Economic–Environmental Dispatch of a Hybrid Microgrid Using an Improved Red-Billed Blue Magpie Optimizer

The intermittency and seasonal variation of wind and photovoltaic generation complicate the economic–environmental dispatch of microgrids with high renewable-energy penetration. This study develops a grid-connected hybrid microgrid dispatch model that uses min–max normalization to place operating and environmental treatment costs on comparable scales. An improved red-billed blue magpie optimizer (IRBMO) is proposed to solve the resulting nonlinear optimization problem. IRBMO integrates composite chaotic initialization, an anti-predation mechanism, a multi-stage fine foraging strategy, and a simulated annealing acceptance mechanism to improve population diversity and solution accuracy while reducing the risk of becoming trapped in local optima. The algorithm is evaluated on ten CEC2019 benchmark functions and then applied to three microgrid configurations under typical summer and winter conditions. Its performance is compared with the red-billed blue magpie optimizer (RBMO), particle swarm optimization (PSO), snake optimizer (SO), dung beetle optimizer (DBO), hippopotamus optimization (HO), artificial rabbits optimization (ARO), multi-verse optimizer (MVO), and differential evolution (DE). The results show that IRBMO provides competitive solution accuracy and optimization stability. Among the three configurations, the complete system comprising photovoltaic (PV), wind turbine (WT), microturbine (MT), fuel cell (FC), and storage battery (SB) achieves the best overall dispatch performance. Its mean comprehensive costs are 1578.20 Chinese yuan (CNY) in summer and 1535.46 CNY in winter, with corresponding mean pollutant emissions of 850.66 kg and 792.51 kg. Under the profiles considered in this study, retaining both renewable sources reduces the comprehensive cost and pollutant emissions of the grid-connected microgrid.