Profit based unit commitment problem solution using metaheuristic optimisation approach

Abstract

Earlier power utilities had generated electrical power with the goal of minimising the costs and meeting both demand and reserves. Generation companies (GENCOs) schedule their generators under a new structure to maximise the profit. This paper provides a computational methodology based on monarch butterfly optimisation (MBO) to find a solution to the problem of profit-based unit commitment (PBUC), taking into account the power and reserve conditions. The proposed method allows GENCOs to determine how much power and reserve should be sold in the markets in order to achieve maximum profit. The binary variables of unit commitment problems are handled by modifying the continuous-time nature of the monarch butterfly algorithm. The computational approach maximises the profit but also handles the mixed constraints of the commitment problem. The effect of thermal turbine valve point loading is also taken into consideration. The computational technique has been used to solve two test systems in the absence and presence of steam turbine valve point loading effects. The results obtained are in agreement with the recent results available in the literature. Comparative analysis shows the effectiveness of the proposed MBO based solution methodology in terms of profit earned and execution time in relation to other techniques.

KEYWORDS:

• Unit Commitment (UC)
• Profit Based Unit Commitment (PBUC)
• Monarch butterfly optimisation (MBO)
• Profit (PR)

Acknowledgement

The research work is funded under the Human Resources Development Group project grant 22(0815)/19 / EMR-II approved to the second author by the Council of Scientific and Industrial Research (CSIR), New Delhi, India.

Disclosure statement

Submitted with approval from the supervisor (Dr R. Naresh, e-mail: [email protected]). No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research work is funded under the Human Resources Development Group project grant [number 22(0815)/19 / EMR-II] approved to the second author by the Council of Scientific and Industrial Research (CSIR), New Delhi, India.

Notes on contributors

V. Kumar

V. Kumar was born in Lucknow in 1991 and he received his B.Tech. Degree in Electrical and Electronics Engineering from ABES Engineering College, Ghaziabad, India in 2013, and the M.Tech degree in Power System from NIT Hamirpur, India in 2017. He is currently a research scholar in Department of Electrical Engineering, NIT Hamirpur, India. He is working in unit commitment area.

R. Naresh

R. Naresh was born in 1965, in Himachal Pradesh India. He graduated in 1987 from Thapar Institute of Engineering and Technology, Patiala, India with a BE degree in electrical engineering, post graduate (M.Tech in power system) from Punjab Engineering College, Chandigarh in 1990 and doctoral degree (PhD) in 1999 from IIT Roorkee, India. He currently serves as a professor in the Department of Electrical Engineering, National Institute of Technology, Hamirpur, India. He is expertise in applications of AI techniques, evolutionary and recent heuristic optimisation techniques that are helpful in modern power system planning, operation and control.

Veena Sharma

Veena Sharma received her B.Tech degree in Electrical Engineering from REC Hamirpur, Himachal Pradesh, India, in 1990, and M.Tech degree in Instrumentation and Control Engineering from Punjab Agricultural University Ludhiana, India, in 1993 and Ph.D. 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. She has published a number of research papers in national and international journals. She has been providing consultancy services to electric power industry. Her research interests include power system optimisation, power generation, operation and control.