Multi-objective optimal scheduling of charging stations based on deep reinforcement learning Article Swipe
Feifei Cui
,
Xixiang Lin
,
Ruining Zhang
,
Qingyu Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2022.1042882
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2022.1042882
With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.
Related Topics
Concepts
Reinforcement learning
Markov decision process
Computer science
Microgrid
Mathematical optimization
Scheduling (production processes)
Renewable energy
Charging station
Grid
Energy management
Electric vehicle
Markov process
Power (physics)
Energy (signal processing)
Artificial intelligence
Engineering
Control (management)
Mathematics
Geometry
Quantum mechanics
Electrical engineering
Statistics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenrg.2022.1042882
- https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdf
- OA Status
- gold
- Cited By
- 12
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4316466763
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4316466763Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fenrg.2022.1042882Digital Object Identifier
- Title
-
Multi-objective optimal scheduling of charging stations based on deep reinforcement learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-16Full publication date if available
- Authors
-
Feifei Cui, Xixiang Lin, Ruining Zhang, Qingyu YangList of authors in order
- Landing page
-
https://doi.org/10.3389/fenrg.2022.1042882Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdfDirect OA link when available
- Concepts
-
Reinforcement learning, Markov decision process, Computer science, Microgrid, Mathematical optimization, Scheduling (production processes), Renewable energy, Charging station, Grid, Energy management, Electric vehicle, Markov process, Power (physics), Energy (signal processing), Artificial intelligence, Engineering, Control (management), Mathematics, Geometry, Quantum mechanics, Electrical engineering, Statistics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 9Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4316466763 |
|---|---|
| doi | https://doi.org/10.3389/fenrg.2022.1042882 |
| ids.doi | https://doi.org/10.3389/fenrg.2022.1042882 |
| ids.openalex | https://openalex.org/W4316466763 |
| fwci | 1.99060678 |
| type | article |
| title | Multi-objective optimal scheduling of charging stations based on deep reinforcement learning |
| biblio.issue | |
| biblio.volume | 10 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10768 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Electric Vehicles and Infrastructure |
| topics[1].id | https://openalex.org/T10663 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9990000128746033 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2203 |
| topics[1].subfield.display_name | Automotive Engineering |
| topics[1].display_name | Advanced Battery Technologies Research |
| topics[2].id | https://openalex.org/T10603 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9950000047683716 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Smart Grid Energy Management |
| is_xpac | False |
| apc_list.value | 2490 |
| apc_list.currency | USD |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2490 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C97541855 |
| concepts[0].level | 2 |
| concepts[0].score | 0.778265118598938 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[0].display_name | Reinforcement learning |
| concepts[1].id | https://openalex.org/C106189395 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7394698262214661 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q176789 |
| concepts[1].display_name | Markov decision process |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6882221102714539 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2776784348 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6642904281616211 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5762595 |
| concepts[3].display_name | Microgrid |
| concepts[4].id | https://openalex.org/C126255220 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6360377073287964 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[4].display_name | Mathematical optimization |
| concepts[5].id | https://openalex.org/C206729178 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5958274602890015 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2271896 |
| concepts[5].display_name | Scheduling (production processes) |
| concepts[6].id | https://openalex.org/C188573790 |
| concepts[6].level | 2 |
| concepts[6].score | 0.468919575214386 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12705 |
| concepts[6].display_name | Renewable energy |
| concepts[7].id | https://openalex.org/C2779607880 |
| concepts[7].level | 4 |
| concepts[7].score | 0.46228575706481934 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2140665 |
| concepts[7].display_name | Charging station |
| concepts[8].id | https://openalex.org/C187691185 |
| concepts[8].level | 2 |
| concepts[8].score | 0.43996915221214294 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2020720 |
| concepts[8].display_name | Grid |
| concepts[9].id | https://openalex.org/C7817414 |
| concepts[9].level | 3 |
| concepts[9].score | 0.42449936270713806 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1779504 |
| concepts[9].display_name | Energy management |
| concepts[10].id | https://openalex.org/C2776422217 |
| concepts[10].level | 3 |
| concepts[10].score | 0.3173328936100006 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q13629441 |
| concepts[10].display_name | Electric vehicle |
| concepts[11].id | https://openalex.org/C159886148 |
| concepts[11].level | 2 |
| concepts[11].score | 0.28265446424484253 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q176645 |
| concepts[11].display_name | Markov process |
| concepts[12].id | https://openalex.org/C163258240 |
| concepts[12].level | 2 |
| concepts[12].score | 0.23178565502166748 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[12].display_name | Power (physics) |
| concepts[13].id | https://openalex.org/C186370098 |
| concepts[13].level | 2 |
| concepts[13].score | 0.17554154992103577 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q442787 |
| concepts[13].display_name | Energy (signal processing) |
| concepts[14].id | https://openalex.org/C154945302 |
| concepts[14].level | 1 |
| concepts[14].score | 0.1592504382133484 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[14].display_name | Artificial intelligence |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.14455866813659668 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| concepts[16].id | https://openalex.org/C2775924081 |
| concepts[16].level | 2 |
| concepts[16].score | 0.11679515242576599 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q55608371 |
| concepts[16].display_name | Control (management) |
| concepts[17].id | https://openalex.org/C33923547 |
| concepts[17].level | 0 |
| concepts[17].score | 0.08991652727127075 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[17].display_name | Mathematics |
| concepts[18].id | https://openalex.org/C2524010 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[18].display_name | Geometry |
| concepts[19].id | https://openalex.org/C62520636 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[19].display_name | Quantum mechanics |
| concepts[20].id | https://openalex.org/C119599485 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[20].display_name | Electrical engineering |
| concepts[21].id | https://openalex.org/C105795698 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[21].display_name | Statistics |
| concepts[22].id | https://openalex.org/C121332964 |
| concepts[22].level | 0 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[22].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[0].score | 0.778265118598938 |
| keywords[0].display_name | Reinforcement learning |
| keywords[1].id | https://openalex.org/keywords/markov-decision-process |
| keywords[1].score | 0.7394698262214661 |
| keywords[1].display_name | Markov decision process |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6882221102714539 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/microgrid |
| keywords[3].score | 0.6642904281616211 |
| keywords[3].display_name | Microgrid |
| keywords[4].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[4].score | 0.6360377073287964 |
| keywords[4].display_name | Mathematical optimization |
| keywords[5].id | https://openalex.org/keywords/scheduling |
| keywords[5].score | 0.5958274602890015 |
| keywords[5].display_name | Scheduling (production processes) |
| keywords[6].id | https://openalex.org/keywords/renewable-energy |
| keywords[6].score | 0.468919575214386 |
| keywords[6].display_name | Renewable energy |
| keywords[7].id | https://openalex.org/keywords/charging-station |
| keywords[7].score | 0.46228575706481934 |
| keywords[7].display_name | Charging station |
| keywords[8].id | https://openalex.org/keywords/grid |
| keywords[8].score | 0.43996915221214294 |
| keywords[8].display_name | Grid |
| keywords[9].id | https://openalex.org/keywords/energy-management |
| keywords[9].score | 0.42449936270713806 |
| keywords[9].display_name | Energy management |
| keywords[10].id | https://openalex.org/keywords/electric-vehicle |
| keywords[10].score | 0.3173328936100006 |
| keywords[10].display_name | Electric vehicle |
| keywords[11].id | https://openalex.org/keywords/markov-process |
| keywords[11].score | 0.28265446424484253 |
| keywords[11].display_name | Markov process |
| keywords[12].id | https://openalex.org/keywords/power |
| keywords[12].score | 0.23178565502166748 |
| keywords[12].display_name | Power (physics) |
| keywords[13].id | https://openalex.org/keywords/energy |
| keywords[13].score | 0.17554154992103577 |
| keywords[13].display_name | Energy (signal processing) |
| keywords[14].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[14].score | 0.1592504382133484 |
| keywords[14].display_name | Artificial intelligence |
| keywords[15].id | https://openalex.org/keywords/engineering |
| keywords[15].score | 0.14455866813659668 |
| keywords[15].display_name | Engineering |
| keywords[16].id | https://openalex.org/keywords/control |
| keywords[16].score | 0.11679515242576599 |
| keywords[16].display_name | Control (management) |
| keywords[17].id | https://openalex.org/keywords/mathematics |
| keywords[17].score | 0.08991652727127075 |
| keywords[17].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.3389/fenrg.2022.1042882 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2595193159 |
| locations[0].source.issn | 2296-598X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2296-598X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Energy Research |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Frontiers in Energy Research |
| locations[0].landing_page_url | https://doi.org/10.3389/fenrg.2022.1042882 |
| locations[1].id | pmh:oai:doaj.org/article:5fc7f90c7da045a8852d7647882017d2 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Frontiers in Energy Research, Vol 10 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/5fc7f90c7da045a8852d7647882017d2 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5087216286 |
| authorships[0].author.orcid | https://orcid.org/0009-0008-7529-3940 |
| authorships[0].author.display_name | Feifei Cui |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| authorships[0].institutions[0].id | https://openalex.org/I87445476 |
| authorships[0].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Feifei Cui |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| authorships[1].author.id | https://openalex.org/A5078115214 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3213-1534 |
| authorships[1].author.display_name | Xixiang Lin |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| authorships[1].institutions[0].id | https://openalex.org/I87445476 |
| authorships[1].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xixiang Lin |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| authorships[2].author.id | https://openalex.org/A5053672501 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0136-2420 |
| authorships[2].author.display_name | Ruining Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I119454577 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China |
| authorships[2].institutions[0].id | https://openalex.org/I119454577 |
| authorships[2].institutions[0].ror | https://ror.org/05td3s095 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I119454577 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Nanjing Agricultural University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ruining Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China |
| authorships[3].author.id | https://openalex.org/A5037772904 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Qingyu Yang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I87445476 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| authorships[3].institutions[0].id | https://openalex.org/I87445476 |
| authorships[3].institutions[0].ror | https://ror.org/017zhmm22 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I87445476 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Xi'an Jiaotong University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Qingyu Yang |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Multi-objective optimal scheduling of charging stations based on deep reinforcement learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10768 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Electric Vehicles and Infrastructure |
| related_works | https://openalex.org/W2333017163, https://openalex.org/W2347673489, https://openalex.org/W2964937504, https://openalex.org/W2169268775, https://openalex.org/W3013373080, https://openalex.org/W2371966636, https://openalex.org/W2981497404, https://openalex.org/W2362149433, https://openalex.org/W4378211444, https://openalex.org/W3203839526 |
| cited_by_count | 12 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 9 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3389/fenrg.2022.1042882 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2595193159 |
| best_oa_location.source.issn | 2296-598X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2296-598X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Energy Research |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Frontiers in Energy Research |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fenrg.2022.1042882 |
| primary_location.id | doi:10.3389/fenrg.2022.1042882 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2595193159 |
| primary_location.source.issn | 2296-598X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2296-598X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Energy Research |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1042882/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Energy Research |
| primary_location.landing_page_url | https://doi.org/10.3389/fenrg.2022.1042882 |
| publication_date | 2023-01-16 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3090667645, https://openalex.org/W6755254405, https://openalex.org/W2515947593, https://openalex.org/W6741872501, https://openalex.org/W6810393848, https://openalex.org/W3123212791, https://openalex.org/W6757865261, https://openalex.org/W6811396470, https://openalex.org/W2114588688, https://openalex.org/W3006624343, https://openalex.org/W3206739682, https://openalex.org/W3127850951, https://openalex.org/W2944339461, https://openalex.org/W2802445149, https://openalex.org/W3181341067, https://openalex.org/W2883398862, https://openalex.org/W2783064730, https://openalex.org/W3100630095, https://openalex.org/W2962809298, https://openalex.org/W6810807888, https://openalex.org/W4206398417, https://openalex.org/W3200898618, https://openalex.org/W3014321438, https://openalex.org/W6809461956, https://openalex.org/W6684588646, https://openalex.org/W2525707554, https://openalex.org/W6793901347, https://openalex.org/W3202331697, https://openalex.org/W6757225213, https://openalex.org/W2899639849, https://openalex.org/W6782993138, https://openalex.org/W6747232964, https://openalex.org/W6790382555, https://openalex.org/W6806573182, https://openalex.org/W3048479713, https://openalex.org/W4205745186, https://openalex.org/W6767440500, https://openalex.org/W6804569950, https://openalex.org/W4206004614, https://openalex.org/W3088182102, https://openalex.org/W4225786141, https://openalex.org/W2905708007, https://openalex.org/W2910950104, https://openalex.org/W4226455455, https://openalex.org/W3153428702, https://openalex.org/W2741177915, https://openalex.org/W4214606885, https://openalex.org/W3131248943, https://openalex.org/W2783347048, https://openalex.org/W2896130544, https://openalex.org/W2166606493, https://openalex.org/W4200004120, https://openalex.org/W4226388845, https://openalex.org/W2969699448 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 34, 46, 73, 107, 124 |
| abstract_inverted_index.EV | 22 |
| abstract_inverted_index.In | 17, 99 |
| abstract_inverted_index.To | 161 |
| abstract_inverted_index.an | 54 |
| abstract_inverted_index.as | 59, 106 |
| abstract_inverted_index.by | 133, 195, 205, 211 |
| abstract_inverted_index.in | 156 |
| abstract_inverted_index.is | 69, 131, 197 |
| abstract_inverted_index.it | 68, 92 |
| abstract_inverted_index.of | 4, 20, 41, 84, 148, 165 |
| abstract_inverted_index.on | 113 |
| abstract_inverted_index.to | 13, 24, 71, 76, 94, 118 |
| abstract_inverted_index.up | 171 |
| abstract_inverted_index.we | 44, 102, 151, 169 |
| abstract_inverted_index.MDP | 129 |
| abstract_inverted_index.TD3 | 196 |
| abstract_inverted_index.The | 188 |
| abstract_inverted_index.and | 28, 88, 180, 207 |
| abstract_inverted_index.are | 9 |
| abstract_inverted_index.can | 200 |
| abstract_inverted_index.for | 50 |
| abstract_inverted_index.has | 32 |
| abstract_inverted_index.key | 35 |
| abstract_inverted_index.set | 170 |
| abstract_inverted_index.the | 1, 18, 25, 38, 60, 82, 96, 120, 128, 134, 145, 149, 157, 163, 166, 176, 192 |
| abstract_inverted_index.two | 172 |
| abstract_inverted_index.EVA, | 150 |
| abstract_inverted_index.EVs, | 43 |
| abstract_inverted_index.With | 0, 123 |
| abstract_inverted_index.also | 152 |
| abstract_inverted_index.cost | 204 |
| abstract_inverted_index.deep | 114, 137, 181 |
| abstract_inverted_index.each | 78 |
| abstract_inverted_index.face | 19 |
| abstract_inverted_index.fuel | 15 |
| abstract_inverted_index.show | 190 |
| abstract_inverted_index.that | 191 |
| abstract_inverted_index.this | 100 |
| abstract_inverted_index.twin | 135 |
| abstract_inverted_index.user | 89 |
| abstract_inverted_index.with | 53, 64 |
| abstract_inverted_index.(DRL) | 117 |
| abstract_inverted_index.(EVA) | 58 |
| abstract_inverted_index.(EVs) | 8 |
| abstract_inverted_index.(MDP) | 111 |
| abstract_inverted_index.10.9% | 206 |
| abstract_inverted_index.While | 143 |
| abstract_inverted_index.avoid | 119 |
| abstract_inverted_index.based | 112 |
| abstract_inverted_index.being | 10 |
| abstract_inverted_index.body. | 62 |
| abstract_inverted_index.grid, | 26 |
| abstract_inverted_index.makes | 91 |
| abstract_inverted_index.model | 103, 130 |
| abstract_inverted_index.power | 202, 209 |
| abstract_inverted_index.using | 175 |
| abstract_inverted_index.which | 199 |
| abstract_inverted_index.(DDPG) | 185 |
| abstract_inverted_index.(TD3). | 142 |
| abstract_inverted_index.69.4%. | 212 |
| abstract_inverted_index.Markov | 108 |
| abstract_inverted_index.access | 23 |
| abstract_inverted_index.action | 126 |
| abstract_inverted_index.become | 33 |
| abstract_inverted_index.demand | 90 |
| abstract_inverted_index.energy | 86 |
| abstract_inverted_index.ensure | 77, 153 |
| abstract_inverted_index.method | 179 |
| abstract_inverted_index.model. | 98 |
| abstract_inverted_index.paper, | 101 |
| abstract_inverted_index.policy | 139, 183 |
| abstract_inverted_index.power. | 160 |
| abstract_inverted_index.reduce | 201, 208 |
| abstract_inverted_index.solved | 132 |
| abstract_inverted_index.space, | 127 |
| abstract_inverted_index.verify | 162 |
| abstract_inverted_index.benefit | 147 |
| abstract_inverted_index.delayed | 136 |
| abstract_inverted_index.energy, | 5 |
| abstract_inverted_index.maximum | 146 |
| abstract_inverted_index.method, | 168, 186 |
| abstract_inverted_index.minimal | 154 |
| abstract_inverted_index.process | 110 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.rapidly | 12 |
| abstract_inverted_index.replace | 14 |
| abstract_inverted_index.results | 189 |
| abstract_inverted_index.vehicle | 56 |
| abstract_inverted_index.However, | 63 |
| abstract_inverted_index.charging | 30, 39, 51, 104, 178 |
| abstract_inverted_index.decision | 109 |
| abstract_inverted_index.disorder | 177 |
| abstract_inverted_index.electric | 6, 55 |
| abstract_inverted_index.ensuring | 144 |
| abstract_inverted_index.exchange | 159 |
| abstract_inverted_index.gradient | 140, 184 |
| abstract_inverted_index.learning | 116 |
| abstract_inverted_index.multiple | 65 |
| abstract_inverted_index.obtained | 194 |
| abstract_inverted_index.optimal, | 198 |
| abstract_inverted_index.problem. | 36 |
| abstract_inverted_index.proposed | 167 |
| abstract_inverted_index.purchase | 203 |
| abstract_inverted_index.stations | 52 |
| abstract_inverted_index.strategy | 75, 193 |
| abstract_inverted_index.vehicles | 7 |
| abstract_inverted_index.Moreover, | 81 |
| abstract_inverted_index.algorithm | 141 |
| abstract_inverted_index.developed | 11 |
| abstract_inverted_index.different | 42 |
| abstract_inverted_index.difficult | 93 |
| abstract_inverted_index.effective | 29 |
| abstract_inverted_index.establish | 95 |
| abstract_inverted_index.formulate | 72 |
| abstract_inverted_index.framework | 49 |
| abstract_inverted_index.microgrid | 158 |
| abstract_inverted_index.problems. | 122 |
| abstract_inverted_index.real-time | 27, 47, 74 |
| abstract_inverted_index.renewable | 85 |
| abstract_inverted_index.vehicles. | 16 |
| abstract_inverted_index.aggregator | 57 |
| abstract_inverted_index.continuous | 125 |
| abstract_inverted_index.generation | 87 |
| abstract_inverted_index.interests. | 80 |
| abstract_inverted_index.management | 31 |
| abstract_inverted_index.scheduling | 48, 105 |
| abstract_inverted_index.transition | 3 |
| abstract_inverted_index.Considering | 37 |
| abstract_inverted_index.challenging | 70 |
| abstract_inverted_index.comparative | 173 |
| abstract_inverted_index.fluctuation | 155 |
| abstract_inverted_index.large-scale | 21 |
| abstract_inverted_index.objectives, | 67 |
| abstract_inverted_index.uncertainty | 83 |
| abstract_inverted_index.experiments, | 174 |
| abstract_inverted_index.fluctuations | 210 |
| abstract_inverted_index.optimization | 66, 97 |
| abstract_inverted_index.deterministic | 138, 182 |
| abstract_inverted_index.effectiveness | 164 |
| abstract_inverted_index.reinforcement | 115 |
| abstract_inverted_index.respectively. | 187 |
| abstract_inverted_index.green-oriented | 2 |
| abstract_inverted_index.afore-mentioned | 121 |
| abstract_inverted_index.characteristics | 40 |
| abstract_inverted_index.decision-making | 61 |
| abstract_inverted_index.participant’s | 79 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5037772904 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I87445476 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.85172388 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |