0 23.8 23.8 24.4 23.7 25.1 25.0 April 27.7 26.1 27.4 27.0 27.9 28.0 27.1 27.3 34.3 31.4 32.5 32.5 33.2 34.1 30.0 31.2 23.3 22.9 23.7 23.7 24.5 23.9 25.3 24.8 May 27.6 26.3 27.5 27.1 28.1 27.8 27.4 27.4 34.7 32.0 32.6 32.8 33.4 33.4 30.7 31.3 22.9 22.5 23.2 23.2 23.9 22.9 25.1 24.5 June 27.5 26.1 27.2 27.0 27.9 26.8 27.2 27.1 35.0 32.3 32.5 32.9 33.5 32.1 30.7 31.0 22.4 22.2 22.3 23.1 23.1 22.3 25.1 24.1 July 27.1 25.8 26.8 26.8 27.8 26.5 27.1 26.8 34.7 31.9 32.7 32.6 33.9 32.2 30.5 30.8 22.6 22.3 22.2 23.3 23.1 21.9 25.1 24.2 August 27.1 26.0 27.1 26.8 28.3 26.7 27.2 27.0 34.5 32.2 33.6 32.8 34.8 33.7 31.0 31.1 22.6 22.3 22.7 23.2 23.5 22.3 25.0 24.4 September 26.7 26.2 27.6 26.7 29.1 28.0 27.5 27.2 33.5 32.6 34.5 32.3 36.1 36.0 32.0 31.5 22.8 22.6 23.5 23.3 24.5 23.7 25.0 24.5 October 26.5 26.0 27.9 26.5 29.8 29.4 27.5 27.5 32.7 31.8 34.2 31.7 36.4 37.2 31.6 32.0 23.2 22.9 23.8 23.8 25.0 24.5 25.1 24.8 November 26.4 25.8 27.6 26.4 29.5 30.0 27.2 27.5 31.9 30.7 33.3 30.7 36.2 37.6 30.5 31.7 23.1 23.0 23.7 23.7 24.3 24.6 25.4 25.0 December 26.3 25.6 27.0 26.3 27.9 28.3 27.0 27.4 31.4 30.0 31.9 30.6 33.8 34.2 29.5 31.3 YEAR 27.0 25.9 27.1 26.7 28.2 27.9 27.1 27.2 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 48 of 86 40 35 Monthly air temperature [°C] 30 25 20 15 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Minimum - Maximum Figure 3.1: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 49 of 86 3.3 Global Horizontal Irradiation Map 3.12: Global Horizontal Irradiation – long term average of daily and yearly totals. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 50 of 86 Map 3.13: Global Horizontal Irradiation – long-term monthly averages of daily totals. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 51 of 86 Global Horizontal Irradiation (GHI) is used as a reference value for comparing solar potential related to PV electricity systems, as it eliminates the possible variations, given by the choice of technical components and the PV system design. The highest GHI is identified in the southern islands of the archipelago, where the average daily totals reach 2 2 5.6 kWh/m (average annual 2045 kWh/m ) and higher (Map 3.12). Further north, average daily totals of GHI 2 2 2 values occurs between 3.8 kWh/m and 4.8 kWh/m (annual sums are between 1400 and 1750 kWh/m ). 2 Minimum daily GHI values in the country are lower than 3.6 kWh/m (average annual value lower than 2 1300 kWh/m ), but the solar resource in these areas is still sufficiently high for small-scale PV. Table 3.3: Daily averages and average minima and maxima of Global Horizontal Irradiation at 8 sites 2 Global Horizontal Irradiation [kWh/m ] Variability Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 3.82 3.61 3.32 3.95 4.10 3.97 3.90 3.96 January 4.24 4.03 3.94 4.51 4.78 5.07 4.29 4.71 8.7 4.60 4.57 5.02 5.40 5.55 6.46 4.83 5.60 4.13 4.07 2.97 3.94 4.02 4.24 4.41 4.23 February 4.70 4.46 3.90 4.83 4.73 5.43 4.84 5.01 9.3 5.32 4.79 4.68 5.37 5.41 6.36 5.71 5.49 4.46 4.53 4.34 4.26 4.16 4.39 4.11 3.87 March 5.29 4.79 4.69 4.99 4.94 5.58 5.31 4.66 6.6 5.73 5.32 5.71 5.52 5.63 6.41 6.11 5.85 4.52 3.98 4.37 4.72 4.32 5.01 4.20 4.58 April 4.98 4.60 4.71 4.98 4.82 5.92 5.15 5.04 8.0 5.30 4.95 5.24 5.25 5.27 6.47 6.13 5.50 4.16 4.16 4.07 4.74 3.96 4.60 4.39 4.33 May 4.86 4.53 4.63 4.92 4.89 5.32 4.85 4.69 5.0 5.31 4.78 5.24 5.31 5.66 5.85 5.51 4.95 4.30 4.04 3.84 4.60 3.77 4.52 3.88 4.01 June 4.81 4.44 4.47 4.85 4.92 5.16 4.60 4.42 5.7 5.23 5.02 4.98 5.10 5.48 5.62 4.99 5.10 4.15 3.94 3.63 4.47 4.64 4.82 4.25 3.84 July 4.74 4.42 4.74 4.93 5.38 5.45 4.88 4.36 8.1 5.18 4.90 5.42 5.37 5.86 5.79 6.02 5.13 4.17 4.04 4.55 4.44 5.45 5.79 4.84 4.31 August 4.75 4.60 5.28 4.92 6.16 6.23 5.43 4.90 11.8 5.28 4.93 5.64 5.22 6.50 6.56 6.53 5.69 4.39 3.73 4.39 4.38 4.89 5.86 4.99 4.43 September 4.82 4.73 5.60 4.97 6.66 6.83 5.69 4.92 15.0 5.56 5.30 6.29 5.42 7.18 7.40 6.86 5.43 4.05 2.58 4.15 4.19 4.91 6.14 4.80 4.51 October 4.52 4.35 5.21 4.54 6.47 7.07 5.44 5.25 18.1 5.01 4.76 6.15 4.86 7.36 7.77 6.57 5.85 3.72 3.93 4.19 3.46 4.50 5.64 4.25 4.40 November 4.22 4.29 4.63 4.28 5.32 6.52 4.66 4.99 15.8 4.54 4.55 5.16 4.63 6.47 7.13 4.98 5.69 3.39 3.63 3.41 3.76 3.86 4.21 4.17 4.46 December 3.88 4.00 4.17 4.21 4.40 5.08 4.53 4.88 9.5 4.36 4.33 4.56 4.94 5.27 5.60 5.06 5.51 4.54 4.27 4.39 4.61 4.81 5.44 4.65 4.56 YEAR 4.65 4.44 4.67 4.74 5.29 5.80 4.97 4.82 8.9 4.74 4.55 5.02 4.86 5.73 6.13 5.60 5.12 9.0 8.0 7.0 Daily sums of GHI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Min - Max Figure 3.2: Long-term monthly averages, minima and maxima of Global Horizontal Irradiation. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 52 of 86 Table 3.3 shows long-term average, and average minima and maxima of daily totals of Global Horizontal Irradiation (GHI) for a period 1999 to 2016 at Binjai and Jambi sites, while the remaining 6 sites encompasses the data for a period 2007 to 2016. Figure 3.2 compares monthly averages of daily values of Global horizontal irradiation (GHI). Most stable weather, but with lower GHI values is from May to July. Highest GHI daily sums and also higher variability is seen during the period from August to November. Due to the equatorial position of Indonesia, the solar irradiance is very evenly distributed over the year, with some higher differences at Kupang and Surabaya sites. These are influenced by dry winds from Australia; thus lower occurrence of clouds or aerosols is the main driver for higher GHI. Weather changes in cycles and has also stochastic nature. Therefore, annual solar radiation in each year can deviate from the long-term average in the range of few percent. Figure 3.3 shows interannual variability, i.e. the magnitude of the year-by-year GHI change. Locally, this variability might be exaggerated by the volcanic activity or forest fires. The interannual variability is calculated from the unbiased standard deviation of GHI over 18 years (for Binjai and Jambi sites) and 10 years (for all other sites), considering a simplified assumption of normal distribution of the annual sums. Sites Surabaya and Kupang show similar varying patterns of GHI over the recorded period, probably because their similar climatic conditions, influenced by winds from Australia. Other sites have different characteristics and relatively small extremes (minimum and maximum GHI) are found: for example, in years 2009, 2010, 2013 or 2015. The most stable GHI (the smallest interannual variability) is observed in Binjai, Jambi and Pontianak. The sites with the highest interannual variability are Manado and Surabaya. 6.5 2374 Average yearly sum of Global Horizontal Irradiation 6.0 2192 Average daily sum of Global Horizontal Irradiation 5.5 2009 5.0 1826 4.5 1644 4.0 1461 [kWh/m2] [kWh/m2] 3.5 1278 3.0 1096 2.5 913 2.0 730 1.5 548 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Binjai 1.9% Jambi 2.1% Jakarta 5.9% Pontianak 2.4% Surabaya 7.3% Kupang 4.8% Manado 7.7% Jayapura 4.1% Figure 3.3: Interannual variability of Global Horizontal Irradiation for selected sites. Map 3.14 delineates ratio of diffuse to global horizontal irradiation. This ratio is important for understanding the performance of PV systems. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 53 of 86 Map 3.14: Long-term average for ratio of diffuse to global irradiation (DIF/GHI). Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 54 of 86 3.4 Direct Normal Irradiation Map 3.15: Direct Normal Irradiation – long-term average of daily and yearly totals. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 55 of 86 Direct Normal Irradiation (DNI) is one of the primary solar resource parameters, needed for computation of Global Tilted Irradiation (GTI, Chapter 3.5). Table 3.4 and Figure 3.4 show long-term average daily totals and average daily minimum and maximum of DNI for eight selected sites, assuming a period 1999 (2007) to 2016. The highest DNI is found in Kupang, while the lowest at the Jambi airport. Table 3.4: Daily averages and average minima and maxima of Direct Normal Irradiation at 8 sites 2 Direct Normal Irradiation [kWh/m ] Variability Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 1.66 1.22 1.03 1.74 1.70 2.00 2.14 2.14 January 2.27 1.70 1.92 2.61 2.70 3.35 2.84 3.15 22.4 2.79 2.03 3.30 3.72 3.62 6.09 3.88 4.71 1.89 1.59 0.53 1.18 1.37 2.21 2.48 2.21 February 2.54 1.96 1.68 2.78 2.61 4.14 3.32 3.30 28.3 3.24 2.36 2.47 3.59 3.72 5.81 4.75 4.15 2.47 2.06 2.13 2.09 2.19 2.69 2.52 2.05 March 3.22 2.43 2.64 2.93 3.05 4.93 4.03 2.87 25.3 3.81 3.20 4.24 3.65 4.21 6.31 5.29 4.15 2.45 1.73 2.35 3.08 2.65 4.49 2.88 3.01 April 2.91 2.52 2.90 3.30 3.32 6.16 4.15 3.66 31.7 3.49 3.11 3.78 3.78 3.99 7.41 5.66 4.18 2.03 2.04 2.35 3.16 2.77 4.17 3.35 3.01 May 3.00 2.67 3.19 3.61 4.14 5.76 4.14 3.70 25.3 3.82 3.06 4.00 4.21 5.53 6.88 5.31 4.21 2.25 2.26 2.32 3.23 2.58 4.46 2.74 2.80 June 3.04 2.73 3.18 3.73 4.68 5.88 3.80 3.51 26.8 3.67 3.54 3.84 4.04 5.82 7.11 4.27 4.63 2.27 2.12 1.95 3.05 4.14 4.81 3.12 2.18 July 2.94 2.56 3.39 3.63 5.37 6.25 4.13 3.31 32.0 3.59 3.21 4.41 4.40 6.49 7.08 6.44 4.78 2.09 1.78 2.55 2.32 4.73 6.08 3.93 2.89 August 2.68 2.31 3.55 3.05 6.01 7.07 4.64 3.76 40.3 3.43 2.89 4.18 3.61 6.77 8.03 6.75 5.05 2.16 0.83 2.30 1.46 3.52 5.09 3.40 2.81 September 2.64 2.21 3.48 2.67 6.21 6.96 4.55 3.46 43.4 3.56 3.11 4.41 3.40 7.13 8.27 6.39 4.14 1.32 0.47 1.70 1.83 3.32 5.13 3.49 2.83 October 2.37 1.88 2.70 2.35 5.18 6.83 4.29 3.69 46.5 3.14 2.34 3.65 2.98 6.57 8.32 5.79 4.66 1.60 1.62 1.77 1.42 2.17 4.73 3.07 2.40 November 2.32 1.98 2.19 2.42 3.33 5.91 3.57 3.41 40.6 2.65 2.39 2.76 2.86 5.18 7.06 4.09 4.30 1.37 1.34 0.95 1.93 1.48 2.19 2.69 2.78 December 1.97 1.73 1.87 2.43 2.15 3.53 3.41 3.46 30.0 2.56 2.18 2.46 3.53 2.96 4.36 4.34 4.34 2.49 1.93 2.37 2.76 3.25 4.83 3.39 3.11 YEAR 2.66 2.22 2.73 2.96 4.07 5.57 3.91 3.44 31.0 2.92 2.52 3.16 3.22 4.77 6.17 4.97 3.87 9.0 8.0 7.0 Daily sums of DNI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Min - Max Figure 3.4: Daily averages of Direct Normal Irradiation at selected sites. At all sites, minimum DNI values are observed during the period of November to February, which corresponds with the monsoon season in Indonesia. During this season, also the lowest DNI variability (given by minimum and maximum range of monthly values) occurs, probably due to constant and high occurrence of clouds and aerosols (fog, smoke). For the rest of the year, DNI patterns depend on the position of the site. For example, World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 56 of 86 Kupang or Manado sites have two maxima during year, while Jambi or Pontianak only one. All sites show highest DNI variability in September and October. Generally, Eastern Java and the Lesser Sunda Islands have highest DNI potential; lower values are found in Sumatra and Kalimantan, and these are influenced by higher occurrence of clouds, and by higher concentrations of aerosols in the atmosphere. Interannual variability of DNI for selected sites (Figure 3.5) is calculated from the unbiased standard deviation of yearly DNI over 10 years (18 years on Sumatra), and it is based on a simplified assumption of normal distribution of the yearly sums. Every site has its own variability pattern; few events were so strong that they influenced most of the sites - see for example high values in years 2009 or 2015. The most stable DNI (the smallest interannual variability) is observed in Jambi, Pontianak and Binjai, the most unstable DNI is recorded at Manado site. The lowest DNI is at Jambi airport. 6.5 Average daily sum of Direct Normal Irradiation Average yearly sum of Direct Normal Irradiation 6.0 2194 5.5 2011 5.0 1828 [kWh/m2] 4.5 1646 [kWh/m2] 4.0 1463 3.5 1281 3.0 1098 2.5 915 2.0 733 1.5 550 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Binjai 5.6% Jambi 7.6% Jakarta 12.6% Pontianak 6.4% Surabaya 15.0% Kupang 10.2% Manado 15.9% Jayapura 8.6% Figure 3.5: Interannual variability of Direct Normal Irradiation at representative sites Daily totals in any single year can be displayed for a better visual presentation of DNI in relation to GHI. Figure 3.6 shows daily totals for year 2016 in Jakarta. GHI daily totals (blue pattern), are shown in the background to highlight the lower DNI values (yellow pattern). 8 Global Horizontal Direct Normal Daily sums of irradiation [kWh/m2] 6 4 2 0 01.01.2016 01.03.2016 01.05.2016 01.07.2016 01.09.2016 01.11.2016 01.01.2017 Figure 3.6: Daily totals of GHI and DNI in Jakarta, year 2016 Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 57 of 86 3.5 Global Tilted Irradiation Map 3.16: Global Tilted Irradiation at optimum angle – long-term average of daily and yearly totals. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 58 of 86 Map 3.17: Theoretical optimum tilt of PV modules to maximize yearly PV power production. Source: Solargis, in practical applications tilt higher than 10° is applied. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 59 of 86 Global Tilted Irradiation (GTI) is the key source of energy for flat-plate photovoltaic (PV) technologies (Chapter 3.6). The regional trend of GTI received by PV modules tilted at optimum angle (GTI) is similar to DNI 2 (Map 3.15). PV modules tilted at optimum inclination show daily totals of GTI at about 5.6 kWh/m (annual 2 totals about 2045 kWh/m ) and higher, especially in the Eastern Java and the Lesser Sunda Islands. In Indonesia, latitude spans between 6° North and 11° South (Map 3.17). For this region, theoretical optimum tilt is 0° to 4° on a northern hemisphere and 0° to 15° on a southern hemisphere (increasing with distance from the Equator). Table 3.5 shows long-term averages of daily total of Global Tilted Irradiation (GTI) for selected sites. It is assumed that solar radiation is received by PV modules surface inclined at the optimum tilt. Sites close to equator do not benefit much from optimal tilt as is visible on Figure 3.8. Jambi, Pontianak and Jayapura will gain in period from April to September up to 3.2%, but in season from October to March will lose up to 2.5%, thus annual GTI gain is close to 0%. Only Kupang and Surabaya can slightly benefit from installations at optimum tilt and are able to reach annual gains in GTI up to 2.8% and 1.9% respectively. Generally, the main parameter influencing optimum tilt is latitude. With increasing latitude, surface inclined at optimum tilt gains more yearly global irradiation compared to the horizontal surface. As presented in Chapter 2.3, it is recommended to install PV modules at a tilt (inclination angle) close to optimum. However, for sites near equator, optimum angle is very low (or equal to 0°) and azimuth of installation is not important. In this case, it is not recommended to keep the tilt of PV modules close to the horizontal position as this prevents natural self-cleaning of PV modules by rain. The PV modules installed at very low tilt will collect dirt and dust, which will result in reduction of the PV power output. In this report, optimum angles for selected sites are calculated, and GTI and PVOUT for optimum angles are delivered. However, for real projects, it is a good practice to install modules at tilt of 10° or higher for improved self-cleaning by rain. The difference between GTI for tilt 10° and lower is negligible. Table 3.5: Daily averages and average minima and maxima of Global Tilted Irradiation at 8 sites 2 Global Tilted Irradiation [kWh/m ] Variability Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura between Min Min Min Min Min Min Min Min sites [%] Average Average Average Average Average Average Average Average Max Max Max Max Max Max Max Max 3.86 3.53 3.18 3.87 3.90 3.74 3.90 3.89 January 4.29 3.94 3.77 4.41 4.50 4.73 4.32 4.61 8.4 4.65 4.46 4.76 5.28 5.20 5.93 4.83 5.47 4.16 4.01 2.91 3.90 3.90 4.09 4.41 4.18 February 4.73 4.40 3.80 4.77 4.57 5.22 4.86 4.95 10.2 5.36 4.72 4.55 5.30 5.22 6.08 5.71 5.42 4.46 4.52 4.34 4.25 4.17 4.40 4.11 3.87 March 5.31 4.78 4.70 4.98 4.95 5.64 5.32 4.66 7.0 5.75 5.32 5.72 5.51 5.65 6.48 6.11 5.84 4.50 4.03 4.51 4.77 4.49 5.34 4.20 4.64 April 4.96 4.66 4.86 5.04 5.02 6.34 5.14 5.11 11.7 5.28 5.02 5.43 5.32 5.50 6.96 6.13 5.58 4.13 4.26 4.29 4.84 4.22 5.09 4.39 4.44 May 4.82 4.64 4.91 5.03 5.29 5.97 4.82 4.81 9.3 5.26 4.91 5.59 5.44 6.20 6.62 5.51 5.08 4.25 4.16 4.09 4.72 4.08 5.11 3.88 4.13 June 4.76 4.58 4.80 4.98 5.45 5.93 4.57 4.56 10.0 5.17 5.20 5.37 5.24 6.12 6.53 4.99 5.28 4.12 4.04 3.82 4.58 5.07 5.42 4.25 3.93 July 4.69 4.55 5.07 5.05 5.94 6.20 4.86 4.48 12.8 5.13 5.05 5.83 5.52 6.52 6.63 6.02 5.30 4.15 4.10 4.73 4.49 5.80 6.32 4.84 4.38 August 4.73 4.68 5.52 4.99 6.59 6.83 5.41 5.00 17.0 5.25 5.03 5.91 5.30 6.98 7.23 6.53 5.82 4.39 3.73 4.44 4.39 4.97 6.03 4.99 4.46 September 4.82 4.75 5.68 4.98 6.82 7.08 5.69 4.95 18.2 5.56 5.33 6.39 5.43 7.35 7.68 6.86 5.47 4.07 2.56 4.08 4.15 4.80 6.00 4.80 4.48 October 4.54 4.31 5.12 4.51 6.33 6.90 5.46 5.21 20.4 5.04 4.71 6.05 4.82 7.19 7.58 6.57 5.80 3.75 3.85 4.04 3.41 4.29 5.28 4.25 4.33 November 4.27 4.20 4.45 4.20 5.04 6.08 4.69 4.90 15.8 4.59 4.46 4.95 4.54 6.09 6.63 4.98 5.57 3.42 3.55 3.28 3.68 3.65 3.93 4.17 4.35 December 3.93 3.90 3.97 4.11 4.13 4.69 4.56 4.76 7.1 4.42 4.22 4.33 4.81 4.93 5.14 5.06 5.37 4.54 4.28 4.43 4.62 4.88 5.62 4.65 4.57 YEAR 4.65 4.45 4.73 4.75 5.39 5.97 4.97 4.83 11.5 4.75 4.56 5.09 4.87 5.84 6.29 5.60 5.14 Figure 3.7 compares long-term daily averages in selected sites. Similar to GHI, high and variable values are observed in period from August to November, while lower and more stable values occur in season from May to July and December to January. Detailed comparison of daily GTI and GHI values for Jakarta is shown in Table 3.6 and Figure 3.9. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 60 of 86 9.0 8.0 7.0 Daily sums of GTI [kWh/m2] 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Min - Max Figure 3.7: Global Tilted Irradiation – long term daily averages, minima and maxima. 20.0 10.0 Relative gain of GTI to GHI [% ] 0.0 -10.0 -20.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Figure 3.8: Monthly relative gain of GTI relative to GHI at selected sites. Table 3.6: Relative gain of daily GTI (optimum tilt of PV modules 10°) to GHI in Jakarta 2 Average daily sum of irradiation [kWh/m ] Jakarta Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Global Horizontal 3.94 3.90 4.69 4.71 4.63 4.47 4.74 5.28 5.60 5.21 4.63 4.17 4.67 Global Tilted 3.77 3.80 4.70 4.86 4.91 4.80 5.07 5.52 5.68 5.12 4.45 3.97 4.73 Global Tilted vs. Horizontal [%] -4 -3 0 3 6 7 7 5 1 -2 -4 -5 1 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 61 of 86 6.0 15 Average daily sum of irradiation [kWh/m2] 5.0 10 Percentual difference GTI vs. GHI [%] 4.0 5 3.0 0 2.0 -5 1.0 -10 Global Horizontal Global Tilted Global Tilted vs. Horizontal 0.0 -15 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 3.9: GHI and GTI monthly averages and relative gain of GTI to GHI in Jakarta Optimum tilt of PV modules 10° Daily totals, in one single year, are shown for better visual presentation of potential gain of solar electricity for tilted PV modules in comparison to horizontally-mounted. Figure 3.10 shows daily sums for year 2016 in Jakarta Blue pattern, representing GHI totals, is transparent to make visible lower values of red, GTI pattern, during the monsoon season. The GTI gains are visible in dry season, while in monsoon season the horizontally-mounted PV modules gain more energy (blue GHI pattern over red GTI). 8 Global Tilted Global Horizontal Daily sums of irradiation [kWh/m2] 6 4 2 0 01.01.2016 01.03.2016 01.05.2016 01.07.2016 01.09.2016 01.11.2016 01.01.2017 Figure 3.10: Daily values of GHI and GTI for Jakarta, year 2016 Optimum tilt of PV modules 10° World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 62 of 86 3.6 Photovoltaic power potential Map 3.18: PV electricity output from an open space fixed-mounted PV system with PV modules mounted at an optimum tilt and a nominal peak power of 1 kWp. Long-term averages of daily and yearly totals. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 63 of 86 Map 3.19: PV power generation potential for an open-space fixed-mounted PV system. Long-term monthly averages of daily totals. PV modules mounted at optimum tilt. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 64 of 86 Map 3.18 shows the average daily totals of specific PV electricity output from a typical open-space PV system with a nominal peak power of 1 kW; the unit is kWh/kWp. Calculating PV output for 1 kWp of installed power makes it simple to scale the PV power production depending on the size of a power plant. Besides the technology choice, the electricity production depends on a geographical position of the power plant. In Indonesia, the average daily total of specific PV power production from a reference system (Table 2.16) varies between 3.0 kWh/kWp (equals to average annual total of about 1100 kWh/kWp) in high and cloudy mountains, and 4.6 kWh/kWp (about 1680 kWh/kWp yearly) with the highest values see in southern islands of the archipelago. In the mountains, the power production can be reduced by up to 20% (or even more) due to terrain shading. Areas with high PV electricity production potential were previously identified also as areas with high GTI and DNI and lower DIF values. Electricity production in a potential PV power plant depends on the site position and follows a combined pattern of global tilted irradiation and air temperature. High PV power production is identified at Kupang and Surabaya sites; lower potential is in Jambi and Binjai. Considering 8 selected sites (Tab. 3.7), the lowest specific PV production is seen in Jambi, which may be result of microclimatic conditions with highest annual DIF/GHI ratio (there are more clouds and aerosols in this site). The difference in PV power production between the selected sites with highest (Kupang, 4.47 kWh/kWp) and lowest (Jambi, 3.41 kWh/kWp) PV power production is about 24%. Map 3.19 shows monthly production from a PV power system, and Figure 3.11 breaks down the values for eight sites. The season of relatively high PV yield is long enough for an effective operation of a PV system. As shown in Chapter 3.5, for sites Kupang and Surabaya it was recommended to install modules at an optimum tilt rather than at horizontal orientation. Besides higher yield, a benefit of tilted modules is improved self-cleaning of the surface pollution by rain. For other sites, it may be feasible to install modules in tilt at least at 10° for improved self-cleaning, because losses in soiled modules can be higher than losses due to suboptimal position to the sun. Table 3.7: Annual performance parameters of a PV system with modules fixed at optimum angle Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura PVOUT Average daily total 3.52 3.41 3.58 3.62 4.05 4.47 3.80 3.69 [kWh/kWp] PVOUT Yearly total 1287 1246 1309 1323 1480 1633 1389 1348 [kWh/kWp] Optimum angle 2° 5° 10° 4° 12° 14° 1° 4° PV system 180° 0° 0° 0° 0° 0° 180° 0° azimuth Annual ratio 56.7% 61.8% 57.2% 53.8% 45.0% 33.9% 46.1% 50.3% of DIF/GHI System PR 75.7% 76.7% 75.9% 76.2% 75.1% 74.9% 76.4% 76.4% PVOUT - PV electricity yield for fixed-mounted modules at optimum angle; DIF/GHI – Ratio of diffuse/global horizontal irradiation; PR - Performance ratio for fixed-mounted PV This shows potential in Indonesia for PV electricity generation. Southern parts of the archipelago (with highest PV electricity production potential), with existing medium voltage distribution lines, are best-suitable for development of medium- to large-scale grid connected PV power projects. During the day time, the newly- developed systems will improve electricity balance in the distribution grid and will help to reduce oil in the primary energy mix [47]. On the opposite side, remote areas without grid availability can benefit from developing the local micro-grids or small solar systems, as a good option for local electrification [26]. Northern parts of archipelago see higher DIF/GHI ratio, but these parts of country still have very good potential for PV electricity generation projects (see Jambi or Binjai in Table 3.8). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 65 of 86 Monthly power production profiles have similar shape for sites Kupang, Surabaya, Jakarta and Manado. High production can be reached in dry season (from August to November), while rainy season from December to February reduces electricity gains. Different climatic conditions in sites Binjai, Jambi, Pontianak and Jayapura determine more stable electricity production during the whole year, without typical seasonal patterns. Table 3.8: Average daily sums of PV electricity output from an open-space fixed PV system with a nominal peak power of 1 kW [kWh/kWp] Average daily sum of electricity production [kWh/kWp] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai 3.26 3.58 4.00 3.75 3.64 3.60 3.54 3.57 3.65 3.45 3.26 3.00 3.52 Jambi 3.04 3.38 3.67 3.57 3.56 3.51 3.50 3.58 3.62 3.30 3.23 3.00 3.41 Jakarta 2.85 2.89 3.57 3.69 3.74 3.66 3.87 4.19 4.27 3.86 3.37 3.02 3.58 Pontianak 3.38 3.63 3.79 3.83 3.82 3.80 3.85 3.80 3.79 3.43 3.21 3.14 3.62 Surabaya 3.41 3.47 3.74 3.79 4.00 4.13 4.49 4.94 5.05 4.68 3.76 3.12 4.05 Kupang 3.56 3.92 4.23 4.75 4.51 4.52 4.72 5.14 5.24 5.06 4.46 3.51 4.47 Manado 3.31 3.73 4.07 3.93 3.69 3.49 3.72 4.13 4.32 4.14 3.58 3.50 3.80 Jayapura 3.52 3.78 3.55 3.91 3.69 3.50 3.43 3.83 3.77 3.96 3.73 3.63 3.69 5.50 5.00 Electricity production [kWh/kWp] 4.50 4.00 3.50 3.00 2.50 2.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Figure 3.11: Monthly averages of daily totals of power production from the fixed tilted PV systems with a nominal peak power of 1 kW at eight sites [kWh/kWp] Table 3.9 and Figure 3.12 show monthly and yearly performance ratios (PR) for a reference installation at the selected sites. The yearly PR is found in a range between 74.9% (Kupang) and 76.7% (Jambi). Jambi is the site with lowest solar radiation (GHI and DNI), yet a PV system would work there with best performance ratio, compared to the other selected sites; this is due to low air temperature. Monthly variations in PR fall in the range ±0.3% (Jayapura, Pontianak and Jambi) to ±1.4% (Kupang); depending on local climate conditions of a site, especially air temperature. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 66 of 86 Table 3.9: Monthly and annual Performance Ratio of a free-standing PV system with fixed modules Monthly Performance Ratio [%] Site Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Binjai 76.2 75.7 75.4 75.5 75.4 75.5 75.5 75.4 75.8 76.0 76.3 76.3 75.7 Jambi 77.0 76.8 76.7 76.6 76.7 76.7 76.9 76.6 76.4 76.4 76.8 76.9 76.7 Jakarta 75.7 76.0 76.0 75.9 76.1 76.3 76.3 75.8 75.2 75.4 75.8 76.1 75.9 Pontianak 76.5 76.2 76.0 75.9 76.1 76.1 76.2 76.1 76.0 76.2 76.5 76.5 76.2 Surabaya 75.7 75.9 75.6 75.6 75.6 75.9 75.5 74.9 74.0 73.9 74.5 75.5 75.1 Kupang 75.3 75.2 74.9 74.9 75.6 76.3 76.1 75.2 74.0 73.4 73.4 74.9 74.9 Manado 76.8 76.7 76.5 76.5 76.4 76.5 76.6 76.3 75.9 75.9 76.4 76.7 76.4 Jayapura 76.4 76.3 76.3 76.5 76.6 76.7 76.7 76.5 76.2 76.1 76.2 76.3 76.4 Impact of air temperature on the performance of PV power plants is seen when comparing monthly temperature profiles in Figure 3.1 with monthly PR profiles in Figure 3.12. Almost all sites, except Surabaya and Kupang, have similar and stable pattern of performance ratio, not influenced by significant changes in air temperature. Sites Surabaya and Kupang with visible temperature peak in September, October and November, see visible PR drop in the same months. 80.0 78.0 Performance ratio [%] 76.0 74.0 72.0 70.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Binjai Jambi Jakarta Pontianak Surabaya Kupang Manado Jayapura Figure 3.12: Monthly performance ratio of a PV system at selected sites. Fixed mounted modules at optimum tilt are considered World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 67 of 86 3.7 Solar climate The power production and performance efficiency of photovoltaic systems is primarily driven by global horizontal irradiance, GHI (Map 3.12). GHI determines absolute values of energy production of a PV system, and variability patterns of PV power production (interannual, seasonal, daily and very short-term variability). In general, the higher GHI, the higher PV energy is expected. Patterns of power generation are also determined by the ratio of diffuse to global radiation (Map 3.14). The simplified assumption about solar radiation is modulated by air temperature, TEMP (Map 3.11), as it affects operation efficiency of a PV system. In general, while high temperature reduces performance efficiency of a PV system, lower air temperature makes power conversion in the PV modules more efficient. In addition, high temperature areas represent regions where PV operates under higher stress. For development of solar power systems, it is also important to consider other geographical and meteorological factors, e.g. terrain elevation, terrain shading, wind speed, rainfall, and land cover. These factors are also important for development and operation of solar power systems: • Terrain: maps of elevation and slope inclination show limitations of installation and operating for the meteorological stations, but also for the PV power systems. Higher elevation above sea level (Map 3.2) in Indonesia relates to volcanos and high mountains with low population. Shading from high terrain blocks direct sunlight and it reduces power generation. Therefore, if possible a solar meteorological station and a PV system should be located in a place with no or limited shading. High slope inclination (Map 3.3) is challenging for logistics and operation, and indicates various geo- hazards in Indonesia, such as possible earthquakes, volcano eruptions, landslides, avalanches and floods. • Wind speed: Low and medium speed wind, close to the ground, has a cooling effect on PV modules, which in turn increases their conversion efficiency and increases power production. However, occurrence of stronger winds poses a risk of damaging the modules and construction components. Wind speed map is not supplied in this report. • Rainfall (Map 3.10): amount and periodicity of rainfall determine cleaning occurrence and intensity of surface of the PV modules. Lack of rainfall is not an issue in Indonesia. • Water bodies (Map 3.6): in areas close to water bodies and the sea the PV power plants may be affected by microclimate features such as increased humidity, salinity or morning fog. • Industrial and highly urbanised areas (Map 3.5 and 3.7): these areas typically correlate with higher air pollution that triggers higher intensity of soiling of PV modules. PV modules that are covered by dust or atmospheric pollution may show substantial reduction of power production and require more frequent cleaning of surface of the PV modules. The proximity of PV power plant to heavy industry or transport lines should be avoided. Note: map of rainfall provides only indicative information at a regional level. It does not respect detailed and complex orography, and thus it does not represent the local climate. Below, the geographical diversity of Indonesia is described by several climate zones relevant to PV power production. These climate zones are identified by combining maps of yearly GHI and TEMP (Tables 3.10 and 3.11). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 68 of 86 Map 3.20: Solar climate zones of Indonesia – indicative classification based on a combination of GHI and TEMP. Source: Solargis World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 69 of 86 Table 3.10: Categories of long-term yearly average of global horizontal irradiation and their relative area Category Yearly average of daily global horizontal irradiation (GHI) 2 A Low < 4.5 kWh/m 30.6% 2 B Middle range From 4.5 to 5.5 kWh/m 67.6% 2 C High > 5.5 kWh/m 1.7% Table 3.11: Categories of long-term yearly average of air temperature and their relative area Category Yearly average of air temperature (TEMP) 1 Low < 20°C 7.6% 2 Middle range From 20°C to < 25°C 42.2% 3 High > 25°C 50.2% In this Chapter, we identified climate regions of Indonesia that may be relevant for taking decision from the perspective of solar power generation and performance efficiency of PV systems (Map 3.20, Tables 3.10 and 3.11, see also Maps 3.11 and 3.12). The zones are quite fragmented, which is given by geography of Indonesia: • Solar climate zone A shows lower solar radiation areas (GHI long-term yearly average below 2 4.5 kWh/m per day). This zone is spread in mostly unpopulated or very sparsely populated areas on the Sumatra, Sulawesi and Papua islands. It is mostly represented by ridges and slopes of high mountains with high occurrence of clouds and air temperature in the middle and lower range (A1 and A2). Occasionally, solar resource is reduced by terrain shading. This climate zone is dominating by forests. The challenge here is also accessibility. 2 • Solar climate zone B with GHI yearly average between 4.5 and 5.5 kWh/m per day is prevailing in Indonesia. This region offers opportunities for installing large-scale PV power systems, where limitation factors are availability of land (in populated areas), terrain (high slope and volcanoes), accessibility (proximity to roads or airports) and the nature protected areas. • Solar climate zone C is also widely spread in Indonesia and indicates areas with higher solar radiation 2 (average above 5.5 kWh/m per day). In Java and Southern islands the deployment of solar energy systems is favourable, as it coincides with the high population and high energy demand. Similar limitation factors apply as in zone B. Higher temperature slightly reduces performance of PV modules. The factor reducing efficiency of PV power systems in urban areas could be air pollution. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 70 of 86 3.8 Evaluation The Chapters above describe various aspects of PV power generation potential in Indonesia, and its relevance for development and operation of photovoltaic systems. Large parts of the country show daily specific PV electricity output in the range between 3.2 kWh/kWp and 4.2 kWh/kWp (equals to average yearly totals between approximately 1170 kWh/kWp and 1530 kWh/kWp). Compared to other countries of the world, Indonesia has very favourable potential for PV power generation (Map 3.21). In addition, the seasonal variability in the country is very low, compared to other regions, further from the equator. The ratio between months with maximum and minimum GHI is about 1.43, compared to other examples, such as Upington, South Africa, it is 2.29 and in Sevilla, Spain, 3.54 (Figure 3.13). Map 3.21: PV power potential of Indonesia in the global context. Fixed mounted modules at optimum tilt are considered 10 8 6 kWh/m2 4 2 Sevilla, 1838 kWh/m2 year Jakarta, 1705 kWh/m2 year Upington, 2272 kWh/m2 year 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3.13: Comparing seasonal variability in three locations World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 71 of 86 Figure 3.14 demonstrates how PV power production depends on solar resource and air temperature in non- linear way. Even that the PV power production is feasible everywhere, for the case of large-scale PV power plants there might be some geographical preferences. ` 3.5 4.0 GHI daily average [kWh/kWp] Jambi 1246 kWh/kWp 4.5 Binjai 1287 Jakarta kWh/kWp 1309 kWh/kWp Pontianak Jayapura 1323 1348 5.0 kWh/kWp kWh/kWp Manado 1388 kWh/kWp Surabaya 1480 kWh/kWp 5.5 Kupang 1633 kWh/kWp 6.0 6.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 TEMP yearly average [°C] Figure 3.14: Comparing yearly GHI and TEMP with potential PV power output at selected sites Indonesia has good potential for development of solar power generation, predominantly in southern part of the country. A lot of population in the country still lacks access to electricity. Medium or small installations are feasible in small and remote communities (off-grids, mini-grids) across the country. Monsoon season has largest impact on decreasing the amount of solar radiation in Southern and coastal region from June to September. The microclimate factors should be considered as well for choosing a best site (see Chapter 3.7). Based on the outcomes of this study, Table 3.12 provides indicative SWOT analysis relative to the exploitation of solar resources in Indonesia. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 72 of 86 Table 3.12: SWOT analysis relative to the solar resource and photovoltaic potential in Indonesia Strengths Weaknesses • Good solar resource and PV power potential • Islands or areas with small and isolated communities • Existing and mature technology for off-grid and • Terrain constrains: terrain with high elevation, steep mini-grids systems for remote communities slope, shading, and limited accessibility • Existing programs for PV installations [43] • High costs of grid connection, long time of connection into HV lines for remote areas [44] • Air pollution in large urban areas Opportunities Threats • Growing demand for electricity • Geographical risks and extreme events: volcanic • International support programs eruptions earthquakes, landslides, tsunamis, etc.) • Positive attitude to renewable energy • Very short term variability of solar resource should be • Reduced cost of PV analyzed for effective PV integration (reduction of minute-scale ramps) • Combination with other renewable energy sources (mainly hydro) helps dealing with variability of solar • Volcanic eruptions resource • Potential synergy with gas-fired power plants World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 73 of 86 4 Priority areas for meteorological stations 4.1 Localisation criteria Based on the analysis of maps presented in this report, and set of criteria, we identify areas proposed for prospective deployment of solar meteorological stations. Here we have in mind the need for the validation of the solar and meteorological models, and reduction of the solar resource data uncertainty for the need of effective development of large-scale photovoltaic power systems. The methodology includes two steps: • Identification of solar climate regions in Indonesia, which may show some difference regarding the performance of large-scale photovoltaic power plants (Chapter 3.7), • Identification of areas preferred for deployment of solar meteorological stations (Chapter 4.2). 4.2 Areas suitable for solar meteorological stations Based on the analysis in Chapter 3.7, we propose location of solar measuring stations by excluding areas that are not suitable. One of requirements of the solar measurement campaign is to receive high quality, continuous data sets that can be used for satellite data validation and regional adaptation of the solar model. From the regional perspective, suitable areas for deployment of solar meteorological stations should be geographically representative, i.e. they should represent a wider territory, certain type of climate where solar resource, terrain, air temperature and land use are similar, and where they do not change abruptly. We also identify exclusion areas, where installation of stations is not recommended, for reasons such as fast changing terrain and landscape, in industrial zones, but also in remote and difficult-to-access areas. From the local perspective, there are several additional localisation criteria for deployment of solar meteorological stations, such as accessibility, availability of personnel for maintenance and cleaning, security, sustainability of running the measurement campaign in a long term, acceptance/interest of the land owner, and other economical and logistical criteria. This report focuses on the regional selection criteria. The local criteria are not considered in this report. Map 4.1 is based on the overlay of the following factors: • Areas close to the transport means and populated areas are preferred, due to requirements for logistics, regular maintenance and likelihood that the meteo station will serve the data needs for local solar power plant. In the low populated zones, areas at a distance more than 5 km from the nearest main road are excluded. • Areas in the distance closer than 3 km from the coast and water are excluded, due to higher uncertainty of solar model data and possible effect from nearby sea or lake. • Areas with slope inclination higher than 10 degrees are excluded; these are areas with rapidly changing terrain and landscape and they are not suitable for localisation of solar meteorological stations (from the perspective of the model validation). • Excluded are also areas with elevation higher than 3000 m. Map 4.1 presents areas excluded (white colour on the map) and prioritized (green colour on the map) from the point of view of localisation of the solar measuring stations. Consulting also Map 3.20 and Figure 3.14, we propose that the meteorological stations are deployed in all three GHI zones: A, B and C (see Chapter 3.7). World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 74 of 86 Map 4.1: Preferred areas for deployment of solar meteorological stations (green colour) Note: areas with high air pollution are not shown in this map Quality and reliability of the measuring campaign is also of the highest importance. Besides choosing the high- accuracy sensors, implementation of the best measurement practices is necessary precondition for achieving reliable data sets required for calibration and validation of solar models. The meteorological instruments must World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 75 of 86 be regularly maintained, cleaned and calibrated. Measurement sites should be in the areas, which are not affected by excessive dust and pollution. Locally shaded areas, caused by surrounding buildings, structures and vegetation, should be also avoided. If shading takes place, the affected solar radiation values should be identified and flagged. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 76 of 86 5 Solargis data delivery for Indonesia The key features of the delivered data and maps for Indonesia are: • Harmonized solar, meteorological and geographical data based on the best available methods and input data sources. • Historical long-term averages representing 18/10 years at high spatial and temporal resolution, available for any location. • The Solargis database and energy simulation software is extensively validated by company Solargis, and by independent organizations. They are also verified within monitoring of commercial PV power plants and solar measuring stations worldwide. • Additional data can be accessed online at http://solargis.com. The delivered data and maps offer a good basis for knowledge-based decision-making and project development. The Solargis data are updated in real time, and it can be further used in solar monitoring, performance assessment and forecasting. 5.1 Spatial data products High-resolution Solargis data have been delivered in the format suitable for common GIS software. The Primary data represent solar radiation, meteorological data and PV power potential. The Supporting data include various vector data, such administrative divisions, etc. Tables 5.1 and 5.2 show information about the data layers and the technical specification is summarized in Tables 5.3 and 5.4. File name convention, used for the individual data sets, is described in Table 5.5. Table 5.1: General information about GIS data layers Geographical extent Federal Democratic Republic of Indonesia with buffer 20 km along the borders and the coast 2 (approx. 3 180 000 km ) Map projection Geographic (Latitude/Longitude), datum WGS84 (also known as GCS_WGS84; EPSG: 4326) Data formats ESRI ASCII raster (AAIGRID) data format (asc) GeoTIFF raster data format (tif) Metadata Each data layer is accompanied by metadata, provided in separate files according to ISO19139 standards in two formats: PDF - human readable; XML - for machine-to-machine communication Notes: • Data layers of both formats (asc and tif) contain the same information, the user is free to choose the preferential data format. Data layers can be also converted to other standard raster formats. • More information about ESRI ASCII grid format can be found at http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/ESRI_ASCII_raster_format/009t0000000z000000/ • More information about GeoTIFF format can be found at https://trac.osgeo.org/geotiff/ World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 77 of 86 Table 5.2: Description of primary GIS data layers Acronym Full name Unit Type of use Type of data layers 2 GHI Global Horizontal kWh/m Reference information for the Long-term yearly and monthly Irradiation assessment of flat-plate PV average of daily totals (photovoltaic) and solar heating technologies (e.g. hot water) 2 DNI Direct Normal kWh/m Assessment of Concentrated PV Long-term yearly and monthly Irradiation (CPV) and Concentrated Solar Power average of daily totals (CSP) technologies, but also calculation of GTI for fixed mounting and sun-tracking flat plate PV 2 DIF Diffuse Horizontal kWh/m Complementary parameter to GHI and Long-term yearly and monthly Irradiation DNI average of daily totals 2 GTI Global Irradiation kWh/m Assessment of solar resource for PV Long-term yearly and monthly at optimum tilt technologies average of daily totals OPTA Optimum angle ° Optimum tilt to maximize yearly PV - production PVOUT Photovoltaic kWh/kWp Assessment of power production Long-term yearly and monthly power potential potential for a PV power plant with average of daily totals free-standing fixed-mounted c-Si modules, mounted at optimum tilt to maximize yearly PV production TEMP Air Temperature at °C Defines operating environment of the Long-term (diurnal) annual and 2 m above ground solar power plants monthly averages level Table 5.3: Technical specification of primary GIS data layers Acronym Full name Data Spatial resolution Time No. of data format representation layers GHI Global horizontal Raster 9 arc-sec. 1999 (2007) to 2016 12+1 irradiation (approx. 275x275 m) DNI Direct normal Raster 9 arc-sec. 1999 (2007) to 2016 12+1 irradiation (approx. 275x275 m) DIF Diffuse horizontal Raster 9 arc-sec. 1999 (2007) to 2016 12+1 irradiation (approx. 275x275 m) GTI Global irradiation Raster 9 arc-sec. 1999 (2007) to 2016 12+1 at optimum tilt (approx. 275x275 m) OPTA Optimum angle (tilt) Raster 2 arc-min - 1 of PV modules (approx. 3700x3700 m) PVOUT Photovoltaic power Raster 9 arc-sec. 1999 (2007) to 2016 12+1 potential (approx. 275x275 m) TEMP Air temperature at 2 m Raster 30 arc-sec. 1999 (2007) to 2016 12+1 above ground level (approx. 925x925 m) World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 78 of 86 Table 5.4: Characteristics of the raster output data files Characteristics Range of values West − East 94:00:00E − 142:00:00E North − South 7:00:00N − 12:00:00S Resolution (GHI, DNI, GTI, DIF, PVOUT) 00:00:09 (19200 columns x 7600 rows) Resolution (TEMP) 00:00:30 (5760 columns x 2280 rows) Resolution (OPTA) 00:02 (1440 columns x 570 rows) Data type Float (OPTA as Integer) No data value -9999, NaN Data layers are provided as separate files in a tree structure, organized according to • File format: ESRI ASCII grid (AAIGRID) and GEOTIFF • Time summarization: yearly and monthly Complementary files: • Project files (*.prj) complement ESRI ASCII grid files (*.asc) The support GIS data are provided in a vector format (ESRI shapefile, Table 5.5). Filename convention of data layers The filename consits of: • Parameter acronym (GHI, DNI, etc) • Filenames with no number represent yearly data. • Filenames with numbers represent monthly data, where January = 01, …, December = 12 • File extension (asc, prj, tif, xml, pdf) Examples of filenames: • GHI.asc: ESRI ASCII grid of long-term average of annual sum of GHI • GHI.prj: projection metadata file for GHI.asc data layer • GHI.asc.xml: metadata for ASC file in XML format • GHI.asc.pdf: metadata for ASC file in PDF format (human readable and printable) • GHI_01.asc: ESRI ASCII grid of long-term average of monthly sum (January) GHI • GHI_01.prj: projection metadata file for GHI_01.asc data layer • GHI_01.asc.xml: metadata in XML format • GHI_01.asc.pdf: metadata in PDF format (human readable and printable) • GHI.tif: GEOTIF file containing data of long-term average of annual sum of GHI • GHI.tif.xml: metadata for GEOTIFF file in XML format • GHI.tif.pdf: metadata for GEOTIFF file in PDF format (human readable and printable) Approximate size of provided data • AAIGRID files uncompressed: 52 GB • GeoTIFF files (LZW compression): 5.8 GB • Complete compressed archive (ZIP): 6.9 GB World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 79 of 86 Table 5.5: Support GIS data Data type Source Data format Cities OpenStreetMap.org contributors, GeoNames.org, Point shapefile adapted by Solargis Administrative boundaries Cartography Unit, GSDPM, World Bank Group Polyline shapefile Roads OpenStreetMap.org contributors Polyline shapefile Water bodies OpenStreetMap.org contributors Polygon shapefile 5.2 Project in QGIS format For easy manipulation with the GIS data files, selected vector and raster data files are integrated into ready-to- open QGIS project file with colour schemes and annotation (see Figure 5.1). QGIS is state-of-art open-source GIS software allowing visualization, query and analysis on the provided data. QGIS includes a rich toolbox to manipulate with data. More information about the software and download packages can be found at http://qgis.org. Figure 5.1: Screenshot of the map and data in the QGIS software 5.3 Digital maps Besides the GIS data layers, digital maps are also delivered for selected data layers for presentation purposes. Digital maps are prepared in three types; each suitable for different purpose: • High-resolution poster maps • Medium-resolution maps for presentations Digital images for high-resolution poster printing (size 120 x 80 cm). The colour-coded maps are prepared in a TIFF format at 300 dpi density and lossless compression. World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 80 of 86 Following three map files are delivered for high-resolution poster printing: • Global Horizontal Irradiation – Yearly average of the daily totals • Direct Normal Irradiation − Yearly average of the daily totals • Photovoltaic electricity production from a free-standing power plant with optimally tilted c-Si modules − Yearly average of the daily totals Digital images prepared in a resolution suitable for A4 printing or on-screen presentation. The colour-coded maps are prepared in PNG format at 300 dpi density and lossless compression. Following map files are delivered: • Long-term yearly average of Global Horizontal Irradiation • Annual and monthly long-term averages of ratio Diffuse/Global Horizontal Irradiation • Long-term yearly average of Global Tilted Irradiation (for optimum tilt) • Long-term yearly average of Direct Normal Irradiation • Long-term yearly average of Air Temperature • Long-term yearly average of Photovoltaic (PV) Electricity Potential • High resolution Terrain Elevation • Indonesia in the world context of Global Horizontal Irradiation map The maps also include visualization of the following layers: • Main cities, location and names • Administrative borders • Water bodies World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 81 of 86 6 List of maps Map 2.1: Coverage of solar geostationary satellite data in Indonesia ...................................................................... 17 Map 2.2: Solar radiation validation sites ..................................................................................................................... 21 Map 2.3: Regions of solar resource uncertainty in Indonesia ................................................................................... 23 Map 2.4: Position of meteorological stations considered in the validation. ............................................................ 25 Map 3.1: Position of eight selected representative sites in Indonesia. .................................................................... 35 Map 3.2: Terrain elevation above sea level. ................................................................................................................ 37 Map 3.3: Terrain slope.................................................................................................................................................. 38 Map 3.4: Land cover. .................................................................................................................................................... 39 Map 3.5: Main roads and urban centres. .................................................................................................................... 40 Map 3.6: Nature protection areas ................................................................................................................................ 41 Map 3.7: Population density. ....................................................................................................................................... 42 Map 3.8: Occurrence of forest fires ............................................................................................................................ 43 Map 3.9: Volcanic eruptions in Indonesia between 1999 and 2016 ......................................................................... 44 Map 3.10: Long-term yearly average sum of rainfall (precipitation). ........................................................................ 45 Map 3.11: Long-term yearly average of air temperature at 2 metres. ...................................................................... 46 Map 3.12: Global Horizontal Irradiation – long term average of daily and yearly totals. ........................................ 49 Map 3.13: Global Horizontal Irradiation – long-term monthly averages of daily totals. .......................................... 50 Map 3.14: Long-term average for ratio of diffuse to global irradiation (DIF/GHI). .................................................. 53 Map 3.15: Direct Normal Irradiation – long-term average of daily and yearly totals. .............................................. 54 Map 3.16: Global Tilted Irradiation at optimum angle – long-term average of daily and yearly totals................... 57 Map 3.17: Theoretical optimum tilt of PV modules to maximize yearly PV power production. ............................. 58 Map 3.18: PV electricity output from an open space fixed-mounted PV system .................................................... 62 Map 3.19: PV power generation potential for an open-space fixed-mounted PV system. ...................................... 63 Map 3.20: Solar climate zones of Indonesia – indicative classification .................................................................. 68 Map 3.21: PV power potential of Indonesia in the global context. ........................................................................... 70 Map 4.1: Preferred areas for deployment of solar meteorological stations (green colour) .................................... 74 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 82 of 86 7 List of figures Figure 2.1: Simplified Solargis PV simulation chain .................................................................................................. 31 Figure 3.1: Monthly averages, minima and maxima of air-temperature at 2 m for selected sites. ........................ 48 Figure 3.2: Long-term monthly averages, minima and maxima of Global Horizontal Irradiation. .......................... 51 Figure 3.3: Interannual variability of Global Horizontal Irradiation for selected sites. ............................................ 52 Figure 3.4: Daily averages of Direct Normal Irradiation at selected sites. ............................................................... 55 Figure 3.5: Interannual variability of Direct Normal Irradiation at representative sites ........................................... 56 Figure 3.6: Daily totals of GHI and DNI in Jakarta, year 2016 ................................................................................... 56 Figure 3.7: Global Tilted Irradiation – long term daily averages, minima and maxima. .......................................... 60 Figure 3.8: Monthly relative gain of GTI relative to GHI at selected sites................................................................. 60 Figure 3.9: GHI and GTI monthly averages and relative gain of GTI to GHI in Jakarta ............................................ 61 Figure 3.10: Daily values of GHI and GTI for Jakarta, year 2016............................................................................... 61 Figure 3.11: Monthly averages of daily totals of power production from the fixed tilted PV systems .................. 65 Figure 3.12: Monthly performance ratio of a PV system at selected sites. ............................................................. 66 Figure 3.13: Comparing seasonal variability in three locations ................................................................................ 70 Figure 3.14: Comparing yearly GHI and TEMP with potential PV power output at selected sites .......................... 71 Figure 5.1: Screenshot of the map and data in the QGIS software ........................................................................... 79 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 83 of 86 8 List of tables Table 2.1: Theoretically-achievable uncertainty of pyranometers at 95% confidence level .............................. 15 Table 2.2: Input data used in the Solargis solar radiation model and related GHI and DNI outputs ................ 18 Table 2.3: Comparing solar data from solar measuring stations and from satellite models ........................... 20 Table 2.4: Selected validation sites in the region ................................................................................................. 21 Table 2.5: Global Horizontal Irradiance – quality indicators in the region ......................................................... 22 Table 2.6: Direct Normal Irradiance – quality indicators in the region ............................................................... 22 Table 2.7: Uncertainty of long-term yearly estimates for GHI, GTI and DNI values in Indonesia ...................... 23 Table 2.8: Original source of Solargis meteorological data for Indonesia: models CFSR and CFSv2. ............. 24 Table 2.9: Comparing data from meteorological stations and weather models ............................................... 25 Table 2.10: Meteorological stations and time periods considered in the model validation .......................... 26 Table 2.11: Air temperature at 2 m: accuracy indicators of the model outputs [ºC]...................................... 26 Table 2.12: Wind speed at 10 m: accuracy indicators of the model outputs [m/s]. ...................................... 27 Table 2.13: Relative humidity at 2 m: accuracy indicators of the model outputs [%]. ................................... 28 Table 2.14: Expected uncertainty of modelled meteorological parameters in Indonesia. ............................ 28 Table 2.15: Specification of Solargis database used in the PV calculation in this study .............................. 31 Table 2.16: Reference configuration - photovoltaic power plant with fixed-mounted PV modules .............. 32 Table 2.17: Yearly energy losses and related uncertainty in PV power simulation ........................................ 33 Table 3.1: Position of eight selected sites in Indonesia ...................................................................................... 34 Table 3.2: Monthly averages and average minima and maxima of air-temperature at 2 m at 8 sites ............. 47 Table 3.3: Daily averages and average minima and maxima of Global Horizontal Irradiation at 8 sites ......... 51 Table 3.4: Daily averages and average minima and maxima of Direct Normal Irradiation at 8 sites ............... 55 Table 3.5: Daily averages and average minima and maxima of Global Tilted Irradiation at 8 sites ................. 59 Table 3.6: Relative gain of daily GTI (optimum tilt of PV modules 10°) to GHI in Jakarta ................................ 60 Table 3.7: Annual performance parameters of a PV system with modules fixed at optimum angle ............... 64 Table 3.8: Average daily sums of PV electricity output from an open-space fixed PV system ........................ 65 Table 3.9: Monthly and annual Performance Ratio of a free-standing PV system with fixed modules ........... 66 Table 3.10: Categories of long-term yearly average of global horizontal irradiation and their relative area 69 Table 3.11: Categories of long-term yearly average of air temperature and their relative area .................... 69 Table 3.12: SWOT analysis relative to the solar resource and photovoltaic potential in Indonesia ............. 72 Table 5.1: General information about GIS data layers ......................................................................................... 76 Table 5.2: Description of primary GIS data layers ................................................................................................ 77 Table 5.3: Technical specification of primary GIS data layers ............................................................................ 77 Table 5.4: Characteristics of the raster output data files .................................................................................... 78 Table 5.5: Support GIS data ................................................................................................................................... 79 World Bank Group (ESMAP) • Solar Resource and Photovoltaic Power Potential of Indonesia 84 of 86 9 References [1] Parangtopo, H. 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