Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling

ABSTRACT

This paper presents a new approach for solving the short-term hydrothermal scheduling (STHTS) problem using a disruption operator in an oppositional gravitational search algorithm. The nonlinear and non-convex nature of the STHTS problem coupled with the cascading nature of reservoirs, water transport delays and scheduling time linkages makes the solution of this optimization problem quite difficult using the conventional optimization methods. Here, an opposition-based learning concept is introduced in a gravitational search algorithm to improve the quality of the current population towards global optimal solutions and a disruption operator is integrated to accelerate the convergence of solutions. This method is evaluated on two test systems consisting of four hydro and an equivalent thermal plant and four hydro and three thermal plants. The detailed statistical results prove that the proposed approach performs better in terms of production cost and smooth convergence characteristics when compared with other recently reported methods in the literature.

Keywords:

• Disruption operator
• gravitational search algorithm
• oppositional-based learning
• short-term hydrothermal scheduling

Acknowledgments

The authors gratefully acknowledge the financial support given by Government of India under Technical Education Quality Improvement Program-Phase II (TEQIP-II) via Grant No. [F.No.16-6/2013-TS.VII (Pt)].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

N. Gouthamkumar

N. Gouthamkumar received his BTech degree in electrical and electronics engineering from LBRCE, Mylavaram, Andhra Pradesh, India, in 2009, and his MTech degree in electrical engineering from the National Institute of Technology, Hamirpur, Himachal Pradesh, India, in 2012. He is currently working as a full-time research scholar in the Department of Electrical Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India. His primary research focuses on stochastic multi-objective short-term hydrothermal generation scheduling.

Email: [email protected]

Veena Sharma

Veena Sharma received her BTech degree in electrical engineering from REC Hamirpur, Himachal Pradesh, India, in 1990, her MTech degree in instrumentation and control engineering from Punjab Agricultural University Ludhiana, India, in 1993, and PhD from Punjab Technical University, Jalandhar, in 2006. She is currently working as an associate professor in EED, National Institute of Technology, Hamirpur, Himachal Pradesh, India. Her research interests include power system optimization, power generation, operation, and control.

Email: [email protected]

R. Naresh

R. Naresh received his BE in electrical engineering in 1987, ME in power systems from Punjab Engineering College, Chandigarh, in 1990, and PhD from the University of Roorkee, India, in 1999. He joined REC, Hamirpur in 1989. He is currently working as a professor in EED, National Institute of Technology, Hamirpur, Himachal Pradesh, India. He has published a number of research papers in national & international journals. He has been providing consultancy services to electric power industry. His research interests are artificial intelligence applications to power system optimization problems, evolutionary computation, neural networks, and fuzzy systems.

Email: [email protected]