Hybrid nutcracker optimization algorithm for multi-objective energy scheduling in grid-connected microgrid systems

Abstract

The growing demand for clean and sustainable energy has driven rapid advancements in hybrid microgrid systems to mitigate climate change and environmental degradation. This paper proposes a novel multi-objective scheduling framework for hybrid microgrids aimed at minimizing operational costs while maximizing environmental benefits. To efficiently solve this complex optimization problem, we introduce a Hybrid Nutcracker Optimization Algorithm (HNOA), which combines the recently developed Nutcracker Optimization Algorithm (NOA) with the Bat Algorithm (BAT). This hybridization enhances NOA’s exploration–exploitation balance and search capability, as demonstrated by rigorous validation on 12 benchmark functions. HNOA achieves superior accuracy and computational efficiency compared to several state-of-the-art metaheuristics. The proposed HNOA is then applied to solve the scheduling of a grid-connected hybrid microgrid under various scenarios to evaluate its performance. Simulation results indicate that the optimal microgrid configuration, consisting of PV/WT/turbine/diesel/battery, achieves an investment cost of 80,789.02 yuan. The findings of this study offer valuable insights for advancing renewable energy integration and promoting environmental sustainability.