Combined Use of an Improved PSO and GA to Solve the Unit Commitment Problem

Abstract

Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination of an improved the Particle Swarm Optimization method and the genetic algorithm applied to an IEEE electrical network 30 buses containing 6 production units to solve the Unit Commitment problem in one side and to find an optimized combination scheduling in the other side leading to minimize the total production cost. Our strategy differs from other evolutionary computing in enhancing the searching ability and helping to find more optimal solutions enabling a minimal production cost while considering a best unit commitment scheduling.