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

Author

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  • 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
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    References listed on IDEAS

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    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.

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