User satisfaction-based genetic algorithm for load shifting in smart grid

Abstract

This paper presents a new load shifting strategy for smart grid systems based on both power consumers’ day-ahead power forecast and their Service Level Agreement (SLA) in order to reduce their electricity bills, guaranties user satisfaction, and for smart grid system to reduce as well the overall power consumption at the peak hours. We provide an analytical model that formulated the load shifting process as a cost minimization problem. A Genetic Algorithm (GA) approach based on a two dimensional chromosome representation is used to solve the optimization problem by collecting a day-ahead forecast and SLAs as an input from the power consumers. The output of the GA consists of giving the best power task plan for the day-ahead which satisfy all consumers in terms of minimizing their consumption bill and reduces the peak demand. Experimental results using simulation show that the proposed load shifting strategy not only guaranty SLA requirements but it reduces the total cost by more than 16%, and in general it achieves a substantial cost savings of 38% compared to the recent algorithms from the literature.

KEYWORDS:

• Smart grid
• load shifting
• optimization
• genetic algorithms
• service level agreement

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No potential conflict of interest was reported by the author(s).

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Additional information

Notes on contributors

Abderezak Touzene

Dr. Abderezak Touzene is a full professor of Computer Science at the College of Science, Sultan Qaboos University, Muscat, Sultanate of Oman. He has more than 30 years of teaching and research experience. Before joining Sultan Qaboos University, he has also worked at King Saud University, Saudi Arabia, and Grenoble Institute of Technology, France. He obtained his Ph.D. from Institut Polytechnique de Grenoble, France (1992), M.Sc. from Paris University, France (1989), and B.Sc. from University of Technology Houari Boumedien, Algeria (1987) His area of interest include Smart Systems, Cloud computing, Cybersecurity, Machine Learning, Big Data analytics, IoT.

Manar Al Moqbali

Manar Al Moqbali has an Msc. in Computer Science from Sultan Qaboos University, she is currently a programmer in the center of education and technology at SQU.