Integrating remote sensing, GIS, and multi-criteria decision making for assessing PV potential in mountainous regions

Xiaofan Feng, Zhengjia Zhang, Qi Chen, Zhiling Guo, Haoran Zhang, Mengmeng Wang, Wei Gao and Xiuguo Liu

Renewable Energy, 2025, vol. 241, issue C

Abstract: Installing photovoltaic (PV) facilities in mountainous areas can address the challenge of land scarcity in PV development, improve the energy structure, and promote economic growth in rural mountainous regions. In this study, a framework was proposed to assess the feasibility and generation potential of solar PV in mountainous areas by remote sensing (RS), geographic information systems (GIS), and multi-criteria decision-making (MCDM) model. Climatic, geographic, policy, and social factors were integrated into the optimal site selection for PV facilities construction based on the characteristics of mountainous regions. The weight of each criterion was calculated using the ordinal priority approach (OPA) for subsequent land suitability assessment. Furthermore, the impact of ecological and land use policies on the optimal site selection for solar PV power stations have been considered in the proposed framework. The method was applied in Yongren County, Yunnan Province. The study results show that the optimal areas for the construction of PV plants within Yongren County is 85.45 km2. The identified location aligns seamlessly with the current PV facilities, affirming the effectiveness of the approach. Additionally, the estimated power generation potential of the optimal areas is 665 million kWh, which can satisfy the electricity demands of Chuxiong Prefecture. The levelized cost of electricity (LCOE) is 0.3963 RMB/kWh. This work will provide valuable support for the construction of PV power plants in mountainous areas, which will be crucial in reducing carbon emissions and increasing the share of clean energy.

Keywords: PV potential assessment; Mountainous areas; Policy; Remote sensing; GIS (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:241:y:2025:i:c:s0960148125000023

DOI: 10.1016/j.renene.2025.122340

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