Optimum Battery Depth of Discharge of Stand-alone Hybrid System Using the MOPSO Method

TL;DR

This study optimizes a stand-alone solar PV/battery system using MOPSO to find the best battery DOD for minimal cost and maximum reliability, validated by NSGA-II, showing 70% DOD as optimal.

Contribution

It introduces a combined MOPSO and NSGA-II approach for optimizing battery DOD in off-grid solar systems, highlighting the benefits of 70% DOD for cost and reliability.

Findings

Optimal DOD of approximately 70% minimizes COE and ensures zero LLP.

MOPSO achieves faster convergence compared to NSGA-II.

Both methods produce consistent optimization results.

Abstract

This paper presents an optimized design of a Standalone Solar PV/Battery (SSPVB) system to address energy reliability and cost efficiency challenges in off-grid environments. The proposed system integrates a Multi-Objective Particle Swarm Optimization (MOPSO) approach and validates the results using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The optimization process 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 and lifecycle cost. Results indicate that an optimal DOD of approximately 70% yields a COE of 0.2059 USD/kWh with zero LLP, demonstrating strong reliability and cost-effectiveness. Comparative analysis shows that both MOPSO and NSGA-II methods achieve consistent outcomes, with MOPSO exhibiting faster convergence. The study provides valuable insights into optimal battery sizing for stand-alone systems, contributing to modern optimization practices in renewable energy applications.