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H2-optimal transactive control of electric power regulation from fast-acting demand response in the presence of high renewables

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  • Chassin, David P.
  • Behboodi, Sahand
  • Shi, Yang
  • Djilali, Ned

Abstract

This paper presents an H2-optimal power regulation scheme for balancing authorities to provide regulation services using both generation and load resources in the presence of a significant amount of intermittent renewable generation. The optimal controller is designed to minimize the loss of total economic surplus due to deviations from the schedule because of generation contingencies. The results show that the optimal controller outperforms the conventional ACE control policy by (1) providing faster return to the schedule under varying demand response levels, (2) reducing the cost of using reserve units for regulation services, and (3) minimizing deviations from the global surplus-maximizing schedule.

Suggested Citation

  • Chassin, David P. & Behboodi, Sahand & Shi, Yang & Djilali, Ned, 2017. "H2-optimal transactive control of electric power regulation from fast-acting demand response in the presence of high renewables," Applied Energy, Elsevier, vol. 205(C), pages 304-315.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:304-315
    DOI: 10.1016/j.apenergy.2017.07.099
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    4. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2017. "Interconnection-wide hour-ahead scheduling in the presence of intermittent renewables and demand response: A surplus maximizing approach," Applied Energy, Elsevier, vol. 189(C), pages 336-351.
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    Citations

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    Cited by:

    1. Chassin, David P. & Behboodi, Sahand & Djilali, Ned, 2018. "Optimal subhourly electricity resource dispatch under multiple price signals with high renewable generation availability," Applied Energy, Elsevier, vol. 213(C), pages 262-271.
    2. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    3. Nguyen, Hieu Trung & Battula, Swathi & Takkala, Rohit Reddy & Wang, Zhaoyu & Tesfatsion, Leigh, 2019. "An integrated transmission and distribution test system for evaluation of transactive energy designs," Applied Energy, Elsevier, vol. 240(C), pages 666-679.
    4. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    5. Yu, Min Gyung & Pavlak, Gregory S., 2021. "Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems," Applied Energy, Elsevier, vol. 282(PB).
    6. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    7. Janko, Samantha A. & Johnson, Nathan G., 2018. "Scalable multi-agent microgrid negotiations for a transactive energy market," Applied Energy, Elsevier, vol. 229(C), pages 715-727.
    8. Khalid Alnowibet & Andres Annuk & Udaya Dampage & Mohamed A. Mohamed, 2021. "Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework," Sustainability, MDPI, vol. 13(21), pages 1-32, October.

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