A long-term effort from Sokline and Karodine is now accepted and published in Renewable and Sustainable Energy Reviews, the No. 1 journal (IF: 14.982) in Green & Sustainable Science & Technology.
This is a milestone of the CHESS lab. Big congratulations!
Tuy S., Lee H.S.*, and Chreng K. 2022. Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 Satellite imagery, optimal sites, annual energy production and equivalent CO2 reduction. Renew. Sust. Energ. Rev., 163, 112501. Open access
- Offshore wind resources are estimated using WRF model and Sentinel-1 ocean products.
- Estimated offshore winds by WRF shows good agreement with observed surface winds by Sentinel-1.
- Optimal site selection of offshore wind farm is conducted by analytic hierarchy process.
- Wind power density, annual energy production and equivalent CO2 reduction are estimated.
- The study demonstrates a well-designed exemplary case study for integrated offshore wind power estimation.
This study aims to assess the offshore wind power potential in Cambodia using the WRF and Sentinel-1 level 2 ocean (L2 OCN) products, to evaluate potential sites and to estimate the annual energy production (AEP) and equivalent CO2 reduction. The model is initially calibrated and validated against observed onshore winds at four weather stations before its main simulation for the two-years study period of 2018–2019. As a result, the spatially averaged annual wind speed errors between the WRF and L2 OCN data are 0.70 m/s for mean bias error and 0.79 m/s for root mean square error, indicating good model performance. The annual mean wind speeds over the Cambodian Sea are 5.15 m/s, 5.20 m/s and 5.27 m/s at 80 m, 100 m and 140 m above sea level, respectively. Then, analytic hierarchy process (AHP) is applied to evaluate the optimal sites for offshore wind power generation. The most potential areas in Cambodian sea are along Kampot and Kep shoreline. The resulting AEPs are 11,949.02 GWh for 80-m V112 turbines, 20,013.34 GWh for 100-m V164 turbines and 31,880.48 GWh for 150-m HX12 turbines, which could reduce CO2 emissions by 5.48 Mt-CO2, 9.18 Mt-CO2 and 14.63 Mt-CO2 per year, respectively. If 10% of the total AEP could be generated by 2030, the offshore wind source would contribute to 1.95%, 3.27%, or 5.20% of the country’s electric demands forecasted for 2030. The integrated assessment methodology and resources adopted in this study can be applicable to other regions particularly where offshore measurements are not readily available.