Active distribution network operational optimization problem: A multi-objective tuna swarm optimization model

Abstract

This study proposes multi-objective tuna swarm optimization through multi-objective transformation, initialization improvement and population variation for active distribution network (ADN). The ADN energy optimization model with dynamic reconfiguration, reactive power compensation, on-load tap changer and controllable load coordinated control are established with the minimum economic and environmental cost. Several controllable resources increase and increase the complexity of energy optimization problem in realized the effective clean energy consumption and the power grid stable operation. The minimum control cost and the minimum node voltage deviation are proposed. This study also proposes a decision-making method based on pareto front and an index to evaluate the maximum extensible dimension of intelligent algorithms in the process of analyzing energy optimization problems with intelligent algorithms. The proposed ADN energy optimization method based on multi-objective tuna swarm optimization shows excellent results after testing with the improved IEEE33 system. The voltage deviation and network loss are reduced by 51.62% and 22.16% on average compared with the single means control. The proposed model provides the support for the new energy consumption and the power grid stable operation, has strong engineering significance in the intelligent upgrading and power grid active control transformation.