Research On Reactive Power Optimization Of Distribution Network With Distributed Generation

Distributed generation is an effective supplement to the traditional large-scale centralized power supply,which uses the various energy scattered at the user side.Especially,the distributed generation generated by clean and renewable energy technologies such as wind and solar energy is an important way to alleviate the global energy crisis and tackle the worsening environmental problems.Because distributed generation is usually influenced by weather,it brings new problems to the planning,operation and control of the power system when it is connected to the power system.Power system reactive Power optimization ensures the safety and stability of the system and improves the economy of the system.But the access of distributed generation brings great changes to the system.Therefore,this paper fully considers the probability characteristic of distributed generation based on probability theory and method.The influence of the randomness of distributed generation to the reactive power optimization of distribution network is analyzed quantitatively.On the basis,some adjustments are made to the traditional reactive power planning and optimization model,which are as follows:1.Probabilistic modeling of wind farms and photovoltaic power plants with certain correlationsThe probabilistic models of wind farm and PV power station output based on parameter estimation and non-kernel density estimation method are established respectively.The fitting degree and fitting accuracy test of various models are also verified to validate the fitting effect of the models.In view of the correlation between wind farms and photovoltaic power stations in the same area,three correlation indices are adopted to describe the correlation.Then the Copula theory is Introduced to establish a joint probabilistic model for wind power and photovoltaic plant output.Finally,the fitting effect of different probability models is examined by measured wind velocity and illumination intensity data in a certain area of the United States.2.multi-objective reactive power planning of distribution network based on multiple scenesAiming at the problem of reactive power planning in distribution network with distributed generation,a scene analysis method is proposed to deal with the stochastic output of distributed power supply,which changes the randomness problem into a deterministic problem under multiple scenes.The Wasserstein distance indicator is applied to generate the optimal sub-points,producing a certain number of wind farms and photovoltaic power station output scenes.Then,the clustering analysis method is used to reduce the scene to the specified number,which can avoid the problem of inefficient computation.To select the reactive power compensation nodes,the sensitivity analysis method is adopted where the node with high sensitivity is selected as reactive compensation node.On the basis of this,a multi-objective reactive power optimization model whose objective is minimum reactive power investment and the minimum expectation of the node voltage deviation is established,and the model is solved by the improved non-dominant ranking genetic algorithm.At last,the IEEE33 node distribution system is used to validate the established reactive power planning model and improved non-dominant ranking genetic algorithm.3.multi-objective reactive power optimization of distribution network based on probabilistic load flowReactive power optimization of distribution network is studied under the premise that the node and compensating capacity of reactive power compensation is determined.The probabilistic power flow calculation is used to solve the randomness of distributed generation.Based on this,a reactive power optimization model with minimal expectation of active network loss,minimum value of node voltage deviation and maximum voltage static stability margin is established.The improved non-dominant genetic algorithm proposed in this paper is also used to solve the model.At last,the model and algorithm are verified by the IEEE33 node system and the distribution system in a region of Shandong province.