Modelling and Degradation Cost Assessment of Batteries in Capacity Markets

  • Ahmed Gailani

    Student thesis: Doctoral Thesis


    Climate change concerns are encouraging the international community to adopt policies to decarbonise the energy system by increasing the reliance on renewable energy sources (RES). Battery energy storage (BES) is widely recognised as a key to resolving RES’s intermittency by providing several grid services. However, large-scale deployment of BES on EU energy networks is hindered due to regulatory and market barriers that prevent it from stacking multiple services across different markets. Nevertheless, there is a lack of understanding of the impact of battery degradation on its business case. This thesis aims to inform the integration of BES in electricity networks considering the impact of battery degradation on battery revenue when providing power/energy services in the capacity market (CM).
    The inaccurate assessment of battery degradation when providing CM services may result in: i) choosing a less-profitable battery de-rating factor; ii) increasing the exposure to penalties; iii) increasing the maintenance cost for the battery owner; and iv) affecting energy security in decentralised local energy networks. This work contributes to solving these issues by accurately modelling battery degradation and consider battery degradation cost as a deciding factor in choosing de-rating factors in the CM. Accurate physics-based battery models (PBMs) are formulated and validated to quantify the degradation costs for batteries in the CM. The model improves on the previous techno-economic studies by formulating a pseudo-two-dimensional (p2d) PBM coupled with a degradation model representing three lithium-ion battery degradation mechanisms. Therefore, battery profitability in the CM can be assessed in advance of participating in the CM auction leading to maximise revenue, minimise cost and time.
    The key findings of this thesis are: i) battery degradation cost can significantly impact the overall revenue in Great Britain’s CM, affecting the profitability for all the de-rated batteries; and ii) battery PBMs can accurately predict calendar and cycle degradation battery conditions for a wide range of battery temperatures (from 5C ̶ 45C) and SoC levels (20 ̶ 100%) compared to other models substantiating the business case for the batteries in the CM. For instance, the profit for the 1h de-rated battery at 25C can reach nearly £3738 using the PBMs compared to £2550 using the empirical model at the end of 1-year CM contract.
    Date of Award11 Nov 2022
    Original languageEnglish
    Awarding Institution
    • Teesside University
    SupervisorTracey Crosbie (Supervisor), Maher Al-Greer (Supervisor) & Michael Short (Supervisor)

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