Optimum Battery Depth of Discharge of Stand-alone Hybrid System Using the MOPSO Method (Comprehensive Journal of Science, 2025)
This paper presents an optimized design of a Standalone Solar PV/Battery (SSPVB) system toaddress energy reliability and cost efficiency challenges in off-grid environments. The proposedsystem integrates a Multi-Objective Particle Swarm Optimization (MOPSO) approach and validatesthe results using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The optimizationprocess aims to minimize both the Cost of Energy (COE) and Loss of Load Probability (LLP),while examining the effects of Battery Depth of Discharge (DOD) on system reliability andlifecycle cost. Results indicate that an optimal DOD of approximately 70% yields a COE of 0.2059USD/kWh with zero LLP, demonstrating strong reliability and cost-effectiveness. Comparativeanalysis shows that both MOPSO and NSGA-II methods achieve consistent outcomes, withMOPSO exhibiting faster convergence. The study provides valuable insights into optimal batterysizing for stand-alone systems, contributing to modern optimization practices in renewable energyapplications.
Keywords: Stand-alone PV-battery system, Multi-objective Optimization, MOPSO method, Loss of load probability, Cost of energy