IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7061-d1258486.html
   My bibliography  Save this article

A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market

Author

Listed:
  • Seong-Hyeon Cha

    (Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea)

  • Sun-Hyeok Kwak

    (Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea)

  • Woong Ko

    (Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea)

Abstract

As the share of distributed generation increases, so do the opportunities for aggregators to participate in the electricity market. In particular, aggregators participating in both the day-ahead and real-time markets contribute to improving the reliability of the power system. In addition, aggregators seeking additional revenue can benefit from providing reserves in a joint electricity market environment. However, aggregated resources with uncertainty are limited because of the uncertain nature of both reserve provision and the amount of reserves they can provide. Therefore, this study proposes a robust optimization model for an aggregator to formulate a strategy for participation in the day-ahead markets and deploys energy control in the real-time operation. The serving ratio reflects the availability of the aggregator’s reserve participation. Both the deployed up/down power and renewable energy in the real-time operation are considered as uncertain parameters to reflect the uncertainty. In the case study, we analyze the profit-maximization strategy of an aggregator that owns renewable energy resources and energy-storage systems under the variation interval for uncertain parameters and the serving ratio. The bidding strategies vary by the variation interval and the serving ratio.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7061-:d:1258486
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7061/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7061/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(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. Zhang, Rufeng & Sun, Haihang & Li, Guoqing & Jiang, Tao & Li, Xue & Chen, Houhe & Zou, He, 2023. "Reserve provision of combined-cycle unit in joint day-ahead energy and reserve markets," Applied Energy, Elsevier, vol. 336(C).
    4. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).
    5. Joseph Nyangon & John Byrne, 2023. "Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(1), January.
    6. Zengqiang Mi & Yulong Jia & Junjie Wang & Xiaoming Zheng, 2018. "Optimal Scheduling Strategies of Distributed Energy Storage Aggregator in Energy and Reserve Markets Considering Wind Power Uncertainties," Energies, MDPI, vol. 11(5), pages 1-17, May.
    7. Castagneto Gissey, Giorgio & Subkhankulova, Dina & Dodds, Paul E. & Barrett, Mark, 2019. "Value of energy storage aggregation to the electricity system," Energy Policy, Elsevier, vol. 128(C), pages 685-696.
    8. Khojasteh, Meysam & Faria, Pedro & Vale, Zita, 2022. "A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets," Energy, Elsevier, vol. 238(PB).
    9. Sun, Guoqiang & Shen, Sichen & Chen, Sheng & Zhou, Yizhou & Wei, Zhinong, 2022. "Bidding strategy for a prosumer aggregator with stochastic renewable energy production in energy and reserve markets," Renewable Energy, Elsevier, vol. 191(C), pages 278-290.
    10. Iria, José & Soares, Filipe, 2019. "Real-time provision of multiple electricity market products by an aggregator of prosumers," Applied Energy, Elsevier, vol. 255(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. Senpeng Gao & Xiaoqing Bai & Qinghua Shang & Zonglong Weng & Yinghe Wu, 2024. "A Joint Electricity Market-Clearing Mechanism for Flexible Ramping Products with a Convex Spot Market Model," Sustainability, MDPI, vol. 16(6), pages 1-25, March.

    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. Ioannidis, Filippos & Georgitsioti, Tatiani & Kosmidou, Kyriaki & Zopounidis, Constantinos & Andriosopoulos, Kostas, 2025. "A simulation model for imbalance costs of renewable energy aggregators: The case of Greek balancing market," Energy Economics, Elsevier, vol. 142(C).
    2. Zhang, Rufeng & Sun, Haihang & Li, Guoqing & Jiang, Tao & Li, Xue & Chen, Houhe & Zou, He, 2023. "Reserve provision of combined-cycle unit in joint day-ahead energy and reserve markets," Applied Energy, Elsevier, vol. 336(C).
    3. 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).
    4. Pareek, Parikshit & Sampath, L.P. Mohasha Isuru & Singh, Anshuman & Goel, Lalit & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2024. "Degradation-infused energy portfolio allocation framework: Risk-averse fair storage participation," Energy, Elsevier, vol. 313(C).
    5. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2022. "Optimal strategy of electricity and natural gas aggregators in the energy and balance markets," Energy, Elsevier, vol. 257(C).
    6. Joseph Nyangon & Ruth Akintunde, 2024. "Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis," Papers 2412.07787, arXiv.org.
    7. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, vol. 13(1), pages 1-16, December.
    8. Zhang, Yin & Qian, Tong & Tang, Wenhu, 2022. "Buildings-to-distribution-network integration considering power transformer loading capability and distribution network reconfiguration," Energy, Elsevier, vol. 244(PB).
    9. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    10. Sohani, Ali & Cornaro, Cristina & Shahverdian, Mohammad Hassan & Moser, David & Pierro, Marco & Olabi, Abdul Ghani & Karimi, Nader & Nižetić, Sandro & Li, Larry K.B. & Doranehgard, Mohammad Hossein, 2023. "Techno-economic evaluation of a hybrid photovoltaic system with hot/cold water storage for poly-generation in a residential building," Applied Energy, Elsevier, vol. 331(C).
    11. Leisen, Robin & Radek, Julian & Weber, Christoph, 2024. "Modeling combined-cycle power plants in a detailed electricity market model," Energy, Elsevier, vol. 298(C).
    12. Zhihan Shi & Guangming Zhang & Xiaoxiong Zhou & Weisong Han & Mingxiang Zhu & Zhiqing Bai & Xiaodong Lv, 2023. "Research on Integrated Energy Distributed Sharing in Distribution Network Considering AC Power Flow and Demand Response," Sustainability, MDPI, vol. 15(22), pages 1-23, November.
    13. 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).
    14. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.
    15. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    16. Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    17. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2023. "A hierarchy model to use local resources by DSO and TSO in the balancing market," Energy, Elsevier, vol. 267(C).
    18. Li, Xiaozhu & Chen, Laijun & Sun, Fan & Hao, Yibo & Du, Xili & Mei, Shenwei, 2023. "Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game," Renewable Energy, Elsevier, vol. 208(C), pages 679-692.
    19. Nyangon, Joseph & Darekar, Ayesha, 2024. "Advancements in hydrogen energy systems: A review of levelized costs, financial incentives and technological innovations," Innovation and Green Development, Elsevier, vol. 3(3).
    20. Joseph Nyangon & Ruth Akintunde, 2024. "Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:16:y:2023:i:20:p:7061-:d:1258486. 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.