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Machine Learning and Optimization for Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 1375

Special Issue Editors


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Guest Editor
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, UK
Interests: optimization; machine learning; power systems

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Imperial College London, London, UK
Interests: integration of renewable energy; optimization in energy system planning and operation; smart grid; economics and regulations in power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes contributions on machine learning applied to power systems. This may include forecasts or any other type of application of machine learning to power systems.

In terms of optimization, the Special Issue welcomes contributions on this topic and specifically stochastic optimization.

This can also include contributions in which both optimization and machine learning are combined in relation to power systems.

Given the uncertainty that characterizes energy systems, contributions in this area can be very helpful to the wider power systems community, as they can provide significant insights into new methods and concepts in these evolving fields.

Topics of interest for publication include, but are not limited to, the following:

  • Machine learning forecasting methods related to power systems;
  • Other machine learning applications to power systems;
  • Topics that involve the application of optimization within the context of machine learning;
  • Case studies can include the following topics: electric vehicles, energy investments, network planning, etc.

Dr. Spyros Giannelos
Dr. Danny Pudjianto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • optimization
  • uncertainty
  • linear regression
  • deep learning
  • neural networks

Published Papers (1 paper)

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Research

18 pages, 2739 KiB  
Article
A Resilience-Oriented Approach for Microgrid Energy Management with Hydrogen Integration during Extreme Events
by Masoumeh Sharifpour, Mohammad Taghi Ameli, Hossein Ameli and Goran Strbac
Energies 2023, 16(24), 8099; https://doi.org/10.3390/en16248099 - 16 Dec 2023
Cited by 4 | Viewed by 855
Abstract
This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and [...] Read more.
This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and demand response programs (DRPs). It seeks to optimize the delicate balance between maximizing operating revenues and minimizing costs, catering to both normal and critical operational modes. The evaluation of the R-OEMA framework is conducted through numerical simulations on a test system comprising three microgrids (MGs). The simulations consider various disaster scenarios entailing the diverse durations of power outages. The results underscore the efficacy of the R-OEMA approach in augmenting NMG resilience and refining operational efficiency during extreme events. Specifically, the approach integrates hydrogen systems, demand response, and controllable DGs, orchestrating their collaborative operation with predictive insights. This ensures their preparedness for emergency operations in the event of disruptions, enabling the supply of critical loads to reach 82% in extreme disaster scenarios and 100% in milder scenarios. The proposed model is formulated as a mixed-integer linear programming (MILP) framework, seamlessly integrating predictive insights and pre-scheduling strategies. This novel approach contributes to advancing NMG resilience, as revealed by the outcomes of these simulations. Full article
(This article belongs to the Special Issue Machine Learning and Optimization for Energy Systems)
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