Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to th...
Saved in:
| Published in: | IEEE transactions on smart grid Vol. 13; no. 4; pp. 2935 - 2958 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1949-3053, 1949-3061 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well. |
|---|---|
| AbstractList | With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well. |
| Author | Li, Na Qu, Guannan Tang, Yujie Chen, Xin Low, Steven |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0000-0002-1357-3970 surname: Chen fullname: Chen, Xin email: chenxin2336@gmail.com organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA – sequence: 2 givenname: Guannan orcidid: 0000-0002-5466-3550 surname: Qu fullname: Qu, Guannan email: gqu@andrew.cmu.edu organization: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 3 givenname: Yujie orcidid: 0000-0002-4921-8372 surname: Tang fullname: Tang, Yujie email: yujietang@seas.harvard.edu organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA – sequence: 4 givenname: Steven orcidid: 0000-0001-6476-3048 surname: Low fullname: Low, Steven email: slow@caltech.edu organization: Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA – sequence: 5 givenname: Na orcidid: 0000-0001-9545-3050 surname: Li fullname: Li, Na email: nali@seas.harvard.edu organization: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA |
| BookMark | eNp9kM1PAjEQxRuDiajcTbw08Qz2a7vUGyGCRhKN4nlTurNYsnTXtmD47y1CPHhwLjOZvDeT9ztHHdc4QOiKkgGlRN3O36YDRhgbcJqJnA5PUJcqofqcSNr5nTN-hnohrEgqzrlkqovWr2Bd1XgDa3ARz0B7Z90SpxV-gxpMtFvAT7DDo7atrdHRNi5g6_BL8wVJswsR1uEOv4LZHxiVW-0MBKxdiSebuPGAxx-6rsEtIVyi00rXAXrHfoHeJ_fz8UN_9jx9HI9mfcMUjX21EMYYZoYVAWIE0zIThBMKUou8XGTlotQpU8WN0ExzmSUIC8mBVykvUOAX6OZwt_XN5wZCLFbNxrv0smAyV1yJPJdJJQ8q45sQPFSFsfEnYPTa1gUlxZ5ukegWe7rFkW4ykj_G1tu19rv_LNcHiwWAX7nKGRWE8W96q4eC |
| CODEN | ITSGBQ |
| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_105833 crossref_primary_10_1049_rpg2_12887 crossref_primary_10_3390_en16041608 crossref_primary_10_1016_j_enbuild_2025_116071 crossref_primary_10_1016_j_rineng_2024_102741 crossref_primary_10_1109_ACCESS_2024_3396449 crossref_primary_10_1109_TIA_2024_3435170 crossref_primary_10_1595_205651325X17458327898748 crossref_primary_10_1080_00207721_2025_2469821 crossref_primary_10_1109_TPWRS_2024_3423381 crossref_primary_10_1109_TTE_2024_3519215 crossref_primary_10_3390_en17092167 crossref_primary_10_1109_TPWRS_2024_3523262 crossref_primary_10_1109_ACCESS_2024_3439015 crossref_primary_10_1109_TPWRS_2024_3407977 crossref_primary_10_1016_j_egyai_2025_100620 crossref_primary_10_1109_TCYB_2025_3528200 crossref_primary_10_1109_JPROC_2023_3303358 crossref_primary_10_1016_j_engappai_2023_106896 crossref_primary_10_1109_ACCESS_2025_3560312 crossref_primary_10_1109_TCSII_2024_3367184 crossref_primary_10_1016_j_apenergy_2025_125541 crossref_primary_10_1109_TPWRS_2023_3319518 crossref_primary_10_1109_TSG_2022_3233766 crossref_primary_10_1080_15325008_2024_2311340 crossref_primary_10_1016_j_apenergy_2024_124155 crossref_primary_10_1080_23335777_2022_2130434 crossref_primary_10_1109_TIA_2025_3531321 crossref_primary_10_1016_j_asoc_2025_113321 crossref_primary_10_1109_TIA_2023_3326433 crossref_primary_10_3390_math11040884 crossref_primary_10_3390_en15196920 crossref_primary_10_1016_j_apenergy_2023_121740 crossref_primary_10_1016_j_asoc_2024_111878 crossref_primary_10_1109_TSP_2024_3386396 crossref_primary_10_1016_j_apenergy_2024_123507 crossref_primary_10_12688_digitaltwin_17632_2 crossref_primary_10_1016_j_rser_2024_114282 crossref_primary_10_3390_en17215425 crossref_primary_10_3390_en17215307 crossref_primary_10_1016_j_rser_2022_113052 crossref_primary_10_1109_TPWRS_2023_3298007 crossref_primary_10_1109_TTE_2025_3528255 crossref_primary_10_1016_j_eswa_2024_125017 crossref_primary_10_1016_j_egyr_2025_04_008 crossref_primary_10_1016_j_epsr_2024_110269 crossref_primary_10_1016_j_prime_2025_100940 crossref_primary_10_1109_TII_2024_3399885 crossref_primary_10_1109_TPWRS_2023_3326121 crossref_primary_10_1109_TIA_2024_3472655 crossref_primary_10_1007_s13042_023_01981_9 crossref_primary_10_1016_j_conengprac_2023_105499 crossref_primary_10_1016_j_apenergy_2025_126445 crossref_primary_10_1109_LCSYS_2023_3289435 crossref_primary_10_1109_TAC_2024_3494656 crossref_primary_10_1109_TASE_2024_3357204 crossref_primary_10_1016_j_egyr_2024_09_060 crossref_primary_10_1109_TSG_2024_3410322 crossref_primary_10_1109_TSG_2023_3304135 crossref_primary_10_1016_j_ijepes_2024_109980 crossref_primary_10_1063_5_0147592 crossref_primary_10_1016_j_ijepes_2025_110621 crossref_primary_10_1016_j_apenergy_2024_124944 crossref_primary_10_1016_j_apenergy_2024_123611 crossref_primary_10_1109_TPWRS_2023_3266773 crossref_primary_10_1109_JSYST_2023_3253041 crossref_primary_10_1088_2515_7620_adf530 crossref_primary_10_1109_TPWRS_2023_3340674 crossref_primary_10_3390_electronics14122336 crossref_primary_10_1016_j_inoche_2024_113726 crossref_primary_10_1016_j_ress_2023_109340 crossref_primary_10_1109_TSTE_2024_3452489 crossref_primary_10_3390_app15158188 crossref_primary_10_1016_j_est_2024_114805 crossref_primary_10_1109_ACCESS_2023_3337118 crossref_primary_10_1109_TII_2024_3514183 crossref_primary_10_1007_s40435_025_01709_3 crossref_primary_10_1109_TSG_2023_3343373 crossref_primary_10_1016_j_est_2025_116676 crossref_primary_10_1109_TIA_2024_3463608 crossref_primary_10_1109_TPWRS_2023_3319699 crossref_primary_10_1109_TSG_2025_3540416 crossref_primary_10_1016_j_chaos_2024_115690 crossref_primary_10_1016_j_applthermaleng_2025_125752 crossref_primary_10_1016_j_egyr_2024_09_073 crossref_primary_10_1016_j_apenergy_2024_124773 crossref_primary_10_3390_jsan14030051 crossref_primary_10_1016_j_rser_2023_113627 crossref_primary_10_1109_TSG_2023_3324474 crossref_primary_10_3390_en17092075 crossref_primary_10_1109_TSTE_2023_3330842 crossref_primary_10_1016_j_est_2024_114080 crossref_primary_10_1016_j_ijepes_2025_110879 crossref_primary_10_1016_j_rser_2025_115977 crossref_primary_10_3390_en18112959 crossref_primary_10_1016_j_eswa_2023_121070 crossref_primary_10_1016_j_epsr_2024_110648 crossref_primary_10_3390_en18030653 crossref_primary_10_1109_TSG_2024_3388258 crossref_primary_10_1109_TSTE_2025_3550563 crossref_primary_10_1016_j_rser_2025_116022 crossref_primary_10_1109_TSG_2024_3396435 crossref_primary_10_1016_j_epsr_2025_111455 crossref_primary_10_1109_TSG_2024_3399705 crossref_primary_10_1145_3727112 crossref_primary_10_3389_fnbot_2024_1364587 crossref_primary_10_1016_j_arcontrol_2025_101009 crossref_primary_10_3390_en15176392 crossref_primary_10_1109_TSG_2024_3377910 crossref_primary_10_1016_j_tej_2022_107130 crossref_primary_10_1109_TIA_2024_3462900 crossref_primary_10_1016_j_compchemeng_2023_108413 crossref_primary_10_1109_LCSYS_2023_3332297 crossref_primary_10_1016_j_jobe_2024_111030 crossref_primary_10_1109_ACCESS_2023_3280558 crossref_primary_10_3390_app15073638 crossref_primary_10_1016_j_trac_2024_117852 crossref_primary_10_1038_s41598_025_03310_2 crossref_primary_10_1109_ACCESS_2025_3609317 crossref_primary_10_1109_TII_2025_3567398 crossref_primary_10_1109_ACCESS_2022_3226446 crossref_primary_10_3390_en18071809 crossref_primary_10_3390_en16052371 crossref_primary_10_1109_TSG_2024_3466768 crossref_primary_10_1016_j_apenergy_2024_124351 crossref_primary_10_1016_j_apenergy_2024_124753 crossref_primary_10_1016_j_ijepes_2025_110691 crossref_primary_10_1016_j_ijepes_2024_110279 crossref_primary_10_1109_TSG_2025_3564105 crossref_primary_10_1016_j_knosys_2025_113844 crossref_primary_10_1016_j_sysconle_2024_105753 crossref_primary_10_1109_TPWRS_2023_3320172 crossref_primary_10_1016_j_segan_2024_101580 crossref_primary_10_1109_TSTE_2024_3376369 crossref_primary_10_1088_2516_1083_acb987 crossref_primary_10_1109_JAS_2024_124218 crossref_primary_10_1109_TSG_2022_3222323 crossref_primary_10_1109_TTE_2024_3434750 crossref_primary_10_1109_TSG_2022_3169361 crossref_primary_10_1109_LCSYS_2024_3408068 crossref_primary_10_1016_j_automatica_2023_111364 crossref_primary_10_1109_TPWRS_2024_3496936 crossref_primary_10_3390_app13126937 crossref_primary_10_1016_j_compeleceng_2024_109417 crossref_primary_10_1088_1361_6668_ad3d10 crossref_primary_10_1109_ACCESS_2025_3584719 crossref_primary_10_1007_s10994_023_06422_w crossref_primary_10_1016_j_apenergy_2025_126409 crossref_primary_10_1109_TPWRS_2022_3220799 crossref_primary_10_32604_ee_2024_047794 crossref_primary_10_1109_TPWRS_2023_3289334 crossref_primary_10_1109_TSMC_2024_3427646 crossref_primary_10_3390_electronics14142826 crossref_primary_10_1007_s42452_025_06509_0 crossref_primary_10_1109_TCNS_2023_3338240 crossref_primary_10_1109_JAS_2023_123135 crossref_primary_10_1109_JSYST_2022_3222262 crossref_primary_10_1016_j_apenergy_2022_120500 crossref_primary_10_3390_electronics12102219 crossref_primary_10_1016_j_apenergy_2024_124970 crossref_primary_10_1016_j_segan_2025_101826 crossref_primary_10_1016_j_egyr_2025_01_002 crossref_primary_10_1109_JPROC_2025_3584656 crossref_primary_10_1016_j_segan_2024_101402 crossref_primary_10_1016_j_apenergy_2025_126018 crossref_primary_10_1109_ACCESS_2023_3249151 crossref_primary_10_1109_ACCESS_2023_3321135 crossref_primary_10_3390_en15228739 crossref_primary_10_3390_en18061380 crossref_primary_10_1109_JAS_2023_123126 crossref_primary_10_1016_j_apenergy_2023_120759 crossref_primary_10_1016_j_eswa_2023_120495 |
| Cites_doi | 10.1109/TSG.2019.2945691 10.1109/TSG.2019.2936142 10.1109/TPWRS.2011.2157180 10.17775/CSEEJPES.2019.00920 10.1038/nature16961 10.1109/PESGM41954.2020.9281614 10.1016/j.egyai.2021.100092 10.1162/0899766053011528 10.1145/3054912 10.1109/TSG.2019.2962625 10.1109/TCNS.2020.3024489 10.1109/JIOT.2020.2992117 10.1109/TPWRS.2004.831259 10.1109/TSG.2020.2976771 10.1109/TSG.2020.3010130 10.1109/TSG.2019.2924025 10.1145/3061639.3062224 10.1109/ISGTEurope.2012.6465777 10.1109/TPWRS.2020.2990179 10.1109/TSG.2019.2933502 10.1109/TSG.2019.2942593 10.1201/9781315140223 10.1109/JPROC.2020.2993787 10.1109/TSG.2020.3014055 10.1049/iet-gtd.2016.1734 10.1007/978-3-030-60990-0_12 10.1016/j.ifacol.2019.08.164 10.1109/TETCI.2020.2964886 10.1109/TPWRS.2020.3000652 10.1145/2939672.2945397 10.1109/TPWRS.2019.2919522 10.1109/TSG.2017.2679238 10.1109/TIE.2017.2668983 10.1109/TSG.2020.3011739 10.1016/j.apenergy.2020.114772 10.1109/TSG.2019.2955437 10.1109/ACCESS.2020.3019535 10.1109/ACCESS.2021.3064354 10.1109/TSG.2021.3052998 10.1111/j.1541-0420.2011.01572.x 10.1109/TAC.2017.2713529 10.1162/neco.1997.9.8.1735 10.1177/0278364913495721 10.1109/TIE.2015.2420792 10.1007/BF00993306 10.1109/ACCESS.2019.2933020 10.1023/A:1022140919877 10.1007/BF00992698 10.23919/ACC53348.2022.9867476 10.1109/TSG.2019.2952331 10.1109/9.580874 10.1109/TII.2011.2166794 10.35833/MPCE.2020.000557 10.1609/aaai.v35i6.16638 10.1109/ACCESS.2019.2946282 10.1109/JIOT.2020.2966232 10.1016/j.energy.2018.04.042 10.1201/b10869 10.1109/JSYST.2019.2931879 10.1109/TSG.2020.2970768 10.23919/ACC.2019.8814865 10.1109/TPWRS.2019.2941134 10.1109/TPWRS.2014.2357079 10.1109/JIOT.2019.2957289 10.1109/TPWRS.2020.2973761 10.1109/TIT.2021.3120096 10.1109/TPWRS.2021.3092220 10.1214/aoms/1177729586 10.1561/2200000086 10.1109/TSG.2021.3090039 10.1109/TSG.2021.3058996 10.1007/s10994-023-06303-2 10.1109/TSG.2019.2951769 10.1007/978-3-642-27645-3_2 10.1201/9781351006620-6 10.1126/science.1127647 10.35833/MPCE.2020.000522 10.1049/iet-gtd.2019.0218 10.1109/CDC.2018.8619829 10.1109/TSG.2020.3005270 10.35833/MPCE.2020.000552 10.1109/TSG.2018.2879572 10.2352/ISSN.2470-1173.2017.19.AVM-023 10.1142/9789814360616_0007 10.1109/ACCESS.2020.3041007 10.1109/TSG.2020.2972208 10.1109/TPWRS.2019.2948132 10.1049/iet-gtd.2019.0554 10.1109/TPWRS.2020.2999890 10.1109/TPWRS.2020.2987292 10.1109/EI250167.2020.9346692 10.1109/PowerTech46648.2021.9494982 10.1109/TSG.2020.2996274 10.1109/TPWRS.2018.2881359 10.1109/TPWRS.2019.2931685 10.1109/CDC.2018.8619572 10.1287/opre.1050.0216 10.1109/TSG.2021.3060027 10.1109/TSG.2020.2971427 10.1109/ACCESS.2019.2940005 10.1109/TSG.2018.2834219 10.1609/aaai.v33i01.33013387 10.1109/ACCESS.2020.2974286 10.1109/TNNLS.2018.2801880 10.17775/CSEEJPES.2019.02890 10.1287/moor.1040.0129 10.1109/TSG.2020.3035127 10.1109/ACCESS.2019.2894756 10.1109/TSG.2019.2930299 10.1109/TSG.2019.2933191 10.1016/j.apenergy.2021.117634 10.1109/LCSYS.2020.3003190 10.1109/TNNLS.2019.2955857 10.1109/TGCN.2021.3061789 10.1016/j.arcontrol.2019.09.008 10.1109/TSG.2020.2986333 10.1038/nature14539 10.1109/TSG.2018.2790704 10.1016/j.arcontrol.2020.03.001 10.1109/TPWRS.2019.2897948 10.1561/2200000070 10.1109/TSG.2020.2978061 10.1109/TEC.2020.2990937 10.1109/ACCESS.2021.3060620 10.1609/aaai.v30i1.10295 10.3390/en13051250 10.1109/CDC40024.2019.9029268 10.1038/nature14236 10.1109/TSG.2020.3041620 10.15607/RSS.2014.X.031 10.21105/joss.02435 10.1016/j.automatica.2013.09.043 10.1109/TII.2020.3001095 10.1109/TSG.2019.2909266 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M |
| DOI | 10.1109/TSG.2022.3154718 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Civil Engineering Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1949-3061 |
| EndPage | 2958 |
| ExternalDocumentID | 10_1109_TSG_2022_3154718 9721402 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Caltech Center for Autonomous Systems and Technologies (CAST) – fundername: NSF AI Institute grantid: 2112085 – fundername: NSF grantid: ECCS-1931662; AitF-1637598; CNS-1518941 funderid: 10.13039/100000001 – fundername: Cyber-Physical Systems (CPS) grantid: ECCS-1932611 – fundername: PIMCO Fellowship – fundername: Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology; Resnick Sustainability Institute funderid: 10.13039/100007287 – fundername: Amazon AI4Science Fellowship – fundername: NSF CAREER grantid: ECCS-1553407 funderid: 10.13039/100000001 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M |
| ID | FETCH-LOGICAL-c291t-9b4ccc2c8f0e0c42a6540301e6a47db5dbda194f3c4a2a365110b63e3f061e1e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 231 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000814692300041&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1949-3053 |
| IngestDate | Mon Jun 30 09:38:52 EDT 2025 Tue Nov 18 22:32:09 EST 2025 Sat Nov 29 03:46:00 EST 2025 Wed Aug 27 02:23:54 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c291t-9b4ccc2c8f0e0c42a6540301e6a47db5dbda194f3c4a2a365110b63e3f061e1e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-6476-3048 0000-0002-4921-8372 0000-0001-9545-3050 0000-0002-1357-3970 0000-0002-5466-3550 |
| PQID | 2679394776 |
| PQPubID | 2040408 |
| PageCount | 24 |
| ParticipantIDs | ieee_primary_9721402 crossref_citationtrail_10_1109_TSG_2022_3154718 crossref_primary_10_1109_TSG_2022_3154718 proquest_journals_2679394776 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-07-01 |
| PublicationDateYYYYMMDD | 2022-07-01 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE transactions on smart grid |
| PublicationTitleAbbrev | TSG |
| PublicationYear | 2022 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | Wang (ref39) 2017 ref59 Cui (ref57) 2020 ref58 ref55 Krueger (ref35) 2020 Jaksch (ref17) 2010; 11 Paszke (ref161) 2019; 32 ref45 ref47 Achiam (ref123) ref44 Tessler (ref170) 2018 ref49 (ref102) 2006 ref8 ref7 Mankowitz (ref181) 2019 ref9 ref4 ref3 Dertat (ref46) 2017 ref6 ref5 ref100 ref101 ref40 Garcıa (ref168) 2015; 16 ref36 ref30 Abadi (ref160) Ernst (ref31) 2005; 6 Ng (ref37); 1 Marot (ref156) 2021 ref22 Mukherjee (ref86) 2021 Derman (ref185) 2018 ref21 Fan (ref159) 2021 Levine (ref34) 2020 Schulman (ref54) 2017 Berkenkamp (ref164) Lee (ref80) 2021 ref128 ref129 ref126 ref96 ref127 ref124 Fan (ref48) ref125 Pan (ref180) 2021 Qin (ref172) Sutton (ref28) ref93 ref133 ref92 ref134 Barto (ref199) 2003; 13 ref95 ref131 ref94 ref132 ref130 ref91 ref90 Verma (ref200) ref89 ref139 ref137 ref85 ref138 ref88 ref135 ref87 ref136 ref82 ref144 ref81 ref145 ref84 ref142 ref83 ref143 ref140 ref141 ref79 ref108 ref78 ref109 ref106 ref107 ref75 ref104 ref74 ref105 ref77 ref76 ref103 Agarwal (ref20) Cheung (ref15) ref71 ref111 ref70 ref112 ref73 ref72 ref110 Bertsekas (ref12) 2012 ref68 ref119 ref67 ref117 ref69 ref118 ref64 ref115 ref63 ref116 Watkins (ref24) 1992; 8 ref66 ref113 ref65 ref114 Degris (ref27) 2012 Li (ref153) 2020 (ref163) 2021 Ghavamzadeh (ref198) 2016 Agarwal (ref33) Srikant (ref23) 2019 ref60 ref122 ref62 ref120 ref61 ref121 Lin (ref187) 2020 ref169 ref178 ref176 ref173 ref174 ref171 Wang (ref50) ref179 ref188 ref189 ref182 Zhang (ref184) ref183 ref148 ref149 ref146 Mnih (ref51) ref147 Lagoudakis (ref32) 2003; 4 ref155 Goodfellow (ref42) 2016 ref151 ref152 Silver (ref29) Schulman (ref53) ref150 ref158 Pattanaik (ref177) 2017 Brockman (ref154) 2016 Bevrani (ref56) 2017 ref165 ref162 (ref43) 2021 ref13 Fu (ref38) 2017 ref11 ref10 ref16 ref19 ref18 Wang (ref196) 2016 Achiam (ref175) 2017 Qu (ref186); 1 Henri (ref157) 2020 Chow (ref166) 2018 Fan (ref167) 2019 Sutton (ref2) 2018 Qu (ref191) 2020 Sun (ref41) 2019 Liu (ref98) 2021 Lecarpentier (ref14) 2019 ref1 Rummery (ref26) 1994 ref192 ref190 Pinto (ref97) 2017 ref193 ref194 Zhuo (ref197) 2019 Qu (ref25) Che (ref195) Parisotto (ref99) 2015 Haarnoja (ref52) 2018 |
| References_xml | – start-page: 1 volume-title: Proc. ICLR ident: ref195 article-title: Combining model-based and model-free RL via multi-step control variates – start-page: 486 volume-title: Proc. Learn. Dyn. Control ident: ref48 article-title: A theoretical analysis of deep Q-learning – ident: ref78 doi: 10.1109/TSG.2019.2945691 – year: 2015 ident: ref99 article-title: Actor-mimic: Deep multitask and transfer reinforcement learning publication-title: arXiv:1511.06342 – year: 2019 ident: ref14 article-title: Non-stationary Markov decision processes, a worst-case approach using model-based reinforcement learning, extended version publication-title: arXiv:1904.10090 – ident: ref140 doi: 10.1109/TSG.2019.2936142 – ident: ref143 doi: 10.1109/TPWRS.2011.2157180 – ident: ref8 doi: 10.17775/CSEEJPES.2019.00920 – year: 2016 ident: ref154 article-title: Openai gym publication-title: arXiv:1606.01540 – ident: ref4 doi: 10.1038/nature16961 – ident: ref71 doi: 10.1109/PESGM41954.2020.9281614 – ident: ref155 doi: 10.1016/j.egyai.2021.100092 – ident: ref169 doi: 10.1162/0899766053011528 – ident: ref192 doi: 10.1145/3054912 – year: 2019 ident: ref181 article-title: Robust reinforcement learning for continuous control with model misspecification publication-title: arXiv:1906.07516 – year: 2020 ident: ref153 article-title: Real-time aggregate flexibility via reinforcement learning publication-title: arXiv:2012.11261 – ident: ref93 doi: 10.1109/TSG.2019.2962625 – ident: ref72 doi: 10.1109/TCNS.2020.3024489 – ident: ref129 doi: 10.1109/JIOT.2020.2992117 – ident: ref89 doi: 10.1109/TPWRS.2004.831259 – ident: ref107 doi: 10.1109/TSG.2020.2976771 – ident: ref85 doi: 10.1109/TSG.2020.3010130 – ident: ref125 doi: 10.1109/TSG.2019.2924025 – start-page: 1843 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref15 article-title: Reinforcement learning for non-stationary Markov decision processes: The blessing of (more) optimism – ident: ref128 doi: 10.1145/3061639.3062224 – start-page: 104 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref33 article-title: An optimistic perspective on offline reinforcement learning – ident: ref100 doi: 10.1109/ISGTEurope.2012.6465777 – ident: ref92 doi: 10.1109/TPWRS.2020.2990179 – start-page: 1995 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref50 article-title: Dueling network architectures for deep reinforcement learning – ident: ref116 doi: 10.1109/TSG.2019.2933502 – start-page: 1 volume-title: Proc. NeurIPS ident: ref184 article-title: Robust multi-agent reinforcement learning with model uncertainty – year: 2020 ident: ref57 article-title: Reinforcement learning for optimal frequency control: A lyapunov approach publication-title: arXiv:2009.05654 – start-page: 8682 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref172 article-title: Density constrained reinforcement learning – ident: ref151 doi: 10.1109/TSG.2019.2942593 – ident: ref174 doi: 10.1201/9781315140223 – ident: ref105 doi: 10.1109/JPROC.2020.2993787 – ident: ref132 doi: 10.1109/TSG.2020.3014055 – ident: ref68 doi: 10.1049/iet-gtd.2016.1734 – ident: ref55 doi: 10.1007/978-3-030-60990-0_12 – volume-title: Applied Deep Learning—Part 3: Autoencoders year: 2017 ident: ref46 – volume: 11 start-page: 1563 issue: 51 year: 2010 ident: ref17 article-title: Near-optimal regret bounds for reinforcement learning publication-title: J. Mach. Learn. Res. – ident: ref66 doi: 10.1016/j.ifacol.2019.08.164 – ident: ref60 doi: 10.1109/TETCI.2020.2964886 – ident: ref95 doi: 10.1109/TPWRS.2020.3000652 – ident: ref162 doi: 10.1145/2939672.2945397 – volume-title: Reinforcement Learning: An Introduction year: 2018 ident: ref2 – ident: ref194 doi: 10.1109/TPWRS.2019.2919522 – ident: ref75 doi: 10.1109/TSG.2017.2679238 – year: 2019 ident: ref197 article-title: Federated reinforcement learning publication-title: arXiv:1901.08277 – ident: ref69 doi: 10.1109/TIE.2017.2668983 – ident: ref130 doi: 10.1109/TSG.2020.3011739 – ident: ref87 doi: 10.1016/j.apenergy.2020.114772 – ident: ref122 doi: 10.1109/TSG.2019.2955437 – ident: ref59 doi: 10.1109/ACCESS.2020.3019535 – ident: ref124 doi: 10.1109/ACCESS.2021.3064354 – year: 2020 ident: ref191 article-title: Combining model-based and model-free methods for nonlinear control: A provably convergent policy gradient approach publication-title: arXiv:2006.07476 – year: 2017 ident: ref175 article-title: Constrained policy optimization publication-title: arXiv:1705.10528 – ident: ref84 doi: 10.1109/TSG.2021.3052998 – start-page: 22 volume-title: Proc. 34th Int. Conf. Mach. Learn. ident: ref123 article-title: Constrained policy optimization – ident: ref7 doi: 10.1111/j.1541-0420.2011.01572.x – ident: ref73 doi: 10.1109/TAC.2017.2713529 – ident: ref44 doi: 10.1162/neco.1997.9.8.1735 – ident: ref5 doi: 10.1177/0278364913495721 – ident: ref145 doi: 10.1109/TIE.2015.2420792 – volume-title: MATLAB and Reinforcement Learning Toolbox (R2021a) year: 2021 ident: ref163 – ident: ref19 doi: 10.1007/BF00993306 – year: 2017 ident: ref39 article-title: On the origin of deep learning publication-title: arXiv:1702.07800 – ident: ref147 doi: 10.1109/ACCESS.2019.2933020 – volume: 13 start-page: 41 issue: 1 year: 2003 ident: ref199 article-title: Recent advances in hierarchical reinforcement learning publication-title: Discr. Event Dyn. Syst. doi: 10.1023/A:1022140919877 – year: 2020 ident: ref35 article-title: Active reinforcement learning: Observing rewards at a cost publication-title: arXiv:2011.06709 – volume: 8 start-page: 279 issue: 3 year: 1992 ident: ref24 article-title: Q-learning publication-title: Mach. Learn. doi: 10.1007/BF00992698 – ident: ref79 doi: 10.23919/ACC53348.2022.9867476 – ident: ref138 doi: 10.1109/TSG.2019.2952331 – year: 2021 ident: ref156 article-title: Learning to run a power network challenge: A retrospective analysis publication-title: arXiv:2103.03104 – ident: ref21 doi: 10.1109/9.580874 – start-page: 387 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref29 article-title: Deterministic policy gradient algorithms – start-page: 908 volume-title: Advances in Neural Information Processing Systems ident: ref164 article-title: Safe model-based reinforcement learning with stability guarantees – ident: ref1 doi: 10.1109/TII.2011.2166794 – ident: ref114 doi: 10.35833/MPCE.2020.000557 – ident: ref190 doi: 10.1609/aaai.v35i6.16638 – year: 2016 ident: ref198 article-title: Bayesian reinforcement learning: A survey publication-title: arXiv:1609.04436 – ident: ref142 doi: 10.1109/ACCESS.2019.2946282 – ident: ref115 doi: 10.1109/JIOT.2020.2966232 – ident: ref67 doi: 10.1016/j.energy.2018.04.042 – volume-title: Intelligent Automatic Generation Control year: 2017 ident: ref56 doi: 10.1201/b10869 – year: 2018 ident: ref52 article-title: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor publication-title: arXiv:1801.01290 – ident: ref193 doi: 10.1109/JSYST.2019.2931879 – ident: ref77 doi: 10.1109/TSG.2020.2970768 – year: 2019 ident: ref167 article-title: Safety-guided deep reinforcement learning via online Gaussian process estimation publication-title: arXiv:1903.02526 – ident: ref176 doi: 10.23919/ACC.2019.8814865 – ident: ref94 doi: 10.1109/TPWRS.2019.2941134 – ident: ref70 doi: 10.1109/TPWRS.2014.2357079 – volume: 32 start-page: 8026 volume-title: Advances in Neural Information Processing Systems year: 2019 ident: ref161 article-title: PyTorch: An imperative style, high-performance deep learning library – volume-title: Benefit of Demand Response in Electricity Market and Recommendations for Achieving Them year: 2006 ident: ref102 – ident: ref137 doi: 10.1109/JIOT.2019.2957289 – ident: ref149 doi: 10.1109/TPWRS.2020.2973761 – ident: ref188 doi: 10.1109/TIT.2021.3120096 – ident: ref117 doi: 10.1109/TPWRS.2021.3092220 – ident: ref22 doi: 10.1214/aoms/1177729586 – ident: ref16 doi: 10.1561/2200000086 – ident: ref103 doi: 10.1109/TSG.2021.3090039 – volume: 4 start-page: 1107 year: 2003 ident: ref32 article-title: Least-squares policy iteration publication-title: J. Mach. Learn. Res. – ident: ref83 doi: 10.1109/TSG.2021.3058996 – ident: ref189 doi: 10.1007/s10994-023-06303-2 – year: 2018 ident: ref185 article-title: Soft-robust actor-critic policy-gradient publication-title: arXiv:1803.04848 – volume: 1 start-page: 256 volume-title: Proc. Mach. Learn. Res. ident: ref186 article-title: Scalable reinforcement learning of localized policies for multi-agent networked systems – ident: ref91 doi: 10.1109/TSG.2019.2951769 – ident: ref30 doi: 10.1007/978-3-642-27645-3_2 – year: 2020 ident: ref34 article-title: Offline reinforcement learning: Tutorial, review, and perspectives on open problems publication-title: arXiv:2005.01643 – year: 2021 ident: ref80 article-title: A graph policy network approach for Volt-VAR control in power distribution systems publication-title: arXiv:2109.12073 – year: 2021 ident: ref159 article-title: PowerGym: A reinforcement learning environment for Volt-VAR control in power distribution systems publication-title: arXiv:2109.03970 – ident: ref3 doi: 10.1201/9781351006620-6 – year: 1994 ident: ref26 article-title: On-line Q-learning using connectionist systems – ident: ref45 doi: 10.1126/science.1127647 – ident: ref113 doi: 10.35833/MPCE.2020.000522 – ident: ref63 doi: 10.1049/iet-gtd.2019.0218 – year: 2017 ident: ref177 article-title: Robust deep reinforcement learning with adversarial attacks publication-title: arXiv:1712.03632 – start-page: 1057 volume-title: Advances in Neural Information Processing Systems ident: ref28 article-title: Policy gradient methods for reinforcement learning with function approximation – ident: ref173 doi: 10.1109/CDC.2018.8619829 – ident: ref141 doi: 10.1109/TSG.2020.3005270 – ident: ref10 doi: 10.35833/MPCE.2020.000552 – ident: ref121 doi: 10.1109/TSG.2018.2879572 – ident: ref6 doi: 10.2352/ISSN.2470-1173.2017.19.AVM-023 – year: 2019 ident: ref23 article-title: Finite-time error bounds for linear stochastic approximation and TD learning publication-title: arXiv:1902.00923 – year: 2020 ident: ref157 article-title: Pymgrid: An open-source Python microgrid simulator for applied artificial intelligence research publication-title: arXiv:2011.08004 – ident: ref101 doi: 10.1142/9789814360616_0007 – ident: ref112 doi: 10.1109/ACCESS.2020.3041007 – year: 2021 ident: ref180 article-title: Improving robustness of reinforcement learning for power system control with adversarial training publication-title: arXiv:2110.08956 – year: 2021 ident: ref86 article-title: Scalable voltage control using structure-driven hierarchical deep reinforcement learning publication-title: arXiv:2102.00077 – ident: ref88 doi: 10.1109/TSG.2020.2972208 – ident: ref90 doi: 10.1109/TPWRS.2019.2948132 – ident: ref111 doi: 10.1049/iet-gtd.2019.0554 – ident: ref58 doi: 10.1109/TPWRS.2020.2999890 – start-page: 8092 volume-title: Advances in Neural Information Processing Systems year: 2018 ident: ref166 article-title: A lyapunov-based approach to safe reinforcement learning – volume: 1 start-page: 1 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref37 article-title: Algorithms for inverse reinforcement learning – ident: ref110 doi: 10.1109/TPWRS.2020.2987292 – ident: ref108 doi: 10.1109/EI250167.2020.9346692 – ident: ref178 doi: 10.1109/PowerTech46648.2021.9494982 – ident: ref127 doi: 10.1109/TSG.2020.2996274 – volume: 6 start-page: 503 year: 2005 ident: ref31 article-title: Tree-based batch mode reinforcement learning publication-title: J. Mach. Learn. Res. – volume-title: CS231n: Convolutional Neural Networks for Visual Recognition year: 2021 ident: ref43 – ident: ref64 doi: 10.1109/TPWRS.2018.2881359 – ident: ref76 doi: 10.1109/TPWRS.2019.2931685 – year: 2016 ident: ref196 article-title: Learning to reinforcement learn publication-title: arXiv:1611.05763 – year: 2020 ident: ref187 article-title: Multi-agent reinforcement learning in time-varying networked systems publication-title: arXiv:2006.06555 – start-page: 265 volume-title: Proc. 12th Symp. Oper. Syst. Design Implement. ident: ref160 article-title: TensorFlow: A system for large-scale machine learning – ident: ref165 doi: 10.1109/CDC.2018.8619572 – ident: ref183 doi: 10.1287/opre.1050.0216 – ident: ref81 doi: 10.1109/TSG.2021.3060027 – ident: ref133 doi: 10.1109/TSG.2020.2971427 – ident: ref139 doi: 10.1109/ACCESS.2019.2940005 – year: 2017 ident: ref97 article-title: Robust adversarial reinforcement learning publication-title: arXiv:1703.02702 – ident: ref131 doi: 10.1109/TSG.2018.2834219 – ident: ref171 doi: 10.1609/aaai.v33i01.33013387 – ident: ref135 doi: 10.1109/ACCESS.2020.2974286 – ident: ref120 doi: 10.1109/TNNLS.2018.2801880 – year: 2012 ident: ref27 article-title: Off-policy actor-critic publication-title: arXiv:1205.4839 – ident: ref136 doi: 10.17775/CSEEJPES.2019.02890 – volume: 16 start-page: 1437 issue: 1 year: 2015 ident: ref168 article-title: A comprehensive survey on safe reinforcement learning publication-title: J. Mach. Learn. Res. – start-page: 5045 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref200 article-title: Programmatically interpretable reinforcement learning – start-page: 3185 volume-title: Proc. Conf. Learn. Theory ident: ref25 article-title: Finite-time analysis of asynchronous stochastic approximation and Q-learning – year: 2018 ident: ref170 article-title: Reward constrained policy optimization publication-title: arXiv:1805.11074 – ident: ref182 doi: 10.1287/moor.1040.0129 – year: 2017 ident: ref38 article-title: Learning robust rewards with adversarial inverse reinforcement learning publication-title: arXiv:1710.11248 – ident: ref118 doi: 10.1109/TSG.2020.3035127 – volume-title: Dynamic Programming and Optimal Control year: 2012 ident: ref12 – ident: ref65 doi: 10.1109/ACCESS.2019.2894756 – start-page: 1889 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref53 article-title: Trust region policy optimization – ident: ref119 doi: 10.1109/TSG.2019.2930299 – ident: ref144 doi: 10.1109/TSG.2019.2933191 – ident: ref109 doi: 10.1016/j.apenergy.2021.117634 – ident: ref152 doi: 10.1109/LCSYS.2020.3003190 – ident: ref179 doi: 10.1109/TNNLS.2019.2955857 – ident: ref106 doi: 10.1109/TGCN.2021.3061789 – year: 2019 ident: ref41 article-title: Optimization for deep learning: Theory and algorithms publication-title: arXiv:1912.08957 – start-page: 1928 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref51 article-title: Asynchronous methods for deep reinforcement learning – ident: ref9 doi: 10.1016/j.arcontrol.2019.09.008 – ident: ref126 doi: 10.1109/TSG.2020.2986333 – ident: ref40 doi: 10.1038/nature14539 – ident: ref148 doi: 10.1109/TSG.2018.2790704 – start-page: 64 volume-title: Proc. Conf. Learn. Theory ident: ref20 article-title: Optimality and approximation with policy gradient methods in Markov decision processes – year: 2017 ident: ref54 article-title: Proximal policy optimization algorithms publication-title: arXiv:1707.06347 – ident: ref11 doi: 10.1016/j.arcontrol.2020.03.001 – ident: ref74 doi: 10.1109/TPWRS.2019.2897948 – ident: ref18 doi: 10.1561/2200000070 – ident: ref134 doi: 10.1109/TSG.2020.2978061 – ident: ref146 doi: 10.1109/TEC.2020.2990937 – ident: ref82 doi: 10.1109/ACCESS.2021.3060620 – ident: ref49 doi: 10.1609/aaai.v30i1.10295 – ident: ref61 doi: 10.3390/en13051250 – year: 2021 ident: ref98 article-title: Bi-level off-policy reinforcement learning for Volt/VAR control involving continuous and discrete devices publication-title: arXiv:2104.05902 – ident: ref150 doi: 10.1109/CDC40024.2019.9029268 – year: 2016 ident: ref42 publication-title: Deep Learning – ident: ref47 doi: 10.1038/nature14236 – ident: ref96 doi: 10.1109/TSG.2020.3041620 – ident: ref36 doi: 10.15607/RSS.2014.X.031 – ident: ref158 doi: 10.21105/joss.02435 – ident: ref13 doi: 10.1016/j.automatica.2013.09.043 – ident: ref62 doi: 10.1109/TII.2020.3001095 – ident: ref104 doi: 10.1109/TSG.2019.2909266 |
| SSID | ssj0000333629 |
| Score | 2.7091775 |
| Snippet | With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges,... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2935 |
| SubjectTerms | Communication networks Decision making Distributed generation Energy management Energy sources Frequency regulation Heuristic algorithms Learning Markov processes Mathematical models Power system dynamics Power systems Reinforcement learning smart grid Smart sensors voltage control |
| Title | Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges |
| URI | https://ieeexplore.ieee.org/document/9721402 https://www.proquest.com/docview/2679394776 |
| Volume | 13 |
| WOSCitedRecordID | wos000814692300041&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1949-3061 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000333629 issn: 1949-3053 databaseCode: RIE dateStart: 20100101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA9z-KAPfk1xOiUPvgjWdUn6Ed_GcArKGG6KbyVNrzLQTtZN8L_3knZlogi-lZCEkl_ucpe7_I6QM90xdinvOKmG0DHPZh0lQs8BEXLFmfQSm_L_dB8MBuHzsxzWyEX1FgYAbPIZXJpPG8tPpnphrsrahmlGGObItSDwi7da1X2KyznqYmmDyMKE8z2-jEq6sj0e3aAvyBi6qJ5Rx99OIVtW5YcutgdMf_t_v7ZDtkpDknYL5HdJDbI9srlCL9ggbw9geVG1vQKkJZXqC8UmOrL1b1DV0Tv4pN2VMDadZHRoaqfRks38iqJtaSboFvkCOVVZQvuWjIT2lsVY8n3y2L8e926dsryCo5nszB0ZC60102HqgqsFUz5abyjv4CsRJLGXxInCtUy5Foop7qNp5sY-B56iDQAd4Aeknk0zOCQ0DkL0fFSiA5wUZBiik8VRtNNEKM9jcZO0l8sd6ZJ73JTAeI2sD-LKCAGKDEBRCVCTnFcj3gvejT_6NgwgVb8SiyZpLRGNSsHMI-ajQpIC98_R76OOyYaZu8jIbZH6fLaAE7KuP-aTfHZq99wXbKDTPw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD6ICuqDd3E6NQ--CNZ1SXqJb0OcinOITvGtpOmpDLSK2wT_vSdpNxRF8K2UJC35kpNzy3cA9k3T6qWi6eUGY89em_W0jAMPZSy04CrIXMr_fSfqduOHB3U9BYeTuzCI6JLP8Mg-ulh-9mJG1lXWsEwz0jJHzgRScr-8rTXxqPhCkDRWLowsbUA_EOO4pK8avdszsgY5JyM1sAL52znkCqv8kMbuiGkv_e_nlmGxUiVZq8R-BaawWIWFLwSDa_B8g44Z1TgnIKvIVB8ZvWK3rgIOCTt2iR-s9SWQzfoFu7bV01jFZ37MSLu0A7TKjIEB00XG2o6OhJ2My7EM1uGufdo7OfeqAgue4ao59FQqjTHcxLmPvpFch6S_0Y7HUMsoS4MszTTNZS6M1FyLkJQzPw0Fipy0AGyi2IDp4qXATWBpFJPtozMT0aCo4pjMLEGbO8-kDgKe1qAxnu7EVOzjtgjGU-KsEF8lBFBiAUoqgGpwMOnxWjJv_NF2zQIyaVdhUYP6GNGk2pqDhIckkpSMonDr9157MHfeu-oknYvu5TbM2--U-bl1mB6-jXAHZs37sD9423Xr7xORDdaG |
| 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=Reinforcement+Learning+for+Selective+Key+Applications+in+Power+Systems%3A+Recent+Advances+and+Future+Challenges&rft.jtitle=IEEE+transactions+on+smart+grid&rft.au=Chen%2C+Xin&rft.au=Qu%2C+Guannan&rft.au=Tang%2C+Yujie&rft.au=Low%2C+Steven&rft.date=2022-07-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1949-3053&rft.eissn=1949-3061&rft.volume=13&rft.issue=4&rft.spage=2935&rft_id=info:doi/10.1109%2FTSG.2022.3154718&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1949-3053&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1949-3053&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1949-3053&client=summon |