Application of non‐dominated sorting gravitational search algorithm with disruption operator for stochastic multiobjective short term hydrothermal scheduling

Abstract

The hydrothermal scheduling (HTS) is formulated as a stochastic multiobjective short term HTS problem by considering stochastic production cost, NO x emission, SO2 emission and CO2 emission curves for thermal generation plants and uncertainty in load demand. This study presents, a non-dominated sorting gravitational search algorithm integrated with disruption operator (NSGSA-D) to solve this problem. In this approach, a set of Pareto optimal solutions are obtained by adopting the concept of non-dominated sorting. Further, an elite external archive is introduced to keep the Pareto optimal solutions and guide the search process. In addition, a disruption operator is utilised to intensify the search process and also speed up the convergence of the solutions. Furthermore, the most suitable and efficient solution from the non-dominated solution set is obtained with the help of fuzzy decision making policy. The effectiveness of NSGSA-D approach is demonstrated on three sample test systems and simulation results thus obtained are compared with the results reported in literature. The obtained results affirm that the NSGSA-D approach yields good quality solutions and competitive performance for solving stochastic multiobjective short term HTS, while handling the diverse constraints of the problem effectively.