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Energies
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Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids
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Advanced Coordinated 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: 31 July 2024 | Viewed by 11863

Special Issue Editors

Dr. Martina Kajanova

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Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia
Interests: power systems modeling and control; renewable energy sources; electric vehicles; smart grid
Dr. Marek Höger

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia
Interests: power system modeling; operation of distribution networks; protective relaying
Dr. Peter Bracinik

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia
Interests: control of power electrical systems; distribution automation; control and information systems applied in power engineering
Prof. Dr. Pavol Špánik

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia
Interests: power electronic systems; semiconductor structures; power electronics for automotive

Special Issue Information

Dear Colleagues,

A shift in society towards decarbonization and environmentally friendly lifestyles has brought many challenges for the operation and control of electrical power systems. An increasing share of renewable energy sources, a decreasing dependency on fossil fuels, changes in energy consumers’ behavior, and other similar trends can cause stability issues for power systems operated in a traditional way. Moreover, the prevailing trend of electric vehicle utilization links electrical power systems more closely with the transportation sector and electric vehicle drivers’ daily habits. Therefore, new coordinated strategies and tools considering these recent trends and different perspectives are needed to ensure the reliability and resiliency of smart grids while satisfying consumers’ requirements and preferences.

This Special Issue aims to gather and present original contributions and reviews focused on advanced optimization strategies related to electric vehicle operation, charging, and utilization within smart grids.

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

  • Electric vehicle operation modeling;
  • Battery charging modeling;
  • Electric vehicle operation and charging predictions;
  • Optimization of electric vehicle fleet coordination;
  • Coordination between transportation and electrical power system sectors;
  • Optimization of electric vehicle charging;
  • Impact of different electric vehicle charging strategies on electrical power systems;
  • Advanced strategies of smart grid operation and control;
  • Vehicle-to-X concepts;
  • Cooperation of renewables and electric vehicle charging stations.

Dr. Martina Kajanova
Dr. Marek Höger
Dr. Peter Bracinik
Prof. Dr. Pavol Špánik
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
  • charging
  • modeling
  • optimization
  • control
  • system operation

Published Papers (10 papers)

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Research

Jump to: Review

12 pages, 590 KiB  
Article
Decision Process for Identifying Appropriate Devices for Power Transfer between Voltage Levels in Distribution Grids
by Nassipkul Dyussembekova, Reiner Schütt, Ingmar Leiße and Bente Ralfs
Energies 2024, 17(9), 2158; https://doi.org/10.3390/en17092158 (registering DOI) - 30 Apr 2024
Abstract
During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids [...] Read more.
During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
16 pages, 707 KiB  
Article
Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators
by Yi-An Chen, Wente Zeng, Adil Khurram and Jan Kleissl
Energies 2024, 17(7), 1745; https://doi.org/10.3390/en17071745 - 05 Apr 2024
Viewed by 467
Abstract
In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead [...] Read more.
In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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34 pages, 6339 KiB  
Article
Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations
by Pavol Belany, Peter Hrabovsky and Zuzana Florkova
Energies 2024, 17(5), 1073; https://doi.org/10.3390/en17051073 - 23 Feb 2024
Viewed by 462
Abstract
In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize [...] Read more.
In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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20 pages, 8338 KiB  
Article
Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling
by Ahmad Almaghrebi, Kevin James, Fares Al Juheshi and Mahmoud Alahmad
Energies 2024, 17(4), 925; https://doi.org/10.3390/en17040925 - 16 Feb 2024
Viewed by 586
Abstract
In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected [...] Read more.
In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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26 pages, 1430 KiB  
Article
A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration
by Bahman Ahmadi and Elham Shirazi
Energies 2023, 16(19), 6959; https://doi.org/10.3390/en16196959 - 05 Oct 2023
Cited by 1 | Viewed by 967
Abstract
The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and [...] Read more.
The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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14 pages, 10937 KiB  
Article
Parametric Optimization of Ferrite Structure Used for Dynamic Wireless Power Transfer for 3 kW Electric Vehicle
by Mohamed Bensetti, Karim Kadem, Yao Pei, Yann Le Bihan, Eric Labouré and Lionel Pichon
Energies 2023, 16(14), 5439; https://doi.org/10.3390/en16145439 - 18 Jul 2023
Cited by 3 | Viewed by 1315
Abstract
The current charging technology for electric vehicles consists of plugging the cable from the AC utility to charge the batteries. This requires heavy gauge cables to connect to electric vehicles, which can be difficult to handle, presents tripping hazards, and is prone to [...] Read more.
The current charging technology for electric vehicles consists of plugging the cable from the AC utility to charge the batteries. This requires heavy gauge cables to connect to electric vehicles, which can be difficult to handle, presents tripping hazards, and is prone to vandalism. In addition to these inconveniences, electric vehicles must be immobilized for hours before being fully charged. Dynamic wireless power transfer has been studied worldwide as a promising technology. It is safe and convenient and allows electric vehicles to charge while moving. To improve the efficiency of a dynamic wireless power transfer system, the magnetic coupling coefficient must be maximized between the primary pad, which is integrated into the road, and the secondary pad installed in the electric vehicle. This article presents a parametric optimization of the ferrite structure used for a 3 kW dynamic wireless power transfer prototype. Different ferrite configurations are compared while studying the effect of the parameter values on their magnetic coupling coefficient. Finally, the proposed structure was validated during the experimental test, and its coupling coefficient was improved by 26% compared to the original structure. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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15 pages, 2265 KiB  
Article
Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method
by Klemen Deželak, Klemen Sredenšek and Sebastijan Seme
Energies 2023, 16(14), 5393; https://doi.org/10.3390/en16145393 - 14 Jul 2023
Cited by 2 | Viewed by 1002
Abstract
The widespread adoption of electric vehicles poses certain challenges to the distribution grid, which refers to the network of power lines, transformers, and other infrastructure that delivers electricity from power plants to consumers. This higher demand can strain the distribution grid, particularly in [...] Read more.
The widespread adoption of electric vehicles poses certain challenges to the distribution grid, which refers to the network of power lines, transformers, and other infrastructure that delivers electricity from power plants to consumers. This higher demand can strain the distribution grid, particularly in areas with a high concentration of electric vehicles. Grid operators need to ensure that the grid infrastructure can handle this additional load and prevent overloading and consequences in terms of additional losses. As part of the task, a methodology was developed for the assessment of the electricity consumption of battery electric vehicles in Slovenia. The approach used for the calculation includes the number of electric cars, average consumption, distance travelled and efficiency of the system. Additionally, the results of the modelling approach for an integrated distribution grid model in terms of steady-state simulations are presented. The regular situation of the power losses within the distribution grid is managed together with an optimal result. In this sense, an application of the particle swarm optimisation-based strategy is suggested to minimise reliance on grid systems. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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24 pages, 22093 KiB  
Article
Techno-Economic Potential of V2B in a Neighborhood, Considering Tariff Models and Battery Cycle Limits
by Yannick Pohlmann and Carl-Friedrich Klinck
Energies 2023, 16(11), 4387; https://doi.org/10.3390/en16114387 - 29 May 2023
Viewed by 868
Abstract
To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place. In the long term, it can be assumed that a large proportion of passenger cars will be [...] Read more.
To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place. In the long term, it can be assumed that a large proportion of passenger cars will be battery–electric. On the one hand, this conversion will result in higher energy and power requirements for the electricity network; on the other hand, it also offers the potential for vehicles to provide energy for various systems in the future. Battery–electric vehicles can be used to shift grid purchases, optimize the operation of other components and increase the self-consumption rate of photovoltaic systems. An LP model for the optimal energy management of the neighborhood consisting of buildings with electricity and heat demand, a PV system, a BEV fleet, a heat pump and thermal storage was formulated. The potential of the BEV fleet to provide energy via V2B in the neighborhood was investigated, considering electricity tariff models and individual charging/discharging efficiencies of vehicles and stochastic mobility profiles. The vehicle fleet provides between 4.8kWh−1sqm−1a (flat-fee) and 25.3kWh−1sqm−1a (dynamic tariff) per year, corresponding to 6.7, 9.5% and 35.7% of the annual energy demand of the neighborhood. All tariff models lead to optimization of self-consumption in summer. Dynamic pricing also leads to arbitrage during winter, and a power price tariff avoids peaks in grid draw. Due to individual charging efficiencies, the power supplied by the fleet is distributed unevenly among the vehicles, and setting limits for additional equivalent full cycles distributes the energy more evenly across the fleet. The limits affect the V2B potential, especially below the limits of 20 yearly cycles for flat and power tariffs and below 80 cycles for a dynamic tariff. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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Review

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25 pages, 2084 KiB  
Review
A Review of Advanced Control Strategies of Microgrids with Charging Stations
by Matej Tkac, Martina Kajanova and Peter Bracinik
Energies 2023, 16(18), 6692; https://doi.org/10.3390/en16186692 - 18 Sep 2023
Cited by 5 | Viewed by 1153
Abstract
In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, [...] Read more.
In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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33 pages, 4046 KiB  
Review
A Comprehensive Review of the Bidirectional Converter Topologies for the Vehicle-to-Grid System
by Suresh Panchanathan, Pradeep Vishnuram, Narayanamoorthi Rajamanickam, Mohit Bajaj, Vojtech Blazek, Lukas Prokop and Stanislav Misak
Energies 2023, 16(5), 2503; https://doi.org/10.3390/en16052503 - 06 Mar 2023
Cited by 12 | Viewed by 4202
Abstract
Over the past decade, there has been a great interest in the changeover from cars powered by gasoline to electric vehicles, both within the automotive industry and among customers. The electric vehicle–grid (V2G) technology is a noteworthy innovation that enables the battery of [...] Read more.
Over the past decade, there has been a great interest in the changeover from cars powered by gasoline to electric vehicles, both within the automotive industry and among customers. The electric vehicle–grid (V2G) technology is a noteworthy innovation that enables the battery of an electric vehicle during idling conditions or parked can function as an energy source that can store or release energy whenever required. This results in energy exchange between the grid and EV batteries. This article reviews various bidirectional converter topologies used in the V2G system. Additionally, it can reduce the cost of charging for electric utilities, thus increasing profits for EV owners. Normally electric grid and the battery of an electric vehicle can be connected through power electronic converters, especially a bidirectional converter, which allows power to flow in both directions. The majority of research work is carried out over the converters for V2G applications and concerns utilizing two conversion stages, such as the AC-DC conversion stage used for correcting the power factor and the DC-DC conversion stage for matching the terminal voltage. Furthermore, a bidirectional conversion can be made for an active power transfer between grid–vehicle (G2V) and V2G effectively. This review explores and examines several topologies of bidirectional converters which make it possible for active power flow between the grid and the vehicle and vice versa. Moreover, different types of charging and discharging systems, such as integrated/non-integrated and on/off board, etc., which have been used for electric vehicle applications, are also discussed. A comparison study is carried out based on several other factors that have been suggested. The utilization of semiconductors in power converters and non-conventional resources in charging and discharging applications are the two improving technologies for electric vehicles. Full article
(This article belongs to the Special Issue Advanced Coordinated Optimization Strategy of Electric Vehicle and Smart Grids)
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