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Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory

Author

Listed:
  • Ghasem Ansari

    (Faculty of Electrical and Computer Engineering, Semnan-University, Semnan 35131-19111, Iran)

  • Reza Keypour

    (Faculty of Electrical and Computer Engineering, Semnan-University, Semnan 35131-19111, Iran)

Abstract

Power systems face challenges with regard to handling the high penetration of renewable energies, including energy intermittency and fluctuations, which are not present in conventional electricity systems. Various flexibility models have been developed to address these fluctuations, including demand-side flexibility, which offers a practical solution with which to overcome these challenges in all demand sectors, including the commercial sector. This paper proposes a new structure for the participation of the commercial sector in the electricity market to integrate and coordinate the consumption of the commercial sector. Unlike previous studies that had commercial consumers participate in the electricity market individually and sometimes fail to meet the requirements for flexibility programs, this study adopts a commercial aggregator to enhance the responsiveness of commercial systems. The proposed structure includes a mathematical model for commercial systems, e.g., shopping centers, with responsive ventilation systems to achieve demand flexibility. The study also uses the information-gap decision theory to address time-based commercial demand response planning from 24 h ahead to near real time. Moreover, a multi-layered structure is proposed to integrate the flexibility of shopping centers from the demand side to the supply side through a newly invented commercial demand response aggregator. The proposed approach was implemented in the New York electricity market, and the results show that it provides demand flexibility for up to 18% of the nominal level of electricity consumption compared to the traditional system. The paper aims to present a responsive structure for commercial systems, addressing the challenges of integrating renewable energies with the electricity system.

Suggested Citation

  • Ghasem Ansari & Reza Keypour, 2023. "Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory," Sustainability, MDPI, vol. 15(7), pages 1-31, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6243-:d:1116446
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    References listed on IDEAS

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    1. Kim, Youngjin & Norford, Leslie K., 2017. "Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation," Applied Energy, Elsevier, vol. 193(C), pages 308-324.
    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. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    4. Al-Sumaiti, Ameena Saad & Salama, Magdy M.A. & El-Moursi, Mohamed, 2017. "Enabling electricity access in developing countries: A probabilistic weather driven house based approach," Applied Energy, Elsevier, vol. 191(C), pages 531-548.
    5. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai & Chi-Chun Lo, 2015. "Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems," Energies, MDPI, vol. 8(9), pages 1-18, September.
    6. Majidi, M. & Mohammadi-Ivatloo, B. & Soroudi, A., 2019. "Application of information gap decision theory in practical energy problems: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 157-165.
    7. Moradi-Dalvand, M. & Mohammadi-Ivatloo, B. & Amjady, N. & Zareipour, H. & Mazhab-Jafari, A., 2015. "Self-scheduling of a wind producer based on Information Gap Decision Theory," Energy, Elsevier, vol. 81(C), pages 588-600.
    8. Ah-Yun Yoon & Hyun-Koo Kang & Seung-II Moon, 2020. "Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction," Energies, MDPI, vol. 13(4), pages 1-20, February.
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