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Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

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  • Yu, Min Gyung
  • Huang, Bowen
  • Ma, Xu
  • Devaprasad, Karthik

Abstract

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing multiple objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generates optimal solutions, and forms the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to diverse strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of thermal and battery storage systems in buildings, providing valuable guidance to stakeholders.

Suggested Citation

  • Yu, Min Gyung & Huang, Bowen & Ma, Xu & Devaprasad, Karthik, 2024. "Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy," Applied Energy, Elsevier, vol. 372(C).
  • Handle: RePEc:eee:appene:v:372:y:2024:i:c:s0306261924012029
    DOI: 10.1016/j.apenergy.2024.123819
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    References listed on IDEAS

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    1. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang, 2019. "Flexible dispatch of a building energy system using building thermal storage and battery energy storage," Applied Energy, Elsevier, vol. 243(C), pages 274-287.
    2. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    3. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    4. Chen, Xi & Liu, Zhongbing & Wang, Pengcheng & Li, Benjia & Liu, Ruimiao & Zhang, Ling & Zhao, Chengliang & Luo, Songqin, 2023. "Multi-objective optimization of battery capacity of grid-connected PV-BESS system in hybrid building energy sharing community considering time-of-use tariff," Applied Energy, Elsevier, vol. 350(C).
    5. Shah, Sheikh Khaleduzzaman & Aye, Lu & Rismanchi, Behzad, 2020. "Multi-objective optimisation of a seasonal solar thermal energy storage system for space heating in cold climate," Applied Energy, Elsevier, vol. 268(C).
    6. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Bre, Facundo & Lamberts, Roberto & Flores-Larsen, Silvana & Koenders, Eduardus A.B., 2023. "Multi-objective optimization of latent energy storage in buildings by using phase change materials with different melting temperatures," Applied Energy, Elsevier, vol. 336(C).
    8. Squalli, Jay, 2017. "Renewable energy, coal as a baseload power source, and greenhouse gas emissions: Evidence from U.S. state-level data," Energy, Elsevier, vol. 127(C), pages 479-488.
    9. Yu, Min Gyung & Pavlak, Gregory S., 2021. "Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems," Applied Energy, Elsevier, vol. 282(PB).
    Full references (including those not matched with items on IDEAS)

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