Power distribution network multi-objective probabilistic reactive power optimization method

Abstract

The invention provides a power distribution network multi-objective probabilistic reactive power optimization method. The method includes the following steps that: a stochastic model is constructed according to the predetermined stochastic factors of a power distribution network; probabilistic power flow is calculated, so that the probabilistic density function of node voltage can be obtained; the voltage entropy of the power distribution network is calculated according to the probabilistic density function of the node voltage; and a multi-objective probabilistic reactive power optimization model is constructed and solved. With the power distribution network multi-objective probabilistic reactive power optimization method provided by the technical schemes provided by the invention adopted, the probabilistic distribution of the node voltage is more centralized compared with probabilistic distribution obtained by using a single-objective reactive power optimization method, and the voltage percent of pass of a system can be improved.

Description

technical field [0001] The invention relates to the application of a probabilistic method in the field of reactive power optimization of a distribution network, in particular to a multi-objective probabilistic reactive power optimization method of a distribution network. Background technique [0002] As an important branch of the smart grid, the smart power distribution system contains a large number of distributed power sources and fluctuating loads. The distribution network presents the randomness of load demand, and the randomness of the power generated by DG such as wind power and photovoltaics, all of which provide power flow control for the distribution network. , Voltage fluctuations, power quality, reactive power optimization and many other aspects have brought great challenges. [0003] With the rapid development of intelligent power distribution system, the uncertainty of load and the randomness of distributed power generation have brought new challenges to reactiv...

AI Technical Summary

Problems solved by technology

Most of the mathematical models and solution methods used in traditional reactive power optimization research belong to the category of deterministic analysis, that is, assuming that the load level remains unchanged and optimize the reactive power control scheme of certain operating indicators, however, the operation of the power system is faced with many random disturbances , the control variables obtained by reactive power optimization are extremely sensitive to uncertain factors in the power grid, which will affect its implementation effect, and even lead to unfeasible operation mode or deterioration of system security and stability

[0004] In the research of reactive power optimization involving stochasticity, the stochastic reactive power optimization chance constrained programming model is generally used to calculate the probability distribution of active power loss and state variables through probabilistic power flow, and the deterministic voltage constraints are treated as probabilistic voltage constraints. The obtained optimization The result is that the objective function is sacrificed to a certain extent to improve its feasibility in an uncertain environment, but this method cannot change the standard deviation of the probability distribution of the state variable, which may increase the probability of the state variable exceeding the limit. Large, so the objective function will be sacrificed to a greater extent

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