Abstract:
Iran's electricity generation relies heavily on fossil fuels, resulting in frequent power shortages and widespread blackouts in major cities. Given the high levels of solar irradiance across the country, photovoltaic (PV) and concentrating solar power (CSP) technologies could provide a sustainable alternative. Existing studies focus on specific technologies or individual regions. Currently, there is no consistent, comprehensive mapping of the scope for political decision-making in Iran. This study aims to address this issue by providing the first nationwide assessment of solar energy potential in Iran, evaluating both PV and CSP. This GIS-based assessment uses an expanded set of environmental and technical criteria and performs sensitivity analyses to ensure robust results and identify the most effective and sustainable locations for PV and CSP plants. The model incorporates specific constraints, such as protected natural areas, to exclude unsuitable sites, and assesses suitability based on criteria such as solar irradiation levels and proximity to grid infrastructure. These factors are categorised into four suitability classes, ranging from 'high' to 'very low' for both PV and CSP installations. ... mehrBy synthesising the constraint and suitability maps, the model identifies feasible sites and assesses their relative desirability. A sensitivity analysis, focusing on the weighting of the suitability criteria, confirms the robustness of the results. The results highlight Iran's considerable capacity for solar power generation and suggest that the country could exceed its current electricity production by a multiple through the development of solar power plants. The model applies 14 exclusion criteria, revealing that 70% of Iran’s land is unsuitable for PV and 83% for CSP. The results show that 14.5% of Iran’s land is suitable for PV and 7.5% for CSP (medium and high suitable), with central and eastern regions offering the highest potential. Additionally, the study highlights the promising prospects of GIS modeling in renewable energy siting, emphasizing improved data integration, global scalability, environmental impact assessment, and policy harmonization.