JOURNAL ARTICLE
Cost-Saving Synergy: Energy Stacking in Battery Energy Storage Systems.
Published In: Management Science (INFORMS), 2026, v. 72, n. 5. P. 3906 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Bae, Joonho; Kapuscinski, Roman; Silberholz, John 3 of 3
Abstract
This article analyzes the economic benefits of energy stacking—providing multiple grid services simultaneously using a single battery energy storage system (BESS)—to improve battery profitability despite high upfront costs and degradation. Focusing on two common services, day-ahead energy arbitrage and frequency regulation, the study develops an analytical model incorporating usage-based battery degradation costs and shows that simultaneous stacking often yields cost-saving synergy, where the combined degradation cost is less than the sum of individual costs. The model demonstrates that stacking can more than double profits compared to the best standalone service and induces synergistic behavior by increasing optimal service quantities. Extensions considering correlated prices, battery inefficiency (round-trip energy loss), nonuniform regulation signals, and power capacity constraints confirm that stacking’s benefits generally persist or even strengthen under realistic conditions. The findings suggest that stacking enhances battery utilization and deployment viability, particularly benefiting less efficient or second-hand batteries, and highlight stacking as a key strategy for sustainable grid integration of renewable energy.
Additional Information
- Source:Management Science (INFORMS). 2026/05, Vol. 72, Issue 5, p3906
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2026
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.03477
- Accession Number:193596717
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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