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Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk

Author

Listed:
  • Shiduo Jia

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

  • Xiaoning Kang

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

Abstract

A combined heating and power (CHP) microgrid has high flexibility and economy, but the output of renewable energy is uncertain. Meanwhile, excessive flexible load adjustment in the demand response process will increase user dissatisfaction. In order to solve the above problems, this paper quantifies uncertainty with the conditional value-at-risk (CVaR) of relative disturbance. Additionally, a multi-objective optimal scheduling model that takes into account both the operating economy and the demand-side power consumption satisfaction is established. In order to solve the multi-objective mixed-integer nonlinear programming problem well, we propose an improved sparrow search algorithm (ISSA), which solves the problem that the sparrow search algorithm (SSA) is prone to low accuracy, insufficient in population diversity and easy to be trapped in local optimum. Combined with the non-dominated solution ranking method, ISSA has the ability of multi-objective optimization. Finally, simulation on a typical CHP microgrid is performed. The optimization results under different confidence levels and risk preference coefficients are compared and analyzed. When the risk preference coefficient is 0.1, 2 and 5, the minimum rotating reserve capacity is 75.17 kW, 82.83 kW, and 105.70 kW in the electric part and 40.08 kW, 59.89 kW, and 61.94 kW in the thermal part. The effectiveness of the proposed CVaR of relative disturbance is verified.

Suggested Citation

  • Shiduo Jia & Xiaoning Kang, 2022. "Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk," Energies, MDPI, vol. 15(9), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3394-:d:809667
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    References listed on IDEAS

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    1. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Ghoreishi, Niloofar & Catalão, João P.S., 2020. "Optimal power management of dependent microgrid considering distribution market and unused power capacity," Energy, Elsevier, vol. 200(C).
    2. Jun Dong & Yaoyu Zhang & Yuanyuan Wang & Yao Liu, 2021. "A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    3. Bo Hu & Nan Wang & Zaiming Yu & Yunqing Cao & Dongsheng Yang & Li Sun, 2021. "Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory," Energies, MDPI, vol. 15(1), pages 1-21, December.
    4. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response," Energy, Elsevier, vol. 239(PA).
    5. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    6. Sergio Bruno & Gabriella Dellino & Massimo La Scala & Carlo Meloni, 2019. "A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †," Energies, MDPI, vol. 12(6), pages 1-20, March.
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    Cited by:

    1. Shiduo Jia & Xiaoning Kang & Jinxu Cui & Bowen Tian & Shuwen Xiao, 2022. "Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles," Energies, MDPI, vol. 15(15), pages 1-23, July.
    2. Seyed Hasan Mirbarati & Najme Heidari & Amirhossein Nikoofard & Mir Sayed Shah Danish & Mahdi Khosravy, 2022. "Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

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