Research On Operation Optimization Of Active Distribution Network Based On Second-order Cone Programming

With the large number of access of Distributed generation(DG)and active management equipment,the traditional distribution network is gradually developing to Active distribution network(ADN),which can be actively managed and controlled.Therefore,the operation of ADN becomes complicated,and its operation optimization has become a research hotspot.The structural parameters of active distribution network and the uncertainty of the output of wind and solar have brought many difficulties to the study of ADN operation optimization.First,after the addition of DG to the distribution network,the original unidirectional flow power will be changed.Thus,the power flow distribution of distribution network is affected.Second,the addition of active management equipment increases the difficulty and complexity of solving the model and reduces the speed of solving the system.Thirdly,when renewable energy such as wind and solar are connected to the power system,it has strong uncertainty and randomness,which puts forward higher requirements for the operation and scheduling of the system.Fourth,the economic impact should be considered when the system is optimized due to cost problems such as the change of electricity price at different times.Based on the above problems,the main research work carried out in this thesis is as follows:(1)According to the characteristics of radial distribution network,a more efficient second-order cone programming method is introduced to analyze the impact of DG access on the voltage and network loss of distribution network.Taking the IEEE33-node system as an example,the influence of different location,capacity and quantity of distributed generation on the voltage and network loss of the distribution network is studied.Finally,the second-order cone programming is compared with the traditional method,and the effectiveness of the second-order cone programming is verified.(2)Due to the addition of active management equipment,the operation optimization problem of ADN has become a mixed integer non-convex and nonlinear problem.In order to solve the problem more efficiently and accurately,this thesis uses second-order cone relaxation and Big-M method to transform this problem into a mixed integer second-order cone programming model.The second order cone programming is used to solve the network reconstruction problem of active distribution network considering DG,and an example is given to verify that this method can be effectively improve the voltage stability of the system and reduce the network loss.(3)In order to alleviate the uncertainty of wind power and photovoltaic power output,the correlation between wind power and photovoltaic power output is considered.The correlation between wind power and photovoltaic power output is modeled by kernel density estimation and Frank Copula function.Then K-means algorithm is used to reduce a large number of wind-landscape output scenes,and typical combined wind power and photovoltaic power output scenarios are obtained,which make the wind power and photovoltaic power output models more accurate and fit the actual situation.(4)A dynamic operation optimization model of active distribution network considering the correlation between wind and solar is proposed.The model comprehensively considers the cost of network loss,power purchase cost and the cost of wind and solar abandonment,and makes full use of the correlation between wind and solar,which makes the model closer to the actual situation.The second order cone programming is used to solve the model,and it is proved that the operation optimization strategy can effectively reduce the operating cost of the system.In addition,an example is given to verify that the method can guarantee the accuracy of calculation and greatly improve the calculation speed.