Multi-objective Moth Flame Optimization for Renewable Energy Integration

Investigating the optimization of power flow within an energy grid enriched with renewable sources wind, solar, and hybrid solar-hydroelectric systems this study conducts a techno-economic assessment to determine the most efficient energy distribution under varied scenarios. The challenge of calculating optimal power flow (OPF) is heightened by the unpredictable output from renewable resources. Our approach leverages Lognormal, Weibull and Gumbel distributions to forecast power generation, providing a quantitative basis for managing uncertainty. The objective functions that penalize underestimations and allocate reserves for overestimations, fostering financial prudence and encouraging accurate modeling. Utilizing a non-dominated Multi-objective Moth Flame Optimizer integrated with a fuzzy decision-making framework, optimization seek to balance supply and demand efficiently. The effectiveness of this method is validated against three recent algorithms applied to the hybrid Renewable Energy Resources (RER) using the IEEE-30 bus system as a benchmark. Result findings offer valuable insights into advanced strategies for managing renewable energy flows, highlighting potential improvements in grid reliability and economic efficiency.