Recent Advances in Modeling and Control of Electric Energy Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 863

Special Issue Editor

Special Issue Information

Dear Colleagues,

Over the past few years, electric energy systems are rapidly evolving, driven by the increasing demand for renewable energy integration, advancements in smart grid technologies, and the necessity for enhanced reliability and efficiency. The complexities involved in the modern electric energy systems necessitate robust modeling and control strategies to ensure their stable and efficient operation. This Special Issue on "Recent Advances in Modeling and Control of Electric Energy Systems" aims to showcase cutting-edge research findings and innovative solutions that address the challenges faced by today's electric grids. We seek to highlight significant advancements in modeling techniques, control strategies, and their applications in real-world scenarios.

We hope that this Special Issue will serve as a platform for researchers, engineers, and practitioners to share their latest findings and exchange ideas.

The topics include, but are not limited to, the following:

  • Advanced modeling techniques for electric energy systems;
  • The dynamic modeling of power systems;
  • The modeling of renewable energy sources and their integration;
  • Control strategies for renewable energy integration;
  • Energy storage systems and their management;
  • The control of distributed energy resources (DERs);
  • Demand response and demand-side management;
  • The modeling and control of battery energy storage systems;
  • The integration of energy storage with renewable sources;
  • Advanced energy management systems for storage solutions;
  • New topologies and control methods of inverters for electric vehicles;
  • The thermal management of battery systems;
  • Advanced charging systems for electric vehicles.

Dr. Dmitry Baimel
Guest Editor

Manuscript Submission Information

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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. Electronics 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 2400 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

  • electric energy system
  • power system
  • renewable energy source
  • energy storage system
  • electric vehicle

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Published Papers (1 paper)

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Research

21 pages, 9797 KiB  
Article
Artificial Intelligence-Driven Optimal Charging Strategy for Electric Vehicles and Impacts on Electric Power Grid
by Umar Jamil, Raul Jose Alva, Sara Ahmed and Yu-Fang Jin
Electronics 2025, 14(7), 1471; https://doi.org/10.3390/electronics14071471 - 6 Apr 2025
Viewed by 497
Abstract
Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an [...] Read more.
Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs. Full article
(This article belongs to the Special Issue Recent Advances in Modeling and Control of Electric Energy Systems)
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