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Decision tree-based optimization for flexibility management for sustainable energy microgrids

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  • Huo, Yuchong
  • Bouffard, François
  • Joós, Géza

Abstract

In this paper, we apply a flexibility based operational planning paradigm to microgrid energy dispatch. The classic energy dispatch problem with energy storage and dispatchable thermal generation assets requires the solution of mixed-integer optimization problems. Such approaches are not amenable to most remote microgrids and practical field microgrid implementations, where controls are rule-based and typically implemented by programmable logic controllers. Albeit such rule-based dispatch controls are always feasible, they cannot optimize fully over the availability of renewable generation and asset capacities of microgrids, especially energy storage. In this paper we propose a systematic method to generate the microgrid dispatch rule base with the objective of matching as much as possible the control performance obtained by full mixed-integer optimization. To achieve this we develop a rigorous control mapping method based on decision trees. The numerical results demonstrate that the decision tree-based dispatch strategy can provide feasible and near optimal dispatch decisions for microgrids. Its computational efficiency is very high, a feature promising for real-time in-field implementation.

Suggested Citation

  • Huo, Yuchong & Bouffard, François & Joós, Géza, 2021. "Decision tree-based optimization for flexibility management for sustainable energy microgrids," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002774
    DOI: 10.1016/j.apenergy.2021.116772
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    References listed on IDEAS

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    1. Holjevac, Ninoslav & Capuder, Tomislav & Zhang, Ning & Kuzle, Igor & Kang, Chongqing, 2017. "Corrective receding horizon scheduling of flexible distributed multi-energy microgrids," Applied Energy, Elsevier, vol. 207(C), pages 176-194.
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    3. Quashie, Mike & Bouffard, François & Joós, Géza, 2017. "Business cases for isolated and grid connected microgrids: Methodology and applications," Applied Energy, Elsevier, vol. 205(C), pages 105-115.
    4. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Meng, He & Li, Peng & Yu, Li, 2018. "Distributed voltage control for active distribution networks based on distribution phasor measurement units," Applied Energy, Elsevier, vol. 229(C), pages 804-813.
    5. Moutis, Panayiotis & Skarvelis-Kazakos, Spyros & Brucoli, Maria, 2016. "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, Elsevier, vol. 161(C), pages 197-205.
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    Cited by:

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    2. Anastasia Ioannou & Gioia Falcone & Christina Baisch & Georgie Friederichs & Jan Hildebrand, 2023. "A Decision Support Tool for Social Engagement, Alternative Financing and Risk Mitigation of Geothermal Energy Projects," Energies, MDPI, vol. 16(3), pages 1-25, January.
    3. Huang, Guizao & Wu, Guangning & Yang, Zefeng & Chen, Xing & Wei, Wenfu, 2023. "Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning," Applied Energy, Elsevier, vol. 333(C).
    4. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    5. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    6. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    7. João Faria & Carlos Marques & José Pombo & Sílvio Mariano & Maria do Rosário Calado, 2023. "Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach," Energies, MDPI, vol. 16(21), pages 1-33, October.

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