Multi-objective generation scheduling using modified non-dominated sorting genetic algorithm- II

Abstract

This paper presents a Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II) solution to Multi-objective Generation Scheduling (MOGS) problem. The MOGS problem involves the decisions with regards to the unit start-up, shut down times and the assignment of the load demands to the committed generating units, considering conflicting objectives such as minimization of system operational cost and minimization of emission release. Through an intelligent encoding scheme, hard constraints such as minimum up/down time constraints are automatically satisfied. For maintaining good diversity in the performance of NSGA-II, the concepts of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given the name as MNSGA-II. In order to prove the capability of the proposed approach 10 units, 24-hour test system is considered. The performance of the MNSGA-II are compared with NSGA-II and validated with reference Pareto front generated by conventional weighted sum method using Real Coded Genetic Algorithm (RGA). Numerical results demonstrate the ability of the proposed approach, to generate well distributed pareto front solutions for MOGS problem.