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Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems

Author

Listed:
  • Mingyue He

    (Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

  • Zahra Soltani

    (Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

  • Mojdeh Khorsand

    (Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

  • Aaron Dock

    (Salt River Project (SRP) Power and Water, 1500 N. Mill Ave., Tempe, AZ 85288, USA)

  • Patrick Malaty

    (Salt River Project (SRP) Power and Water, 1500 N. Mill Ave., Tempe, AZ 85288, USA)

  • Masoud Esmaili

    (Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA)

Abstract

Recently there has been a considerable increase in the penetration level of distributed energy resources (DERs) due to various factors, such as the increasing affordability of these resources, the global movement towards sustainable energy, and the energy democracy movement. However, the uncertainty and variability of DERs introduce new challenges for power system operations. Advanced techniques that account for the characteristics of DERs, i.e., their intermittency and human-in-the-loop factors, are essential to improving distribution system operations. This paper proposes a behavior-aware approach to analyze and aggregate prosumers’ participation in demand response (DR) programs. A convexified AC optimal power flow (ACOPF) via a second-order cone programming (SOCP) technique is used for system scheduling with DERs. A chance-constrained framework for the system operation is constructed as an iterative two-stage algorithm that can integrate loads, DERs’ uncertainty, and SOCP-based ACOPF into one framework to manage the violation probability of the distribution system’s security limits. The benefits of the analyzed prosumers’ behaviors are shown in this paper by comparing the optimal system scheduling with socially aware and non-socially aware approaches. The case study illustrates that the socially aware approach within the chance-constrained framework can utilize up to 43% more PV generation and improve the reliability and operation of distribution systems.

Suggested Citation

  • Mingyue He & Zahra Soltani & Mojdeh Khorsand & Aaron Dock & Patrick Malaty & Masoud Esmaili, 2022. "Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9420-:d:1001640
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    References listed on IDEAS

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    3. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    4. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
    5. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
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    Cited by:

    1. Francesco Gulotta & Edoardo Daccò & Alessandro Bosisio & Davide Falabretti, 2023. "Opening of Ancillary Service Markets to Distributed Energy Resources: A Review," Energies, MDPI, vol. 16(6), pages 1-25, March.
    2. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.

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