Multi-target probabilistically optimal power flow fuzzy modelling and solving method for power distribution network

Abstract

The invention relates to a multi-target probabilistically optimal power flow fuzzy modelling and solving method for a power distribution network. The multi-target probabilistically optimal power flow fuzzy modelling and solving method comprises the following steps: obtaining n (n is less than t) random variables in t nodes in a network, and establishing a random variable model; obtaining 2<n> sample points corresponding to the n random variables and weighting coefficients omega<1> and k corresponding to the sample points by adopting a two-point estimation method; establishing a multi-target probabilistically optimal power flow nonlinear programming model; converting the multi-target probabilistically optimal power flow model into a single-target nonlinear programming model on the basis of a fuzzy set theory, and obtaining optimal power flow values Z<1> and k corresponding to the 2<n> sample points; and obtaining origin moments of various orders of an optimal power flow Z. According to the multi-target probabilistically optimal power flow fuzzy modelling and solving method provided by the invention, the random distributed power supply output problem is solved by adopting a point estimation method; therefore, the probabilistically optimal power flow problem is converted into deterministic optimal power flow calculation and characteristic value statistics; various samples are calculated by adopting the multi-target optimal power flow modelling and solving method respectively; and thus, the statistical characteristic value of a probabilistically optimal power flow can be finally obtained.

Description

technical field [0001] The invention relates to a distribution network optimal power flow simulation method, in particular to a distribution network multi-objective probability optimal power flow fuzzy modeling and solving method. Background technique [0002] The optimal power flow is when the structural parameters and load conditions of the system are given, through the optimization of the control variables, find the power flow distribution that can satisfy all the specified constraints and make one or more performance indicators of the system optimal. Constrained nonlinear programming problems, in the traditional optimal power flow research, experts and scholars have introduced various optimization methods, such as linear programming method, nonlinear programming method, quadratic programming method, interior point method and artificial intelligence method; [0003] The optimal power flow problem is a multi-objective nonlinear programming problem with scalable constraints...

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Problems solved by technology

[0003] The optimal power flow problem is a multi-objective nonlinear programming problem with scalable constraints. In terms of optimal power flow models, most of the current research has simplified and approximated this problem. For multiple objectives, weighted summation is usually used to construct The main problem of this method is that it is difficult to select an appropriate weight factor, and it cannot handle multi-objective functions of different dimensions; in addition, for the processing of constraints, conventional models often use hard constraint models for all constraints, which greatly reduces the feasible region ;

[0005] As distributed power is connected to the distribution network, the structure of the distribution network becomes more and more complex. In power flow calculation, distributed power cannot be simply treated as traditional PQ nodes or PV nodes. At the same time, the randomness of distributed power output makes The calculation of optimal power flow is more complicated

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