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Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid

Author

Listed:
  • Hisham Alghamdi
  • Lyu-Guang Hua
  • Ghulam Hafeez
  • Sadia Murawwat
  • Imen Bouazzi
  • Baheej Alghamdi

Abstract

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.

Suggested Citation

  • Hisham Alghamdi & Lyu-Guang Hua & Ghulam Hafeez & Sadia Murawwat & Imen Bouazzi & Baheej Alghamdi, 2024. "Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-28, November.
    • Handle: RePEc:plo:pone00:0307228
    DOI: 10.1371/journal.pone.0307228
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    References listed on IDEAS

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    1. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    2. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).
    3. Qudrat-Ullah, Hassan, 2022. "A review and analysis of renewable energy policies and CO2 emissions of Pakistan," Energy, Elsevier, vol. 238(PB).
    4. Youssef, Heba & Kamel, Salah & Hassan, Mohamed H. & Nasrat, Loai, 2023. "Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm," Energy, Elsevier, vol. 278(C).
    5. Sun, Yue & Luo, Zhiwen & Li, Yu & Zhao, Tianyi, 2024. "Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm," Energy, Elsevier, vol. 296(C).
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