Electric vehicle ordered charging and discharging dynamic optimization strategy based on particle swarm optimization

Abstract

Aiming at the problem that a new load peak is easily generated in a load valley period in the traditional TOUT and RTP charging strategies, the peak regulation demand of a power grid side and the charging demand of a user side are considered; an electric vehicle ordered charging and discharging dynamic optimization strategy based on the particle swarm optimization is put forward, and the electric vehicle ordered charging and discharging dynamic optimization strategy takes establishment of an electric vehicle charging load model, establishment of an electric vehicle charging and discharging optimization algorithm model and an ordered charging and discharging two-stage dynamic optimization strategy as contents; through dynamically updating electricity price information when each electric vehicle is connected with a power grid, orderly charging and discharging optimization of the electric vehicle is realized by using the particle swarm algorithm. Compared with TOUT and RTP charging strategies and disordered charging, the electric vehicle ordered charging and discharging dynamic optimization strategy based on the particle swarm algorithm can obviously reduce the peak-valley difference of a load curve and the charging cost of a user, and has the advantages of being scientific, reasonable, high in applicability and good in effect.

Description

technical field [0001] The invention relates to the field of vehicle-to-grid interaction (Vehicle to Grid, V2G), and is a dynamic optimization strategy for orderly charging and discharging of electric vehicles based on a particle swarm algorithm. Background technique [0002] In recent years, the electric vehicle (Electric Vehicle, EV) industry has developed rapidly. However, due to the great randomness and uncertainty of users' charging behavior, it will increase the difficulty of controlling the grid after large-scale EVs are connected to the grid. Studies have shown that the time for users to charge EVs is roughly consistent with their daily electricity consumption time, which means that when large-scale EVs are charged in disorder, the charging load will be superimposed on the basic load, further increasing the peak-to-valley difference of the load curve. Both the traditional Time-of-Use Tariff (TOUT) and Real-Time Price (RTP) demand response mechanisms will generate new...

AI Technical Summary

Problems solved by technology

[0002] In recent years, the electric vehicle (Electric Vehicle, EV) industry has developed rapidly. However, due to the great randomness and uncertainty of users' charging behavior, it will increase the difficulty of controlling the grid after large-scale EVs are connected to the grid.

Studies have shown that the charging time of users for EVs roughly coincides with their daily electricity consumption time, which means that when large-scale EVs are charged in disorder, the charging load will be superimposed on the basic load, further increasing the peak-to-valley difference of the load curve

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