PEV Charging Infrastructure Integration into Smart Grid

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute to the reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. With the increasing popularity of PEVs, public electric-vehicle charging infrastructure (EVCI) becomes indispensable to meet the PEV user requirements. EVCI can consist of various types of charging technologies, offering multiple charging services for PEV users. Proper integration of the charging infrastructure into smart grid is key to promote widespread adoption of PEVs. Planning and operation of EVCI are technically challenging, since PEVs are characterized by their limited driving range, long charging duration, and high charging power, in addition to the randomness in driving patterns and charging decisions of PEV users. EVCI planning involves both the siting and capacity planning of charging facilities. Charging facility siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. Capacity planning of charging facilities must ensure a satisfactory charging service for PEV users in addition to a reliable operation of the power grid. During the operation of EVCI, price-based coordination mechanisms can be leveraged to dynamically preserve the quality-of-service (QoS) requirements of charging facilities and ensure the profitability of the charging service. This research is to investigate and develop solutions for integrating the EVCI into the smart grid. It consists of three research topics: First, we investigate PEV charging infrastructure siting. We propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. In the proposed model, we consider transportation network dynamics and congestion, in addition to different characteristics and usage patterns of each charging facility type. Second, we propose a QoS aware capacity planning of EVCI. The proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. Lastly, we investigate the coordination of PEV charging demands. We develop a differentiated pricing mechanism for a multiservice EVCI using deep reinforcement learning (RL). The proposed framework enhances the performance of charging facilities by motivating PEV users to avoid over-usage of particular service classes. Since customer-side information is stochastic, non-stationary, and expensive to collect at scale, the proposed pricing mechanism utilizes the model-free deep RL approach. In the proposed RL approach, deep neural networks are trained to determine a pricing policy while interacting with the dynamically changing environment. The neural networks take the current EVCI state as input and generate pricing signals that coordinate the anticipated PEV charging demand

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Evaluating the effects of road hump on speed and noise level at a university setting

This study is carried out to determine the effectivness of road humps to reduce the traffic speed and traffic noise in institutional area. The difference in hump profiles in terms of height, width and length are the main factors in determing the effectiveness of road humps. The difference in the profiles of the road hump will cause changing driver behaviour of the users especially when approaching the road hump. The road humps with different design profiles are selected to measure the speed and noise level of the vehicles at, before and after each of the selected road humps. Radar speed gun and noise level meters are used to measure speed and noise level of the vehicles at each of designated points along the major circular road in IIUM. The changes in speed and noise level at different selected points at each of the different profiles of the road humps are the expected findings of this study

Evaluating the effects of road hump on the speed of vehicles in an institutional environment

Vehicles travelling at speed above the permissible speed limit have jeopardized the safety of road users. The concern is greater at institutional environment whereby most road users travel by walking. Road hump is considered as an efficient traffic calming measure in reducing the speed of the vehicle. This paper investigates the effects of different road hump dimensions in decreasing the speed of vehicles at the main road of International Islamic University Malaysia. Six (6) road humps with different design profile were selected. The design profile and spot speed of the vehicles at all six (6) road humps were measured. The speed of vehicles at the road hump was analyzed by using descriptive analysis and t-test. The findings of this study suggest that road hump is effective in lowering the speed of vehicles in an institutional environment. The dimensions of road hump, especially height, influence significantly the speed reduction of vehicles

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities

Enabling long journeys in electric vehicles:design and demonstration of an infrastructure location model

This research develops a methodology and model formulation which suggests locations for rapid chargers to help assist infrastructure development and enable greater battery electric vehicle (BEV) usage. The model considers the likely travel patterns of BEVs and their subsequent charging demands across a large road network, where no prior candidate site information is required. Using a GIS-based methodology, polygons are constructed which represent the charging demand zones for particular routes across a real-world road network. The use of polygons allows the maximum number of charging combinations to be considered whilst limiting the input intensity needed for the model. Further polygons are added to represent deviation possibilities, meaning that placement of charge points away from the shortest path is possible, given a penalty function. A validation of the model is carried out by assessing the expected demand at current rapid charging locations and comparing to recorded empirical usage data. Results suggest that the developed model provides a good approximation to real world observations, and that for the provision of charging, location matters. The model is also implemented where no prior candidate site information is required. As such, locations are chosen based on the weighted overlay between several different routes where BEV journeys may be expected. In doing so many locations, or types of locations, could be compared against one another and then analysed in relation to siting practicalities, such as cost, land permission and infrastructure availability. Results show that efficient facility location, given numerous siting possibilities across a large road network can be achieved. Slight improvements to the standard greedy adding technique are made by adding combination weightings which aim to reward important long distance routes that require more than one charge to complete

Electric Mobility: Smart Transportation in Smart Cities

2014 - 2015One of the mega trends over the past century has been humanity’s move towards cities. Public Administration and Municipalities are facing a challenging task, to harmonize a sustainable urban development offering to people in city the best living conditions. Smart cities are now considered a winning urban strategy able to increase the quality of life by using technology in urban space, both improving the environmental quality and delivering better services to the citizens. Mobility is a key element to support this new approach in the growth of the cities. In fact, transport produces several negative impacts and problems for the quality of life in cities, such as, pollution, traffic and congestion. Therefore, Sustainable Mobility is one of the most promising topics in smart city, as it could produce high benefits for the quality of life of almost all the city stakeholders. The boldest and imminent challenge awaiting mobility in smart cities is the introduction of the electricity as energy vector instead of fossil fuels, concerning both the collective and the private transports. Electric public transport include electric city buses, trolleybuses, trams (or light rail), passenger trains and rapid transit (metro/subways/undergrounds, etc.). Even though railway systems are the most energy efficient than other transport modes, the enhancement of energy efficiency is an important issue to reduce their contributions to climate change further as well as to save and enlarge competition advantages involved. One key means for improving energy efficiency is to deploy advanced systems and innovative technologies. Additionally, electrification of the private road transport has emerged as a trend to support energy efficiency and CO2 emissions reduction targets. According to the International Energy Agency, in order to limit average global temperature increases to 2°C - the critical threshold that scientists say will prevent dangerous climate change -, by 2050, 21% of carbon reductions must come from the transport sector. Full electric vehicles (EVs) use electric motor and battery energy for propulsion, which has higher efficiency and lower operating cost compared to the conventional internal combustion engine vehicle. Today, there are more than 20 models offered by different brands covering different range of sizes, styles, prices and powertrains to suit the wider range of consumers as possible. The continuous development of lithium ion battery and of fast charging technology will be the major facilitators for EVs roll out in the very near future. However, the present EVs industry meets many technical limitations, such as high initial price, long battery recharge time, limited charging facilities and driving range. Although it is desirable a fast development from the start of electric mobility, its impact on the existing power grid must be assessed beforehand to see if it is necessary prior an adjustment of power infrastructure or/and the introduction of new services in the power grid. In fact, the interconnection of EVs on the power grid for charging their batteries potentially introduces negative impacts on grid operation: uncontrolled charging can significantly increase average load in the existing power systems, with problems in terms of reliability and overloads. If uncontrolled EV charging is added to the system, this can have effects both at the distribution and at the generation level. Controlled or smart charging will allow a much greater number of cars in the cities, avoiding local overload and allowing a faster EVs penetration without requiring an imminent improvement of the electricity generating and grid capacity. Smart charging might also allow load balancing both at sub-station and at the grid level, particularly with charging at peak wind supply times. This kind of use of EV battery capacity for storing electric energy may ease the integration of large scale intermittent electricity sources such as renewable energy sources. The proposed PhD Dissertation is developed in the context just described, mainly focusing the attention on the impact that electric mobility will have on the power systems and the effectiveness of solutions aimed to increase the reliability and resilience in the smart grid. In particular, it is addressed a scenario analysis regarding the electric vehicles charging management and some innovative solutions to increase energy efficiency in electrified transport systems. The first chapter emphasizes on the key aspects related to the sustainable mobility in the smart cities of the future. It provides a brief overview on the transport sector energy consumption expected in the next years. In particular, the chapter shows the significant contribution that the electrification of urban transport may provide to the sustainable mobility, and the serious concerns related to its impact on existing power systems. Chapter 2 proposes a solution method for an optimal generation rescheduling and load-shedding (GRLS) problem in microgrids in order to determine a stable equilibrium state following unexpected outages of generation or sudden increase in demand. The chapter mainly focuses on the mathematical formulation of the GRLS problem and the proposed solution algorithm. Finally, simulations results carried out by using a real case study data are presented and discussed. In Chapter 3, a simple and effective methodology is proposed to analyze data acquired during the fulfillment of the COSMO research project, and to identify typical load pattern for the EVs charging. The chapter also presents a novel scheduling problem formulation, flattening the demand load profile and minimizing the EVs charging costs, according to the electricity prices during the day. Finally, some simulations results are discussed, showing the effectiveness of the proposed methodology. Chapter 4 introduces some innovative solutions for energy efficiency in urban railway systems focusing, in particular, on energy storage systems and eco-drive operations in metro networks. The mathematical formulation of these optimization problems and the proposed solution algorithms are illustrated and discussed. The obtained results are part of the activity carried out in the SFERE research project. Finally, Chapter 5 ends the Dissertation with some concluding remarks and further developments of the proposed research activity. [edited by author]Una delle grandi tendenze nel corso del secolo scorso è stata la concentrazione della popolazione nelle città. Attualmente, le Pubbliche Amministrazioni e i Comuni si trovano ad affrontare un compito impegnativo per armonizzare uno sviluppo urbano sostenibile e offrire agli abitanti delle città le migliori condizioni di vita. Le smart cities sono ormai considerate una strategia urbana vincente in grado di aumentare la qualità della vita utilizzando la tecnologia, sia per il miglioramento della qualità ambientale che per fornire servizi migliori ai cittadini. A tale scopo, la mobilità risulta essere un elemento chiave per sostenere questo nuovo approccio nella crescita delle città. Infatti, i sistemi di trasporto urbano producono diversi effetti negativi sulla qualità della vita urbana, come ad esempio, inquinamento, traffico e congestione. Pertanto, la mobilità sostenibile è uno degli argomenti più interessanti per le smart cities, in quanto in grado produrre elevati benefici per la qualità della vita di quasi tutte le parti interessate degli agglomerati urbani. La sfida più audace e imminente per la mobilità nelle smart cities del futuro è l'introduzione dell'elettricità come vettore energetico al posto dei combustibili fossili, per quanto riguarda sia il trasporto collettivo che quello privato. I mezzi per il trasporto pubblico comprendono autobus elettrici, filobus, tram, treni passeggeri e trasporto rapido (metropolitane, etc.). Anche se i sistemi di trasporto su ferro sono più efficienti rispetto ad altri modi di trasporto, l’incremento dell'efficienza energetica è un tema importante per ridurre ulteriormente il loro contributo alle emissioni inquinanti e al consumo di energia. Le più promettenti soluzioni per migliorarne l'efficienza energetica consistono nell’implementazione di sistemi avanzati per il recupero dell’energia di frenata e tecnologie di controllo innovative. D’altro canto, l'elettrificazione del trasporto individuale su strada è emersa come una tendenza finalizzata a sostenere gli obiettivi di efficienza energetica e di riduzione delle emissioni di CO2. Secondo l'Agenzia Internazionale per l'Energia, al fine di limitare, entro il 2050, l'aumento della temperatura media globale a 2 °C - la soglia critica che gli scienziati suggeriscono di non superare per evitare pericolosi cambiamenti climatici -, il 21% delle riduzioni di biossido di carbonio deve provenire dal settore trasporti. I veicoli elettrici (EV) utilizzano un motore elettrico e l'energia accumulata nelle batterie per la propulsione, in modo da avere una maggiore efficienza e minori costi operativi rispetto ai veicoli convenzionali con motore a combustione interna. Oggi, esistono in commercio più di 20 modelli offerti da diverse case produttrici che coprono una ampia gamma di modelli che differiscono per dimensione, stile, prezzo e motorizzazione in modo da soddisfare il maggior numero di consumatori possibile. Il continuo sviluppo delle batterie al litio e delle tecnologie di ricarica rapida saranno i principali fattori abilitanti per la diffusione degli EV in un futuro molto prossimo. Tuttavia, l'attuale industria dei veicoli elettrici incontra molte limitazioni tecnico-economiche, come elevati costi, autonomia e tempi di ricarica della batteria, capillarità delle infrastrutture di ricarica. Sebbene sia auspicabile un rapido sviluppo della mobilità elettrica, il suo impatto sulla rete elettrica esistente deve essere investigato a fondo per verificare la necessità di potenziamenti delle infrastrutture e/o l'introduzione di nuovi servizi nella rete elettrica. Infatti, l'interconnessione dei veicoli elettrici con la rete di distribuzione dell’energia necessaria per la ricarica delle batterie può causare effetti negativi sul normale funzionamento del sistema elettrico: una ricarica degli EV non controllata può aumentare significativamente il carico medio negli impianti esistenti, introducendo problemi di affidabilità e sovraccarico. La ricarica intelligente o controllata degli EV consente, invece, di gestire un numero molto maggiore di autovetture elettriche nelle città, riducendo le possibilità di sovraccarico locale e di velocizzare la penetrazione della mobilità elettrica senza che rendere necessari imminenti potenziamenti dei sistemi di produzione di energia elettrica e incrementi della capacità di rete. La ricarica intelligente, inoltre, può anche influire sul bilanciamento del carico sia a livello della sottostazione elettrica che a livello di rete di distribuzione, in particolare quando si verificano molte sessioni di ricarica nelle ore di punta. Infatti, l’utilizzo della capacità della batteria degli EV per l’accumulo di energia elettrica può facilitare l'integrazione su larga scala delle fonti di energia non programmabili, come quelle rinnovabili. Il lavoro di tesi si sviluppa nel contesto di riferimento appena descritto, focalizzando l'attenzione soprattutto sull'impatto che la mobilità elettrica ha sui sistemi elettrici e sull'efficacia di nuove soluzioni finalizzate all’incremento dell'affidabilità nelle smart grids. In particolare, viene proposta un'analisi di scenario per quanto riguarda la gestione intelligente delle ricariche dei veicoli elettrici e alcune soluzioni innovative per aumentare l'efficienza energetica nei sistemi di trasporto elettrificati. Il primo capitolo sottolinea gli aspetti chiave relativi alla mobilità sostenibile nelle smart cities del futuro e fornisce una breve panoramica sul consumo energetico del settore trasporti previsto nel prossimo futuro. In particolare, vengono evidenziate da un lato il significativo contributo che l'elettrificazione dei trasporti urbani può fornire alla causa della mobilità sostenibile, e dall’altro, le gravi preoccupazioni legate all’impatto sui sistemi elettrici esistenti di un notevole incremento della domanda. Il Capitolo 2 propone un metodo per la soluzione del problema congiunto di scheduling dei generatori e load shedding (GRLS) all’interno di microgrids portando in conto l’incertezza sia sulla domanda che lato generazione. Il fine è determinare un nuovo stato di equilibrio stabile in seguito a guasti, riduzione della generazione da fonte rinnovabile o improvviso aumento della domanda. Il capitolo si concentra principalmente sulla formulazione matematica del problema GRLS e sull'algoritmo di soluzione proposto. Infine, sono presentati e commentati i risultati di simulazione basati su un caso studio reale. Nel Capitolo 3, è proposta una metodologia semplice ed efficace per identificare profili di carico tipico relativi alla ricarica di veicoli elettrici: in particolare, l’analisi condotta si basa sull’analisi dei dati acquisiti durante lo svolgimento del progetto di ricerca COSMO. Il capitolo, inoltre, introduce una formulazione matematica del problema dello scheduling delle ricariche dei veicoli elettrici, che garantisce un appiattimento del profilo di carico e riduce allo stesso tempo il costo della ricarica per gli utenti. Infine, sono commentati i risultati delle simulazioni eseguite dimostrando l'efficacia della metodologia proposta. Il Capitolo 4 introduce alcune soluzioni innovative per l'efficienza energetica nei sistemi di trasporto urbani: l’attenzione viene posta, in particolare, sui sistemi di accumulo dell’energia e sulla condotta di guida Eco-Drive in reti metropolitane. In dettaglio, nel capitolo, vengono introdotti e commentati la formulazione matematica dei problemi di ottimizzazione proposti e i rispettivi algoritmi di soluzione. I risultati ottenuti fanno parte delle attività svolte nell’ambito del progetto di ricerca SFERE. Infine, il Capitolo 5 conclude la tesi con alcune osservazioni finali e con i possibili sviluppi dell'attività di ricerca proposta. [a cura dell'autore]XIV n.s