Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models

Tom Goldstein-Tweg, Elinor Ginzburg-Ganz, Juri Belikov and Yoash Levron ()
Additional contact information
Tom Goldstein-Tweg: The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Elinor Ginzburg-Ganz: The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Juri Belikov: Department of Software Science, Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, Estonia
Yoash Levron: The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel

Energies, 2025, vol. 18, issue 6, 1-30

Abstract: This paper contributes to the field of analytic and semi-analytic solutions for optimal power flow problems involving storage systems. Its primary contribution is a rigorous proof establishing the uniqueness of the “shortest path” optimal solution, a key element in this class of algorithms, building upon a graphical design procedure previously introduced. The proof is constructed through five consequential lemmas, each defining a distinct characteristic of the optimal solution. These characteristics are then synthesized to demonstrate the uniqueness of the optimal solution, which corresponds to the shortest path of generated energy within defined bounds. This proof not only provides a solid theoretical foundation for this algorithm class but also paves the way for developing analytic solutions to more complex optimal control problems incorporating storage. Furthermore, the efficacy of this unique solution is validated through two comparative tests. The first one uses synthetic data to benchmark the proposed solution in comparison to recent reinforcement learning algorithms, including actor–critic, PPO, and TD3. The second one compares the proposed solution to the optimal solutions derived from other numerical methods based on real-world data from an electrical vehicle storage device.

Keywords: battery lifetime; energy storage; load balancing; load leveling; optimal efficiency; power management; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1483/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1483/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:6:p:1483-:d:1614352

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().