IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9420-d1001640.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9420/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9420/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    3. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid & Josep M. Guerrero, 2018. "A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems," Energies, MDPI, vol. 11(7), pages 1-19, July.
    4. 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.
    5. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominique Barth & Benjamin Cohen-Boulakia & Wilfried Ehounou, 2022. "Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers," Energies, MDPI, vol. 15(4), pages 1-19, February.
    2. Hong, Bowen & Zhang, Weitong & Zhou, Yue & Chen, Jian & Xiang, Yue & Mu, Yunfei, 2018. "Energy-Internet-oriented microgrid energy management system architecture and its application in China," Applied Energy, Elsevier, vol. 228(C), pages 2153-2164.
    3. Zhenghao Wang & Yonghui Liu & Zihao Yang & Wanhao Yang, 2021. "Load Frequency Control of Multi-Region Interconnected Power Systems with Wind Power and Electric Vehicles Based on Sliding Mode Control," Energies, MDPI, vol. 14(8), pages 1-15, April.
    4. Carmine Cancro & Camelia Delcea & Salvatore Fabozzi & Gabriella Ferruzzi & Giorgio Graditi & Valeria Palladino & Maria Valenti, 2022. "A Profitability Analysis for an Aggregator in the Ancillary Services Market: An Italian Case Study," Energies, MDPI, vol. 15(9), pages 1-26, April.
    5. Ricardo Echeverri Mart nez & Eduardo Caicedo Bravo & Wilfredo Alfonso Morales & Juan David Garcia-Racines, 2020. "A Bi-level Multi-objective Optimization Model for the Planning, Design and Operation of Smart Grid Projects. Case Study: An Islanded Microgrid," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 325-341.
    6. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    7. Qinqin Cai & Yongqiang Zhu & Xiaohua Yang & Lin E, 2020. "Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction," Sustainability, MDPI, vol. 12(5), pages 1-12, March.
    8. Mahtab Kaffash & Glenn Ceusters & Geert Deconinck, 2021. "Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    9. Ioan Sarbu & Matei Mirza & Daniel Muntean, 2022. "Integration of Renewable Energy Sources into Low-Temperature District Heating Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-28, September.
    10. Nak Heon Choi & Diego del Olmo & Diego Milian & Nadia El Kissi & Peter Fischer & Karsten Pinkwart & Jens Tübke, 2020. "Use of Carbon Additives towards Rechargeable Zinc Slurry Air Flow Batteries," Energies, MDPI, vol. 13(17), pages 1-12, August.
    11. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    12. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.
    13. Peng Tian & Zetao Li & Zhenghang Hao, 2019. "A Doubly-Fed Induction Generator Adaptive Control Strategy and Coordination Technology Compatible with Feeder Automation," Energies, MDPI, vol. 12(23), pages 1-21, November.
    14. Jun Dong & Zhenjie Chen & Xihao Dou, 2022. "The Influence of Multiple Types of Flexible Resources on the Flexibility of Power System in Northwest China," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    15. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    16. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    17. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    18. Long Wang, 2023. "Optimal Scheduling Strategy for Multi-Energy Microgrid Considering Integrated Demand Response," Energies, MDPI, vol. 16(12), pages 1-17, June.
    19. Xia, Yuanxing & Xu, Qingshan & Tao, Siyu & Du, Pengwei & Ding, Yixing & Fang, Jicheng, 2022. "Preserving operation privacy of peer-to-peer energy transaction based on Enhanced Benders Decomposition considering uncertainty of renewable energy generations," Energy, Elsevier, vol. 250(C).
    20. Rapeepat Techarungruengsakul & Anongrit Kangrang, 2022. "Application of Harris Hawks Optimization with Reservoir Simulation Model Considering Hedging Rule for Network Reservoir System," Sustainability, MDPI, vol. 14(9), pages 1-21, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9420-:d:1001640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.