Energy storage configuration model for reliability services of active distribution networks

The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply, increasing the demand for energy storage in distribution networks. Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services. This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network. First, an analytical reliability assessment model is constructed and embedded into the energy storage allocation model, where the impact of renewable energy uncertainty is described using chance constraints. Considering the interests of both the distribution network and shared energy storage operators, a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established, which is then transformed into a mixed-integer linear programming (MILP) model for efficient solution. Case studies show that the proposed method, through cooperation between the distribution system operator and shared energy storage operators, significantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.

Authors

Institutions

Publication Details

Journal
iEnergy
Published
2025-09-01
DOI
https://doi.org/10.23919/ien.2025.0015
Citations
1
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Energy storage configuration model for reliability services of active distribution networks

Wenchuan Wu, Qi Wang, Yue Zhou, Shuwei Xu et al.
1 citations
iEnergy
Power Systems and Technologies
0.73
article

Energy storage configuration model for reliability services of active distribution networks

Wenchuan Wu, Qi Wang, Yue Zhou, Shuwei Xu, Yaqi Sun, Haotian Zhao
article en
1 citations

Abstract

The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply, increasing the demand for energy storage in distribution networks. Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services. This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network. First, an analytical reliability assessment model is constructed and embedded into the energy storage allocation model, where the impact of renewable energy uncertainty is described using chance constraints. Considering the interests of both the distribution network and shared energy storage operators, a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established, which is then transformed into a mixed-integer linear programming (MILP) model for efficient solution. Case studies show that the proposed method, through cooperation between the distribution system operator and shared energy storage operators, significantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.

iEnergyVol. 4(3)
Cardiff University (GB), Tsinghua University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality
Openalex Percentile: Top 24%
Power Systems and Technologies
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.