Distribution network reactive power optimization method and system oriented to multiple random uncertainty

Abstract

The invention discloses a distribution network reactive power optimization method oriented to multiple random uncertainty. The method comprises the following steps: acquiring power grid data; substituting the data into a pre-built reactive power optimization model; and solving the reactive power optimization model through a particle swarm optimization algorithm to obtain an optimal solution so asto achieve distribution network reactive power optimization, wherein the reactive power optimization model takes the lowest active loss as a target, and equality and inequality constraints are introduced. The randomness of distribution network loads, the randomness of distributed generation output and the randomness of a reactive compensation device are considered at the same time, so that the actual condition of a distribution network is more accurately reflected.

Description

technical field [0001] The invention relates to the technical field of distribution network operation control, in particular to a reactive power optimization method and system for a distribution network facing multiple stochastic uncertainties. Background technique [0002] The distribution network reactive power optimization problem has the following characteristics: (1) Non-linear. The objective function and constraints Regal nonlinearity. (2) Discrete type. The adjustment times of adjustable transformer taps and the number of switching groups of compensation capacitors are both discrete variables. (3) Complexity. Equality constraints and inequality constraints coexist, and the number of constraints increases with the expansion of the grid scale. [0003] The existing reactive power optimization methods for distribution networks can be roughly divided into four categories: conventional optimization methods, artificial intelligence methods, and hybrid algorithms. With ...

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Problems solved by technology

(3) Complexity

With the development of the current distribution network, these algorithms have the following problems in practical application: (1) The network reconfiguration caused by the grid connection of distributed power generation, the existing reactive power optimization model and algorithm can no longer accurately reflect the actual situation of the current system

(2) With the complexity of the distribution network structure and the expansion of the distribution network scale, the current optimization algorithm cannot effectively solve the reactive power optimization problem of various scale distribution networks

(3) The current distribution network has strict requirements on real-time reactive power optimization control, mainly including real-time response speed, s...

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