Optimal Configuration of Transformer–Energy Storage Deeply Integrated System Based on Enhanced Q-Learning with Hybrid Guidance

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Processes Ročník 13; číslo 10; s. 3267
Hlavní autori: Li, Zhe, You, Li, Kang, Yiqun, Tan, Daojun, Cai, Xuan, Xiong, Haozhe, Liu, Yonghui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 13.10.2025
Predmet:
Algorithms
Alternative energy sources
Analysis
Arbitrage
Artificial intelligence
Convergence
Economic impact
Electric power systems
Electric transformers
Electrical equipment
Electricity
Energy industry
Energy management systems
Energy storage
Learning
Machine learning
Multiple objective analysis
Optimization
Optimization models
Renewable resources
Resource allocation
Technological change
China
ISSN:2227-9717, 2227-9717
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance.
AbstractList This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance.
Audience Academic
Author Cai, Xuan
Tan, Daojun
Li, Zhe
Xiong, Haozhe
Liu, Yonghui
You, Li
Kang, Yiqun
Author_xml – sequence: 1
  givenname: Zhe
  surname: Li
  fullname: Li, Zhe
– sequence: 2
  givenname: Li
  surname: You
  fullname: You, Li
– sequence: 3
  givenname: Yiqun
  surname: Kang
  fullname: Kang, Yiqun
– sequence: 4
  givenname: Daojun
  surname: Tan
  fullname: Tan, Daojun
– sequence: 5
  givenname: Xuan
  surname: Cai
  fullname: Cai, Xuan
– sequence: 6
  givenname: Haozhe
  surname: Xiong
  fullname: Xiong, Haozhe
– sequence: 7
  givenname: Yonghui
  surname: Liu
  fullname: Liu, Yonghui
BookMark eNpNUdtKAzEQDaLg9cUvCPgmrG6SveVRa61CQaT6vGSTyRppkzVJkX3rP_iHfokpFXTmYS6cM8OcOUb71llA6JzkV4zx_HrwhJGc0areQ0eU0jrjNan3_-WH6CyE9zwZJ6wpqyO0eRqiWYklnjirTb_2IhpnsdP4xQsbtPMr8N-br6kF3494EZ0XPeA7gGE54kcboU8UUHgxhggrfCtCKtKEqX0TVqb8OZuD8NbYHn-a-IYfxs4bhWdro7aAU3SgxTLA2W88Qa_305fJQzZ_mj1ObuaZpLyMWQeNVBXpSkqUagolBOiSA-WMVLSpVFFzSej2ek0AJGl0XeiOy8SjspQdO0EXu7mDdx9rCLF9d2tv08o2KVbyguWEJdTVDtWLJbTGahe9kMkVrIxMcmuT-jdNRQvaFE2RCJc7gvQuBA-6HXzS048tydvtV9q_r7AfFtWDQQ
Cites_doi 10.1109/TSG.2017.2738610
10.1109/ICISCE.2017.242
10.1109/TSG.2021.3061619
10.1109/TPWRS.2003.821625
10.1109/ICISC.2018.8399083
10.1109/TSG.2021.3064312
10.1016/j.apenergy.2020.116172
10.1016/j.est.2020.101224
10.1109/TSG.2018.2872521
ContentType Journal Article
Copyright COPYRIGHT 2025 MDPI AG
2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: COPYRIGHT 2025 MDPI AG
– notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
7SR
8FD
8FE
8FG
8FH
ABJCF
ABUWG
AFKRA
AZQEC
BBNVY
BENPR
BGLVJ
BHPHI
CCPQU
D1I
DWQXO
GNUQQ
HCIFZ
JG9
KB.
LK8
M7P
PDBOC
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
DOI 10.3390/pr13103267
DatabaseName CrossRef
Engineered Materials Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Natural Science Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
Biological Science Collection
ProQuest Central
Technology collection
Natural Science Collection
ProQuest One Community College
ProQuest Materials Science Collection
ProQuest Central Korea
ProQuest Central Student
SciTech Premium Collection
Materials Research Database
Materials Science Database
Biological Sciences
Biological Science Database
Materials Science Collection
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
DatabaseTitle CrossRef
Publicly Available Content Database
Materials Research Database
ProQuest Central Student
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
Materials Science Collection
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Natural Science Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
Engineered Materials Abstracts
Natural Science Collection
ProQuest Central Korea
Biological Science Collection
Materials Science Database
ProQuest Central (New)
ProQuest Materials Science Collection
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
Biological Science Database
ProQuest SciTech Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList CrossRef
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: KB.
  name: Materials Science Database
  url: http://search.proquest.com/materialsscijournals
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Sciences (General)
EISSN 2227-9717
ExternalDocumentID A862428484
10_3390_pr13103267
GeographicLocations China
GeographicLocations_xml – name: China
GroupedDBID 5VS
8FE
8FG
8FH
AADQD
AAFWJ
AAYXX
ABJCF
ACIWK
ACPRK
ADBBV
ADMLS
AFFHD
AFKRA
AFZYC
ALMA_UNASSIGNED_HOLDINGS
BBNVY
BCNDV
BENPR
BGLVJ
BHPHI
CCPQU
CITATION
D1I
HCIFZ
IAO
IGS
ITC
KB.
KQ8
LK8
M7P
MODMG
M~E
OK1
PDBOC
PHGZM
PHGZT
PIMPY
PQGLB
PROAC
RNS
7SR
8FD
ABUWG
AZQEC
DWQXO
GNUQQ
JG9
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
ID FETCH-LOGICAL-c295t-be8cd61b521dd84daaef59e29316286d479c121032f1eec18f74fb9cbe82c5cb3
IEDL.DBID KB.
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001601591600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2227-9717
IngestDate Tue Oct 28 21:21:50 EDT 2025
Tue Nov 11 03:52:46 EST 2025
Sat Nov 29 07:15:07 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 10
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c295t-be8cd61b521dd84daaef59e29316286d479c121032f1eec18f74fb9cbe82c5cb3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/3265943013?pq-origsite=%requestingapplication%
PQID 3265943013
PQPubID 2032344
ParticipantIDs proquest_journals_3265943013
gale_infotracacademiconefile_A862428484
crossref_primary_10_3390_pr13103267
PublicationCentury 2000
PublicationDate 2025-10-13
PublicationDateYYYYMMDD 2025-10-13
PublicationDate_xml – month: 10
  year: 2025
  text: 2025-10-13
  day: 13
PublicationDecade 2020
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Processes
PublicationYear 2025
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References Li (ref_12) 2022; 42
Walker (ref_10) 2021; 282
Nazemi (ref_8) 2021; 12
Dai (ref_11) 2021; 12
Kang (ref_13) 2017; 41
Ci (ref_7) 2021; 4
Ding (ref_5) 2014; 34
Xiao (ref_3) 2022; 54
Liu (ref_1) 2023; 43
Luo (ref_21) 2011; 35
Kim (ref_9) 2019; 10
Liang (ref_6) 2020; 44
Damousis (ref_18) 2004; 19
ref_19
Huber (ref_15) 2019; 10
ref_16
Xiao (ref_20) 2022; 29
Sheng (ref_4) 2021; 47
Guo (ref_14) 2020; 44
Zhang (ref_2) 2024; 39
Datta (ref_17) 2020; 28
References_xml – volume: 10
  start-page: 317
  year: 2019
  ident: ref_15
  article-title: Applicability of Solid-State Transformers in Today’s and Future Distribution Grids
  publication-title: IEEE Trans. Smart Grid
  doi: 10.1109/TSG.2017.2738610
– ident: ref_16
  doi: 10.1109/ICISCE.2017.242
– volume: 12
  start-page: 3163
  year: 2021
  ident: ref_11
  article-title: The Utilization of Shared Energy Storage in Energy Systems: A Comprehensive Review
  publication-title: IEEE Trans. Smart Grid
  doi: 10.1109/TSG.2021.3061619
– volume: 41
  start-page: 2
  year: 2017
  ident: ref_13
  article-title: New Form of Energy Storage for Future Power Systems: Cloud Energy Storage
  publication-title: Autom. Electr. Power Syst.
– volume: 4
  start-page: 427
  year: 2021
  ident: ref_7
  article-title: Modeling and Operation Control of Digital Energy Storage System Based on Reconfigurable Battery Network: A Case Study of Base Station Energy Storage Application
  publication-title: J. Glob. Energy Interconnect.
– volume: 43
  start-page: 4899
  year: 2023
  ident: ref_1
  article-title: An Overview of Morphological Development and Ol Technology of Power Electronics Dominated Distribution Area
  publication-title: Proc. CSEE
– volume: 19
  start-page: 1165
  year: 2004
  ident: ref_18
  article-title: A solution to the unit-commitment problem using integer-coded genetic algorithm
  publication-title: IEEE Trans. Power Syst.
  doi: 10.1109/TPWRS.2003.821625
– volume: 34
  start-page: 1
  year: 2014
  ident: ref_5
  article-title: A Review on the Effect of Large-Scale PV Generation on Power Systems
  publication-title: Proc. CSEE
– volume: 42
  start-page: 6611
  year: 2022
  ident: ref_12
  article-title: Game-Theoretic Optimal Dispatch of Distribution Network with Multi-Microgrid Leasing Shared Energy Storage
  publication-title: Proc. CSEE
– volume: 39
  start-page: 2784
  year: 2024
  ident: ref_2
  article-title: Coordinated Operation Method of Renewable Energy Power Systems Based on Feasible Region Projection Theory
  publication-title: Trans. China Electrotech. Soc.
– ident: ref_19
  doi: 10.1109/ICISC.2018.8399083
– volume: 12
  start-page: 3200
  year: 2021
  ident: ref_8
  article-title: Uncertainty-aware Deployment of Mobile Energy Storage Systems for Distribution Grid Resilience
  publication-title: IEEE Trans. Smart Grid
  doi: 10.1109/TSG.2021.3064312
– volume: 54
  start-page: 47
  year: 2022
  ident: ref_3
  article-title: New Power Systems Dominated by Renewable Energy Towards the Goal of Emission Peak & Carbon Neutrality: Contribution, Key Techniques, and Challenges
  publication-title: Adv. Eng. Sci.
– volume: 44
  start-page: 1
  year: 2020
  ident: ref_6
  article-title: Analysis of Development Trend for Intelligent Distribution Transformer
  publication-title: Autom. Electr. Power Syst.
– volume: 47
  start-page: 3072
  year: 2021
  ident: ref_4
  article-title: Key Technologies and Application Prospects for Operation and Maintenance of Power Equipment in New Type Power System
  publication-title: High Volt. Eng.
– volume: 282
  start-page: 116172
  year: 2021
  ident: ref_10
  article-title: Analysis on Impact of Shared Energy Storage in Residential Community: Individual Versus Shared Energy Storage
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2020.116172
– volume: 29
  start-page: 1874
  year: 2022
  ident: ref_20
  article-title: Two-player Optimization Control Based on Off-policy Q-learning Algorithm
  publication-title: Control Eng. China
– volume: 28
  start-page: 101224
  year: 2020
  ident: ref_17
  article-title: Smart Control of BESS in PV Integrated EV Charging Station for Reducing Transformer Overloading and Providing Battery-to-Grid Service
  publication-title: J. Energy Storage
  doi: 10.1016/j.est.2020.101224
– volume: 44
  start-page: 1611
  year: 2020
  ident: ref_14
  article-title: Comprehensive Optimal Allocation of Electricity/Heat Cloud Energy Storage in Regional Integrated Energy System
  publication-title: Power Syst. Technol.
– volume: 10
  start-page: 4996
  year: 2019
  ident: ref_9
  article-title: Enhancing Distribution System Resilience with Mobile Energy Storage and Microgrids
  publication-title: IEEE Trans. Smart Grid
  doi: 10.1109/TSG.2018.2872521
– volume: 35
  start-page: 207
  year: 2011
  ident: ref_21
  article-title: Applications of life cycle cost theory in Decision-Making of investment for distribution transformers renovation
  publication-title: Power Syst. Technol.
SSID ssj0000913856
Score 2.3057916
Snippet This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a...
SourceID proquest
gale
crossref
SourceType Aggregation Database
Index Database
StartPage 3267
SubjectTerms Algorithms
Alternative energy sources
Analysis
Arbitrage
Artificial intelligence
Convergence
Economic impact
Electric power systems
Electric transformers
Electrical equipment
Electricity
Energy industry
Energy management systems
Energy storage
Learning
Machine learning
Multiple objective analysis
Optimization
Optimization models
Renewable resources
Resource allocation
Technological change
Title Optimal Configuration of Transformer–Energy Storage Deeply Integrated System Based on Enhanced Q-Learning with Hybrid Guidance
URI https://www.proquest.com/docview/3265943013
Volume 13
WOSCitedRecordID wos001601591600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: M~E
  dateStart: 20130101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Biological Science Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: M7P
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/biologicalscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Materials Science Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: KB.
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/materialsscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: BENPR
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: PIMPY
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NT9RAFH9R8KAHENS4fGwmgUQ9jNB2ph8nwkIRQlirYgKnpp03xU10d-numnAx_A_8h_wlzJtOXQ_Gi5deptM2eZ_z-t7vB7C9ixbWJuIyFMgJ4pwXItrlotSiUF4ZFRIt2UTU78cXF0nmCm4T11bZ-kTrqHGkqEa-Y9IMSVDhXrA3vubEGkV_Vx2FxmNYJJQEom447b3_XWMhzMtYhg0qaWBO9zvj2iNiLd_Sys_j0N-9sQ0xR8v_-3HPYckll2y_0YYVeKSHq_DsD8jBVVhxxjxhbx3i9LsXcPvROI4fZifN_w2uZo1WsFHFztu8Vtf3t3epHRRkX8w53bghdqj1-PsNO2kRJ5A1-OesZ0IjMvOEdPjNdhiwT9wBuV4xqvyy4xuaFGMfZgOkG17C16P0_OCYO2oGrvxETnmpY4WhV5rgjxgLLApdyUSb3MGjWVcUUaIImizwK09r5cVVJKoyUWafr6Qqg1ewMBwN9WtgFDtlmGBZSZPNRBhjIIvExFCBIapKd2CrFVQ-bhA4cnNyIXHmc3F24A3JMCeznNaFKtx0gXkHAVzl-3YQJhax6MBGK8Pc2esknwtw7d_L6_DUJwZg6mkJNmBhWs_0JjxRP6eDSd2FxV7azz53rRp2qY80o-uv1KxkJ2fZ5QMi3uwj
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VggQ9AC0gAgVWAgQcVo3tXXt9QFU_UhK1hCKC1Juxd9YhUpukjgPKrf-B_8GP6i9hZ20TDohbD5ztXUvr5zdv1zNvAF600dnaRFyGAjlZnPNURG0uMiNS7WVRKtE1m4j6fXVyEh-vwM-mFobSKhtOdESNE01n5FtWZkiyCveC7ek5p65R9He1aaFRweLQLL7bLdvsbW_fvt-Xvn_QGex1ed1VgGs_liXPjNIYepmNW4hKYJqaXMbGhj2PyjRRRLEmV63Azz1jtKfySORZrO04X0udBXbea3DdyghfuVTB499nOuSxqWRYuaAGQdzemhYeNfLyXRv7Zdz7O_u7kHZw539bjLtwuxbPbKdC-zqsmPEGrP1hqbgB6zVZzdjr2lH7zT24-GCJ8cyOpPrG0XBeoZ5NcjZodLspLi9-dFwhJPtU2u9iaNi-MdPTBes1jhrIKn93tmtDPzI7Q2f81WVQsI-8NqodMjrZZt0FVcKxd_MR0g334fOVrMsDWB1PxuYhMNIGMowxy6VVaxEqDGQaW40gMESdmxY8b4CRTCuHkcTuzAg-yRI-LXhFmEmIdsoi1WldPWGfQQZeyY4r9FFCiRZsNphJaj6aJUvAPPr35Wdwszt4f5Qc9fqHj-GWT92OKX8n2ITVspibJ3BDfytHs-Kpgz6DL1cNr1_CKEXq
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEB71B1XlALQFNVBgJagoh1Vie9c_B4RaktCoKARIpd6MvbsOkSAJjgPKre_A2_A4PAkz9pr0gLj1wNneXWn9zcy365lvAJ62dClrE3DpC81J4pwnImhxkRqRKCcNEqnLZhNBvx9eXESDNfhZ18JQWmXtE0tHraeK7sibSDMkSYU7XjOzaRGDdvfl7CunDlL0p7Vup1FB5Mwsv-Pxbf6i18Zvfei63c7w1Sm3HQa4ciNZ8NSESvtOijFM61DoJDGZjAyGQIdKNrUIIkUKW56bOcYoJ8wCkaWRwnGukir1cN512AyQZKB1bZ50-oP3f254SHEzlH6liep5Uas5yx1q6-WWTe1XUfDvsaAMcN3b__PW3IFbllaz48oOdmDNTHbh5hWxxV3YsW5szo6s1vbzPbh8iy7zC46kysfxaFHZA5tmbFgzepP_uvzRKUsk2YcCLWZkWNuY2ecl69VaG5pVyu_sBEmBZjhDZ_KpzK1g77iVsB0xuvNmp0uqkWOvF2NNL9yF82vZl3uwMZlOzD4wYg3Sj3SaSeRxgQ61J5MI2YPQvlaZacCTGiTxrNIeifHMRlCKV1BqwDPCT0wOqcgTldi6ClyDpL3i47IEKBShaMBBjZ_Yeqp5vALP_X8_fgxbiKr4Ta9_9gC2XWqDTIk93gFsFPnCPIQb6lsxnuePrB0w-Hjd-PoNfoFP-g
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimal+Configuration+of+Transformer%E2%80%93Energy+Storage+Deeply+Integrated+System+Based+on+Enhanced+Q-Learning+with+Hybrid+Guidance&rft.jtitle=Processes&rft.au=Li%2C+Zhe&rft.au=Li%2C+You&rft.au=Kang%2C+Yiqun&rft.au=Tan+Daojun&rft.date=2025-10-13&rft.pub=MDPI+AG&rft.eissn=2227-9717&rft.volume=13&rft.issue=10&rft.spage=3267&rft_id=info:doi/10.3390%2Fpr13103267&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-9717&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-9717&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-9717&client=summon