Research On Active Management Technology Of Distribution Network Based On Adaptive Particle Swarm Algorithm

With the rapid development of the economic society,human demand for fossil energy is increasing which has caused serious energy shortages and environmental pollution problems.Renewable energy has many advantages such as clean,environmental-friendly,safety and high efficiency,etc.,thus of great value for alleviating energy and environmental problem.The use of renewable energy has two forms including centralized generation and distributed generation.Due to the reverse distribution characteristics of China's energy supply and demand,the centralized use of renewable energy will cause a serious "abandon wind and light" phenomenon.While the flexibility of distributed utilization can effectively solve the problem of centralized utilization,so the distributed use of renewable energy which is called distributed power generation has been more widely used in the actual distribution network.As the impact of high-permeability distributed power on the distribution network becomes more and more significant,in order to avoid its adverse impact on the distribution network,the distributed power supply connected to the distribution network must be controlled.The control of distributed power generation is mainly to optimize its configuration scheme and manage its operating state.The purpose is to improve economic or operating indicators of the distribution network under the premise of meeting the operation constraints of the distribution network system which can play an active role of the distributed power generation.In view of the above problems,the main research contents of this article are as follows:1.The mathematical model of the conventional output power of the photovoltaic,fan and load unit are established and their output characteristics are analyzed.The impact of distributed power access on the voltage of the nodes and the active power loss of the branches are studied by theoretical derivation.The relationship between the access location and capacity of distributed power sources and the degree of influence on the voltage of the nodes are qualitatively analyzed based on the IEEE33 node power distribution model.2.The typical scenario model of distributed power output and load are established.Based on the probabilistic models of distributed power output and load,the typical scenario of distributed power output and load in the area is constructed with historical data of light intensity,wind speed and load demand in the area by multi-scenario technology.The matching of distributed power output and load is analyzed for each scenario.3.The optimal configuration of distributed power in distribution network is solved.The optimal configuration model is established and the adaptive particle swarm algorithm is used to solve the model to get the optimized configuration scheme.The IEEE33 node power distribution system is taken as an example for simulation analysis to verify the validity of the configuration and the superiority of the adaptive particle swarm optimization algorithm.Three quantitative indicators including total cost reduction rate,total active power network loss reduction rate and average voltage offset rate are used to compare the improvement of the economic and operating indicators of the distribution network under different access schemes.4.The active management scheme of the distribution network is determined and its implementation effect is verified.The active management model is established and the interior point method is selected to solve model to obtain the optimal power flow distribution of the distribution network.The IEEE33 node power distribution system is taken as an example for simulation analysis to verify the effectiveness of the active management measures.Three quantitative indicators including total cost reduction rate,total active network loss reduction rate and average voltage offset rate are used to compare the improvement of the economic and operating indicators of the distribution network before and after active management.