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.