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Systematic Literature Review of Heuristic-Optimized Microgrids and Energy-Flexible Factories

Author

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  • Johannes Prior

    (Chair of Production Systems, Faculty of Mechanical Engineering, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany)

  • Tobias Drees

    (Chair of Production Systems, Faculty of Mechanical Engineering, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany)

  • Michael Miro

    (Chair of Production Systems, Faculty of Mechanical Engineering, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany)

  • Bernd Kuhlenkötter

    (Chair of Production Systems, Faculty of Mechanical Engineering, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany)

Abstract

Decentralized renewable energy generation and consumption through microgrids, coupled with short- and long-term storage systems and enhanced demand flexibility, represent a promising strategy for mitigating grid stress and reducing emissions in the industrial sector. However, transitioning into a sustainable industry often poses challenges in terms of economic feasibility. This review surveys current optimization approaches and simulation functionalities to enhance feasibility. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, covering 1066 studies from 2016 to 2023 across three research areas: optimal system sizing of microgrids (OSS), optimization of electrical energy distribution to storage systems and consumers (EED), and energy flexibilization of factories (EF). As a result, 24 filtered sources from these areas were analyzed. Quantitative analysis indicated that evolutionary and swarm-inspired metaheuristics are predominantly applied in OSS, whereas exact linear problem solvers are favored for EED and EF optimization. A range of functionalities is available, and approaches often prioritize individual functionalities, such as load forecasting, dynamic electricity pricing, and statistical representation of energy generation, rather than comprehensively integrating them. Furthermore, no current approach simultaneously integrates optimization and simulation models across all three research areas.

Suggested Citation

  • Johannes Prior & Tobias Drees & Michael Miro & Bernd Kuhlenkötter, 2024. "Systematic Literature Review of Heuristic-Optimized Microgrids and Energy-Flexible Factories," Clean Technol., MDPI, vol. 6(3), pages 1-28, August.
    • Handle: RePEc:gam:jcltec:v:6:y:2024:i:3:p:55-1141:d:1461826
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    References listed on IDEAS

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    Cited by:

    1. Minghong Liu & Mengke Liao & Ruilong Zhang & Xin Yuan & Zhaoqun Zhu & Zhi Wu, 2025. "Quantum Computing as a Catalyst for Microgrid Management: Enhancing Decentralized Energy Systems Through Innovative Computational Techniques," Sustainability, MDPI, vol. 17(8), pages 1-27, April.
    2. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.

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