IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v328y2022ics0306261922014246.html
   My bibliography  Save this article

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

Author

Listed:
  • Han, Rushuai
  • Hu, Qinran
  • Cui, Hantao
  • Chen, Tao
  • Quan, Xiangjun
  • Wu, Zaijun

Abstract

With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

Suggested Citation

  • Han, Rushuai & Hu, Qinran & Cui, Hantao & Chen, Tao & Quan, Xiangjun & Wu, Zaijun, 2022. "An optimal bidding and scheduling method for load service entities considering demand response uncertainty," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014246
    DOI: 10.1016/j.apenergy.2022.120167
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922014246
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120167?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dimitriadis, Christos N. & Tsimopoulos, Evangelos G. & Georgiadis, Michael C., 2022. "Strategic bidding of an energy storage agent in a joint energy and reserve market under stochastic generation," Energy, Elsevier, vol. 242(C).
    2. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
    3. Li, Peng & Wang, Zixuan & Wang, Jiahao & Yang, Weihong & Guo, Tianyu & Yin, Yunxing, 2021. "Two-stage optimal operation of integrated energy system considering multiple uncertainties and integrated demand response," Energy, Elsevier, vol. 225(C).
    4. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    5. Heidari, A. & Mortazavi, S.S. & Bansal, R.C., 2020. "Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies," Applied Energy, Elsevier, vol. 261(C).
    6. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
    7. Venkat Durvasulu & Timothy M. Hansen, 2018. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets," Energies, MDPI, vol. 11(12), pages 1-21, December.
    8. Zeng, Bo & Wei, Xuan & Zhao, Dongbo & Singh, Chanan & Zhang, Jianhua, 2018. "Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties," Applied Energy, Elsevier, vol. 229(C), pages 186-200.
    9. Liu, Wenxia & Huang, Yuchen & Li, Zhengzhou & Yang, Yue & Yi, Fang, 2020. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response," Energy, Elsevier, vol. 198(C).
    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. Sicheng Wang & Weiqing Sun, 2023. "Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

    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. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    4. Iria, José & Scott, Paul & Attarha, Ahmad & Gordon, Dan & Franklin, Evan, 2022. "MV-LV network-secure bidding optimisation of an aggregator of prosumers in real-time energy and reserve markets," Energy, Elsevier, vol. 242(C).
    5. Tao, Peng & Xu, Fei & Dong, Zengbo & Zhang, Chao & Peng, Xuefeng & Zhao, Junpeng & Li, Kangping & Wang, Fei, 2022. "Graph convolutional network-based aggregated demand response baseline load estimation," Energy, Elsevier, vol. 251(C).
    6. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    7. Wang, Fei & Lu, Xiaoxing & Chang, Xiqiang & Cao, Xin & Yan, Siqing & Li, Kangping & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data," Energy, Elsevier, vol. 238(PB).
    8. Liu, Hong & Cao, Yuchen & Ge, Shaoyun & Xu, Zhengyang & Gu, Chenghong & He, Xingtang, 2022. "Load carrying capability of regional electricity-heat energy systems: Definitions, characteristics, and optimal value evaluation," Applied Energy, Elsevier, vol. 310(C).
    9. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    10. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    11. Seong-Hyeon Cha & Sun-Hyeok Kwak & Woong Ko, 2023. "A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market," Energies, MDPI, vol. 16(20), pages 1-27, October.
    12. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    13. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    14. Li, Na & Okur, Özge, 2023. "Economic analysis of energy communities: Investment options and cost allocation," Applied Energy, Elsevier, vol. 336(C).
    15. 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.
    16. Sun, Weijia & Wang, Qi & Ye, Yujian & Tang, Yi, 2022. "Unified modelling of gas and thermal inertia for integrated energy system and its application to multitype reserve procurement," Applied Energy, Elsevier, vol. 305(C).
    17. Liu, Zhiqiang & Cui, Yanping & Wang, Jiaqiang & Yue, Chang & Agbodjan, Yawovi Souley & Yang, Yu, 2022. "Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties," Energy, Elsevier, vol. 254(PC).
    18. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    19. Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.
    20. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).

    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:eee:appene:v:328:y:2022:i:c:s0306261922014246. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.