Study on reactive power optimization of ACDC hybrid distribution network with electric vehicles

This paper briefly introduces the charging and discharging characteristics of electric vehicles, and calculates the charging and discharging load of electric vehicles by Monte Carlo simulation method. A dynamic scene analysis method is used to deal with the stochastic uncertainty of electric vehicles charging and discharging, and the dynamic neural network is used to predict the load. The paper uses the Latin hypercube sampling method to sample the predictive error, uses the K-means clustering method to reduce scenes, and finally produces dynamic load scenarios of the same time section. The AC/DC hybrid power distribution system is the research object and the reactive power optimization model is established with the lowest operating cost of the whole system as objective function. The second-order cone relaxation is applied to AC distribution network power flow constraints, DC distribution network power flow constraints and voltage source converter operation controlling constraints. The paper analyzes the reactive power optimization and control of the system with electric vehicles by Particle Swarm Optimization algorithm, obtains the compensation scheme of multi scenes reactive power optimization at the same time section, and a robust optimization scheme is proposed to guide the optimization of reactive power compensation in the system. This scheme is applicable to other scenarios through model testing, and the validity of the algorithm is verified.