Research On Peak Shaving And Valley Filling Technology Of Flexible Distribution Network Based On Quantum Particle Swarm Algorithm

With the high proportion of distributed renewable power sources,flexible loads such as electric vehicles have grown rapidly,and users have increasingly higher requirements for green environmental protection,power supply reliability,and power supply quality.A flexible distribution network constructed by a large number of advanced power electronic equipment emerged as the times require.Flexible Distribution Network(FDN)is one of the representative applications of advanced power electronic technology.Compared with the traditional distribution network,it has the characteristics of high flexibility and reliability.Generally,high-power voltage source converters and other power electronic equipment are used to interconnect the AC distribution network and the DC distribution network to form a distributed power distribution network.AC and DC hybrid closed-loop power supply structure.Compared with the traditional distribution network,it has greatly improved in terms of power supply quality,power supply reliability,and renewable energy consumption capacity.This paper takes the flexible distribution network as the research object,and studies the DC distribution center(DC Distribution Centre,DDC)multi-objective peak shaving and valley filling optimization strategy under the scenario of wind and solar storage access,taking into account energy storage The battery has the characteristic of "energy time shift".A multi-objective optimization model of peak-shaving and valley-filling based on DC distribution center load is established,and an improved multi-objective QPSO optimization algorithm is proposed,which realizes the optimization based on DC distribution center load.Cut peaks and fill valleys.The specific research content is as follows:First,an improved multi-objective quantum particle swarm algorithm is proposed,which introduces dynamic ? infeasibility constraint dominance function and individual crowding distance judgment in distinguishing feasible solutions from infeasible solutions to make improvements;introduces dynamic optimization in algorithm optimization The second optimization method of inertia weight and probability variation is improved;through fuzzy membership function analysis method,the optimal compromise solution is selected in the Pareto optimal solution set obtained.The accuracy and effectiveness of the proposed algorithm are verified by an energy storage constant volume calculation example.Secondly,the DC side structure of the DC distribution center and the coupling relationship with the AC system are described in detail,and a 33AC-5DC flexible distribution network structure is established.Considering the peak-shaving and valley-filling of the net load curve of the DC distribution center after the wind and wind absorption,the economics of distributed power operation and the environmental processing cost,the peak-shaving and valley-filling of the DC distribution center load in the flexible distribution network is established.Target optimization model,and proposed an optimization process based on improved multi-objective QPSO algorithm.Finally,for an improved IEEE 33-node flexible interconnected distribution network example with a three-terminal DC distribution center,the above optimization method is applied to it,and the peak-shaving and valley-filling of the DC distribution center load is achieved.The results before and after the optimization verify the results.Improve the rationality of the algorithm and the effectiveness of the strategy.