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 lifecycle 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 Optimization (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 $2.024 million, LPSP to 0.05, and cost of energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CEs, operational carbon emissions only) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.981 million and LPSP at 0.01. Thirty independent runs of each algorithm were conducted for statistical validation, and sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, wind speed fluctuations, battery price volatility, and carbon tax changes. The study also expanded the analysis to multiple climatic scenarios, providing an economical, reliable, low-carbon solution with strong generalizability.