Electric vehicle charging and discharging scheduling strategy based on local search and competitive learning particle swarm optimization algorithm

It is great significance for environmental protection, energy conservation and emission reduction to replace fuel vehicles with EVs(electric vehicles).However, as a kind of random mobile load, large-scale integration into the power grid may lead to power quality problems such as line overload, line loss increase, voltage reduction and so on. In order to minimize the adverse effect of the disordered charging of EVs on the distribution grid, this paper takes the typical IEEE-33 node distribution system as the research object, a backward learning competitive particle swarm optimization (PSO) algorithm based on local search (SW-OBLCSO) is proposed. The SW-OBLCSO algorithm competitive learning and reverse learning mechanisms. In order to verify the performance of the algorithm, 4 common test functions are used, test functions compare the SW-OBLCSO algorithm with multiple optimization algorithms in different dimensions. The experimental results show that the proposed algorithm has outstanding performance in convergence speed and global search ability. This paper takes the minimum operation cost, the minimum environmental pollution, the minimum peak valley difference of load, the minimum node voltage offset rate, the minimum system grid loss and lowest charge cost as the optimization objectives; results shows that the proposed scheme can realize the transfer of charging load in time and space, so as to stabilize the load fluctuation of distribution grid, improve the operation quality of power grid, reduce the charging cost of users, and achieve the expected research objectives.

Automated summary

This paper proposes a particle swarm optimization algorithm based on competitive learning and reverse learning to minimize the adverse effects of disordered EV charging on the distribution grid, showing outstanding performance in experiments.