Multi-objective stochastic programming method for electric vehicle EV charging load

Abstract

OF THE INVENTION The invention discloses a multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm. In combination with the requirements of the best operation of the distribution system and in consideration of the influence of multiple random factors, it establishes a new multi-objective stochastic optimization model of the distribution network based on EV charging load, which utilizes the improved non-dominated sorting genetic algorithm- II (non-dominated sorting genetic algorithm-2, NSGA-2) to solve, takes fully charged EV battery, charging power within limit and distribution network tide constraints as constraint conditions and takes distribution network loss, power node peak load and load fluctuation optimization as sub-goals to achieve multi-objective stochastic programming of EV charging load.

Description

technical field [0001] This application relates to electric vehicle charging, and especially designs a multi-objective random programming method for electric vehicle EV charging load based on non-dominated sorting genetic algorithm. Background technique [0002] Battery state of charge (SOC) is an important parameter used to describe the state of battery capacity, which is of great significance to the operation of battery vehicles and the maintenance and management of batteries. Since the capacity released by the battery is affected by many factors such as the discharge rate, battery temperature, and the number of battery charge and discharge cycles, the parameter SOC, which represents the state of battery capacity, must also be related to these factors. Computational difficulties arise for SOC estimation under varying discharge current conditions. The SOC at the initial moment of battery charging directly affects the charging time of the battery. [0003] Electric vehicle...

AI Technical Summary

Problems solved by technology

Researchers at home and abroad have done a lot of research on the charging of electric vehicles, but there are still various problems in the existing EV charging load optimization strategy, or assume that the charging behavior of electric vehicles is completely controllable, ignoring randomness, or strategy optimization The target is too single, or the EV battery state of charge model at the initial moment cannot truly reflect the actual charging load state, etc.

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