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An Optimal Energy Management System for University Campus Using the Hybrid Firefly Lion Algorithm (FLA)

Author

Listed:
  • Haneef Ullah

    (Faculty of Electrical & Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Murad Khan

    (Faculty of Electrical & Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Irshad Hussain

    (Faculty of Electrical & Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Ibrar Ullah

    (Faculty of Electrical & Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Peerapong Uthansakul

    (School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Naeem Khan

    (Faculty of Electrical & Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

Abstract

As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.

Suggested Citation

  • Haneef Ullah & Murad Khan & Irshad Hussain & Ibrar Ullah & Peerapong Uthansakul & Naeem Khan, 2021. "An Optimal Energy Management System for University Campus Using the Hybrid Firefly Lion Algorithm (FLA)," Energies, MDPI, vol. 14(19), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6028-:d:640685
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    References listed on IDEAS

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    1. Awais Manzoor & Nadeem Javaid & Ibrar Ullah & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes," Energies, MDPI, vol. 10(9), pages 1-28, August.
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    Citations

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    Cited by:

    1. Satish Suresh Tanavade & Ganesh Nagraj Patil & C. V. Sudhir & A. M. Saravanan, 2023. "Strategic Energy Management and Carbon Footprint Reduction in University Campuses: A Comprehensive Review," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 15-27, November.
    2. Ibrar Ullah & Irshad Hussain & Khalid Rehman & Piotr Wróblewski & Wojciech Lewicki & Balasubramanian Prabhu Kavin, 2022. "Exploiting the Moth–Flame Optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector," Energies, MDPI, vol. 15(10), pages 1-27, May.
    3. Hongli Liu & Luoqi Wang & Ji Li & Lei Shao & Delong Zhang, 2023. "Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China," Energies, MDPI, vol. 16(8), pages 1-18, April.
    4. Ganesh Nagraj Patil & Satish Suresh Tanavade, 2024. "Eco-Friendly Energy Efficient Classrooms and Sustainable Campus Strategies: A Case Study on Energy Management and Carbon Footprint Reduction," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 188-197, May.

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