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Special Issue "Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (16 June 2017)

Special Issue Editors

Guest Editor
Dr. Hugo Morais

Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), ISEP/IPP, Porto, Portugal
E-Mail
Interests: distributed energy resources management; electric vehicles integration in power systems; virtual power players, microgrids and smart grids management; multi-agents systems and power systems visualization
Guest Editor
Prof. Dr. Juan Manuel Corchado

Bioinformática, Sistemas Inteligentes y Tecnología Educativa, Universidad de Salamanca, Salamanca, Spain
Website | E-Mail
Interests: multi-agent systems based on hybrid algorithms for artificial intelligence; systems case-based reasoning; neuro-symbolic model for real-time forecasting problems; artificial Intelligence systems applied to knowledge management; case-based reasoning systems; plan-based reasoning systems; deliberative reasoning systems; artificial neural networks; genetic algorithms
Guest Editor
Prof. Dr. Lei Wang

College of Electronic and Information Engineering, TongJi Unversity, Shanghai, China
Website | E-Mail
Interests: integration of different intelligent algorithms such as neural networks, fuzzy algorithms, swarm intelligence, and apply them for intelligent decision-making
Guest Editor
Dr. Junjie Hu

Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
Website | E-Mail
Interests: optimal charging of electric vehicles and storages for secure power distribution system operation and power system balancing; Transactive control for managing distributed energy resources; multi-agent theory and simulation
Guest Editor
Dr. Emanuele Principi

Center for Electric Power and Energy, Università Politecnica delle Marche, Ancona, Italy
Website | E-Mail
Interests: machine learning and pattern recognition for the smart grid (e.g., energy task scheduling, non-intrusive load monitoring, computational Intelligence for vehicle to grid) and intelligent audio analysis (e.g., multiroom voice activity detection and speaker localization)

Special Issue Information

Dear Colleagues,

The fast developments of the electric vehicles impose new challenges for power system management and planning. However, considering the recent EVs’ evolution perspectives, such as vehicle-to-grid technology, EVs also present opportunity for a smart grid in the near future. Within the smart grids context, the integration of EVs can be seen as a flexible load, as well as a generation resource, with the capacity to provide different services to the system, such as the frequency regulation and peak shaving.

Beyond the integration with power systems, the management of EVs can be integrated in a more embracing perspective like smart energy network management and planning, e.g., an integrated planning of traffic network, heating network, and power distribution network. With smart energy network planning technology, it can help in building an efficient and low-carbon society.

In the present Special Issue, we invite original and unpublished submissions concerning the integration of electric vehicles in future power systems allowing the development of the smart grids and smart energy network. Intelligent computing methods developments and applications in electric vehicles fields should be specifically addressed in the papers.

Potential topics include, but are not limited to:

  • Electric vehicle charging infrastructure planning
  • Electric vehicle fleet operation management
  • Multi-agents’ application on electric vehicles charging and discharging
  • Energy resources management considering electric vehicles
  • Stochastic analysis and optimization of electric vehicles management in smart grids
  • Use of electric vehicles/battery for frequency regulation, peak shaving and load leveling
  • Power quality enhancement with electric vehicles
  • Integrated management of electric vehicles considering the power grid and other critical infrastructures in a smart energy network context
  • Electric vehicle driving pattern analysis and prediction
  • Intelligent methods for electric motor fault detection
  • Impact of communication delay on system integration of electric vehicles within a smart grid context

Dr. Hugo Morais
Prof. Dr. Juan Manuel Corchado
Prof. Dr. Lei Wang
Dr. Junjie Hu
Dr. Emanuele Principi
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 papers will be 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 monthly 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 1500 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
  • smart cities
  • intelligent energy resources management
  • electric vehicle fleet
  • stochastic analysis
  • driving patterns forecast
  • charging stations planning
  • intelligent charging technologies
  • battery management and operation

Published Papers (7 papers)

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Research

Open AccessArticle A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm
Energies 2017, 10(9), 1333; doi:10.3390/en10091333
Received: 18 June 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 4 September 2017
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Abstract
To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to
[...] Read more.
To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving. Full article
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Open AccessArticle A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles
Energies 2017, 10(8), 1159; doi:10.3390/en10081159
Received: 16 June 2017 / Revised: 2 August 2017 / Accepted: 3 August 2017 / Published: 7 August 2017
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Abstract
Renewable energy generation and electric vehicles (EVs) have attracted much attention in the past decade due to increasingly serious environmental problems as well as less and less fossil energy reserves. Moreover, the forms of energy utilization are changing with the development of information
[...] Read more.
Renewable energy generation and electric vehicles (EVs) have attracted much attention in the past decade due to increasingly serious environmental problems as well as less and less fossil energy reserves. Moreover, the forms of energy utilization are changing with the development of information technology and energy technology. The term “energy hub” has been introduced to represent an entity with the capability of energy production, conversion and storage. A residential quarter energy-hub-optimization model including a concentrating solar power (CSP) unit is proposed in this work, with solar energy and electricity as its inputs to supply thermal and electrical demands, and the operating objective is to minimize the involved operation costs. The optimization model is a mixed integer linear programming (MILP) problem. Demand side management (DSM) is next implemented by modeling shiftable electrical loads such as EVs and washers, as well as flexible thermal loads such as hot water. Finally, the developed optimization model is solved with the commercial CPLEX solver based on the YALMIP/MATLAB toolbox, and sample examples are provided for demonstrating the features of the proposed method. Full article
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Open AccessArticle Stochastic Navigation in Smart Cities
Energies 2017, 10(7), 929; doi:10.3390/en10070929
Received: 18 April 2017 / Revised: 9 June 2017 / Accepted: 30 June 2017 / Published: 5 July 2017
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Abstract
In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end
[...] Read more.
In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies. Full article
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Open AccessArticle Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration
Energies 2017, 10(5), 729; doi:10.3390/en10050729
Received: 18 January 2017 / Revised: 27 April 2017 / Accepted: 28 April 2017 / Published: 20 May 2017
Cited by 2 | PDF Full-text (5532 KB) | HTML Full-text | XML Full-text
Abstract
This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure
[...] Read more.
This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF) problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC) of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs. Full article
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Open AccessArticle Stochastic Optimal Control of Parallel Hybrid Electric Vehicles
Energies 2017, 10(2), 214; doi:10.3390/en10020214
Received: 8 September 2016 / Revised: 4 February 2017 / Accepted: 7 February 2017 / Published: 13 February 2017
PDF Full-text (2637 KB) | HTML Full-text | XML Full-text
Abstract
Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet
[...] Read more.
Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet this problem, this paper adopts a stochastic dynamic programming (SDP) method for the EMS of a specially designed vehicle, a pre-transmission single-shaft torque-coupling parallel HEV. In this parallel HEV, the auto clutch output is connected to the transmission input through an electric motor, which benefits an efficient motor assist operation. In this EMS, demanded torque of driver is modeled as a one-state Markov process to represent the uncertainty of future driving situations. The obtained EMS has been evaluated with ADVISOR2002 over two standard government drive cycles and a self-defined one, and compared with a dynamic programming (DP) one and a rule-based one. Simulation results have shown the real-time performance of the proposed approach, and potential vehicle performance improvement relative to the rule-based one. Full article
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Open AccessArticle Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism
Energies 2017, 10(2), 200; doi:10.3390/en10020200
Received: 10 November 2016 / Accepted: 6 February 2017 / Published: 10 February 2017
PDF Full-text (7350 KB) | HTML Full-text | XML Full-text
Abstract
The growing penetration of electric vehicles (EVs) represents an operational challenge to system operators, mainly at the distribution level by introducing congestion and voltage drop problems. To solve these potential problems, a two-level coordination approach is proposed in this study. An aggregation entity,
[...] Read more.
The growing penetration of electric vehicles (EVs) represents an operational challenge to system operators, mainly at the distribution level by introducing congestion and voltage drop problems. To solve these potential problems, a two-level coordination approach is proposed in this study. An aggregation entity, i.e., an EV virtual power plant (EV-VPP), is used to facilitate the interaction between the distribution system operator (DSO) and EV owners considering the decentralized electricity market structure. In level I, to prevent the line congestion and voltage drop problems, the EV-VPP internally respects the line and voltage constraints when making optimal charging schedules. In level II, to avoid power transformer congestion problems, this paper investigates three different coordination mechanisms, or power transformer capacity allocation mechanisms, between the DSO and the EV-VPPs, considering the case of EVs charging and discharging. The three mechanisms include: (1) a market-based approach; (2) a pro-rata approach; and (3) a newly-proposed constrained market-based approach. A case study considering a 37-bus distribution network and high penetration of electric vehicles is presented to demonstrate the effectiveness of the proposed coordination mechanism, comparing with the existing ones. Full article
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Open AccessArticle Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids
Energies 2017, 10(2), 147; doi:10.3390/en10020147
Received: 10 November 2016 / Revised: 10 January 2017 / Accepted: 18 January 2017 / Published: 24 January 2017
Cited by 1 | PDF Full-text (2169 KB) | HTML Full-text | XML Full-text
Abstract
A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with
[...] Read more.
A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies. Full article
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