Lifetime Profit-Maximising Co-optimisation of Multi-Service Stacking for Battery Storage
Abstract
Grid-scale battery energy storage can generate revenue by stacking services across electricity and frequency response markets, yet identifying the lifetime profit-maximising stacking strategy remains challenging. Decisions across services are coupled through shared battery system capacity, constrained by system operator energy management rules, and further shaped by product-specific technical requirements that govern system operation, degradation, and lifetime profitability. This paper presents an ageing-aware receding-horizon framework for co-optimising multi-service stacking that explicitly captures product-specific characteristics and state-of-energy compliance rules. The framework is applied to the Great Britain market, where storage operators can stack electricity trading with multiple dynamic frequency response services procured through the newly introduced 'Enduring Auction Capability' platform under energy management requirements imposed by the National Energy System Operator. Using real market data, we demonstrate that degradation modelling, discount rate, and battery ageing jointly govern both lifetime value and optimal stacking strategy. Accounting for ageing increases lifetime revenue by up to 32% relative to a degradation-agnostic benchmark, while higher-fidelity ageing modelling can provide a further revenue improvement of up to 16% over simpler formulations. Lower discount rates favour strategies that balance calendar and cycling ageing, while higher discount rates favour aggressive operation. Across all strategies, services responding to positive frequency deviations are consistently preferred over those responding to negative deviations. Lifetime profit is maximised by adapting the optimal service mix as the battery ages.
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BibTeX
@misc{xia2026lifetime,
title = {{Lifetime Profit-Maximising Co-optimisation of Multi-Service Stacking for Battery Storage}},
author = {Yuxin Xia and Frederik Schiele and Yihong Zhou and Volkan Kumtepeli and David Howey and Iacopo Savelli and Thomas Morstyn},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03767},
eprint = {2609.03767},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03767},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Lifetime Profit-Maximising Co-optimisation of Multi-Service Stacking for Battery Storage
AU - Yuxin Xia
AU - Frederik Schiele
AU - Yihong Zhou
AU - Volkan Kumtepeli
AU - David Howey
AU - Iacopo Savelli
AU - Thomas Morstyn
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.03767
UR - https://arxiv.org/abs/2609.03767
AB - Grid-scale battery energy storage can generate revenue by stacking services across electricity and frequency response markets, yet identifying the lifetime profit-maximising stacking strategy remains challenging. Decisions across services are coupled through shared battery system capacity, constrained by system operator energy management rules, and further shaped by product-specific technical requirements that govern system operation, degradation, and lifetime profitability. This paper presents an ageing-aware receding-horizon framework for co-optimising multi-service stacking that explicitly captures product-specific characteristics and state-of-energy compliance rules. The framework is applied to the Great Britain market, where storage operators can stack electricity trading with multiple dynamic frequency response services procured through the newly introduced 'Enduring Auction Capability' platform under energy management requirements imposed by the National Energy System Operator. Using real market data, we demonstrate that degradation modelling, discount rate, and battery ageing jointly govern both lifetime value and optimal stacking strategy. Accounting for ageing increases lifetime revenue by up to 32% relative to a degradation-agnostic benchmark, while higher-fidelity ageing modelling can provide a further revenue improvement of up to 16% over simpler formulations. Lower discount rates favour strategies that balance calendar and cycling ageing, while higher discount rates favour aggressive operation. Across all strategies, services responding to positive frequency deviations are consistently preferred over those responding to negative deviations. Lifetime profit is maximised by adapting the optimal service mix as the battery ages.
ER -