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Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid

Author

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  • Xiaogang Guo

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zhejing Bao

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zhijie Li

    (Shandong Longkou Electricity Supply Company, State Grid Corporation of China, Longkou 267000, China)

  • Wenjun Yan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

In this paper, an adaptively constrained stochastic model predictive control (MPC) is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs) in a microgrid (MG). Besides the economic objective of MG operation, the limits of state-of-charge (SOC) and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.

Suggested Citation

  • Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:243-:d:127827
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    References listed on IDEAS

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    1. Zhenya Ji & Xueliang Huang & Changfu Xu & Houtao Sun, 2016. "Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach," Energies, MDPI, vol. 9(11), pages 1-18, November.
    2. Seaseung Oh & Suyong Chae & Jason Neely & Jongbok Baek & Marvin Cook, 2017. "Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid," Energies, MDPI, vol. 10(7), pages 1-16, July.
    3. Young-Sik Jang & Mun-Kyeom Kim, 2017. "A Dynamic Economic Dispatch Model for Uncertain Power Demands in an Interconnected Microgrid," Energies, MDPI, vol. 10(3), pages 1-16, March.
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    Cited by:

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    2. Bao, Zhejing & Ye, Yangli & Liu, Ruijie & Cheng, Weidong & Zhao, Qiang & Wu, Ting, 2022. "Scheduling coordination of back pressure CHP coupled electricity-heat energy system with adaptive constraint strategy to accommodate uncertain wind power," Energy, Elsevier, vol. 240(C).
    3. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    4. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    5. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    6. Janko, Samantha & Johnson, Nathan G., 2020. "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks," Applied Energy, Elsevier, vol. 277(C).
    7. Changyu Zhou & Guohe Huang & Jiapei Chen, 2018. "A Multi-Objective Energy and Environmental Systems Planning Model: Management of Uncertainties and Risks for Shanxi Province, China," Energies, MDPI, vol. 11(10), pages 1-21, October.

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