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Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Author

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  • Sara Ayub

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia
    Department of Electrical Engineering, Faculty of Information and Communication Technology, BUITEMS, Quetta 87300, Pakistan)

  • Shahrin Md Ayob

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia)

  • Chee Wei Tan

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia)

  • Saad M. Arif

    (Department of Electrical Engineering, Aligarh Muslim University, Aligarh 202002, India)

  • Muhammad Taimoor

    (Faculty of Electrical and Computer Engineering, University of Kassel, 34132 Kassel, Germany)

  • Lubna Aziz

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia
    Department of Electrical Engineering, Faculty of Information and Communication Technology, BUITEMS, Quetta 87300, Pakistan)

  • Abba Lawan Bukar

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia
    Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Maiduguri, Borno State, P.O. Box 1069, Maiduguri 600004, Nigeria)

  • Qasem Al-Tashi

    (Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 78712, USA
    Faculty of Administrative and Computer Sciences, University of Albaydha, Albaydha CV46+6X, Yemen)

  • Razman Ayop

    (Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Skudai 81310, Johor, Malaysia)

Abstract

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

Suggested Citation

  • Sara Ayub & Shahrin Md Ayob & Chee Wei Tan & Saad M. Arif & Muhammad Taimoor & Lubna Aziz & Abba Lawan Bukar & Qasem Al-Tashi & Razman Ayop, 2023. "Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer," Sustainability, MDPI, vol. 15(2), pages 1-38, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:957-:d:1025462
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    References listed on IDEAS

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