Research On State Estimation Of Active Distribution Network With Distributed Energy

Due to the intermittent generation of distributed generation(DG)with high permeability,the state estimation of active distribution net(ADN)must consider the uncertainties brought by the introduction of DG.However,the traditional distribution state estimation(DSE)may not satisfy the standard of real-time power grid operation and scheduling.In order to improve the accuracy of ADN state estimation,it is necessary to correctly model the uncertain system with DG and propose an efficient and accurate state estimation method.In this paper,ADN with distributed energy is taken as the research object,to improve the accuracy of ADN state estimation,the following aspects are mainly studied:(1)The mathematical model of DSE is described,including the distribution network measurement system and the network parameters of the distribution network;Then the theory of state estimation based on weighted least squares(WLS)is introduced.(2)In order to improve the accuracy of state estimation,ADN power flow is carried out for the uncertainty caused by the introduction of DG in ADN.The probabilistic models of pv,energy storage,generator and different loads are established.To solve the problem of low precision of Monte Carlo.probabilistic power flow,a probabilistic power flow calculation based on semi-invariant and Gram-Charlier method is proposed.The feasibility of the method is verified by an example of IEEE30 distribution network.Simulation results show that DG access to the overall improvement of the distribution network operating environment,and provide more complete information for the system;At the same time,the deterministic power flow data is obtained by solving the example with the improved forward push-back method.(3)In order to solve the problem of insufficient state estimation of firefly algorithm,an improved leapfrog and firefly algorithm based on chaos strategy is proposed.The frog leaping algorithm was introduced to improve the defects of initial population.The concept of group optimization is introduced into the movement formula.Finally,logistic chaotic strategy was used to overcome the shortcoming of the algorithm which was strongly dependent on the control parameters.The feasibility and efficiency of the proposed algorithm are verified.(4)In order to study the state estimation of ADN with DG,the state estimation model of active distribution network with DG is established.The calculation results of power flow are used as measurement matrix.The improved algorithm is used to estimate the state of ADN containing DG.The results show that the accuracy of state estimation and the speed of solution are both improved,which can intuitively show the range of ADN state estimation including DG.