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Integrating flexible demand response toward available transfer capability enhancement

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  • Hou, Lingxi
  • Li, Weiqi
  • Zhou, Kui
  • Jiang, Qirong

Abstract

Available transfer capability (ATC) has been widely adopted as a crucial measure to guarantee free and reliable power trading. The existing literature generally evaluates the ATC of a power grid considering inelastic load. However, the impacts of responsive load have not yet been systematically explored, which underestimates the ability of demand side resources in ATC enhancement. Therefore, we propose a two-stage ATC evaluation framework in this paper integrating flexible demand response (FDR). In the first stage, i.e., a day-ahead market, a demand side bidding scheme is developed in which electricity users can make flexible choices to participate in demand response (DR) programs according to their preferences. In this paper, FDR is categorized into three typical patterns: (i) deferrable DR, (ii) switchable DR and (iii) adjustable DR. The objective is to minimize the total generation and dispatch costs, while respecting the constraints for FDR, the minimal accommodation of renewable energy, the inter-area power trading, etc. Then based on generation and demand side bidding, a day-ahead unit commitment model is formulated to schedule thermal generators, interchange power and FDR. In the second stage, a real-time ATC evaluation model based on the aforementioned unit commitment results is proposed to quantify the contributions of FDR on ATC enhancement. Case studies based on IEEE 14-bus system and a provincial power grid in China demonstrate that FDR can effectively improve real-time ATC by load shifting and peak shaving, and facilitate the accommodation of renewable energy.

Suggested Citation

  • Hou, Lingxi & Li, Weiqi & Zhou, Kui & Jiang, Qirong, 2019. "Integrating flexible demand response toward available transfer capability enhancement," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:98
    DOI: 10.1016/j.apenergy.2019.113370
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    References listed on IDEAS

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    1. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    2. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    3. Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan & Sun, Yonghui & Zang, Haixiang, 2016. "Probabilistic available transfer capability calculation considering static security constraints and uncertainties of electricity–gas integrated energy systems," Applied Energy, Elsevier, vol. 167(C), pages 305-316.
    4. Xiong, Yingqi & Wang, Bin & Chu, Chi-cheng & Gadh, Rajit, 2018. "Vehicle grid integration for demand response with mixture user model and decentralized optimization," Applied Energy, Elsevier, vol. 231(C), pages 481-493.
    5. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    6. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    7. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
    8. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    9. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    10. Wang, Jianxiao & Zhong, Haiwang & Lai, Xiaowen & Xia, Qing & Shu, Chang & Kang, Chongqing, 2017. "Distributed real-time demand response based on Lagrangian multiplier optimal selection approach," Applied Energy, Elsevier, vol. 190(C), pages 949-959.
    11. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
    12. Yang, Ting & Zhao, Yingjie & Pen, Haibo & Wang, Zhaoxia, 2018. "Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation," Applied Energy, Elsevier, vol. 231(C), pages 277-287.
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    Cited by:

    1. Yuwei Zhang & Wenying Liu & Yue Huan & Qiang Zhou & Ningbo Wang, 2020. "An Optimal Day-Ahead Thermal Generation Scheduling Method to Enhance Total Transfer Capability for the Sending-Side System with Large-Scale Wind Power Integration," Energies, MDPI, vol. 13(9), pages 1-19, May.
    2. Jiang, Tao & Li, Xue & Kou, Xiao & Zhang, Rufeng & Tian, Guoda & Li, Fangxing, 2022. "Available transfer capability evaluation in electricity-dominated integrated hybrid energy systems with uncertain wind power: An interval optimization solution," Applied Energy, Elsevier, vol. 314(C).
    3. Zhu, Yansong & Liu, Jizhen & Hu, Yong & Xie, Yan & Zeng, Deliang & Li, Ruilian, 2024. "Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy," Energy, Elsevier, vol. 288(C).
    4. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    5. Dan Zhou & Qi Zhang & Yangqing Dan & Fanghong Guo & Jun Qi & Chenyuan Teng & Wenwei Zhou & Haonan Zhu, 2022. "Research on Renewable-Energy Accommodation-Capability Evaluation Based on Time-Series Production Simulations," Energies, MDPI, vol. 15(19), pages 1-15, September.
    6. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    7. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    8. Dai, Xuemei & Li, Yaping & Zhang, Kaifeng & Feng, Wei, 2020. "A robust offering strategy for wind producers considering uncertainties of demand response and wind power," Applied Energy, Elsevier, vol. 279(C).

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