A Collaborative Optimisation Strategy of Electric Agricultural Machinery Clusters and Rural Distribution Network in Renewable Energy Storage Application Scenario

To address the impacts of large‐scale electric agricultural machinery (EAM) on rural distribution networks, an integrated framework for analytical modelling and optimal scheduling is proposed. The proposed framework is validated through a case study of a typical farm in Xinjiang, China. Firstly, charging load models of different types of EAM are established. The impact of fast charging of EAM on load curves, power quality and grid losses under typical scenarios were analysed in depth; Secondly, a novel two‐stage optimal scheduling strategy is proposed. In the first stage, fast charging loads are optimised considering peak shaving, valley filling and user satisfaction. In the second stage, coordinated charging and discharging control of PV and EAM resources is implemented to further smooth network loads. The non‐dominated sorting genetic algorithm II (NSGA‐II) is adopted to solve the two‐stage optimisation model. Finally, case studies show that peak load reductions reach 38.16%, 46.78% and 34.81% in tomato, cotton and wheat cultivation areas, respectively, while peak network loss is reduced by about 3.54% and voltage excursions are effectively mitigated.