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Aging-aware predictive control of PV-battery assets in buildings

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  • Cai, Jie
  • Zhang, Hao
  • Jin, Xing

Abstract

This paper presents an aging-aware model predictive control (MPC) approach for operation of sustainable buildings with on-site photovoltaic (PV) and battery systems. On-site batteries can be used to collect excess PV power during low demand hours and to discharge when PV generation is not enough to meet loads. To further increase the value of the PV-battery assets, batteries can be proactively charged/discharged to shed peak demands for utility cost reduction. The battery life span is highly dependent on the operation strategy and aggressive charge/discharge actions lead to faster battery capacity degradation and higher overall operation cost due to more frequent replacement. The control strategy developed in this study incorporates a convex battery capacity loss model, derived from a physically-based degradation model, to capture the battery aging cost in the control optimization. The degradation model used in the control optimization consists of a set of convex and piece-wise linear sub-models to approximate the battery degradation effect at different time instances. A convex MPC problem is solved in the proposed control strategy to seek the optimal balance between the building utility cost and the battery life-cycle cost. Whole month simulation tests were carried out for a typical office building with on-site PV and battery assets. To quantify the benefits of the proposed control approach, three baseline strategies were also simulated that represent the best current practices. Test results showed that conventional battery control strategies seeking minimum utility cost could double the battery degradation rate and lead to a significant increase in the battery life-cycle cost. The proposed strategy could foresee the tradeoff between the utility cost and battery aging cost and make decisions accordingly. It was demonstrated that the proposed control solution could reduce the utility cost by 9% with moderate battery degradation increase, leading to overall building operation cost reduction up to 4%.

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  • Cai, Jie & Zhang, Hao & Jin, Xing, 2019. "Aging-aware predictive control of PV-battery assets in buildings," Applied Energy, Elsevier, vol. 236(C), pages 478-488.
    • Handle: RePEc:eee:appene:v:236:y:2019:i:c:p:478-488
    DOI: 10.1016/j.apenergy.2018.12.003
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    Cited by:

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    4. Kneiske, T.M. & Niedermeyer, F. & Boelling, C., 2019. "Testing a model predictive control algorithm for a PV-CHP hybrid system on a laboratory test-bench," Applied Energy, Elsevier, vol. 242(C), pages 121-137.
    5. Yazhou Zhao & Xiangxi Qin & Xiangyu Shi, 2022. "A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm," Sustainability, MDPI, vol. 14(14), pages 1-34, July.
    6. Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
    7. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    8. Hlanze, Philani & Jiang, Zhimin & Cai, Jie & Shen, Bo, 2023. "Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings," Applied Energy, Elsevier, vol. 336(C).
    9. Steffen Limmer & Nils Einecke, 2022. "An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge," Energies, MDPI, vol. 15(10), pages 1-23, May.
    10. Sarah Ouédraogo & Ghjuvan Antone Faggianelli & Guillaume Pigelet & Jean Laurent Duchaud & Gilles Notton, 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island," Energies, MDPI, vol. 13(17), pages 1-20, September.
    11. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2019. "Optimal operational planning of scalable DC microgrid with demand response, islanding, and battery degradation cost considerations," Applied Energy, Elsevier, vol. 237(C), pages 695-707.
    12. Song, Aoye & Zhou, Yuekuan, 2023. "A hierarchical control with thermal and electrical synergies on battery cycling ageing and energy flexibility in a multi-energy sharing network," Renewable Energy, Elsevier, vol. 212(C), pages 1020-1037.
    13. Liu, Jia & Chen, Xi & Yang, Hongxing & Li, Yutong, 2020. "Energy storage and management system design optimization for a photovoltaic integrated low-energy building," Energy, Elsevier, vol. 190(C).
    14. Ollas, Patrik & Sigarchian, Sara Ghaem & Alfredsson, Hampus & Leijon, Jennifer & Döhler, Jessica Santos & Aalhuizen, Christoffer & Thiringer, Torbjörn & Thomas, Karin, 2023. "Evaluating the role of solar photovoltaic and battery storage in supporting electric aviation and vehicle infrastructure at Visby Airport," Applied Energy, Elsevier, vol. 352(C).
    15. Mishra, Partha Pratim & Latif, Aadil & Emmanuel, Michael & Shi, Ying & McKenna, Killian & Smith, Kandler & Nagarajan, Adarsh, 2020. "Analysis of degradation in residential battery energy storage systems for rate-based use-cases," Applied Energy, Elsevier, vol. 264(C).

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