Co-Optimizing Microgrid Economy, Environment and Reliability: A Comparative Study for PSO-GWO and Meta-heuristic Optimization Algorithms

This study focuses on optimizing hybrid Photovoltaic (PV)-Wind-Lithium-Ion Battery systems, aiming to balance Life-Cycle Cost (LCC) minimization and power supply reliability (measured by Loss of Power Supply Probability, LPSP). A multi-algorithm optimization framework was constructed to compare the performance of Particle Swarm Optimization (PSO), Moth-Flame Optimization (MFO), Grey Wolf Optimizer (GWO), and Hybrid Optimizer of PSO and GWO Merits (PSO-GWO) for off-grid power supply; additionally, a PSO-GWO was proposed to address multi-objective demands of economy, environment, and reliability for remote grid-connected power supply. Combined with system architecture design, energy management strategies, and component availability analysis, the PSO-GWO reduced 25-year LCC to $2.025 million, LPSP to 0.01, and Cost of Energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CE) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.98 million and LPSP at 0.009. Sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, and wind speed fluctuations, providing an economical, reliable, low-carbon solution.