Submit to Energies Review for Energies Propose a Special Issue

Journal Browser

► Journal Browser

Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 16056

Share This Special Issue

Special Issue Editors

Prof. Dr. Komla A. Folly

E-Mail Website1 Website2
Guest Editor

Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
Interests: electrical engineering; power system stability; control; optimization; artificial intelligence

Dr. N Kumarappan

E-Mail Website1 Website2
Guest Editor

Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
Interests: power system operation and control; power system optimization; computational intelligence techniques; power system reliability; power system fault diagnosis; power system price forecasting; smart grid

Special Issue Information

Dear Colleagues,

The development of the internal combustion engine (ICE) has contributed significantly to the growth of modern society by satisfying the needs for greater mobility in everyday life. However, it has been widely argued that transport is one of the major contributors to greenhouse gas (GHG) and pollutant emissions in the form of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), unburned hydrocarbons (HCs), etc. However, the successful commercialization of electrical vehicles (EVs) has demonstrated the potential of electric vehicles in terms of reducing fuel consumption and GHGs from the transport sector. This has brought EVs back into the spotlight worldwide at a moment when fossil fuel prices are unexpectedly high due to increased demand. It is expected that electric vehicles (EVs) will soon represent a large share of the demand for electricity. The adoption of a large number of EVs will offer both benefits and challenges to the electricity grid. On the one hand, the integration of many EVs into the power grid with a large power demand may potentially lead to power grid stability problems if the charging patterns of EVs are not well controlled. On the other hand, EVs with V2G capability can act as an energy storage system for renewable energy, providing ancillary services to the grid via bidirectional power flow if electricity trading and operation models are appropriately set up. There is a need for greater flexibility of active management and better utilization of existing network assets. Optimal scheduling and intelligent energy management strategies are key to integrating large numbers of electric vehicles and the smart grid.

This Special Issue focuses on optimal scheduling and intelligent energy management strategies for electric vehicle charging stations (CSs). Topics of interest for publication include, but are not limited to, the following:

Optimal scheduling methods for EVs;
Artificial intelligence applied to power flow in EV charging stations;
EV charging infrastructure;
Demand–response and V2G;
Decentralized framework for bidirectional operation of EVs;
Integration of renewable energy sources and EV network;
EV electricity market (day-ahead, real time, etc.);
Intelligent energy management systems for EV charging stations;
Advanced flexibility strategies for EV charging/discharging;
EV integration in residential/private/community buildings and self-consumption;
Electric vehicle planning and operation in the smart grid;
Distributed/decentralized V2G/G2V scheduling of EVs;
EV communication, planning and operation.

Prof. Dr. Komla A. Folly
Dr. N Kumarappan
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

EV charging infrastructure
optimal scheduling methods for EVs
EV electricity market
demand–response and V2G
intelligent energy management systems
artificial intelligence applied to power flow in EV charging stations

Published Papers (5 papers)

Download All Papers

Research

Jump to: Review

18 pages, 5719 KiB

Open AccessArticle

Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area

by Nadia V. Panossian, Haitam Laarabi, Keith Moffat, Heather Chang, Bryan Palmintier, Andrew Meintz, Timothy E. Lipman and Rashid A. Waraich

Energies 2023, 16(5), 2189; https://0-doi-org.brum.beds.ac.uk/10.3390/en16052189 - 24 Feb 2023

Cited by 3 | Viewed by 1578

Abstract

This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work. Full article

(This article belongs to the Special Issue Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations)

► Show Figures

Figure 1

19 pages, 3719 KiB

Open AccessArticle

Electric Vehicle User Behavior: An Analysis of Charging Station Utilization in Canada

by Tim Jonas, Noah Daniels and Gretchen Macht

Energies 2023, 16(4), 1592; https://0-doi-org.brum.beds.ac.uk/10.3390/en16041592 - 05 Feb 2023

Cited by 15 | Viewed by 3797

Abstract

For a user-centered deployment of electric vehicle supply equipment (EVSE) infrastructure, it is vital to understand electric vehicle user charging behavior. This study identifies user behavioral patterns by analyzing data from more than 7000 charging stations in Canada, comparing residential vs. public Level 2, and public direct current fast (DCFC) vs. public Level 2 charging. A novel algorithm, CHAODA, was applied to identify differences between DCFC and other Level 2 charging options. Through a multivariate and holistic methodology, various patterns emerge, identifying differences in the utilization and seasonality of different EVSE types. The study provides evidence of an “EV Duck Curve” that amplifies the baseline of the power production “Duck Curve,” confirming future challenges for grid stability. Implementations of this study can support future EVSE infrastructure planning efforts and help improve the overall service of electric vehicle supply equipment and grid stability. Full article

(This article belongs to the Special Issue Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations)

► Show Figures

Figure 1

26 pages, 4073 KiB

Open AccessArticle

Mitigating Adverse Impacts of Increased Electric Vehicle Charging on Distribution Transformers

by Akansha Jain and Masoud Karimi-Ghartemani

Energies 2022, 15(23), 9023; https://0-doi-org.brum.beds.ac.uk/10.3390/en15239023 - 29 Nov 2022

Cited by 9 | Viewed by 1512

Abstract

As the world is transitioning to electric vehicles (EVs), the existing power grids are facing several challenges. In particular, the additional charging power demand may repeatedly overload the traditionally-sized distribution transformers and adversely impact their operational life. To address this challenge, this paper proposes an EV-based reactive power compensation strategy for transformer overloading mitigation. Specifically, a low-bandwidth centralized recursive controller is proposed to determine a set point for the EV’s onboard charger’s reactive power. Importantly, the proposed strategy is practically implementable in existing distribution grids as it does not rely on smart grid infrastructure and is stable under potential communication delays and partial failures. This paper discusses the controller’s structure, design, and stability in detail. The proposed solution is tested with a realistic secondary distribution system considering four different EV charging scenarios with both Level 1 and Level 2 residential EV charging. Specifically, IEEE Standard C57.91-2011 is used to quantify the impact of EV charging on the transformer’s life. It is shown that with the proposed method, transformer overloading is significantly reduced, and the transformer’s life improves by an average of 47% over a year in all four scenarios. Full article

(This article belongs to the Special Issue Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations)

► Show Figures

Figure 1

25 pages, 1792 KiB

Open AccessArticle

An Octopus Charger-Based Smart Protocol for Battery Electric Vehicle Charging at a Workplace Parking Structure

by Edgar Ramos Muñoz and Faryar Jabbari

Energies 2022, 15(17), 6459; https://0-doi-org.brum.beds.ac.uk/10.3390/en15176459 - 04 Sep 2022

Cited by 1 | Viewed by 1510

Abstract

The transportation sector produces a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large-scale adoption of battery electric vehicles and has led to several policies and laws aimed at incentivizing their sales. While electric vehicles comprise a small percentage of the overall fleets of vehicles, the expected production of electric vehicles is soon expected to be in the millions. This will create challenges in providing an adequate charging infrastructure, as well as the ensuing management of the overall electricity demand at the grid level. In this work, a novel smart-charging protocol for battery electric vehicle charging at workplace parking structures is proposed. The Octopus Charger-based Mixed Integer Linear Programming protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging periods for their assigned vehicles. The proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of the proposed protocol. The proposed protocol resulted in improved peak load reductions for all simulated smart-charging scenarios when compared with uncontrolled charging. Critically, the assignment algorithm resulted in a number of required chargers close to the theoretical minimum. Full article

(This article belongs to the Special Issue Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 1435 KiB

Open AccessReview

Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review

by Qin Chen and Komla Agbenyo Folly

Energies 2023, 16(1), 146; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010146 - 23 Dec 2022

Cited by 18 | Viewed by 6447

Abstract

The high penetration of electric vehicles (EVs) will burden the existing power delivery infrastructure if their charging and discharging are not adequately coordinated. Dynamic pricing is a special form of demand response that can encourage EV owners to participate in scheduling programs. Therefore, EV charging and discharging scheduling and its dynamic pricing model are important fields of study. Many researchers have focused on artificial intelligence-based EV charging demand forecasting and scheduling models and suggested that artificial intelligence techniques perform better than conventional optimization methods such as linear, exponential, and multinomial logit models. However, only a few research studies focused on EV discharging scheduling (i.e., vehicle-to-grid, V2G) because the concept of EV discharging electricity back to the power grid is relatively new and evolving. Therefore, a review of existing EV charging and discharging-related studies is needed to understand the research gaps and to make some improvements in future studies. This paper reviews EV charging and discharging-related studies and classifies them into forecasting, scheduling, and pricing mechanisms. The paper determines the linkage between forecasting, scheduling, and pricing mechanism and identifies the research gaps in EV discharging scheduling and dynamic pricing models. Full article

(This article belongs to the Special Issue Optimal Scheduling and Intelligent Energy Management Strategies for Electric Vehicle Charging Stations)

► Show Figures