Current Applications and Future Prospects of Deep Reinforcement Learning in Energy Management for Hybrid Power Systems.
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| Title: | Current Applications and Future Prospects of Deep Reinforcement Learning in Energy Management for Hybrid Power Systems. |
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| Authors: | Li, Zhao1 (AUTHOR), Long, Wuqiang1 (AUTHOR) longwq@dlut.edu.cn, Tian, Hua1 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 9, p2216. 50p. |
| Subject Terms: | *Hybrid power systems, *Reinforcement learning, *Machine learning, *Energy consumption, *Greenhouse gas mitigation |
| Abstract: | Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system's overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193716112 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Current Applications and Future Prospects of Deep Reinforcement Learning in Energy Management for Hybrid Power Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Zhao%22">Li, Zhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Long%2C+Wuqiang%22">Long, Wuqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> longwq@dlut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Hua%22">Tian, Hua</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2216. 50p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Hybrid+power+systems%22">Hybrid power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Greenhouse+gas+mitigation%22">Greenhouse gas mitigation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system's overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193716112 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19092216 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 50 StartPage: 2216 Subjects: – SubjectFull: Hybrid power systems Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Greenhouse gas mitigation Type: general Titles: – TitleFull: Current Applications and Future Prospects of Deep Reinforcement Learning in Energy Management for Hybrid Power Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Zhao – PersonEntity: Name: NameFull: Long, Wuqiang – PersonEntity: Name: NameFull: Tian, Hua IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 9 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
