Compensation mechanism for peak-shaving auxiliary services considering the cost recovery period of energy storage

China's dual carbon targets—peaking emissions by 2030 and achieving carbon neutrality by 2060—require effective integration of renewable energy, creating enhanced peak-shaving auxiliary services. As thermal power plants (TP) shift to auxiliary roles, their profitability diminishes, while energy storage systems (ESS) offer flexibility and rapid response to stabilize supply-demand imbalances. However, ESS adoption has been hindered by weak cost recovery mechanisms. This study introduces a novel economic dispatch model for a wind-fire-storage system, evaluating ESS's income, costs, and cost recovery periods under different compensation mechanisms. The results show that the proposed compensation mechanism reduces ESS cost recovery periods by 15.4 %, boosts wind power profitability, stabilizes TP output, and lowers peak-shaving costs. The findings emphasize the importance of strategic compensation mechanisms in facilitating renewable energy integration, reducing reliance on thermal power, and enhancing ESS participation. This work provides a practical framework for optimizing energy storage applications and supporting China's carbon neutrality goals. • Studies innovative energy storage compensation for renewable peak-shaving services. • Balances cost recovery and incentives for energy storage system adoption. • Focuses on ESS integration for renewable energy and power grid stability. • Employs models used to evaluate compensation mechanisms' economic efficiency. • Supports sustainable energy transitions with practical, scalable solutions.

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Publication Details

Journal
Journal of Energy Storage
Published
2025-05-28
DOI
https://doi.org/10.1016/j.est.2025.117127
Citations
2
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
2.43
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article

Compensation mechanism for peak-shaving auxiliary services considering the cost recovery period of energy storage

Pablo Ballesteros‐Pérez, Martin Skitmore, Jiejia Zhang, Zhaobin Zhu et al.
2 citations
Journal of Energy Storage
Microgrid Control and Optimization
2.43
article

Compensation mechanism for peak-shaving auxiliary services considering the cost recovery period of energy storage

Pablo Ballesteros‐Pérez, Martin Skitmore, Jiejia Zhang, Zhaobin Zhu, Jingxiao Zhang
article en
2 citations

Abstract

China's dual carbon targets—peaking emissions by 2030 and achieving carbon neutrality by 2060—require effective integration of renewable energy, creating enhanced peak-shaving auxiliary services. As thermal power plants (TP) shift to auxiliary roles, their profitability diminishes, while energy storage systems (ESS) offer flexibility and rapid response to stabilize supply-demand imbalances. However, ESS adoption has been hindered by weak cost recovery mechanisms. This study introduces a novel economic dispatch model for a wind-fire-storage system, evaluating ESS's income, costs, and cost recovery periods under different compensation mechanisms. The results show that the proposed compensation mechanism reduces ESS cost recovery periods by 15.4 %, boosts wind power profitability, stabilizes TP output, and lowers peak-shaving costs. The findings emphasize the importance of strategic compensation mechanisms in facilitating renewable energy integration, reducing reliance on thermal power, and enhancing ESS participation. This work provides a practical framework for optimizing energy storage applications and supporting China's carbon neutrality goals. • Studies innovative energy storage compensation for renewable peak-shaving services. • Balances cost recovery and incentives for energy storage system adoption. • Focuses on ESS integration for renewable energy and power grid stability. • Employs models used to evaluate compensation mechanisms' economic efficiency. • Supports sustainable energy transitions with practical, scalable solutions.

Journal of Energy StorageVol. 128
Bond University (AU), China Three Gorges University (CN), Chang'an University (CN), Universitat Politècnica de València (ES)
Affordable and clean energy
Openalex Percentile: Top 11%
Microgrid Control and Optimization
2.43
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