Research On Optimal Operation Strategy Of Distribution Network With Distributed Energy Resources

The access of the distributed energy resources(DERs)has a positive impact on solving the problem of environmental protection and limited resources.Besides,the energy efficiency and network flexibility are enhanced.Nevertheless,the flexibility of distribution network is improved.Wind and solar power are one kind of distributed energy resources,the power uncertainty and fluctuation of which bring great challenges to the safety operation of distribution network.Therefore,the study of distribution network optimization strategy with DERs is one of the urgent issues to be solved.In the paper,a complete research system for distribution network optimization operation strategy that takes into account the uncertainty of wind power and solar power is constructed.The related key technologies,i.e.,the modelling of wind and photovoltaic output characteristics based on scenarios method,distribution network active power optimization method considering the uncertainty of wind power and solar power,and reactive power optimization for three-phase distribution network with single-phase distributed photovoltaics,are systematically and deeply studied.The novel achievements are as follows:A novel dynamic scenarios generation method with the consideration of power prediction error distribution and power time-correlation is proposed to provide data support for distribution network modelling.The probability distribution of wind and solar power is obtained by the prediction box and cumulative empirical distribution function.The power time-correlation is described by the covariance matrix of standard normal distribution.The recursive approach is used to estimate the covariance matrix.In order to make a mathematical theoretical definition to value the key parameter of forgetting factor,a novel method of forgetting factor identification method based on power fluctuation distribution is proposed.A collaborative optimization model of distribution network is proposed,which transfers power fluctuation of main network to natural gas network,so as to improve the absorption capacity of distribution network for new energy.The uncertainty mode of wind and solar power,temperature controlled load model,gas turbine model,energy conversion model of multi-energy coupling system,AC power flow of distribution network and gas flow model of natural gas network are established to make the distribution network with DERs in a cooperative optimal operation state.Gas turbine is one of the main sources of power supply for distribution network with DERs.Power fluctuation of main network can be transferred and suppressed by the electrical and gas coupling device.To solve the mixed integer nonlinear optimization problem,a method combining piecewise linearization and second order cone relaxation is proposed to make the problem to a mixed integer second order cone programming problem.A multi-objective optimization model for three-phase distribution network is proposed to reduce voltage unbalance and network loss simultaneously,so as to solve the problem of high voltage unbalance in three-phase distribution network caused by a large number of singlephase distributed photovoltaics.A multi-objective optimization model of coordinately controlling voltage regulator,capacitor and photovoltaic inverter for three-phase distribution network is established,and the uncertainty of photovoltaic reactive power output is modeled in detail.In order to improve the algorithm global search capability,a modified NSGA-II algorithm is proposed to solve the multi-objective optimization model.The modifications of the algorithm mainly consist of three parts.A discrete variable transformation coding method is proposed to ensure the discrete variables strictly satisfy cross-sectional constraints.A twolevel constraint process strategy is presented to ensure the individuals with small constraint deviation and small objective are preserved firstly.A fuzzy logic controller is designed to dynamically correct crossover and mutation rates at each iteration of the algorithm.