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Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

Author

Listed:
  • Jia Ning

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Sipeng Hao

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Aidong Zeng

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Bin Chen

    (State Grid Changzhou Power Supply Company, Changzhou 213003, China)

  • Yi Tang

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

Abstract

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

Suggested Citation

  • Jia Ning & Sipeng Hao & Aidong Zeng & Bin Chen & Yi Tang, 2021. "Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3400-:d:520235
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    References listed on IDEAS

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    1. Jingshuang Shen & Chuanwen Jiang & Bosong Li, 2015. "Controllable Load Management Approaches in Smart Grids," Energies, MDPI, vol. 8(10), pages 1-16, October.
    2. Jia Ning & Yi Tang & Qian Chen & Jianming Wang & Jianhua Zhou & Bingtuan Gao, 2017. "A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential," Energies, MDPI, vol. 10(4), pages 1-16, April.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, September.
    4. Jun Dong & Shilin Nie & Hui Huang & Peiwen Yang & Anyuan Fu & Jin Lin, 2019. "Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    5. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    6. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
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    1. Jiajie Tang & Jie Zhao & Hongliang Zou & Gaoyuan Ma & Jun Wu & Xu Jiang & Huaixun Zhang, 2021. "Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM," Sustainability, MDPI, vol. 13(19), pages 1-20, September.

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