Reactive Power Planning Of Power System Considering The Randomness And Correlation Of Wind Power

Under the current background of global energy shortage problems and increasingly severe environmental pollution,as an effective supplement to fossil energy power generation,renewable energy power generation(mainly wind and solar)provides new ideas for alleviating the global energy crisis and environmental pollution.Among them,wind power as the most mature technology,the most large-scale development conditions of renewable energy generation technology has been widely used.However,due to the randomness and correlation of the wind turbine output,a large number of grid-connection will not only bring about power quality problems,but also change the original power flow distribution and transmission power of the power grid,which brings a lot of uncertainty to the operation of the power system.At the same time,the load power of the power system also has a certain randomness.Therefore,traditional deterministic reactive power planning methods have been difficult to adapt to these complex and changeable environments,and it is necessary to discuss reactive power planning methods under uncertain parameters.In response to this problem,the reactive power planning of power systems with wind power is studied in the paper.The main contents are as follows:(1)Probability theory is introduced to simulate random input variables in the system,and a probability model of wind turbine output and load is established.The probability power flow based on three-point estimation is used to consider the randomness of wind turbine output and load,and the probability flow calculation is converted into a deterministic power flow calculation at the sampling points.However,the traditional three-point estimation method can not solve the problem of probability power flow with correlation of input variables.It is only applicable to the case where the input variables are independent of each other.Therefore,the Nataf transform is introduced to deal with the random variables with correlation.The simulation analysis of the IEEE33 bus system shows that this method can obtain the probability distribution of the corresponding state variables of the system more accurately.(2)In the paper,the particle swarm optimization algorithm is applied to the solution of power system reactive power planning.In view of the shortcomings of the traditional particle swarm optimization algorithm,such as low convergence accuracy and easy to fall into local optimum,the following three aspects are improved: firstly,to increase the population diversity of the particle,the chaos algorithm is integrated into the particle swarm algorithm,and the particle swarm is initialized with chaos;secondly,adaptive adjustment strategy is used to improve the calculation efficiency of the algorithm;finally,an adaptive mutation operator is added to the position update formula to avoid the algorithm falling into the local optimum.The results of the calculation examples show that the improved particle swarm optimization algorithm can search higher-quality optimized solutions and the calculation speed is faster than the traditional particle swarm optimization algorithm.(3)The problem of reactive power planning can be divided into two sub-problems:the selection of reactive power compensation location and the determination of reactivepower compensation capacity.The stability and economy of system operation can be effectively maintained by reasonably selecting the reactive power compensation position and configuring a reasonable reactive power compensation capacity.Firstly,the reactive power compensation position of the power system is selected by using the reactive power margin analysis method.Then,with the objective of minimizing the overall system cost at different load levels,a mathematical model of reactive power planning with opportunity constraints is established,using probabilistic power flow calculation methods to replace traditional deterministic power flow calculation methods and combining with improved particle swarm optimization algorithm to optimize the model.Finally,the simulation test of IEEE33 bus system shows that the proposed reactive power planning strategy can effectively reduce the power loss of the system and the risk of bus voltage over-limit.