Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1483 |
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| author | Tom Goldstein-Tweg Elinor Ginzburg-Ganz Juri Belikov Yoash Levron |
| author_facet | Tom Goldstein-Tweg Elinor Ginzburg-Ganz Juri Belikov Yoash Levron |
| author_sort | Tom Goldstein-Tweg |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e3e48df0557e4625b569d8b5657d6f08 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e3e48df0557e4625b569d8b5657d6f082025-08-20T03:43:02ZengMDPI AGEnergies1996-10732025-03-01186148310.3390/en18061483Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic ModelsTom Goldstein-Tweg0Elinor Ginzburg-Ganz1Juri Belikov2Yoash Levron3The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, IsraelThe Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, IsraelDepartment of Software Science, Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, EstoniaThe Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, IsraelThis 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.https://www.mdpi.com/1996-1073/18/6/1483battery lifetimeenergy storageload balancingload levelingoptimal efficiencypower management |
| spellingShingle | Tom Goldstein-Tweg Elinor Ginzburg-Ganz Juri Belikov Yoash Levron Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models Energies battery lifetime energy storage load balancing load leveling optimal efficiency power management |
| title | Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models |
| title_full | Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models |
| title_fullStr | Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models |
| title_full_unstemmed | Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models |
| title_short | Uniqueness of Optimal Power Management Strategies for Energy Storage Dynamic Models |
| title_sort | uniqueness of optimal power management strategies for energy storage dynamic models |
| topic | battery lifetime energy storage load balancing load leveling optimal efficiency power management |
| url | https://www.mdpi.com/1996-1073/18/6/1483 |
| work_keys_str_mv | AT tomgoldsteintweg uniquenessofoptimalpowermanagementstrategiesforenergystoragedynamicmodels AT elinorginzburgganz uniquenessofoptimalpowermanagementstrategiesforenergystoragedynamicmodels AT juribelikov uniquenessofoptimalpowermanagementstrategiesforenergystoragedynamicmodels AT yoashlevron uniquenessofoptimalpowermanagementstrategiesforenergystoragedynamicmodels |