Academic Journal
Προβολή σε EDS

Optimizing green port operations with reinforcement learning and model predictive control

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Optimizing green port operations with reinforcement learning and model predictive control
Συγγραφείς: Conte F., Natrella G., Sasso D., Silvestro F., D'Agostino F., D'Antoni F., Merone M.
Συνεισφορές: Conte, F., Natrella, G., Sasso, D., Silvestro, F., D'Agostino, F., D'Antoni, F., Merone, M.
Στοιχεία εκδότη: ELSEVIER SCI LTD
125 London Wall, London, ENGLAND
Έτος έκδοσης: 2025
Συλλογή: Università degli Studi di Genova: CINECA IRIS
Θεματικοί όροι: Smart port, Cold ironing, Neural network, Soft actor critic, Model predictive control
Περιγραφή: While playing a crucial role in global trade, seaports are polluting hubs. To mitigate greenhouse gas emissions from ships at berth, the present study incorporates a combination of solutions to meet ships' demand, including shore-side electricity (Cold ironing), multi-energy storage, renewable generation, and artificial intelligence techniques for forecasting and optimizing renewable energy usage . Key contributions include the sizing of a port based on real ship traffic data and area from the ports of Genova and Trapani, the comparison of reinforcement learning and model predictive control for effective energy management, and the development of artificial intelligence-driven forecasting models for building demand, and photovoltaic and wind generation. Long Short-Term Memory and Time Delay Neural Networks are used to predict renewable generation and demand, while sensitivity analyses demonstrate the flexibility and efficiency of the Soft Actor Critic algorithm in handling forecast uncertainties. The hyperparameters of the artificial intelligence models are optimized on the data of the port of Genova, and tested on both the ports. Results highlight the strength of the developed control algorithms to achieve diesel usage reduction at berth up to 100%. Numerical findings prove the model predictive control ability to extract knowledge from input data and demonstrate the advantages of reinforcement learning in optimizing energy management under limited and uncertain information. The model predictive control and the Soft Actor Critic algorithms were able to achieve 95.9% and 94.4%, respectively, of the maximum achievable cash flow in the port of Genova, and 91.3% and 89.3%, respectively, in the port of Trapani.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: ELETTRONICO
Γλώσσα: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001640122100001; volume:173; firstpage:1; lastpage:16; numberofpages:16; journal:INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS; https://hdl.handle.net/11567/1305862
DOI: 10.1016/j.ijepes.2025.111437
Διαθεσιμότητα: https://hdl.handle.net/11567/1305862
https://doi.org/10.1016/j.ijepes.2025.111437
Αριθμός Καταχώρησης: edsbas.A39FEAF9
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://hdl.handle.net/11567/1305862#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
Header DbId: edsbas
DbLabel: BASE
An: edsbas.A39FEAF9
RelevancyScore: 900
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 899.935607910156
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimizing green port operations with reinforcement learning and model predictive control
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Conte+F%2E%22">Conte F.</searchLink><br /><searchLink fieldCode="AR" term="%22Natrella+G%2E%22">Natrella G.</searchLink><br /><searchLink fieldCode="AR" term="%22Sasso+D%2E%22">Sasso D.</searchLink><br /><searchLink fieldCode="AR" term="%22Silvestro+F%2E%22">Silvestro F.</searchLink><br /><searchLink fieldCode="AR" term="%22D'Agostino+F%2E%22">D'Agostino F.</searchLink><br /><searchLink fieldCode="AR" term="%22D'Antoni+F%2E%22">D'Antoni F.</searchLink><br /><searchLink fieldCode="AR" term="%22Merone+M%2E%22">Merone M.</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: Conte, F.<br />Natrella, G.<br />Sasso, D.<br />Silvestro, F.<br />D'Agostino, F.<br />D'Antoni, F.<br />Merone, M.
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: ELSEVIER SCI LTD<br />125 London Wall, London, ENGLAND
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Università degli Studi di Genova: CINECA IRIS
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Smart+port%22">Smart port</searchLink><br /><searchLink fieldCode="DE" term="%22Cold+ironing%22">Cold ironing</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+network%22">Neural network</searchLink><br /><searchLink fieldCode="DE" term="%22Soft+actor+critic%22">Soft actor critic</searchLink><br /><searchLink fieldCode="DE" term="%22Model+predictive+control%22">Model predictive control</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: While playing a crucial role in global trade, seaports are polluting hubs. To mitigate greenhouse gas emissions from ships at berth, the present study incorporates a combination of solutions to meet ships' demand, including shore-side electricity (Cold ironing), multi-energy storage, renewable generation, and artificial intelligence techniques for forecasting and optimizing renewable energy usage . Key contributions include the sizing of a port based on real ship traffic data and area from the ports of Genova and Trapani, the comparison of reinforcement learning and model predictive control for effective energy management, and the development of artificial intelligence-driven forecasting models for building demand, and photovoltaic and wind generation. Long Short-Term Memory and Time Delay Neural Networks are used to predict renewable generation and demand, while sensitivity analyses demonstrate the flexibility and efficiency of the Soft Actor Critic algorithm in handling forecast uncertainties. The hyperparameters of the artificial intelligence models are optimized on the data of the port of Genova, and tested on both the ports. Results highlight the strength of the developed control algorithms to achieve diesel usage reduction at berth up to 100%. Numerical findings prove the model predictive control ability to extract knowledge from input data and demonstrate the advantages of reinforcement learning in optimizing energy management under limited and uncertain information. The model predictive control and the Soft Actor Critic algorithms were able to achieve 95.9% and 94.4%, respectively, of the maximum achievable cash flow in the port of Genova, and 91.3% and 89.3%, respectively, in the port of Trapani.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: ELETTRONICO
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: info:eu-repo/semantics/altIdentifier/wos/WOS:001640122100001; volume:173; firstpage:1; lastpage:16; numberofpages:16; journal:INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS; https://hdl.handle.net/11567/1305862
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1016/j.ijepes.2025.111437
– Name: URL
  Label: Availability
  Group: URL
  Data: https://hdl.handle.net/11567/1305862<br />https://doi.org/10.1016/j.ijepes.2025.111437
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.A39FEAF9
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.A39FEAF9
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ijepes.2025.111437
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Smart port
        Type: general
      – SubjectFull: Cold ironing
        Type: general
      – SubjectFull: Neural network
        Type: general
      – SubjectFull: Soft actor critic
        Type: general
      – SubjectFull: Model predictive control
        Type: general
    Titles:
      – TitleFull: Optimizing green port operations with reinforcement learning and model predictive control
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Conte F.
      – PersonEntity:
          Name:
            NameFull: Natrella G.
      – PersonEntity:
          Name:
            NameFull: Sasso D.
      – PersonEntity:
          Name:
            NameFull: Silvestro F.
      – PersonEntity:
          Name:
            NameFull: D'Agostino F.
      – PersonEntity:
          Name:
            NameFull: D'Antoni F.
      – PersonEntity:
          Name:
            NameFull: Merone M.
      – PersonEntity:
          Name:
            NameFull: Conte, F.
      – PersonEntity:
          Name:
            NameFull: Natrella, G.
      – PersonEntity:
          Name:
            NameFull: Sasso, D.
      – PersonEntity:
          Name:
            NameFull: Silvestro, F.
      – PersonEntity:
          Name:
            NameFull: D'Agostino, F.
      – PersonEntity:
          Name:
            NameFull: D'Antoni, F.
      – PersonEntity:
          Name:
            NameFull: Merone, M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-locals
              Value: edsbas
ResultId 1