Open AccessApplication of non‐dominated sorting gravitational search algorithm with disruption operator for stochastic multiobjective short term hydrothermal scheduling
Author(s) -
Nadakuditi Gouthamkumar,
Sharma Veena,
Naresh Ram
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.1137
Subject(s) - mathematical optimization , sorting , computer science , pareto principle , solution set , operator (biology) , scheduling (production processes) , multi objective optimization , fuzzy logic , set (abstract data type) , mathematics , algorithm , biochemistry , chemistry , repressor , transcription factor , gene , artificial intelligence , programming language
The hydrothermal scheduling (HTS) is formulated as a stochastic multiobjective short term HTS problem by considering stochastic production cost, NO x emission, SO 2 emission and CO 2 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.