IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1242-d146116.html
   My bibliography  Save this article

Optimal Scheduling Strategies of Distributed Energy Storage Aggregator in Energy and Reserve Markets Considering Wind Power Uncertainties

Author

Listed:
  • Zengqiang Mi

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yulong Jia

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Junjie Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaoming Zheng

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

With continuous technological improvement and economic development of energy storage, distributed energy storage (DES) will be widely connected to the distribution network. If fragmented DES systems are aggregated to form a distributed energy storage aggregator (DESA), the DESA will have great potential to participate in the day-ahead energy and reserve market and the balancing market. The DESA could act as a mediator between the market and DES consumers, enabling beneficial coordination for DES owners and power systems. This paper presents a bilevel optimization model for DESAs in the energy and reserve market under wind power uncertainties. In the lower-level problem, generating companies, wind power plants (WPP), and DESAs are optimized for scheduling day-ahead (DA) energy and the reserve market. In the upper-level problem, operational strategies for DES systems and DESAs are designed to deal with wind power uncertainties in the balancing market. The DESA splits its resources between the energy and reserve markets so that it can reduce total power system consumption, and mutual profit for the system and end customers is achieved. This model is formulated as a mixed-integer linear programming (MILP) program, which can be solved with commercial software. The validity of the bilevel optimization model is verified by the eight-node test transmission system and IEEE-33 bus distribution system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1242-:d:146116
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1242/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1242/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
    2. Ciara O'Dwyer & L. (Lisa B.) Ryan & Damian Flynn, 2017. "Efficient large-scale energy storage dispatch: challenges in future high renewables systems," Open Access publications 10197/9103, School of Economics, University College Dublin.
    3. Singh, A.K. & Parida, S.K., 2018. "A review on distributed generation allocation and planning in deregulated electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4132-4141.
    4. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    5. Chaves-Ávila, J.P. & Hakvoort, R.A. & Ramos, A., 2014. "The impact of European balancing rules on wind power economics and on short-term bidding strategies," Energy Policy, Elsevier, vol. 68(C), pages 383-393.
    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. Liu, Shiyu & Ren, Yanzhe & Zhang, Zhenyu & Xiao, Yao & Bie, Zhaohong & Wang, Xifan, 2021. "Optimal bid-offer strategy for a virtual energy storage merchant: A stochastic bi-level model with all-scenario feasibility," Applied Energy, Elsevier, vol. 299(C).
    2. 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.
    3. 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.
    4. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.

    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. Emblemsvåg, Jan, 2022. "Wind energy is not sustainable when balanced by fossil energy," Applied Energy, Elsevier, vol. 305(C).
    2. Sajjad Haider & Peter Schegner, 2020. "Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid," Energies, MDPI, vol. 13(22), pages 1-19, November.
    3. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    4. Lange, Christopher & Rueß, Alexandra & Nuß, Andreas & Öchsner, Richard & März, Martin, 2020. "Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm," Applied Energy, Elsevier, vol. 280(C).
    5. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    6. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.
    7. Nieta, Agustín A. Sánchez de la & Ilieva, Iliana & Gibescu, Madeleine & Bremdal, Bernt & Simonsen, Stig & Gramme, Eivind, 2021. "Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration," Applied Energy, Elsevier, vol. 283(C).
    8. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    9. Okur, Özge & Voulis, Nina & Heijnen, Petra & Lukszo, Zofia, 2019. "Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation," Applied Energy, Elsevier, vol. 247(C), pages 426-437.
    10. Jefimowski, Włodzimierz & Szeląg, Adam & Steczek, Marcin & Nikitenko, Anatolii, 2020. "Vanadium redox flow battery parameters optimization in a transportation microgrid: A case study," Energy, Elsevier, vol. 195(C).
    11. Semmelmann, Leo & Hertel, Matthias & Kircher, Kevin J. & Mikut, Ralf & Hagenmeyer, Veit & Weinhardt, Christof, 2024. "The impact of heat pumps on day-ahead energy community load forecasting," Applied Energy, Elsevier, vol. 368(C).
    12. Bai, Bo & Lee, Henry & Shi, Yiwei & Wang, Zheng, 2024. "Integrating solar electricity into a fossil fueled system," Energy, Elsevier, vol. 304(C).
    13. Wang, Kaifeng & Ye, Lin & Yang, Shihui & Deng, Zhanfeng & Song, Jieying & Li, Zhuo & Zhao, Yongning, 2023. "A hierarchical dispatch strategy of hybrid energy storage system in internet data center with model predictive control," Applied Energy, Elsevier, vol. 331(C).
    14. Zhang, Xinshuo & Huang, Weibin & Chen, Shijun & Xie, Diya & Liu, Dexu & Ma, Guangwen, 2020. "Grid–source coordinated dispatching based on heterogeneous energy hybrid power generation," Energy, Elsevier, vol. 205(C).
    15. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Power system impacts of community acceptance policies for renewable energy deployment under storage cost uncertainty," Renewable Energy, Elsevier, vol. 156(C), pages 893-912.
    16. Sepúlveda-Mora, Sergio B. & Hegedus, Steven, 2021. "Making the case for time-of-use electric rates to boost the value of battery storage in commercial buildings with grid connected PV systems," Energy, Elsevier, vol. 218(C).
    17. Christensen, Paul A. & Anderson, Paul A. & Harper, Gavin D.J. & Lambert, Simon M. & Mrozik, Wojciech & Rajaeifar, Mohammad Ali & Wise, Malcolm S. & Heidrich, Oliver, 2021. "Risk management over the life cycle of lithium-ion batteries in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    18. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    19. Amirhossein Sajadi & Luka Strezoski & Vladimir Strezoski & Marija Prica & Kenneth A. Loparo, 2019. "Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(1), January.
    20. Kat, B. & Şahin, Ü. & Teimourzadeh, S. & Tör, O.B. & Voyvoda, E. & Yeldan, A.E., 2024. "A new energy-economy-environment modeling framework: Insights from decarbonization of the Turkish power Sector towards net-zero Emission targets," Energy, Elsevier, vol. 302(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:gam:jeners:v:11:y:2018:i:5:p:1242-:d:146116. 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.