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Two-Stage Coordinated Operational Strategy for Distributed Energy Resources Considering Wind Power Curtailment Penalty Cost

Author

Listed:
  • Jing Qiu

    (Commonwealth Scientific and Industrial Research Organization (CSIRO), Mayfield West, Newcastle, NSW 2304, Australia)

  • Junhua Zhao

    (School of Science and Engineering, Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China)

  • Dongxiao Wang

    (Centre for Intelligent Electricity Networks, University of Newcastle, Newcastle, NSW 2308, Australia)

  • Yu Zheng

    (Electric Power Research Institute, CSG, Guangzhou 510080, China)

Abstract

The concept of virtual power plant (VPP) has been proposed to facilitate the integration of distributed renewable energy. VPP behaves similar to a single entity that aggregates a collection of distributed energy resources (DERs) such as distributed generators, storage devices, flexible loads, etc., so that the aggregated power outputs can be flexibly dispatched and traded in electricity markets. This paper presents an optimal scheduling model for VPP participating in day-ahead (DA) and real-time (RT) markets. In the DA market, VPP aims to maximize the expected profit and reduce the risk in relation to uncertainties. The risk is measured by a risk factor based on the mean-variance Markowitz theory. In the RT market, VPP aims to minimize the imbalance cost and wind power curtailment by adjusting the scheduling of DERs in its portfolio. In case studies, the benefits (e.g., surplus profit and reduced wind power curtailment) of aggregated VPP operation are assessed. Moreover, we have investigated how these benefits are affected by different risk-aversion levels and uncertainty levels. According to the simulation results, the aggregated VPP scheduling approach can effectively help the integration of wind power, mitigate the impact of uncertainties, and reduce the cost of risk-aversion.

Suggested Citation

  • Jing Qiu & Junhua Zhao & Dongxiao Wang & Yu Zheng, 2017. "Two-Stage Coordinated Operational Strategy for Distributed Energy Resources Considering Wind Power Curtailment Penalty Cost," Energies, MDPI, vol. 10(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:965-:d:104190
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    References listed on IDEAS

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    Cited by:

    1. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    2. 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.
    3. Mi Dong & Li Li & Lina Wang & Dongran Song & Zhangjie Liu & Xiaoyu Tian & Zhengguo Li & Yinghua Wang, 2018. "A Distributed Secondary Control Algorithm for Automatic Generation Control Considering EDP and Automatic Voltage Control in an AC Microgrid," Energies, MDPI, vol. 11(4), pages 1-18, April.
    4. Fan, Zhi-Ping & Cai, Siqin & Guo, Dongliang & Xu, Bo, 2022. "Facing the uncertainty of renewable energy production: Production decisions of a power plant with different risk attitudes," Renewable Energy, Elsevier, vol. 199(C), pages 1237-1247.

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