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An Optimal and Distributed Demand Response Strategy for Energy Internet Management

Author

Listed:
  • Qian Liu

    (North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Rui Wang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Yan Zhang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Guohua Wu

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Jianmai Shi

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

This study proposes a new model of demand response management for a future smart grid that consists of smart microgrids. The microgrids have energy storage units, responsive loads, controllable distributed generation units, and renewable energy resources. They can buy energy from the utility company when the power generation in themselves cannot satisfy the load demand, and sell extra power generation to the utility company. The goal is to optimize the operation schedule of microgrids to minimize the microgrids’ payments and the utility company’s operation cost. A parallel distributed optimization algorithm based on games theory is developed to solve the optimization problem, in which microgrids only need to send their aggregated purchasing/selling energy to the utility company, thus avoid infringing its privacy. Microgrids can update their operation schedule simultaneously. A case study is implemented, and the simulation results show that the proposed method is effective and efficient.

Suggested Citation

  • Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:215-:d:127273
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    References listed on IDEAS

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    Cited by:

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    2. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    3. V, Kavitha & V, Malathi & Guerrero, Josep M. & Bazmohammadi, Najmeh, 2022. "Energy management system using Mimosa Pudica optimization technique for microgrid applications," Energy, Elsevier, vol. 244(PA).
    4. Xiaohui Yang & Jiating Long & Peiyun Liu & Xiaolong Zhang & Xiaoping Liu, 2018. "Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    6. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    7. Yongsheng Cao & Guanglin Zhang & Demin Li & Lin Wang & Zongpeng Li, 2018. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy," Energies, MDPI, vol. 11(8), pages 1-20, August.
    8. Aleksander Jakimowicz, 2022. "The Energy Transition as a Super Wicked Problem: The Energy Sector in the Era of Prosumer Capitalism," Energies, MDPI, vol. 15(23), pages 1-31, December.
    9. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, June.
    10. Hyung-Chul Jo & Rakkyung Ko & Sung-Kwan Joo, 2019. "Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response," Energies, MDPI, vol. 12(9), pages 1-14, April.

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