Abstract
In this paper, a multi-objective reactive power optimization model of distribution network is established, which takes the minimum of line loss, reactive compensation capacitor switching loss and node voltage deviation as objective, considering the constraints of node voltage, reactive power of wind turbine, capacitor switching times. The improved adaptive genetic annealing algorithm is used to solve the model. IEEE33 system is taken as an example to verify the effectiveness of the reactive power optimization model. When the shunt capacitor and wind turbine are in the optimal reactive power compensation, the line loss of the distribution network can be minimized.
Highlights
Reactive power optimization of distribution network is usually a multi-objective optimization problem which considers the economy and security of distribution network operation[1].At present, many scholars use intelligent optimization algorithm to solve the reactive power optimization problem
Wang[2] proposed a chaos particle swarm optimization algorithm based on golden section to solve the reactive power optimization
Hu[6] added the penalty function of investment scale constraint in the objective function and used genetic algorithm to solve the optimal configuration of reactive power compensation
Summary
Reactive power optimization of distribution network is usually a multi-objective optimization problem which considers the economy and security of distribution network operation[1]. Many scholars use intelligent optimization algorithm to solve the reactive power optimization problem. Wang[2] proposed a chaos particle swarm optimization algorithm based on golden section to solve the reactive power optimization. Zhao[3] designed an adaptive phased genetic algorithm to solve the reactive power planning problem. Yan[4] designed a two-level optimal reactive power compensation model and solved the model by harmonic search particle swarm optimization algorithm. Hu[6] added the penalty function of investment scale constraint in the objective function and used genetic algorithm to solve the optimal configuration of reactive power compensation. The reactive power optimization model is established to minimize the line loss, capacitor switching cost and node voltage deviation in the whole day, and the improved adaptive genetic annealing algorithm is used to solve the model
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