Optimum Battery Depth of Discharge of Stand-alone Hybrid System Using the MOPSO Method
DOI:
https://doi.org/10.65405/.v10i37.654Keywords:
Stand-alone PV-battery system, Multi-objective Optimization, MOPSO method, Loss of load probability, Cost of energyAbstract
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.
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