RuralGrid-PVO: A Reliability-Conscious Multi-Objective Optimization Framework for Distributed PV Siting in Rural Grids - Zhang, Yu; Shi, Jinyue; Ge, Haitao; Geng, Liang; Jia, Lulu (Informatica, 2026)
The rapid growth of distributed photovoltaic (PV) systems offers a sustainable solution to rural energy demands. However, integrating PV into rural distribution grids presents challenges related to power quality and grid reliability. Traditional PV site selection approaches often neglect the impact on distribution reliability metrics, resulting in suboptimal deployments. This paper proposes RuralGridPVO (Rural Grid Photovoltaic Optimization), an optimization modeling framework for PV grid connection site selection in rural areas, incorporating distribution grid reliability considerations. The RuralGrid-PVO method integrates the RTS-GMLC synthetic power system model with solar irradiance data from the NSRDB to simulate realistic rural deployment scenarios. A multi-objective optimization model combines a Genetic Algorithm (GA) with Deep Neural Networks (DNN) for fast reliability assessments, optimizing PV placement based on energy yield, voltage stability, power losses, and reliability indices (SAIDI/SAIFI). GA-DNN framework incorporates SAIDI/SAIFI into optimization, outperforming baseline GA, AHP-GIS, and ReliOpt-Hybrid in energy yield, voltage stability, and reliability improvements, though sensitivity to reliability weights requires further exploration. This paper proposes RuralGrid-PVO, a comprehensive six-module optimization pipeline for distributed photovoltaic (PV) site selection in rural grids with a focus on enhancing grid reliability. The pipeline integrates data acquisition, candidate site identification, capacity estimation via Random Forest regression (R² = 0.93), reliability-aware grid simulation utilizing a Deep Neural Network (DNN) for SAIDI/SAIFI prediction (MAE = 3.8 min/year), multi-objective optimization with a Genetic Algorithm (GA), and final configuration selection. The methodology operates on a hardware/software stack comprising Python-based ML frameworks and power system simulation tools to ensure reproducibility. Experimental evaluation demonstrates that RuralGrid-PVO improves voltage profiles by 15.3%, reduces energy losses by 12.8%, decreases SAIFI by up to 9.6%, and lowers SAIDI by 39%, significantly outperforming baseline site selection methods. These results validate the framework's effectiveness in achieving reliable, energy-efficient rural PV integration.