Distribution Network Planning With Distributed Generation Based On Uncertainty Theory

ABSTRACT:With a new round of energy revolution,electric power has entered the stage of energy interconnection and smart.As a very important form of the future power grid,the planning of future distribution network planning plays a vital role in saving energy,promoting city construction and solving series of problems of emerging technology integration.However,more and more distribution generation(DG)are applied into the power system,which brings new challenges to distribution network planning for its uncertain characteristics like randomness and intermittent.Therefore,under this circumstance,distribution network planning is very necessary to solve various uncertain factors of economy,safety and reliability.And it aimed to improve the ability to accept the renewable energy as well as the best performance.Distribution network planning based on uncertainty is studied in this paper.The main work is as followed:Considering the various planning in distribution network,the uncertainty theory is summarized.In fuzzy programming,the concepts of fuzzy sets and fuzzy expectation values are given and fuzzy simulation is carried out.In the stochastic programming,the concepts of random variables,random expected values and the mathematical principle of stochastic chance constraints are given.The method of uncertain power flow calculation and optimization is studied.First of all,considering the characteristics of distributed power supply and load power,fuzzy power flow and stochastic power flow are calculated respectively according to fuzzy programming and stochastic programming.The fuzzy power flow is based on N-L method,using Taylor series expansion to solve the fuzzy increment of state variables;Stochastic flow is based on cumulant to calculate the unknown probability distribution of each variable node voltage and branch power.Secondly,three improved genetic algorithms,including adaptive genetic algorithm,partheno genetic algorithm and tree structure coding genetic algorithm are introduced,and their respective application characteristics are compared.Distribution network planning model with DG is established.Modeling of wind power generation,photovoltaic power generation and load are built as well as the corresponding fuzzy programming model of expectation programming Models.In the fuzzy expectation programming model,trapezoidal fuzzy number of DG and load are applied to simulate and investment cost minimum is as the objective function.In the chance constrained programming,the confidence level of the node voltage and branch power are taken into consideration.In order to prove the reasonability of the distribution network planning model,the simulation analysis of the 18 bus distribution network is studied based on whether it contains the access of DG,and whether the uncertain planning theory is utilized.The improved TSE-PGA is used to calculate the problems,too.According to the minimum annual expense and power loss,the access of DG can reduce network loss effectively and reduce the cost of network planning to minimized the effect of uncertain variables.Thus,economic benefit is obtained considerably.Through the above research,a solution to solve the problem of distribution network planning with uncertainty is provided based on uncertainty theory,and the feasibility of the method is illustrated by a case study.