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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (10 August 2021) | Viewed by 15366

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Prof. Dr. Andrey V. Savkin

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Guest Editor

School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Interests: robot navigation; deployment of drones; unmanned aerial vehicles; control of wireless communication networks; control of power systems; robust control and filtering; hybrid dynamical systems; control engineering; biomedical engineering
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Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Energies on the subject area of “Integrated Energy Systems and Transportation Electrification”. Electrification is considered to be an effective solution to the environmental impact of transportation, helping to reduce fossil fuel dependency. It is widely believed that electrifying transportation brings enormous economic and environmental benefits and greatly improves quality of life. With the increasing integration of renewable energy and the development of a smart grid, the topic of transportation electrification has attracted a lot of attention in recent years.

Researchers and engineers worldwide are working together to develop novel and efficient tools for integrated energy systems and transportation electrification. This Special Issue is focused on new developments in the field of integrated energy systems and transportation electrification.

Potential topics include but are not limited to the following:

Electric drives for transportation applications
Design, modelling and control of electric vehicles
All types of electric vehicles, including on-road vehicles, off-road vehicles, rail vehicles, aerial drones, surface marine vehicles, and underwater vehicles
Applications and control of solar powered vehicles
Energy-efficient drone delivery
Energy storage for electric vehicles
Optimization and control of charging and charging station deployment for electric vehicles

Prof. Dr. Andrey V. Savkin
Guest Editor

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

Transportation electrification
Electrical systems in transportation
Electric drives
Control of electric vehicles
Drone delivery
Energy storage
Solar powered vehicles
Plug-in electric vehicles
On-off charging
Drone delivery
Control of charging

Published Papers (7 papers)

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Research

21 pages, 6404 KiB

Open AccessArticle

The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey

by Ömer Kaya, Kadir Diler Alemdar, Tiziana Campisi, Ahmet Tortum and Merve Kayaci Çodur

Energies 2021, 14(10), 2756; https://doi.org/10.3390/en14102756 - 11 May 2021

Cited by 24 | Viewed by 3150

Abstract

One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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21 pages, 6754 KiB

Open AccessArticle

Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

by Florian Straub, Simon Streppel and Dietmar Göhlich

Energies 2021, 14(8), 2081; https://doi.org/10.3390/en14082081 - 08 Apr 2021

Cited by 13 | Viewed by 2234

Abstract

With continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m

^{2}

area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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19 pages, 1515 KiB

Open AccessArticle

Path Planning for a Solar-Powered UAV Inspecting Mountain Sites for Safety and Rescue

by Hailong Huang and Andrey V. Savkin

Energies 2021, 14(7), 1968; https://doi.org/10.3390/en14071968 - 02 Apr 2021

Cited by 8 | Viewed by 2241

Abstract

This paper focuses on the application using a solar-powered unmanned aerial vehicle (UAV) to inspect mountain sites for the purpose of safety and rescue. An inspection path planning problem is formulated, which looks for the path for an UAV to visit a set of sites where people may appear while avoiding collisions with mountains and maintaining positive residual energy. A rapidly exploring random tree (RRT)-based planning method is proposed. This method firstly finds a feasible path that satisfies the residual energy requirement and then shortens the path if there is some abundant residual energy at the end. Computer simulations are conducted to demonstrate the performance of the proposed method. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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16 pages, 423 KiB

Open AccessArticle

Benchmarking Flexible Electric Loads Scheduling Algorithms

by Koos van der Linden, Natalia Romero and Mathijs M. de Weerdt

Energies 2021, 14(5), 1269; https://doi.org/10.3390/en14051269 - 25 Feb 2021

Viewed by 1656

Abstract

Due to increasing numbers of intermittent and distributed generators in power systems, there is an increasing need for demand responses to maintain the balance between electricity generation and use at all times. For example, the electrification of transportation significantly adds to the amount of flexible electricity demand. Several methods have been developed to schedule such flexible energy consumption. However, an objective way of comparing these methods is lacking, especially when decisions are made based on incomplete information which is repeatedly updated. This paper presents a new benchmarking framework designed to bridge this gap. Surveys that classify flexibility planning algorithms were an input to define this benchmarking standard. The benchmarking framework can be used for different objectives and under diverse conditions faced by electricity production stakeholders interested in flexibility scheduling algorithms. Our contribution was implemented in a software toolbox providing a simulation environment that captures the evolution of look-ahead information, which enables comparing online planning and scheduling algorithms. This toolbox includes seven planning algorithms. This paper includes two case studies measuring the performances of these algorithms under uncertain market conditions. These case studies illustrate the importance of online decision making, the influence of data quality on the performance of the algorithms, the benefit of using robust and stochastic programming approaches, and the necessity of trustworthy benchmarking. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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33 pages, 6628 KiB

Open AccessArticle

Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

by Jingjing Zhai, Xiaobei Wu, Zihao Li, Shaojie Zhu, Bo Yang and Haoming Liu

Energies 2021, 14(4), 936; https://doi.org/10.3390/en14040936 - 10 Feb 2021

Cited by 1 | Viewed by 1737

Abstract

An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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17 pages, 1428 KiB

Open AccessArticle

Energy-Efficient Autonomous Navigation of Solar-Powered UAVs for Surveillance of Mobile Ground Targets in Urban Environments

by Hailong Huang and Andrey V. Savkin

Energies 2020, 13(21), 5563; https://doi.org/10.3390/en13215563 - 23 Oct 2020

Cited by 8 | Viewed by 1971

Abstract

In this paper, we consider the navigation of a group of solar-powered unmanned aerial vehicles (UAVs) for periodical monitoring of a set of mobile ground targets in urban environments. We consider the scenario where the number of targets is larger than that of the UAVs, and the targets spread in the environment, so that the UAVs need to carry out a periodical surveillance. The existence of tall buildings in urban environments brings new challenges to the periodical surveillance mission. They may not only block the Line-of-Sight (LoS) between a UAV and a target, but also create some shadow region, so that the surveillance may become invalid, and the UAV may not be able to harvest energy from the sun. The periodical surveillance problem is formulated as an optimization problem to minimize the target revisit time while accounting for the impact of the urban environment. A nearest neighbour based navigation method is proposed to guide the movements of the UAVs. Moreover, we adopt a partitioning scheme to group targets for the purpose of narrowing UAVs’ moving space, which further reduces the target revisit time. The effectiveness of the proposed method is verified via computer simulations. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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15 pages, 6831 KiB

Open AccessArticle

Regional Integrated Energy Site Layout Optimization Based on Improved Artificial Immune Algorithm

by Yan Xu and Jianhao Zhang

Energies 2020, 13(17), 4381; https://doi.org/10.3390/en13174381 - 25 Aug 2020

Cited by 6 | Viewed by 1532

Abstract

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers. Full article

(This article belongs to the Special Issue Integrated Energy Systems and Transportation Electrification)

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