Data-Driven Geospatial Analysis for Utility-Scale Solar PV Siting

Abstract

The energy transition to renewable energy is essential for addressing power scarcity and environmental challenges. A key barrier to large-scale renewable energy (RE) deployment is resources assessment and the selection of optimal sites. This study introduces a data-driven methodology for evaluating land suitability and energy potential for utility-scale solar PV plants. The study integrates geographic information systems and multi-criteria decision-making to analyze spatial datasets and conflicting decision-making preferences. Key criteria include climatology, environment, location, and topography. The approach is validated with case study of Saudi Arabia’s Eastern Province. The results show that 83.6% of the region is not suitable for PV deployment due to constraints such as distance from transmission grids, transportation infrastructure, and restricted zones, including oil and gas facilities and flood-prone areas. Conversely, $16.4 \%\left(85,867 \mathrm{~km}^{2}\right)$ of the region is feasible for solar PV installation, classified into five suitability categories: $\mathbf{0. 0 4 \%}$ low, $\mathbf{0. 0 0 0 8 \%}$ moderate, 15.4% high, 0.29% very high, and 0.29% excellent. This research supports Saudi Arabia’s Vision 2030 by identifying viable solar energy sites and contributing to national energy diversification efforts. It supposed to enhance the strategic planning for large-scale solar PV deployment, providing a replicable framework for other regions aiming to optimize RE investments.