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

Towards ‘Smarter’ Systems: Key Cyber-Physical Performance-Cost Tradeoffs in Smart Electric Vehicle Charging with Distributed Generation

The growing penetration of electric vehicles (EV) into the market is driving sharper spikes in consumer power demand. Meanwhile, growing renewable distributed generation (DG) is driving sharper spikes in localised power supply. This leads to growing temporally unsynchronised spikes in generation and consumption, which manifest as localised over- or undervoltage and disrupt grid service quality. Smart Grid solutions can respond to voltage conditions by curtailing charging EVs or available DG through a network of cyber-enabled sensors and actuators. How to optimise efficiency, ensure stable operation, deliver required performance outputs and minimally overhaul existing hardware remains an open research topic. This thesis models key performance-cost tradeoffs relating to Smart EV Charging with DG, including architectural design challenges in the underpinning Information and Communications Technology (ICT). Crucial deployment optimisation balancing various Key Performance Indicators (KPI) is achieved. The contributions are as follows: • Two Smart EV Charging schemes are designed for secondary voltage control in the distribution network. One is optimised for the network operator, the other for consumers/generators. This is used to evaluate resulting performance implications via targeted case study. • To support these schemes, a multi-tier hierarchical distributed ICT architecture is designed that alleviates computation and traffic load from the central controller and achieves user fairness in the network. In this way it is scalable and adaptable to a wide range of network sizes. • Both schemes are modelled under practical latency constraints to derive interlocking effects on various KPIs. Multiple latency-mitigation strategies are designed in each case. • KPIs, including voltage control, peak shaving, user inconvenience, renewable energy input, CO2 emissions and EV & DG capacity are evaluated statistically under 172 days of power readings. This is used to establish key performancecost tradeoffs relevant to multiple invested bodies in the power grid. • Finally, the ICT architecture is modelled for growing network sizes. Quality-of- Service (QoS) provision is studied for various multi-tier hierarchical topologies under increasing number of end devices to gauge performance-cost tradeoffs related to demand-response latency and network deployment

Business Models for SEEV4-City Operational Pilots: From a generic SEEV4-City business model towards improved specific OP business models

This report, led by Northumbria University, provides a final analysis by project partners regarding Business Models for SEEV4-City Operational pilots. It is part of a collection of reports published by the project covering a variation of specific and cross-cutting analysis and evaluation perspectives and spans 6 operational pilots

Charging electric vehicles in the smart city: A survey of economy-driven approaches

International audienceElectric vehicles (EVs), as their penetration increases, do not only challenge the sustainability of the power grid but also stimulate and promote its upgrading. Indeed, EVs can actively reinforce the development of the smart grid if their charging processes are properly coordinated through two-way communications, possibly benefiting all types of actors. Because grid systems involve a large number of actors with nonaligned objectives, we focus on the economic and incentive aspects, where each actor behaves in its own interest. We indeed believe that the market structure will directly impact the actors' behaviors, and as a result, the total benefits that the presence of EVs can earn in the society, hence the need for a careful design. This survey provides an overview of economic models considering unidirectional energy flows and bidirectional energy flows, i.e., with EVs temporarily providing energy to the grid. We describe and compare the main approaches, summarize the requirements on the supporting communication systems, and propose a classification to highlight the most important results and lacks

Novel Charging and Discharging Schemes for Electric Vehicles in Smart Grids

PhD ThesisThis thesis presents smart Charging and Discharging (C&D) schemes in the smart grid that enable a decentralised scheduling with large volumes of Electric Vehicles (EV) participation. The proposed C&D schemes use di erent strategies to atten the power consumption pro le by manipulating the charging or discharging electricity quantity. The novelty of this thesis lies in: 1. A user-behaviour based smart EV charging scheme that lowers the overall peak demand with an optimised EV charging schedule. It achieves the minimal impacts on users' daily routine while satisfying EV charging demands. 2. A decentralised EV electricity exchange process matches the power demand with an adaptive blockchain-enabled C&D scheme and iceberg order execution algorithm. It demonstrates improved performance in terms of charging costs and power consumption pro le. 3. The Peer-to-Peer (P2P) electricity C&D scheme that stimulates the trading depth and energy market pro le with the best price guide. It also increases the EV users' autonomy and achieved maximal bene ts for the network peers while protecting against potential attacks. 4. A novel consensus-mechanism driven EV C&D scheme for the blockchain-based system that accommodates high volume EV scenarios and substantially reduces the power uctuation level. The theoretical and comprehensive simulations prove that the penetration of EV with the proposed schemes minimises the power uctuation level in an urban area, and also increases the resilience of the smart grid system

Energy demand management of electric vehicles

The aim of this thesis is to investigate novel recharging schemes for energy demand management (DM) of electric vehicles (EVs). While there has been a lot of work highlighting the importance of energy DM of EVs, most of the reported works do not expand on suggesting how such a DM system may be implemented. In this thesis the focus is on two aspects of DM system implementation. At the instantaneous control time scale, an alternative mechanism for frequency regulation with the aim of neutralising sudden changes in output power of electric generators is presented. At the recharge planning time scale, the aim is to avoid congestion and undesirable voltage drops in the distribution system, and a novel approach is presented that can improve voltage profiles. The problem of considering both voltage congestion and frequency regulation in a composite DM framework is also addressed. At the instantaneous control time scale, a novel distributed recharging rate controller is presented that is based on non-linear control and that yields a real time and distributed solution. This controller minimises communication overheads and allows EVs to join and leave at arbitrary times. From the perspective of recharging rate allocation, the controller achieves a Pareto efficient allocation which is also proportionally fair. The proposed controller is then applied to a system with a single, isolated, and unregulated synchronous machine and it is shown that the frequency can be used as proxy to the imbalance between produced and consumed electric power and hence communication overhead can be eliminated in such cases. A protocol is also discussed that can modify the controller and can implement the modified controller in a multi-machine system. Simulation is used to show the frequency regulation and fairness of recharging rates of EVs when the protocol and the modified controller are used. Subsequently, the integration of the recharging rate controller with the legacy protection system is also discussed. At the recharge planning time scale, the problem of congestion in the distribution system is addressed. Most of published literature on distribution system voltage issues deals with control of various network elements, for instance, on-load tap changers or banks of shunt capacitors on the distribution feeders. In this thesis, a complementary approach is presented that can also improve voltage profile by scheduling EV load in such a manner that undesirable voltage drops are avoided or their severity is diminished. In this context, a novel approach is presented for recharging EVs in the same geographic neighbourhood that share the same secondary circuits when recharging. The approach is based on a numerical method called Smoothed Particle Hydrodynamics (SPH) that has been previously used by other researchers to solve the equations of fluid dynamics. The characteristics of the method used for the proposed approach as well as its performance in terms of improvement in the reduction of voltage drops and its adaptation to elastic and non-elastic loads is highlighted via simulation. Finally, the approach is extended to also provide a frequency control reserve service.Open Acces

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems