Genetic algorithms-based optimal site selection of solar PV in the north of Afghanistan

Abstract

This study evaluates the renewable energy potential in northern Afghanistan by employing the Analytical Hierarchy Process (AHP) and a genetic optimization algorithm. Eleven geodata layers, including factors like solar radiation, elevation, and proximity to various infrastructures, were considered for assessing suitable solar energy development sites across nine provinces. The research combines AHP and genetic algorithm approaches to determine optimal locations for solar energy projects, aiming to aid policymakers and investors in decision-making. Morphological factors such as slope, elevation, and solar radiation were identified as significant determinants of site suitability for solar photovoltaic systems. The study revealed that Afghanistan's northwest and western regions have the most promising areas for solar PV systems due to their lower topographic complexity. The genetic algorithm accurately outperformed AHP, identifying over 29,000 square kilometers of suitable land for solar power plants in northern Afghanistan. The outputs of genetic algorithm are classified into strongly suitable (4504 km²), moderately suitable (5899 km²), and suitable (7088 km²) categories for solar energy development. Balkh province demonstrated the highest potential with an area of 7960 km², while Badakhshan showed the lowest capacity. These findings offer valuable insights for stakeholders interested in solar energy development in northern Afghanistan, aiding them in making well-informed decisions and investments.

Keywords

• renewable energy
• evolutionary algorithms
• afghanistan
• analytical hierarchy process