An integrated approach to planning charging infrastructure for battery electric vehicles

PhD ThesisBattery electric vehicles (BEVs) could break our dependence on fossil fuels by facilitating the transition to low carbon and efficient transport and power systems. Yet, BEV market share is under 1% and there are several barriers to adoption including the lack of charging infrastructure. This work revealed insights that could inform planning an appropriate charging infrastructure to support the transition towards BEVs. The insights were based on analysis of a comprehensive dataset collected from three early, real world demonstrators in the UK on BEVs and smart grids. The BEV participants had access and used home, work and public charging infrastructure including fast chargers (50 kW). Probabilistic methods were used to combine and analyse the datasets to ensure robustness of findings. The findings confirm that it is essential to consider a new refuelling paradigm for BEV charging infrastructure and not replicate the liquid-fuel infrastructure where all demand is met at public fuelling stations in a very short period of time. BEVs could be charged where they are routinely parked for long periods of time (i.e. home, work) and meet most of the charging needs of drivers. Installing slow charging infrastructure at home and work would be less expensive and less complicated than rolling-out a ubiquitous fast charging infrastructure to meet all charging needs. In addition, ensuring that cars are connected most of the time to the electricity network allows proper management of BEV charging demand. This could support reliable and efficient operation of the power system to minimise network upgrade costs. Finally, when slow charging infrastructure is neither available nor practical to meet charging needs, fast chargers can be used to fill in this gap. Analysing data of BEV drivers with access to private charging locations, the findings show that fast chargers become more important than slow chargers for daily journeys above 240km and could help overcome perceived and actual range barriers. An appropriate infrastructure takes an integrated approach encompassing BEV drivers’ requirements and the characteristics of the distribution networks where BEV charging infrastructure is connected. A non-integrated approach to delivering a charging infrastructure could impede the transition towards BEVs. The findings of this work could support on-going policy development in the UK and are crucial to planning national charging infrastructure to support the adoption of BEVs in a cost-optimal manner

An integrated approach to planning charging infrastructure for battery electric vehicles

PhD ThesisBattery electric vehicles (BEVs) could break our dependence on fossil fuels by facilitating the transition to low carbon and efficient transport and power systems. Yet, BEV market share is under 1% and there are several barriers to adoption including the lack of charging infrastructure. This work revealed insights that could inform planning an appropriate charging infrastructure to support the transition towards BEVs. The insights were based on analysis of a comprehensive dataset collected from three early, real world demonstrators in the UK on BEVs and smart grids. The BEV participants had access and used home, work and public charging infrastructure including fast chargers (50 kW). Probabilistic methods were used to combine and analyse the datasets to ensure robustness of findings. The findings confirm that it is essential to consider a new refuelling paradigm for BEV charging infrastructure and not replicate the liquid-fuel infrastructure where all demand is met at public fuelling stations in a very short period of time. BEVs could be charged where they are routinely parked for long periods of time (i.e. home, work) and meet most of the charging needs of drivers. Installing slow charging infrastructure at home and work would be less expensive and less complicated than rolling-out a ubiquitous fast charging infrastructure to meet all charging needs. In addition, ensuring that cars are connected most of the time to the electricity network allows proper management of BEV charging demand. This could support reliable and efficient operation of the power system to minimise network upgrade costs. Finally, when slow charging infrastructure is neither available nor practical to meet charging needs, fast chargers can be used to fill in this gap. Analysing data of BEV drivers with access to private charging locations, the findings show that fast chargers become more important than slow chargers for daily journeys above 240km and could help overcome perceived and actual range barriers. An appropriate infrastructure takes an integrated approach encompassing BEV drivers’ requirements and the characteristics of the distribution networks where BEV charging infrastructure is connected. A non-integrated approach to delivering a charging infrastructure could impede the transition towards BEVs. The findings of this work could support on-going policy development in the UK and are crucial to planning national charging infrastructure to support the adoption of BEVs in a cost-optimal manner

An Overview of Cyber Security and Privacy on the Electric Vehicle Charging Infrastructure

Electric vehicles (EVs) are key to alleviate our dependency on fossil fuels. The future smart grid is expected to be populated by millions of EVs equipped with high-demand batteries. To avoid an overload of the (current) electricity grid, expensive upgrades are required. Some of the upgrades can be averted if users of EVs participate to energy balancing mechanisms, for example through bidirectional EV charging. As the proliferation of consumer Internet-connected devices increases, including EV smart charging stations, their security against cyber-attacks and the protection of private data become a growing concern. We need to properly adapt and develop our current technology that must tackle the security challenges in the EV charging infrastructure, which go beyond the traditional technical applications in the domain of energy and transport networks. Security must balance with other desirable qualities such as interoperability, crypto-agility and energy efficiency. Evidence suggests a gap in the current awareness of cyber security in EV charging infrastructures. This paper fills this gap by providing the most comprehensive to date overview of privacy and security challenges To do so, we review communication protocols used in its ecosystem and provide a suggestion of security tools that might be used for future research.Comment: 12 pages, 5 tables, 3 figure

Securing the Electric Vehicle Charging Infrastructure

Electric Vehicles (EVs) can help alleviate our reliance on fossil fuels for transport and electricity systems. However, charging millions of EV batteries requires management to prevent overloading the electricity grid and minimise costly upgrades that are ultimately paid for by consumers. Managed chargers, such as Vehicle-to-Grid (V2G) chargers, allow control over the time, speed and direction of charging. Such control assists in balancing electricity supply and demand across a green electricity system and could reduce costs for consumers. Smart and V2G chargers connect EVs to the power grid using a charging device which includes a data connection to exchange information and control commands between various entities in the EV ecosystem. This introduces data privacy concerns and is a potential target for cyber-security attacks. Therefore, the implementation of a secure system is crucial to permit both consumers and electricity system operators to trust smart charging and V2G. In principle, we already have the technology needed for a connected EV charging infrastructure to be securely enabled, borrowing best practices from the Internet and industrial control systems. We must properly adapt the security technology to take into account the challenges peculiar to the EV charging infrastructure. Challenges go beyond technical considerations and other issues arise such as balancing trade-offs between security and other desirable qualities such as interoperability, scalability, crypto-agility, affordability and energy efficiency. This document reviews security and privacy topics relevant to the EV charging ecosystem with a focus on smart charging and V2G

Real-time multi-objective optimisation for electric vehicle charging management

The continuous increase in the uptake of electric vehicles and the interest to use electric vehicles to provide energy services require commercially viable business models for all involved stakeholders. It is, however, challenging to achieve the synergy among different stakeholders since their objectives are often conflicting. This work proposes a real-time multi-objective optimisation method where electric vehicle charging/discharging profile is scheduled in real-time to strike a balance among different objectives, namely electricity cost reduction, battery degradation minimisation and grid stress alleviation as well as meeting the electric vehicle user charging requirement by fulfilling the departure time. Dynamic programming is adopted due to its computational efficiency, which is suitable for real-time applications. The effectiveness of the proposed method is demonstrated using a residential case study where the house is equipped with an electric vehicle and a photovoltaic system, and is validated by experimental implementation. The results show that the proposed multi-objective optimisation algorithm achieves the set objectives to satisfy the stakeholders’ priorities and provides a profit for the electricity end-user that is double as compared to that achieved by a benchmark multi-objective algorithm. The results demonstrate the effectiveness of the proposed multi-objective method and its suitability for real-time charging/discharging scheduling

Value of V2G Frequency Regulation in Great Britain Considering Real Driving Data

Electric Vehicles (EVs) can, when they are not used for driving, create value for the EV owner, by delivering ancillary services to the transmission system operator. Calculating potential earnings from grid services and charging strategies highly depends on the driving time, driving distance, and time spent at different locations. While few datasets describing EV usage exist, this work is based on one of the most extensive datasets gathered from 7,163 Nissan LEAFs. Using the real driving and charging data it was possible to calculate the value of a specific charging strategy for the individual EV. The EV dataset was used in a simulation based on British electricity transmission network operating codes and frequency measurement data. The outcome is the profit from frequency regulation for each EV in the data-set, which is found to range between 50 and 350 £/year, because of the large difference in the EV usage

Green neighbourhoods: the role of big data in low voltage networks’ planning

In this chapter, we aim to illustrate the benefits of data collection and analysis to the maintenance and planning of current and future low voltage net- works. To start with, we present several recently developed methods based on graph theory and agent-based modelling for analysis and short- and long-term prediction of individual households electric energy demand. We show how maximum weighted perfect matching in bipartite graphs can be used for short-term forecasts, and then review recent research developments of this method that allow applications on very large datasets. Based on known individual profiles, we then review agent-based modelling techniques for uptake of low carbon technologies taking into account socio-demographic characteristics of local neighbourhoods. While these techniques are relatively easily scalable, measuring the uncertainty of their results is more challenging. We present confidence bounds that allow us to measure uncertainty of the uptake based on different scenarios. Finally, two case-studies are reported, describing applications of these techniques to energy modelling on a real low-voltage net- work in Bracknell, UK. These studies show how applying agent-based modelling to large collected datasets can create added value through more efficient energy usage. Big data analytics of supply and demand can contribute to a better use of renewable sources resulting in more reliable, cheaper energy and cut our carbon emissions at the same time