Continuous Approximate Dynamic Programming Algorithm to Promote Multiple Battery Energy Storage Lifespan Benefit in Real-Time Scheduling

This paper aims to promote the lifespan benefit of multiple battery energy storage (BES) in real-time scheduling. An effective real-time scheduling model is formulated with the proposed concept of multiple BES (MBES) comprehensive lifespan benefit, which makes a tradeoff between MBES short-term oper...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 15; H. 6; S. 5744 - 5760
Hauptverfasser: Xue, Xizhen, Ai, Xiaomeng, Fang, Jiakun, Jiang, Yazhou, Cui, Shichang, Wang, Jinsong, Ortmeyer, Thomas H., Wen, Jinyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Computation
Continuity (mathematics)
continuous approximate dynamic programming
Costs
decomposed value function approximation
Decomposition
Degradation
Dynamic programming
Energy storage
Job shop scheduling
Life span
Lifespan benefit
Linear functions
multiple battery energy storage
Numerical analysis
Optimization
piece-wise linear function
Processor scheduling
Real-time programming
real-time scheduling
Real-time systems
Scheduling
Stochastic processes
ISSN:1949-3053, 1949-3061
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Zusammenfassung:This paper aims to promote the lifespan benefit of multiple battery energy storage (BES) in real-time scheduling. An effective real-time scheduling model is formulated with the proposed concept of multiple BES (MBES) comprehensive lifespan benefit, which makes a tradeoff between MBES short-term operation and long-term profits. Then, a novel piece-wise linear function (PLF) based continuous ADP (PLFC-ADP) algorithm is proposed to optimize the scheduling model under uncertainties. A new decomposed value function approximation method employing both BES state of charge and BES cumulative life loss is proposed to achieve high optimality and wide applicability. Combined with the difference-based decomposed slope update method to train the PLF slopes with empirical knowledge, the proposed PLFC-ADP algorithm can handle the increasing computation complexity of MBES scheduling and obtain the approximate optimality of stochastic real-time scheduling. Numerical analysis demonstrates the validity of the proposed scheduling model, and superior computation tractability and solution optimality of the proposed PLFC-ADP algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2024.3423321