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A Remedial Strategic Scheduling Model for Load Serving Entities Considering the Interaction between Grid-Level Energy Storage and Virtual Power Plants

Author

Listed:
  • Haiteng Han

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Hantao Cui

    (Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN 37996, USA)

  • Shan Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Qingxin Shi

    (Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN 37996, USA)

  • Anjie Fan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Chen Wu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
    State Grid Jiangsu Electric Power Co., Ltd. Economic Research Institute, Nanjing 210008, China)

Abstract

More renewable energy resources have been connected to the grid with the promotion of global energy strategies, which presents new opportunities for the current electricity market. However, the growing integration of renewable energy also brings more challenges, such as power system reliability and the participants’ marketable behavior. Thus, how to coordinate integrated renewable resources in the electricity market environment has gained increasing interest. In this paper, a bilevel bidding model for load serving entities (LSEs) considering grid-level energy storage (ES) and virtual power plant (VPP) is established in the day-ahead (DA) market. Then, the model is extended by considering contingencies in the intraday (ID) market. Also, according to the extended bidding model, a remedial strategic rescheduling approach for LSE’s daily profit is proposed. It provides a quantitative assessment of LSE’s loss reduction based on contingency forecasting, which can be applied to the power system dispatch to help LSEs deal with coming contingencies. Simulation results verify the correctness and effectiveness of the proposed method.

Suggested Citation

  • Haiteng Han & Hantao Cui & Shan Gao & Qingxin Shi & Anjie Fan & Chen Wu, 2018. "A Remedial Strategic Scheduling Model for Load Serving Entities Considering the Interaction between Grid-Level Energy Storage and Virtual Power Plants," Energies, MDPI, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2420-:d:169447
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    References listed on IDEAS

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    1. Rakkyung Ko & Daeyoung Kang & Sung-Kwan Joo, 2019. "Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. Nasiri, Nima & Mansour Saatloo, Amin & Mirzaei, Mohammad Amin & Ravadanegh, Sajad Najafi & Zare, Kazem & Mohammadi-ivatloo, Behnam & Marzband, Mousa, 2023. "A robust bi-level optimization framework for participation of multi-energy service providers in integrated power and natural gas markets," Applied Energy, Elsevier, vol. 340(C).
    3. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.

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