Multi-population genetic particle swarm optimization method containing micro-grid capacity configuration of electric automobiles

Abstract

The present invention provides a multi-population genetic particle swarm optimization method containing the micro-grid capacity configuration of electric automobiles. The method realizes the energy storage function of an electric automobile on the premise that the electricity demand of the electric automobile can be met. According to the technical scheme of the invention, a multi-target model, with the annual cost, the annual loss of load probability and the peak-valley difference of a load curve as targets, is proposed. Based on the multi-population genetic particle swarm algorithm, a target function is solved out. In this way, the optimal capacity of each unit in a micro-grid system can be obtained precisely. On the premise that the system reliability is ensured and the load fluctuation is stabilized and inhibited, a higher economic benefit is obtained. Through optimizing the micro-grid system containing the electric automobile, the mobile energy-storage device of the electric automobile is utilized to realize the peak-load shifting purpose on the basis that the reliability and the economy of the system are guaranteed. Meanwhile, the peak-valley difference of the system curve is reduced. Not only is the stability of the power system improved, but also the economic benefit is higher. Therefore, the popularization and the utilization of a cleaning device of the electric automobile are facilitated.

Description

technical field [0001] The present invention relates to a calculation method for optimal equipment capacity of a micro-grid system including electric vehicles and intermittent renewable energy sources under the condition that the annual cost, the annual load power shortage probability, and the peak-to-valley difference of the load curve are minimized, and in particular relates to a Multi-population genetic particle swarm optimization method for capacity allocation of electric vehicle microgrid. Background technique [0002] Research results and operating experience at home and abroad show that the optimal allocation of microgrid capacity including electric vehicles is still in its infancy, and the optimization objective is too single. Some use annual cost or annual cost as the objective function, and some consider power supply reliability indicators However, insufficient consideration is given to the reduction of the peak-to-valley difference of the system load curve, so the...

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Problems solved by technology

[0002] Research results and operating experience at home and abroad show that the optimal allocation of microgrid capacity including electric vehicles is still in its infancy, and the optimization objective is too single. Some use annual cost or annual cost as the objective function, and some consider power supply reliability indicators However, insufficient consideration is given to the reduction of the peak-to-valley difference of the system load curve, so the stability of the system cannot be guaranteed, and the optimization of the objective function mostly uses particle swarm optimization or genetic algorithm, both of which have some shortcomings, making the final optimization results insufficient. Accurate, not fast enough

[0003] Since the optimal allocation of microgrid capacity including electric vehicles is a multi-objective nonlinear programming problem, the current system optimization has the disadvantages of single optimization target and defects in the optimization method. Therefore, the present invention applies a multi-population genetic particle swarm optimization algorithm to In the optimization process of the micro-grid system including electric vehicles, the annual cost, the annual load power shortage probability and the minimum load curve peak-to-valley difference are the optimization objectives, and the judgment matrix method is used to transform the multi-objective function into a single objective function. The population genetic particle swarm optimization algorithm optimizes the microgrid system and finds the optimal equipment capacity combination of the system

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