Generator Maintenance Scheduling in a Deregulated Environment Using Hybrid Differential Evolution Algorithm

In this paper, a new approach for preventive maintenance of generating units in a competitive market environment is proposed. The objective of the generator maintenance scheduling (GMS) problem is to find the precise time interval for maintenance of power generating units with an objective criterion of maximizing the profit of individual generation companies (GenCo’s) present in an electricity market. The problem of scheduling of generating units for maintenance is formulated as a mixed integer optimization problem. Differential Evolution (DE) algorithm is suitably modified to handle the integer variables present in the GMS problem. The integer variables are the control variables that denotes the starting period of each generating unit for carrying out maintenance work. The lambda iteration approach is incorporated into the DE procedure in order to assists DE in finding the accurate starting period for maintenance of power generating units. This paper presents a hybrid differential evolution (HDE) to solve maintenance scheduling problem in a power system. The performance of the proposed algorithm is validated by considering 22 units test system. Numerical results obtained by the proposed HDE method are compared with hybrid particle swarm optimization (HPSO) algorithm. The test results reveal the capability of the proposed HDE algorithm in finding near optimal maintenance schedule for the GMS problem under competitive market environment.