Reconfiguration Of Distribution Network With Distributed Generation Under Chance Constraints Of Power Pupply Capability

The reconfiguration of distribution network is an important method to improve the operation efficiency and power quality of the network frame by optimizing the network topology according to the real-time operation status of the system.The power supply capacity reflects the ability of distribution network to provide power load to users,which is one of the important indicators to evaluate the safety of distribution network.At present,the research on distribution network reconfiguration and power supply capacity evaluation is mainly based on deterministic load.However,on the one hand,due to the influence of economic,climate and other factors,the load of distribution system has certain uncertainty.At the same time,distributed generation(DG)containing photovoltaic and wind power have been widely used in transmission and distribution networks due to its short construction cycle,environmental protection and pollution-free.However,the output of uncontrollable distributed generation such as photovoltaic cells and wind turbines is highly random and intermittent.Based on the above background,the optimization and reconfiguration method of distribution network under the constraint of power supply opportunity is studied.The details are as follows:(1)The theory of uncertain programming is studied.Based on the concepts of probability measures in probability theory and credibility measures in fuzzy theory,the concepts of stochastic chance constrained programming and fuzzy chance constrained programming are summarized.Then,the stochastic fuzzy programming theory of uncertainty theory is introduced.The concepts of fuzzy variables,chance measures,and stochastic fuzzy chance constrained programming are analyzed.Finally,considering multiple uncertainties,a hybrid programming model with stochastic fuzzy chance constraint and stochastic fuzzy expected value is studied.(2)A reconfiguration method of distribution network including DG considering the opportunity constraint of power supply capacity is presented.First,the random variables are used to represent the uncertainty of network frame load and DG output.Based on this,a power supply capacity evaluation model that takes into account the above uncertainties is established,and then a distribution network with DG reconfiguration model considering power supply capacity constraint is established,which aims at the maximum safety threshold of power supply capacity,the power supply capacity opportunity constraint conditions under the influence of various uncertain factors are introduced.The improved genetic algorithm is used to solve the reconfiguration model of the distribution network,and the power supply capacity chance constraint test method based on Monte Carlo simulation is given.(3)A reconfiguration method of distribution network with DG under stochastic fuzzy double uncertainty is presented.Firstly,the randomness and fuzziness of DG output and distribution network load are analyzed,they are modeled by random fuzzy variables.Then,a distribution network reconfiguration model with distributed generation(DG)is established,including stochastic fuzzy double uncertainties.The model aims at minimizing the random fuzzy expected value of network loss,and the random fuzzy chance constraints are introduced.The improved genetic algorithm is used to solve the reconfiguration model of the distribution network.The random fuzzy chance constraint checking method and the random fuzzy expected value calculation method based on monte carlo simulation are presented.The power supply capability of the scheme under random fuzzy conditions is evaluated.The distribution network reconfiguration method proposed in this paper considers the influence of uncertain factors such as load and DG output,which can be used to solve the problem of uncertain distribution network optimization and reconfiguration,and to provide a reference for operators' scheduling decisions.The models built in this paper have been verified by the IEEE 14-node and IEEE 33-node examples.The results of the examples show the accuracy and effectiveness of the proposed reconfiguration method.