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Residential load shifting in demand response events for bill reduction using a genetic algorithm

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  • Mota, Bruno
  • Faria, Pedro
  • Vale, Zita

Abstract

Flexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual.

Suggested Citation

  • Mota, Bruno & Faria, Pedro & Vale, Zita, 2022. "Residential load shifting in demand response events for bill reduction using a genetic algorithm," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018771
    DOI: 10.1016/j.energy.2022.124978
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    References listed on IDEAS

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    Cited by:

    1. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    2. Ceglia, Francesca & Marrasso, Elisa & Roselli, Carlo & Sasso, Maurizio, 2023. "Energy and environmental assessment of a biomass-based renewable energy community including photovoltaic and hydroelectric systems," Energy, Elsevier, vol. 282(C).
    3. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    4. Fahad R. Albogamy, 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid," Energies, MDPI, vol. 15(21), pages 1-31, October.
    5. Aparna Kumari & Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Fayez Alqahtani & Amr Tolba & Maria Simona Raboaca & Daniela Lucia Manea, 2023. "Blockchain-Driven Real-Time Incentive Approach for Energy Management System," Mathematics, MDPI, vol. 11(4), pages 1-17, February.

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