Improved multi-objective evolutionary algorithm for day-ahead thermal generation scheduling

This paper presents a multi-objective evolutionary algorithm to solve the day-ahead thermal generation scheduling problem. The objective functions considered to model the scheduling problem are: 1) minimizing the system operation cost and 2) minimizing the emission cost. In the proposed algorithm, the chromosome is formulated as a binary unit commitment matrix (UCM) which stores the generator on/off states and a real power matrix (RPM) which stores the corresponding power dispatch. Problem specific binary genetic operators act on the binary UCM and real genetic operators act on the RPM to effectively explore the large binary and real search spaces separately. Heuristics are used in the initial population by seeding the random population with two Priority list (PL) based solutions for faster convergence. Intelligent repair operator based on PL is designed to repair the solutions for load demand equality constraint violation. The ranking, selection and elitism methods are borrowed from NSGA-II. The proposed algorithm is applied to a large scale 60 generating unit power system and the simulation results are presented and compared with our earlier algorithm [26]. The presented algorithm is found to outperform our earlier algorithm in terms of both convergence and spread in the final Pareto-optimal front.