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Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Author

Listed:
  • Zhongwen Li

    () (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chuanzhi Zang

    () (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

  • Peng Zeng

    () (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

  • Haibin Yu

    () (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

Abstract

Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.

Suggested Citation

  • Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, Open Access Journal, vol. 9(7), pages 1-16, June.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:499-:d:73088
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    References listed on IDEAS

    as
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Holjevac, Ninoslav & Capuder, Tomislav & Kuzle, Igor, 2015. "Adaptive control for evaluation of flexibility benefits in microgrid systems," Energy, Elsevier, vol. 92(P3), pages 487-504.
    3. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    4. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    5. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    6. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
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    Citations

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    Cited by:

    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, Open Access Journal, vol. 9(11), pages 1-18, November.
    2. repec:gam:jeners:v:11:y:2018:i:3:p:610-:d:135557 is not listed on IDEAS
    3. repec:gam:jeners:v:10:y:2017:i:12:p:1941-:d:120067 is not listed on IDEAS

    More about this item

    Keywords

    energy management; microgrid; recording horizon control; stochastic programming; uncertainty;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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