Coordinated Economic Dispatch of Grid-Connected Microgrids Using an Asymmetric GE-RTH-SH Framework

This study focuses on the single-objective day-ahead economic dispatch of grid-connected microgrids. It proposes an asymmetric GE-RTH-SH framework that integrates gradient-guided elite search (GE), Red-Tailed Hawk Optimization (RTH), and SHADE-lite adaptive differential search (SH). Based on the three-stage search mechanism of RTH (high-altitude circling, low-altitude circling, and dive hunting), GE local fine search and SH adaptive differential search based on successful history are introduced to improve local optimization accuracy and parameter adaptability and alleviate search stagnation. An aggregated microgrid model with a 24 h scheduling cycle and a 1 h time resolution is constructed to coordinate wind power, photovoltaic power, micro gas turbines, fuel cells, energy storage, and power exchange with the grid. The model is implemented in Python, aiming to minimize operation and maintenance costs, fuel costs, and net grid transaction costs, while satisfying power balance, equipment output, ramping, energy storage state of charge (SOC), and grid power exchange constraints. With a budget of 30,000 function evaluations per run, the average cost of 50 independent runs is CNY 5295.06, the standard deviation is 10.01, the best cost is CNY 5269.11, and the average renewable energy supply ratio is 62.55%. Through benchmark-function and ablation experiments, the search effects of the GE and SH modules are analyzed, and the optimization performance, economic performance, and repeatability of the complete algorithm are evaluated through comparisons with other algorithms in the microgrid case and four moderate disturbance scenarios. The results show that the proposed method can provide a feasible optimization scheme for coordinated power allocation and economic operation of grid-connected microgrids.