Life-Cycle Co-Optimization of User-Side Energy Storage Systems with Multi-Service Stacking and Degradation-Aware Dispatch

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment.

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Journal
Processes
Published
2026-01-29
DOI
https://doi.org/10.3390/pr14030477
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
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article

Life-Cycle Co-Optimization of User-Side Energy Storage Systems with Multi-Service Stacking and Degradation-Aware Dispatch

Xiangang Peng, Zixuan Guo, Chenxi Zhang, Lixiang Lin et al.
Processes
Microgrid Control and Optimization
article

Life-Cycle Co-Optimization of User-Side Energy Storage Systems with Multi-Service Stacking and Degradation-Aware Dispatch

Xiangang Peng, Zixuan Guo, Chenxi Zhang, Lixiang Lin, Haotian Cai, Yuanliang Zhang, Xin Li
article en

Abstract

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment.

ProcessesVol. 14(3)
Guangdong University of Technology (CN), China Southern Power Grid (China) (CN), Power Grid Corporation (India) (IN)
China Southern Power Grid
Responsible consumption and production
Openalex Percentile: Top 74%
Microgrid Control and Optimization
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