Research On Reactive Power Optimization Of Distribution Network With Distributed Generation Based On Scenario Analysis

With the continuous improvement of the grid connection scale of distributed generation(DG)in the distribution system,the traditional centralized power generation mode is no longer able to meet the problems caused by the increasing number of DG connections and changes in load demand.DG grid connection will change the structure of the distribution system and the distribution of currents in the system,and the intermittent and fluctuating output of DG may affect the stable and economic operation of the distribution system after grid connection.Therefore,considering the operational characteristics of distribution networks with DGs and formulating reactive power optimization strategies have important research significance for reducing network losses and ensuring safe and stable operation of distribution systems.The primary research topics addressed in this article are as follows:Aiming at the problem of changes in the operation mode of distribution systems caused by source load timing fluctuations in distribution networks,a scenario analysis method is proposed to solve the uncertainty of DG output and load demand,and the wind power output scenario is determined through two steps: scenario generation and scenario clustering.The influence of the regulation characteristics of different reactive power sources and different grid connection parameters on the voltage distribution and network losses of the distribution system is analyzed.Aiming at the problem of poor sensitivity judgment of reactive power compensation points in traditional operation modes,a scenario analysis based reactive power compensation node selection and capacity optimization method is proposed.The method uses comprehensive timing sensitivity indicators to determine the location of reactive power compensation,and determines the compensation capacity of reactive power sources with the goal of minimizing system network loss and voltage deviation.The reactive power optimization problem is a multi-objective,multi-constraint nonlinear programming issue.By optimizing the distribution system’s reactive power,network loss can be minimized and the power quality of the system can be enhanced,thus ensuring the system’s secure and reliable functioning.Aiming at the problem that traditional static reactive power optimization cannot accurately reflect the impact of wind power output changes and load fluctuations,as well as the differences between reactive power optimization results and actual situations,a dynamic reactive power optimization allocation method based on dual time scales is proposed.This method is based on the mathematical model of the distribution network with DG.In the day ahead dispatching phase,the day ahead reactive power output plan is made based on the longterm day ahead forecast data,and the day ahead hour level action plan of the parallel capacitor bank,transformer tap changer gear and photovoltaic inverter is determined as the basis of the day ahead reactive power optimization.In the intra-day scheduling phase,real-time intra-day output scheduling is performed using short-term intra-day measurement data,and the day-ahead planning scheme is corrected based on the intraday deviation of DG and load output.Through dual time scale dynamic reactive power optimization allocation,both the economy and stability of long time scale and short time scale distribution system operation are considered.After DG and reactive power supply are connected to the grid,it can realize local grid connection and consumption,which can effectively reduce the loss caused by longdistance transmission of electric energy,and also improve the voltage level of the distribution network,thereby greatly improving the operation stability of the system.At the same time,it can also better meet the power needs of users.Finally,the improved particle swarm algorithm is used to solve the simulation analysis in the IEEE33 node power distribution system,which verifies the effectiveness of the proposed optimization method.