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A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making

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  • Zhou, Yuekuan

Abstract

District building-vehicle interactive energy network with systematic energy interactions can improve techno-economic performances and enhance cleaner power penetration, whereas the battery cycling aging will be accelerated due to an increase in frequency of charging/discharging cycles. In this study, a dynamic self-learning grid-responsive strategy was proposed to improve techno-economic performances, energy flexibility, and the relative capacity (RC) of electrochemical batteries, through dynamically adjustable off-peak grid charging power. A collaborative framework on an energy district with building-integrated photovoltaics and electric vehicles has been formulated, through synergic energy interactions and integrations. Cycling aging of both static batteries in buildings and mobile vehicle batteries has been considered. In respect to intrinsic contradiction characteristics of each objective, trade-off solutions have been proposed through multi-objective optimisation. Furthermore, regarding the multiple optimal solutions along the Pareto front, a weighted Eulerian distance-based methodology was adopted to identify the ‘best of best’ solution and then assist multi-criteria decision-making. Research results show that, with the proposed dynamic self-learning grid-responsive strategy, the grid can provide a more flexible grid-to-battery charging power with a higher RC of batteries, compared to the strategy with dynamic renewable-demand signals. Through the multi-objective optimisation, the import cost can be reduced from 213.2 to 203.8 HK$/m2·a, and the RC can be improved from 0.800 to 0.999. This study also demonstrates a weighted Eulerian distance-based methodology to identify the ‘best of best’ solution to multi-criteria decision-makers. Research results are useful to promote district interactive energy networks in smart cities, with multivariant optimal design, Pareto optimal solutions, and weighted Eulerian distance-based ‘best of best’ solution.

Suggested Citation

  • Zhou, Yuekuan, 2023. "A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making," Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:energy:v:266:y:2023:i:c:s0360544222032832
    DOI: 10.1016/j.energy.2022.126397
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    References listed on IDEAS

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    Cited by:

    1. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(C).
    2. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    3. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Zhou, Yuekuan & Mansouri, Seyed Amir & Jurado, Francisco, 2024. "Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations," Renewable Energy, Elsevier, vol. 225(C).

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