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Control of an isolated microgrid using hierarchical economic model predictive control

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  • Clarke, Will Challis
  • Brear, Michael John
  • Manzie, Chris

Abstract

This article experimentally demonstrates a novel, microgrid control algorithm based on a two-layer economic model predictive control framework that was previously developed by the authors. This algorithm is applied to an isolated microgrid with a solar photovoltaic system, a battery bank and a gasoline-fuelled generator. The control system performance is experimentally compared to a baseline algorithm over 5 min and 10 h periods, while an experimentally validated model is used to compare performance over a full year. The results indicate that applying the proposed, two-layer economic model predictive control algorithm can reduce operating costs and CO2 emissions by 5%–10% relative to conventional, rule based methods, and by 10%–15% if improved solar and demand forecasts are available. Furthermore, the proposed two-level algorithm can achieve reductions of up to 5% compared with current state-of-the-art methods which only attempt to optimize performance in the energy management system.

Suggested Citation

  • Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314148
    DOI: 10.1016/j.apenergy.2020.115960
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    References listed on IDEAS

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    Cited by:

    1. Restrepo, Mauricio & Cañizares, Claudio A. & Simpson-Porco, John W. & Su, Peter & Taruc, John, 2021. "Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility," Applied Energy, Elsevier, vol. 290(C).
    2. Ouédraogo, S. & Faggianelli, G.A. & Notton, G. & Duchaud, J.L. & Voyant, C., 2022. "Impact of electricity tariffs and energy management strategies on PV/Battery microgrid performances," Renewable Energy, Elsevier, vol. 199(C), pages 816-825.
    3. Kaluthanthrige, Roshani & Rajapakse, Athula D., 2021. "Evaluation of hierarchical controls to manage power, energy and daily operation of remote off-grid power systems," Applied Energy, Elsevier, vol. 299(C).
    4. Zhu, Zheng & Liu, Xiangjie & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y. & Xu, Yuping, 2024. "PV/Hydrogen DC microgrid control using distributed economic model predictive control," Renewable Energy, Elsevier, vol. 222(C).
    5. Abulanwar, Sayed & Ghanem, Abdelhady & Rizk, Mohammad E.M. & Hu, Weihao, 2021. "Adaptive synergistic control strategy for a hybrid AC/DC microgrid during normal operation and contingencies," Applied Energy, Elsevier, vol. 304(C).
    6. Maślak, Grzegorz & Orłowski, Przemysław, 2024. "Operational optimisation of a microgrid using non-stationary hybrid switched model predictive control with virtual storage-based demand management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    7. Luis Santiago Azuara-Grande & Santiago Arnaltes & Jaime Alonso-Martinez & Jose Luis Rodriguez-Amenedo, 2021. "Comparison of Two Energy Management System Strategies for Real-Time Operation of Isolated Hybrid Microgrids," Energies, MDPI, vol. 14(20), pages 1-15, October.

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