Solar Grid Placement Optimization: Utilizing Evolutionary Algorithms and Topographical Data to Determine Optimal Distributed Photovoltaic Installation Sites in the Hathidah Region

The transition to renewable energy in densely populated agricultural regions presents a unique spatial challenge: how to maximize energy generation without compromising food security or ecological balance. This research addresses this critical conflict within the specific context of Hathidah, Bihar (25.29 N, 86.08 E). Situated on the banks of the Ganges River, the region features complex topography, including riparian wetlands, railway infrastructure, and the "Tal" agricultural depression. The primary objective of this study is to develop a computational framework to identify optimal sites for distributed solar photovoltaic (PV) installations that respect these geographical constraints.

The study utilizes a Multi-Objective Evolutionary Algorithm (specifically NSGA-II) integrated with high-resolution Digital Elevation Models (NASA SRTM data). Unlike traditional manual planning, this AI-driven approach automates the search for site coordinates by simultaneously optimizing for three conflicting variables: (1) Maximizing Global Horizontal Irradiance (GHI) and topographical suitability, (2) Minimizing transmission distance to the existing Hathidah grid substation, and (3) Minimizing land-use conflict by avoiding fertile double-crop farmland.