Integrated PHEV Charging Loads Forecasting Model and Optimization Strategies

In this dissertation, an integrated Plug-in Electric Vehicle (PHEV) charging loads forecasting model is developed for regular distribution level system and microgrid system. For regular distribution system, charging schedule optimization is followed up. The objectives are 1. Better cooperation with renewable energy sources (especially wind). 2. Relieving the pressure of current distribution transformers in condition of high penetration level PHEVs. As for microgrid, renewable energy power plants (wind, solar) plays a more important role than regular system. Due to the fluctuation of solar and wind plants\u27 output, an empirical probabilistic model is developed to predict their hourly output. On the other hand, PHEVs are not only considered at the charging loads, but also the discharging output via Vehicle to Grid (V2G) method which can greatly affect the economic dispatch for all the micro energy sources in microgrid. Optimization is performed for economic dispatch considering conventional, renewable power plants, and PHEVs. The simulation in both cases results reveal that there is a great potential for optimization of PHEVs\u27 charging schedule. Furthermore, PHEVs with V2G capability can be an indispensable supplement in modern microgrid

Spatial-temporal domain charging optimization and charging scenario iteration for EV

Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies

Prospects for Electric Mobility: Systemic, Economic and Environmental Issues

The transport sector, which is currently almost completely based on fossil fuels, is one of the major contributors to greenhouse gas emissions. Heading towards a more sustainable development of mobility could be possible with more energy efficient automotive technologies such as battery electric vehicles. The number of electric vehicles has been increasing over the last decade, but there are still many challenges that have to be solved in the future. This Special Issue “Prospects for Electric Mobility: Systemic, Economic and Environmental Issues” contributes to the better understanding of the current situation as well as the future prospects and impediments for electro mobility. The published papers range from historical development of electricity use in different transport modes and the recent challenges up to future perspectives

Peer-to-Peer Trading for Enhancing Electric Vehicle Charging with Renewable Energy

Electric vehicles (EVs) are rapidly increasing in popularity as greater attention is paid to climate change and decarbonisation, however the environmental benefits that EVs offer can only be fully realised through the use of renewable energy for their charging. Smart charging solutions are essential for managing the impact of EVs and increasing the utilisation of renewable energy, however, questions remain over whether low-voltage distribution networks can accommodate the upcoming increases in EV charging demand. This thesis addresses both the challenge of increasing the utilisation of renewable energy for EV charging and also the importance of ensuring safe operation of low-voltage distribution networks with the integration of EV charging, distributed renewable energy generation, battery storage and vehicle-to-grid technologies. Chapter 3 examines a scenario where houses equipped with solar photovoltaic panels and EV charge points endeavour to sell surplus solar energy and the use of their EV charge point to visiting EVs that require charging. A peer-to-peer auction is proposed, with a novel matching mechanism presented to increase the amount of EV charging completed using solar energy without any knowledge about future EV arrivals. Chapter 4 presents a full peer-to-peer trading model of Network Impact Tokens and Phase Impact Tokens between houses in a low-voltage network. The Impact Tokens guarantee that all EV charging and renewable energy generation does not cause the network to exceed its voltage, current or transformer loading limits, while ensuring each house retains control over its energy usage, requiring no real-time monitoring or sensors in the network, and no privacy issues are encountered. The Network and Phase Impact Token approach is further verified in Chapter 5, as it forms the basis of a novel approach for Distribution System Operators to evaluate the maximum EV hosting capacity of their networks in conjunction with renewable energy generation and battery storage. The maximum EV capacity results are verified by an alternate Optimisation approach and the maximum EV penetration is evaluated for a number of scenarios

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

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

Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure

Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits. The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities. In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures. Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users. This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments

An Integrated Framework for Modelling and Control of eP2P Interactions based on Model Predictive Control

The energy paradigm is undergoing substantial changes in recent years. In terms of production, it is observable how distributed generation, with an ever-increasing contribution from renewable sources, is displacing large concentrated generation plants. But the fundamental change is not so much about energy supply as about diluting the historical roles of producers and consumers to give way to the concept of prosumers. That is, instead of just being energy consumers, households and industries also become producers. In principle, the purpose of this production, which is inherently distributed, is self-consumption. However, when there is a surplus of production, prosumers can choose between storing the excess, if they have an energy storage system, or sell the unused fraction of energy. An obvious type of prosumers are those industries that have renewable generation facilities and which, as a consequence of their production process, generate by-products that can be used for cogeneration. In this case an obvious problem for the company is to select at all times the power sources that minimize the cost of production, which is known as Optimal Power Dispatch (OPD). If, in addition, the energy consumption time profile of the manufacturing process (per unit of raw material introduced) is known, it is also possible to make an optimal production schedule to minimize energy cost, which is called Optimal Power Scheduling (OPS). Chapter 3 presents an Economic Model Predictive Controller (EMPC) that simultaneously performs OPD and OPS using an olive mill as an example. The emergence of the role of energy prosumers makes it necessary to extend, improve or replace the traditional mechanisms of energy exchange. This thesis includes novel approaches for modelling the behaviour of prosumers. It also proposes new structures to facilitate energy trading, always from the perspective of the peerification of the energy paradigm. Thus, another line of research studies the establishment of peer-to-peer (P2P) markets for the exchange of energy between heterogeneous prosumers (homes, vehicles, intelligent buildings, etc.). The efficiency of markets based on both discrete double auctions (DDAs) and continuous double auctions (CDAs) is compared. An Energy Management System (EMS) is also introduced including market agent software that allows the necessary tasks for participation in the auctions to be carried out automatically (determination of private valuation, role selection and price adaptation). Chapter 4, Chapter 5 and Chapter 6 present some examples of such exchange markets stablished between different types of prosumers: i) energy market for electric vehicles that coincide parked in a large workplace, ii) power market for households within the same neighbourhood and iii) integrated energy and power markets for heterogeneous energy entities. The evolution of aforementioned mechanisms and the appearance of new market models must be accompanied by the development of control techniques that optimise and automate all the processes related to energy saving and trading, by a group of increasingly heterogeneous prosumers. This thesis deals with how different variants of predictive controllers can contribute to this last aspect. For industries with cogeneration capacity, the EMPC contributes to the optimal scheduling of production to maximise the return from energy reuse, either through self-consumption or through the trading of surpluses. The use of stochastic predictive control is proposed in order to maximise the expected return on the participation of prosumers, whatever their type, in continuous markets where the price of energy may undergo stochastic variations.El paradigma energético está experimentando cambios sustanciales en los últimos años. En cuanto a la producción, se observa cómo la generación distribuida, con un aporte cada vez mayor de fuentes renovables, está desplazando a las grandes plantas de generación concentrada. Pero el cambio fundamental no consiste tanto en el suministro de energía como en la dilución de la clasificación tradicional entre productores y consumidores para dar paso al concepto de prosumidores. Es decir, en lugar de ser simplemente consumidores de energía, los hogares y las industrias también se convierten en productores. En principio, el objetivo de esta producción, que es intrínsecamente distribuida, es el autoconsumo. Sin embargo, cuando hay un excedente de producción, los prosumidores pueden elegir entre almacenar el excedente, si tienen un sistema de almacenamiento de energía, o vender la fracción no utilizada de la energía. Un tipo obvio de prosumidores son aquellas industrias que cuentan con instalaciones de generación renovable y que, como consecuencia de su proceso de producción, generan subproductos que pueden ser utilizados para la cogeneración. En este caso, un problema obvio para la empresa es seleccionar en todo momento las fuentes de energía que minimizan el coste de producción, lo que se conoce como Optimal Power Dispatch (OPD). Si, además, se conoce el perfil temporal de consumo de energía asociado al proceso de fabricación (por unidad de materia prima introducida), también es posible realizar un programa de producción óptimo para minimizar el coste de la energía, lo cual se denomina Optimal Power Scheduling (OPS). El capítulo 3 presenta un Controlador Predictivo Económico basado en Modelo (EMPC) que realiza simultáneamente OPD y OPS utilizando como caso de estudio una almazara olivarera. La aparición de la figura de los prosumidores energéticos hace necesario ampliar, mejorar o sustituir los mecanismos tradicionales de intercambio energético. Esta tesis incluye enfoques novedosos para modelar el comportamiento de los prosumidores. También propone nuevas estructuras para facilitar el comercio de energía, siempre desde la perspectiva de la peerificación del paradigma energético. Así, otra línea de investigación estudia el establecimiento de mercados peer-to-peer (P2P) para el intercambio de energía entre prosumidores heterogéneos (viviendas, vehículos, edificios inteligentes, etc.). Se compara la eficiencia de los mercados basados tanto en subastas dobles discretas (Discrete Double Auction - DDA) como en subastas dobles continuas (Continuous Double Auctions - CDA). También se introduce un Sistema de Gestión Energética (Energy Management System - EMS) que incluye un software de agente de mercado que permite que las tareas necesarias para la participación en las subastas (determinación de la valoración privada, selección de roles y adaptación de precios) se lleven a cabo automáticamente. Los capítulos 4, 5 y 6 presentan algunos ejemplos de estos mercados de intercambio establecidos entre diferentes tipos de prosumidores: i) mercado de energía para vehículos eléctricos que coinciden aparcados en un gran lugar de trabajo, ii) mercado de energía para hogares dentro de un mismo barrio y iii) mercados integrados de energía y electricidad para entidades energéticas heterogéneas. La evolución de los mecanismos mencionados y la aparición de nuevos modelos de mercado deben ir acompañados del desarrollo de técnicas de control que optimicen y automaticen todos los procesos relacionados con el ahorro y la comercialización de la energía, por parte de un conjunto de prosumidores cada vez más heterogéneos. Esta tesis trata de cómo las diferentes variantes de los controladores predictivos pueden contribuir a este último aspecto. Para las industrias con capacidad de cogeneración, el EMPC contribuye a la programación óptima de la producción para maximizar el rendimiento de la reutilización de la energía, ya sea a través del autoconsumo o de la comercialización de excedentes. Por otro lado, se propone el uso del control predictivo estocástico para maximizar el rendimiento esperado de la participación de los prosumidores, cualquiera que sea su tipo, en mercados P2P donde el precio de la energía está sujeto a incertidumbres