Method and system for multi-target reactive power optimization of electric power systems

Abstract

The invention discloses a method and system for multi-target reactive power optimization of electric power systems. The method comprises the following steps of: establishing a multi-target reactive power optimization model; generating positions of N initial bird nests by utilizing Kent chaotic mapping, taking the positions of the N bird nests as initial populations, calculating a fitness value of each bird nest, establishing an external file set according to a Pareto dominance relation, updating the positions of the bird nests according to self-adaptive weights, updating the external file set according to the dominance relation and calculating a congestion distance to control the capacity of the file set; carrying out a differential evolution operation on each bird nest and updating the external file set; and when an iteration termination condition is satisfied, outputting an optimum Pareto optimal solution set. According to the method and system, a plurality of target functions are considered, so that the disadvantages that the traditional method is used for converting a plurality of targets into a single target and is difficult to determine the weight coefficients are optimally overcome; an improved cuckoo search algorithm is high in convergence rate, high in precision and good in individual diversity; and the obtained optimal solution set has favorable diversity and uniform distributivity, and can be well adapted to solving the multi-target reactive power optimization problems of the electric power systems.

Description

technical field [0001] The invention relates to the field of power systems, in particular to a method and system for multi-objective reactive power optimization of power systems. Background technique [0002] Power system reactive power optimization refers to adjusting the reactive power flow of the power grid by adjusting the terminal voltage of the generator, the tap of the transformer, the size of the reactive power compensation equipment, etc. Minimum loss, minimum voltage deviation, maximum static voltage stability margin, minimum investment cost of reactive power compensation equipment, etc., in order to achieve safe, stable and economical operation of the system. The reactive power optimization problem is a multi-constraint, multi-variable, multi-objective mixed nonlinear programming problem with the coexistence of equality constraints and inequality constraints. Its control variables include discrete variables and continuous variables. Many existing reactive power o...

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Problems solved by technology

[0003] In terms of reactive power optimization algorithms, the traditional linear programming method, nonlinear programming method, Newton method, and interior point method have certain limitations in solving problems with discrete variables, multi-objectives, and multi-variables. In recent years, genetic algorithms, The emergence of intelligent optimization algorithms such as particle swarm optimization algorithm, tabu search algorithm, immune algorithm, and simulated annealing algorithm has shown strong and effective capabilities in solving reactive power optimization problems in power systems. However, many intelligent algorithms have poor global convergence ability and are prone to fall into local optimum. Therefore, how to overcome the local optimum and obtain the optimal solution more efficiently and quickly is a technical problem to be solved by those skilled in the art

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