Indonesia has a target of achieving 23% of renewable energy share in the total energy mix in 2025. However, Indonesia does not have accurate and comprehensive data on renewable energy potentials, especially wind energy. This article aims to assess the theoretical potential of wind speed and to visualize the wind speed by province for the entire Indonesia. Our assessment relied on the Weather Research and Forecasting (WRF) model using Four-Dimensional Data Assimilation technique, also known as Nudging Newtonian relaxation. The robustness of our analysis is confirmed by using high-resolution data from the National Centers for Environmental Prediction–Final (NCEP - FNL) and Cross-Calibrated Multi-Platform (CCMP) Reanalysis satellite data. This study shows the WRF method is a feasible option to estimate wind speed data.
Indonesia aims to decarbonize the energy sector by accelerating the use of new and renewable energy, expected to reach 31% of total energy supply in 2050. One of important tools to achieve the target is renewable energy potential maps including hydro energy potential maps. Yet, existing hydro energy potential maps have several weaknesses such as sites coordinate not on the river network. This study aims to update and to improve the run-off river system energy hydro maps by using a novel method considering multiple factors that are head values, discharge river, gravity, and the efficiency of the hydro system. In calculating the head value, we use DEM data from SRTM 1 arc second to estimate difference between upstream and downstream elevations. We also did Q90 modeling using WFLOW software as generate the discharge value. In the end, we verified the maps by using field measurement data in 776 sites from previous study. As a result, we estimate the total potential of hydro energy with the run-off river system in Indonesia reaches 94,627 MW distributed in 52,566 sites.
The use of Research Weather and Forecasting (WRF) numerical models to predict the potential of solar energy can be used as a starting point for mapping. The prediction result of WRF should be verified to improve the quality of data. In this study, we used the WRF data from 2001 to 2010. The model results are validated using twelve months' solar radiation data in two locations, Palihan village (Yogjakarta Province) and Aikangkung village (Nusa Tenggara Barat Provinces). The methods used were downscaling, prediction, verification and correction. The verification result showed that location 1 (Palihan village) had a larger deviation (MAPE=22.59%) than location 2 (Aikangkung village, MAPE = 12.95%). Deviations due to the difference between WRF models and observation data were used to correct the solar potential energy map. Finally, MAPE for corrected map were = 0,0007% for Palihan village and 7% for Aikangkung village. ABSTRAKPenggunaan model numerik Weather Research and Forecasting (WRF) untuk memprediksi potensi energi surya dapat digunakan sebagai langkah awal pemetaan. Peta hasil prediksi WRF perlu diverifikasi guna meningkatkan kualitas data. Penelitian ini menggunakan data WRF tahun 2001 hingga 2010. Data observasi radiasi matahari selama 12 bulan pada 2 lokasi, yaitu Desa Palihan (Provinsi Yogjakarta) dan Desa Aikangkung (Provinsi Nusa Tenggara Barat) digunakan untuk verifikasi luaran WRF. Metode yang digunakan adalah penurunan skala (downscaling), prediksi, verifikasi, dan koreksi. Hasil verifikasi memperlihatkan lokasi 1 (Desa Palihan) memiliki deviasi yang lebih besar (MAPE=22,59%) dibanding pada lokasi 2 (Desa Aikangkung) dengan nilai MAPE=12,95%. Perbedaan nilai antara model WRF dan data observasi dimanfaatkan untuk mengoreksi peta potensi energi surya. Hasil peta yang telah terkoreksi, memiliki nilai MAPE = 0,0007% untuk Desa Palihan dan 7% untuk Desa Aikangkung. Kata kunci: evaluasi, potensi energi surya, model, observasi
Electricity subsidies in Indonesia remain high and tend to increase. Existing studies generally propose electricity subsidy reform through economic price adjustment; however, this option potentially arises political and social conflicts. The government and the State Electricity Company have also undertaken several measures to decrease electricity supply costs but those measures remain ineffective due to increasing energy prices needed as fuels for power generations. Our study analyses the effectiveness of two alternative grants for LED lamps and rooftop photovoltaic (PV), to reduce electricity subsidies for low-income residential customers with 450 VA and 900 VA electricity capacity limits. The analysis result is that replacing existing lamps with LED lamps for all those customers will cost the government US$ 313.7 million but potentially decrease electricity subsidies to US$ 208.7 million/ year for 15 years. On the other hand, installing the rooftop PV system is ineffective to bring down the electricity subsidies. The investment cost of the on-grid rooftop PV system is between US$ 827.6 and US$ 1,310.3 per house, while the electricity subsidy savings for 20 years are between US$ 724.1 and US$ 744.8.
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