Research On Optimal Schedule Strategy For Active Distribution Network Based On Particle Swarm Optimization Combined With Bacterial Foraging Optimization Algorithm

With the increasing permeability of distributed generation units, micro grid units and storage units in the distribution network, the traditional distribution network take a large change on the operation, management and control mode. In view of this situation, the traditional distribution network is gradually transited to the active distribution network(ADN). However, the existing distribution network optimization scheduling strategy is no longer suitable for ADN optimization dispatching. Therefore, research on ADN optimization scheduling strategy is extremely important.Considering the fluctuation of the forecasting output power of distributed generation units such as wind and photovoltaic, the restriction of storage units on the output power and the complementary characteristics of mico-grids units, the ADN optimization scheduling strategy is researched based on particle swarm optimization combined with bacterial foraging optimization algorithm(PSO-BFO). Firstly, the required ADN optimization scheduling model is established through the study on the structure characteristics of each related unit. What’s more, by analyzing the composition principle of energy storage unit and its characteristics on charging and discharging, a power difference control strategy improved by the similarity method is proposed, which synthesizes the advantages of traditional method. The improved control strategy can solve the problem that control strategy is failure caused by the peak and valley value and its arrival time of the actual load curve doesn’t match the prediction load curve effectively. After this, the principle and the specific steps of the PSO-BFO algorithm are described. It is used to solve the multi-objective optimization scheduling model and get its Pareto solutions. Finally, the Pareto solutions of the multi-objective optimization problem are evaluated by the entropy weight decision-making method, thus the unique ADN optimization scheduling strategy is obtained.The verification results of specific examples show that the proposed ADN optimization scheduling model is reasonable. Besides this, the optimal scheduling strategy, obtained by solving the ADN optimization scheduling model which uses PSO-BFO algorithm and entropy weight decision-making method, not only gives the output power scheme of each unit in the system during a complete scheduling period, but also shows the position scheme of the contact switch. The optimal scheduling strategy also reflects the ability of overall optimization on the source side, the network side and the load side, which is possessed by the ADN optimization scheduling strategy.