Load-Characteristic Analysis and Energy Management of Microgrid System for Large-Scale Broiler Houses

To address the strong load rigidity and high energy consumption of large-scale broiler breeding, as well as the lack of precise load-characteristic support and efficient scheduling methods for breeding-oriented energy supply and consumption optimization, this paper conducts load-characteristic analysis for large-scale broiler houses and proposes an energy management optimization method based on the improved dream optimization algorithm (IDOA). First, year-round field monitoring was performed on a large-scale broiler farm in Laiyuan, Hebei, to analyze the load characteristics across seasons and breeding cycles and reveal the load evolution rules. Second, a comprehensive operational cost objective is established, considering the renewable operation and maintenance cost, the time-of-use power trading cost, and the carbon emission cost. Third, adaptive weight, dynamic mutation, and opposition-based learning strategies are embedded into the IDOA to better balance global exploration and local exploitation, and the improved algorithm outperforms other meta-heuristic algorithms on the CEC2017 benchmark functions. Simulation tests covering the four-season typical days and key breeding stages demonstrate that, compared with the rule-based dispatch strategy, the proposed method lowers the daily operating cost and effectively smooths the grid power profile, while the ESS state of charge is always maintained within the preset limits. Sensitivity analyses on the ESS capacity and the load and renewable forecasting errors further verify the robustness of the dispatch results, and the single-dispatch runtime of about 23 ms amply satisfies the real-time requirement of field implementation. This study offers theoretical support and practical reference for energy saving and carbon reduction in large-scale livestock breeding and breeding-park energy system scheduling.