Wind Energy Siting Optimization in Fujian Province, China

Samuel Bimenyimana (), Chen Wang (), Godwin Norense Osarumwense Asemota, Jean Marie Vianney Uwizerwa, Jeanne Paula Ihirwe, Mucyo Ndera Tuyizere, Fidele Mwizerwa, Yiyi Mo, Martine Abiyese, Homère Ishimwe and Ange Melissa Ishimwe
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Samuel Bimenyimana: Intelligence and Automation in Construction Provincial Higher-Educational Engineering Research Centre, Huaqiao University, Xiamen 361021, China
Chen Wang: Intelligence and Automation in Construction Provincial Higher-Educational Engineering Research Centre, Huaqiao University, Xiamen 361021, China
Godwin Norense Osarumwense Asemota: African Centre of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda
Jean Marie Vianney Uwizerwa: African Centre of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda
Jeanne Paula Ihirwe: Department of Economics and Management, Hebei University of Technology, Tianjin 300130, China
Mucyo Ndera Tuyizere: Center for Geographical Information System and Remote Sensing, University of Rwanda, Kigali, Rwanda
Fidele Mwizerwa: Center for Geographical Information System and Remote Sensing, University of Rwanda, Kigali, Rwanda
Yiyi Mo: Civil Engineering Department, Huaqiao University, Xiamen 361021, China
Martine Abiyese: Hello Renewables Ltd., Kigali, Rwanda
Homère Ishimwe: Hello Renewables Ltd., Kigali, Rwanda
Ange Melissa Ishimwe: Hello Renewables Ltd., Kigali, Rwanda

Sustainability, 2024, vol. 16, issue 24, 1-39

Abstract: The geographical distribution and scientific evaluation of wind energy potential are crucial for regional energy planning. Wind energy is a renewable energy that can mitigate climate change. Several open-access World Bank databases and the ESRI (Environmental Systems Research Institute) Global were used to gather and process data through wind energy siting optimization in Fujian Province. This paper uses the fuzzy quantifiers of the multi-criteria decision-making (MCDM) approach in arc geographic information system (ArcGIS Pro) and the analytical hierarchy process (AHP) to handle the associated wind data uncertainties to obtain wind energy technology siting optimization for nine cities in Fujian Province. The converted database options and characteristics used the weighted overlay tool (WOT) to reflect the importance of wind farm project objectives. The sensitivity analysis tested the robustness and resilience of the integrated MCDM design for feasibility or viability. The results revealed that 21.743% of the area of Longyan City is suitable for siting wind energy. Other cities’ suitable areas comprise 14.117%, 12.800%, 5.250%, 4.621%, 4.020%, 4.020%, 3.430%, and 2.300%, respectively (Sanming, Ningde, Quanzhou, Putian, Zhangzhou, Nanping, Xiamen, and Fuzhou cities). Furthermore, a considerable amount of wind power is needed to supply the current primary energy deficit (60.0–84.0%) and satisfy the carbon emission reduction target. Wind farm installation in Fujian province is an opportunity to provide inexhaustible energy, generally affected by generation volume and operational span. Wind power is highly acceptable to local Chinese. Reasonably high understanding and excitement for wind farm investments exist among local authorities. Future research should consider wind data of the identified onshore optimization sites and design wind farms for the respective output power for pessimistic, average, and optimistic scenarios for possible wind farm development. Similarly, the long shoreline of about 1680.0 miles (or 2700.0 km) is a considerable source of offshore wind power prospecting, future research, and energy exploitation and harvesting opportunities.

Keywords: site selection; wind energy; China; climatic change; geographic information system; spatial analysis; Fujian (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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