IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i14p3371-d1431873.html
   My bibliography  Save this article

Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives

Author

Listed:
  • Maria Ferrara

    (Department of Energy, TEBE Research Group, Politecnico di Torino, 10129 Turin, Italy)

  • Matteo Bilardo

    (Department of Energy, TEBE Research Group, Politecnico di Torino, 10129 Turin, Italy)

  • Dragos-Ioan Bogatu

    (International Centre for Indoor Environment and Energy—ICIEE, Department of Environmental and Resource Engineering, Technical University of Denmark, Kgs. Lyngby, 2800 Lyngby, Denmark)

  • Doyun Lee

    (Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan)

  • Mahmood Khatibi

    (Department of the Built Environment, Aalborg University Copenhagen, 2450 København, Denmark)

  • Samira Rahnama

    (Department of the Built Environment, Aalborg University Copenhagen, 2450 København, Denmark)

  • Jun Shinoda

    (International Centre for Indoor Environment and Energy—ICIEE, Department of Environmental and Resource Engineering, Technical University of Denmark, Kgs. Lyngby, 2800 Lyngby, Denmark)

  • Ying Sun

    (School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China
    Energy and Environment Group, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Yongjun Sun

    (Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong)

  • Alireza Afshari

    (Department of the Built Environment, Aalborg University Copenhagen, 2450 København, Denmark)

  • Fariborz Haghighat

    (Energy and Environment Group, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Ongun B. Kazanci

    (International Centre for Indoor Environment and Energy—ICIEE, Department of Environmental and Resource Engineering, Technical University of Denmark, Kgs. Lyngby, 2800 Lyngby, Denmark)

  • Ryozo Ooka

    (Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan)

  • Enrico Fabrizio

    (Department of Energy, TEBE Research Group, Politecnico di Torino, 10129 Turin, Italy)

Abstract

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.

Suggested Citation

  • Maria Ferrara & Matteo Bilardo & Dragos-Ioan Bogatu & Doyun Lee & Mahmood Khatibi & Samira Rahnama & Jun Shinoda & Ying Sun & Yongjun Sun & Alireza Afshari & Fariborz Haghighat & Ongun B. Kazanci & Ry, 2024. "Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives," Energies, MDPI, vol. 17(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3371-:d:1431873
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/14/3371/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/14/3371/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barthwal, Mohit & Dhar, Atul & Powar, Satvasheel, 2021. "The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications," Applied Energy, Elsevier, vol. 283(C).
    2. Meinrenken, Christoph J. & Mehmani, Ali, 2019. "Concurrent optimization of thermal and electric storage in commercial buildings to reduce operating cost and demand peaks under time-of-use tariffs," Applied Energy, Elsevier, vol. 254(C).
    3. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    4. Gallardo, Andres & Berardi, Umberto, 2021. "Design and control of radiant ceiling panels incorporating phase change materials for cooling applications," Applied Energy, Elsevier, vol. 304(C).
    5. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).
    6. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
    7. Le, Khoa Xuan & Huang, Ming Jun & Shah, Nikhilkumar N. & Wilson, Christopher & Artain, Paul Mac & Byrne, Raymond & Hewitt, Neil J., 2019. "Techno-economic assessment of cascade air-to-water heat pump retrofitted into residential buildings using experimentally validated simulations," Applied Energy, Elsevier, vol. 250(C), pages 633-652.
    8. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    9. Bilardo, Matteo & Fraisse, Gilles & Pailha, Mickael & Fabrizio, Enrico, 2020. "Design and experimental analysis of an Integral Collector Storage (ICS) prototype for DHW production," Applied Energy, Elsevier, vol. 259(C).
    10. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    11. Hu, Yue & Guo, Rui & Heiselberg, Per Kvols, 2020. "Performance and control strategy development of a PCM enhanced ventilated window system by a combined experimental and numerical study," Renewable Energy, Elsevier, vol. 155(C), pages 134-152.
    12. Kuczyński, T. & Staszczuk, A., 2020. "Experimental study of the influence of thermal mass on thermal comfort and cooling energy demand in residential buildings," Energy, Elsevier, vol. 195(C).
    13. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    3. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    4. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    7. Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
    8. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    9. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).
    10. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    11. Hawks, M.A. & Cho, S., 2024. "Review and analysis of current solutions and trends for zero energy building (ZEB) thermal systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    12. Vivek Aggarwal & Chandan Swaroop Meena & Ashok Kumar & Tabish Alam & Anuj Kumar & Arijit Ghosh & Aritra Ghosh, 2020. "Potential and Future Prospects of Geothermal Energy in Space Conditioning of Buildings: India and Worldwide Review," Sustainability, MDPI, vol. 12(20), pages 1-19, October.
    13. Kung, Kevin S. & Thengane, Sonal K. & Ghoniem, Ahmed F. & Lim, C. Jim & Cao, Yankai & Sokhansanj, Shahabaddine, 2022. "Start-up, shutdown, and transition timescale analysis in biomass reactor operations," Energy, Elsevier, vol. 248(C).
    14. Omar Montero & Pauline Brischoux & Simon Callegari & Carolina Fraga & Matthias Rüetschi & Edouard Vionnet & Nicole Calame & Fabrice Rognon & Martin Patel & Pierre Hollmuller, 2022. "Large Air-to-Water Heat Pumps for Fuel-Boiler Substitution in Non-Retrofitted Multi-Family Buildings—Energy Performance, CO 2 Savings, and Lessons Learned in Actual Conditions of Use," Energies, MDPI, vol. 15(14), pages 1-29, July.
    15. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    16. Hua Jin & Gang Meng & Yuanzhi Pan & Xing Zhang & Changda Wang, 2022. "An Improved Intelligent Control System for Temperature and Humidity in a Pig House," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    17. Chen, Zhidong & Su, Chao & Wu, Zexuan & Wang, Weijia & Chen, Lei & Yang, Lijun & Kong, Yanqiang & Du, Xiaoze, 2023. "Operation strategy and performance analyses of a distributed energy system incorporating concentrating PV/T and air source heat pump for heating supply," Applied Energy, Elsevier, vol. 341(C).
    18. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    19. Zhao, Yaohua & Liu, Zichu & Quan, Zhenhua & Jing, Heran & Yang, Mingguang, 2022. "Experimental investigation and multi-objective optimization of ice thermal storage device with multichannel flat tube," Renewable Energy, Elsevier, vol. 195(C), pages 28-46.
    20. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3371-:d:1431873. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.