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Energies
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Advanced Optimization Strategy of Electric Vehicle and Smart Grids
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Advanced Optimization Strategy of Electric Vehicle and Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 4270

Special Issue Editors

Dr. Sorin Deleanu

E-Mail Website
Guest Editor
Department of Electrical and Electronics Technology, School of Applied Sciences and Technology, Northern Aberta Institute of Technology, 11706-106 Street NW, Edmonton, AB T5G 2R1, Canada
Interests: electric machines, power converters and electric drives, electric vehicles, power systems, reliability, cybersecurity
Prof. Dr. Emil Cazacu

E-Mail Website
Guest Editor
Department of Electrotechnics, Faculty of Electrical Engineering, University “POLITEHNICA” of Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania
Interests: electromagnetic field computation; magnetic levitation; nonlinear circuits; measurement and interpretation of power quality parameters for low-voltage consumers that operates in distorted and/or unbalanced states
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marilena Stanculescu

E-Mail Website
Guest Editor
Department of Electrotechnics, Faculty of Electrical Engineering, University “POLITEHNICA” of Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania
Interests: power electronics; electrical machines and drives; electromagnetic field computation; non-destructive testing; flaw shape reconstruction; research in thermal field computation and radio frequency heating; electromagnetic field inverse problem computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal is pleased to announce a call for papers for a special issue on "Advanced Optimization Strategy of Electric Vehicle and Smart Grid." This special issue aims to explore the emerging field of advanced optimization techniques and strategies in the context of electric vehicles (EVs) and smart grids. We invite researchers, academics, and industry experts to contribute their original research and insights to enhance our understanding and address the challenges in optimizing the integration and operation of EVs and smart grids.

Topics of interest include, but are not limited to:

  • Advanced optimization algorithms for EV charging and discharging scheduling.
  • Intelligent energy management systems for EVs and smart grids.
  • Optimal resource allocation and demand response in EV-grid integration.
  • Grid-friendly charging strategies for large-scale EV penetration.
  • Optimization of power flow and energy storage in EV charging infrastructure.
  • Vehicle-to-Grid (V2G) optimization and control strategies.
  • Multi-objective optimization for EV fleet management and grid stability.
  • Optimization of renewable energy integration and EV charging infrastructure.
  • Energy trading and market mechanisms for EV and smart grid interaction.
  • Optimization models for EV routing, range estimation, and energy consumption.
  • Data analytics and machine learning approaches for EV and smart grid optimization.
  • Cybersecurity and privacy considerations in optimized EV-grid systems.

Dr. Sorin Deleanu
Prof. Dr. Emil Cazacu
Prof. Dr. Marilena Stanculescu
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

  • electric vehicles
  • smart grids
  • optimization algorithms
  • charging and discharging
  • power flow
  • energy storage
  • renewable energy
  • data analytics
  • machine learning

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Published Papers (4 papers)

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Editorial

Jump to: Research

5 pages, 197 KiB  
Editorial
Recent Developments on the Incentives for Users’ to Participate in Vehicle-to-Grid Services
by Davide Astolfi, Antony Vasile, Silvia Iuliano and Marco Pasetti
Energies 2024, 17(21), 5484; https://doi.org/10.3390/en17215484 - 1 Nov 2024
Cited by 1 | Viewed by 865
Abstract
The transportation is the sector of human activities which contributes the most to greenhouse gas emissions by far [...] Full article
(This article belongs to the Special Issue Advanced Optimization Strategy of Electric Vehicle and Smart Grids)

Research

Jump to: Editorial

24 pages, 1197 KiB  
Article
Optimal Control Scheme of Electric Vehicle Charging Using Combined Model of XGBoost and Cumulative Prospect Theory
by Youseok Lim, Sungwoo Bae and Jun Moon
Energies 2024, 17(24), 6457; https://doi.org/10.3390/en17246457 - 22 Dec 2024
Viewed by 489
Abstract
In this paper, we propose the XPaC (XGBoost Prediction and Cumulative Prospect Theory (CPT)) model to minimize the operational losses of the power grid, taking into account both the prediction of electric vehicle (EV) charging demand and the associated uncertainties, such as when [...] Read more.
In this paper, we propose the XPaC (XGBoost Prediction and Cumulative Prospect Theory (CPT)) model to minimize the operational losses of the power grid, taking into account both the prediction of electric vehicle (EV) charging demand and the associated uncertainties, such as when customers will charge, how much electric energy they will need, and for how long. Given that power utilities supply electricity with limited resources, it is crucial to efficiently control EV charging peaks or predict charging demand during specific periods to maintain stable grid operations. While the total amount of EV charging is a key factor, when and where the charging occurs can be even more critical for the effective management of the grid. Although numerous studies have focused on individually predicting EV charging patterns or demand and evaluating the effectiveness of EV charging control, comprehensive assessments of the actual operational benefits and losses resulting from charging control based on predicted charging behavior remain limited. In this study, we firstly compare the performance of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and decision tree-based XGBoost regression models in predicting hourly charging probabilities and the need for grid demand control. Using the predicted results, we applied the CPT algorithm to analyze the optimal operational scenarios and assess the expected profit and loss for the power grid. Since the charging control optimizer with XPaC incorporates real-world operational data and uses actual records for analysis, it is expected to provide a robust solution for managing the demand arising from the rapid growth of electric vehicles, while operating within the constraints of limited energy resources. Full article
(This article belongs to the Special Issue Advanced Optimization Strategy of Electric Vehicle and Smart Grids)
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20 pages, 3049 KiB  
Article
Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm
by Md Jamal Ahmed Shohan, Md Maidul Islam, Sophia Owais and Md Omar Faruque
Energies 2024, 17(21), 5278; https://doi.org/10.3390/en17215278 - 23 Oct 2024
Viewed by 801
Abstract
As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes [...] Read more.
As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications. Full article
(This article belongs to the Special Issue Advanced Optimization Strategy of Electric Vehicle and Smart Grids)
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26 pages, 16564 KiB  
Article
The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid
by Soobok Yoon and Roger Dargaville
Energies 2024, 17(10), 2267; https://doi.org/10.3390/en17102267 - 8 May 2024
Viewed by 1403
Abstract
The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. [...] Read more.
The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus. Full article
(This article belongs to the Special Issue Advanced Optimization Strategy of Electric Vehicle and Smart Grids)
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