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Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration

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  • Ji, Ling
  • Huang, Guohe
  • Xie, Yulei
  • Zhou, Yong
  • Zhou, Jifang

Abstract

With the deregulation of electricity market and the penetration of renewable energy, microgrid system operators may encounter more difficulties in operation management when facing complex economic, technological, and political uncertainties. In this paper, a robust cost-risk tradeoff model is developed for day-ahead schedule optimization in residential microgrid system under uncertainties. This method is an integration of inexact two-stage stochastic programming and worst-case conditional value-at-risk theory, and could handle uncertainties with inexact or partly known probability distribution information. Besides, by introducing the financial risk measurement, it could also hedge against the worst-case scenario caused by multiple independent uncertainties. The proposed model was applied to a hypothetical residential microgrid system with combined heat and power generation for obtaining optimal day-ahead schedule strategies under variable conditions with respect to renewable energy generation, power demand, and electricity market price. The obtained solutions demonstrate that the proposed model could reflect better tradeoff information between economic operation and stable performance according to different risk-aversion attitudes. In general, more conservative risk attitude would be coupled with higher system cost, which implies higher system stability is at the expense of the economic costs. The developed robust cost-risk tradeoff method would be expected to have a potential for wide applications.

Suggested Citation

  • Ji, Ling & Huang, Guohe & Xie, Yulei & Zhou, Yong & Zhou, Jifang, 2018. "Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration," Energy, Elsevier, vol. 153(C), pages 324-337.
    • Handle: RePEc:eee:energy:v:153:y:2018:i:c:p:324-337
    DOI: 10.1016/j.energy.2018.04.037
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    Cited by:

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    3. Yuwei Wang & Yuanjuan Yang & Liu Tang & Wei Sun & Huiru Zhao, 2019. "A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs," Energies, MDPI, vol. 12(20), pages 1-33, October.
    4. Mohammadpour Shotorbani, Amin & Zeinal-Kheiri, Sevda & Chhipi-Shrestha, Gyan & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid," Applied Energy, Elsevier, vol. 304(C).
    5. Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Paschalis A. Gkaidatzis & Athanasios Tryferidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration," Energies, MDPI, vol. 14(10), pages 1-19, May.
    6. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2019. "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Elsevier, vol. 175(C), pages 879-890.
    7. Zhu, Junjie & Huang, Shengjun & Liu, Yajie & Lei, Hongtao & Sang, Bo, 2021. "Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization," Energy, Elsevier, vol. 216(C).
    8. Whei-Min Lin & Chung-Yuen Yang & Chia-Sheng Tu & Ming-Tang Tsai, 2018. "An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment," Energies, MDPI, vol. 11(6), pages 1-17, June.
    9. Liu, Yangyang & Shen, Zhongqi & Tang, Xiaowei & Lian, Hongbo & Li, Jiarui & Gong, Jinxia, 2019. "Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties," Applied Energy, Elsevier, vol. 256(C).
    10. Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    11. Ruixin Gou & Guiping He & Bo Yu & Yanli Xiao & Zhiwei Luo & Yulei Xie, 2022. "An Integrated Energy System Operation Optimization Model for Water Consumption Control Analysis in Park Scale from the Perspective of Energy–Water Nexus," Energies, MDPI, vol. 15(12), pages 1-12, June.
    12. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    13. 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.

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