Digital Visualization and Siting Optimization of Power Plants Using High-Resolution Meteorological Data and GIS

Abstract

The global transition to high-quality renewable energy systems requires precise siting of power plants to maximize generation efficiency and economic returns. Despite similar installed capacities, suboptimal site selection can lead to significant power generation disparities, such as the 14% efficiency gap observed between solar facilities in Australia and Spain. This study presents a data-driven spatial optimization framework that integrates Geographic Information Systems (GIS) with high-resolution meteorological data to address the limitations of traditional, proximity-based siting methodologies. Utilizing a Multi-Criteria Decision Analysis (MCDA) approach, a Python-based TOPSIS algorithm was developed to evaluate candidate sites across four digital layers: meteorological potential, terrain slope, grid infrastructure proximity, and economic/land-use constraints. The model successfully quantified the geographic “Resource Dividend,” demonstrating that the 14% real-world generation gap closely aligned with an 11% disparity in the calculated digital suitability scores (0.82 for Australia versus 0.71 for Spain). By translating complex, heterogeneous spatial data into visual suitability heat maps, this digital optimization framework allows stakeholders to bypass local optima, minimize the Levelized Cost of Energy (LCOE), and enhance the internal rate of return. Ultimately, algorithmic site selection mitigates human bias and ensures a technically efficient and economically sustainable deployment of renewable energy.