Adaptive risk-difference-based network pricing for shared energy storage in distribution networks

Shared energy storage (SES) enables centralized deployment of storage resources to serve multiple users, significantly reducing investment costs and improving asset utilization. However, the profit-driven SES operation may intensify line loadings and introduce additional operational risk, due to its significant impact on distribution network power flows. Existing studies typically incorporate network constraints into storage scheduling models as passive feasibility conditions, without quantifying the network risk induced by SES, thereby failing to provide economic signals that guide SES to mitigate network risk. To address this limitation, this paper proposes a risk-difference-based network pricing and coordinated scheduling approach for SES operation. A continuous risk function based on line loadings is established, and the risk difference between scenarios with and without SES participation is employed for network pricing. Through first-order approximation, the system-level risk difference is transformed into nodal and temporal marginal risk prices, which directly guide storage dispatch decisions according to network operating conditions. Based on this pricing mechanism, a price-guided SES scheduling model is formulated and coordinated with network constraints via the alternating direction method of multipliers (ADMM). Simulation results demonstrate that the proposed mechanism can automatically generate penalty or incentive effects under different conditions, guiding SES to mitigate network operation risk while preserving its economic performance.

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Publication Details

Journal
Applied Energy
Published
2026-06-18
DOI
https://doi.org/10.1016/j.apenergy.2026.128261
Primary Topic
Optimal Power Flow Distribution
Type
article
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article

Adaptive risk-difference-based network pricing for shared energy storage in distribution networks

H Liu, Lidong Huang, Zihan He, Hongtao Wang et al.
Applied Energy
Optimal Power Flow Distribution
article

Adaptive risk-difference-based network pricing for shared energy storage in distribution networks

H Liu, Lidong Huang, Zihan He, Hongtao Wang, Bin Liu, Siyu Ma
article en

Abstract

Shared energy storage (SES) enables centralized deployment of storage resources to serve multiple users, significantly reducing investment costs and improving asset utilization. However, the profit-driven SES operation may intensify line loadings and introduce additional operational risk, due to its significant impact on distribution network power flows. Existing studies typically incorporate network constraints into storage scheduling models as passive feasibility conditions, without quantifying the network risk induced by SES, thereby failing to provide economic signals that guide SES to mitigate network risk. To address this limitation, this paper proposes a risk-difference-based network pricing and coordinated scheduling approach for SES operation. A continuous risk function based on line loadings is established, and the risk difference between scenarios with and without SES participation is employed for network pricing. Through first-order approximation, the system-level risk difference is transformed into nodal and temporal marginal risk prices, which directly guide storage dispatch decisions according to network operating conditions. Based on this pricing mechanism, a price-guided SES scheduling model is formulated and coordinated with network constraints via the alternating direction method of multipliers (ADMM). Simulation results demonstrate that the proposed mechanism can automatically generate penalty or incentive effects under different conditions, guiding SES to mitigate network operation risk while preserving its economic performance.

Applied EnergyVol. 421
Guangxi University (CN)
Affordable and clean energy
Openalex Percentile: Top 10%
Optimal Power Flow Distribution
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Adaptive risk-difference-based network pricing for shared energy storage in distribution networks — H Liu, Lidong Huang, et al. · Applied Energy (2026) | TGRS Research Map | TGRS