Cost-oriented scheduling of battery energy storage with wind turbines in distribution microgrids

This study presents a cost-oriented energy management framework for the coordinated scheduling of wind turbines and battery energy storage systems in a distribution microgrid. Each generation node includes a co-located wind turbine and battery unit, and the optimization model determines their hourly active and reactive power dispatch over a 24-hour horizon. The objective is to minimize daily operating cost while satisfying network constraints, converter apparent power limits, and battery state-of-charge restrictions in both grid-connected and isolated operating modes. The proposed solution strategy is based on a population-based genetic algorithm and is benchmarked against particle swarm optimization and the multiverse optimizer. Representative demand and wind generation profiles from a Colombian region are considered. In the studied configuration, the wind turbines contribute renewable active power and voltage support, while the battery systems provide energy shifting, peak shaving, and reactive power support. Each optimization method is executed 100 independent times to assess solution quality, repeatability, and robustness. The results show that the proposed genetic algorithm achieves the best average performance and the lowest variability among the methods compared, while preserving the microgrid’s technical feasibility and all battery operating constraints. These findings show that coordinated WT and BESS scheduling is an effective strategy for reducing operating cost and supporting the secure integration of wind generation in distribution microgrids. • Storage scheduling coordinates wind generation and battery operation. • A GA-based framework minimizes daily cost with AC power-flow validation. • SOC, converter limits, and reactive support are modeled for grid-connected and isolated operation. • In 100 runs, the proposed method improves cost, robustness, and feasibility.