Optimization of Power Systems based on Ant Colony System Algorithms: An Overview

Abstract

This study presents the ant colony system (ACS) algorithms for optimization of power systems planning. The developed ACS algorithms formulate complex problems as combinatorial optimization problems. They are distributed algorithms composed by a set of cooperating artificial agents, called ants, which cooperate to find an optimum solution of the combinatorial problems. A pheromone matrix that plays the role of global memory provides the cooperation between ants. The study consists of mapping the solution space, expressed by an objective function of the combinatorial problems on the space of control variables, ant system (AS)-graph, where ants walk. In this study an ACS algorithm is applied to the constrained load flow (CLF) problem on IEEE 14-bus test system and 136 bus system. The results are compared with those given by the probabilistic CLF and the reinforcement learning (RL) methods, demonstrating the superiority and flexibility of the ACS algorithm. Moreover, ACS algorithm is applied to the reactive power control problem on the IEEE 14-bus test system in order to minimize the real power losses subject to operating constraints over the whole planning period. Finally, the application of ACS algorithm for active/reactive operational planning of power systems on IEEE 30-bus test system is presented and results are compared to those given by simulated annealing (SA), exhibiting superior performance