Distribution network planning method based on credibility theory

Abstract

The invention discloses a distribution network planning method based on a credibility theory, which comprises the steps of S1, building a distribution network fuzzy planning model based on the credibility theory; S2, building an equivalent model with a given reliability index; and S3, solving the model by adopting fuzzy power flow calculation and a genetic algorithm. According to the invention, the distribution network planning model based on the credibility theory is built on the basis of a fuzzy mathematic theory, the fuzzy uncertainty of load is considered, the minimization of the fixed investment and the fuzzy expected loss cost of the distribution network within the planning period is taken as an objective function, the reliability index is introduced, and the branch power and node voltage constraints are processed by adopting fuzzy change constraints; and secondly, the distribution network planning model under the credibility index is equivalent to a distribution network planningmodel under interval load by adopting a credibility index equivalence theorem, and a cut set corresponding to the fuzzy load distribution is enabled to be expressed by an intuitive interval. In addition, the solving method lowers the model solving difficulty and reduces the model solving time.

Description

technical field [0001] The invention relates to the field of power system planning, in particular to a distribution network planning method based on credibility theory. Background technique [0002] The traditional distribution network planning optimization method is based on certain planning parameters to obtain a deterministic planning scheme that satisfies the environmental constraints and has the best economic indicators. Traditional planning methods do not consider the influence of uncertain factors in planning, resulting in poor flexibility and economical planning of grid structures. [0003] For the treatment of uncertain factors in distribution network planning, there are mainly stochastic programming, fuzzy programming, interval planning, scenario analysis and risk assessment methods. At present, distribution network planning rarely considers the influence of load fuzzy uncertainty. Distribution network planning involves a wide range of content, and its mathematica...

AI Technical Summary

Problems solved by technology

At present, distribution network planning rarely considers the influence of fuzzy uncertainty of load. Distribution network planning involves a wide range of content, and its mathematical model contains many variables and constraints. Therefore, it is difficult for traditional planning algorithms to obtain the optimal solution.

The existing uncertain programming models are all based on uncertain programming theory, the models are complex and the solution speed is slow, the obtained schemes are relatively conservative, and there is a lack of mathematical indicators to effectively evaluate the system risk level

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