Enabling the energy transition on a local level: the case of electrical vehicles
With unprecedented growth in installed photovoltaic (PV) solar energy systems in combination with increased use electric vehicles (EVs) in cities, dependence on fossil fuel sources for energy demand is much reduced, also leading to reduction in greenhouse gas emissions. This brings about new challenges that require trans- and multidisciplinary in-depth studies. On a technical level, local electricity supply and demand management is studied, including so-called Vehicle-to-Grid (V2G), technology, which allows EV batteries to be used as a source of flexibility in the electricity grid. Optimum management of electricity flows is also performed in relation to greenhouse gas emission reductions. However, the behavior of users of EVs, i.e., owners or users of shared EVs, may need to change. Hence, social science research focuses on the study of the willingness of end-users to adapt their behavior. While technical and social research will show what would be possible, this most probably is not allowed (yet) by law. Legal research therefore is also performed, which would lead to recommendations for regulatory changes. In this panel, researchers will discuss recent insights from their collaboration in the ROBUST project, funded by the Topsector Energy in the framework of the MOOI funding scheme.
The role of legal research in transdisciplinary projects regarding the energy transition: harnessing flexibility for congestion management
The electricity system is in the middle of a far-reaching transition. Where it evolved to efficiently transport electricity that was produced in central locations to match demand whenever it occurred, it must now integrate sustainable production sources that are smaller and more dispersed, for which we cannot set when and how much they will produce. Rather than matching production to demand, we must now match demand to production.
One of the concrete issues we have to deal with is net congestion, where transport capacity at a given point in time is insufficient to meet demand. One of the solutions to this problem is to shift demand in time, for example through smart charging.
In the overarching project ROBUST, engineers work to model the potential of this solutions. Social scientists explore the willingness of end-users to adapt their behaviour. This paper explores the role of legal research within this project.
This purpose is twofold: first, the law determine what solutions are possible, and thus, to some extent, which solutions are to be modeled. For example, the Dutch Netcode determines that to participate in one type of congestion management, participants have to offer a minimum volume of 100 kW, which means that for EV’s to participate in this type of congestion management, different charging points have to be grouped in order to reliably be able to offer this product.
On the other hand, the modelling feeds into critical analysis of the law. When a solution is potentially successful in solving congestion, but is not possible under current regulation, this raises the question of whether and how the law should be adapted to facilitate it. This paper illustrates how this type of analysis should take place by looking at the extent to which different connections can be bundled or ‘pooled’ for the purpose of providing congestion management services. It then proceeds to discuss in more general terms how this type of legal research should be conducted, going beyond the question of how the law should be adapted to facilitate technological solutions to discuss the question of how to determine whether such adaptation is indeed desirable.
Data-Driven Modeling of Electric Vehicle Flexibility for Congestion Mitigation Services: A Multi-Objective Optimization Approach Balancing Cost and Emission Reduction
The increasing number of Electric Vehicles (EVs) is causing congestion in the existing power network. While EVs have the potential to improve grid stability due to their flexibility, they can also worsen congestion if not properly managed. The aggregate analysis and control of the charging behaviour of EVs while reducing the costs and environmental impact is crucial to providing useful flexibility, as individual EV behaviour is highly unpredictable. The concept of electric vehicle charging flexibility is well-studied, but many challenges exist in turning feasible flexible solutions into practical ones. These challenges can range from insufficient incentives to complicated legal and regulatory obstacles.
In this paper, real-life data from EV charging sessions in the Netherlands is used to model charging behaviour, its impact on the grid under various scenarios, and the potential ability of the EV fleet to provide grid services and congestion products. Special emphasis is given to studying the dependencies of charging behaviour when cost reduction and CO2 emission are considered as separate objectives.
The analysed dataset includes charging poles with varying locations, occupancy, accessibility, and sessions from both shared and privately owned vehicles. The charging sessions are categorised into groups based on their typical charging behaviour (e.g. residential private use), which is a key factor for both grid impacts and flexibility of charging patterns. The results show the benefits of using data-driven approaches to effectively manage grid congestion considering the cost and emissions caused by the increasing number of EVs.
Pareto efficient frontiers are explored for cost and emission reduction objectives (and combinations thereof). Moreover, a statistical model of charging behaviour is used to explore the ability to deliver dependable congestion management services at various aggregation levels and types of service. These probabilities will help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential.
In conclusion, our findings emphasise the importance of using real-life data to understand the complex relationships between EVs, their impact on the grid and emission levels, and the ability of the EV fleet to offer ancillary services to the grid under multiple objectives. This research will be of significant interest to researchers, practitioners, and policymakers in the smart grid and EV integration field.
Willingness to participate in vehicle-to-grid program: An exploration of battery electric vehicle users with various driving needs and charging preferences
The increased adoption of electric vehicles is beneficial to the environment due to less carbon dioxide emissions but also brings out new issues. In particular, concurrent and unmanaged recharging of EVs brings out much pressure on the grid and therefore results in overwhelming energy consumption. Vehicle-to-grid (V2G), a new technology that enables energy to be pushed back from battery of EVs to the grid helps the energy sector adjust its power distribution by discharging or charging EVs.
The whole concept is beneficial for grid optimization but may bring some inconvenience for the EV drivers, as it cannot guarantee a full-battery status when people are prepared to use the car. In practice, V2G participants have to indicate how much battery should be guaranteed when departure. EV users with various driving needs may have different preference for the guaranteed state of charge, which influence their willingness to participate in the V2G program. Recent studies have explored factors that impact users' willingness to use V2G, but how it works differently among different EV users with various driving needs has not been thoroughly investigated. These gaps will be addressed in this research and two research questions are formulated as follows:
1) How do a series of factors (i.e., guaranteed battery level, remuneration, plug-in time, and discharge circles) impact people’s choice for V2G?
2) To what extent does the driving preferences moderate the impact of the aforementioned factors on the acceptance of V2G?
With the help of a marketing survey company, around 400 BEV drivers’ information across the Netherlands will be collected by a questionnaire-based survey in March 2023. In addition to some basic information we have to collect such as age, gender, education degree, income, working status, and residential and work locations, EV use and driving preference were also collected in the format of a five-point Likert scale in the questionnaire. A cluster analysis was used to classify BEV users into different segments.
A state choice experiment was used to explore factors that impact the choice for V2G, among which four attributes were considered for the choice experiment design: 1) guaranteed minimum battery level (20/40/60%), 2) remuneration (200/600/1000€ per month), 3) average daily plug-in time (6/7/8 per day), 3) discharged circles (1/4/7 times per session). These attributes will be interacted with the aforementioned different EV segments in the multinomial logit model to explore how their impact differs among EV users with various driving preferences.
Identifying Barriers and Facilitating Factors for Smart Charging Behavior of Electric Vehicles. Insights from applying the COM-B framework
To curb climate change, the use of fossil fuels should be reduced, by using, for instance, electrical vehicles instead of conventional vehicles or using solar panels to generate energy. Therefore, electrical vehicle and solar panel use is expected to substantially increase in the coming years. This is likely to lead to problems for current voltage networks in Dutch cities such as net congestion, particularly in the evenings or on (very) sunny days. However, electric vehicles can, beside increasing difficulties, also serve as part of the solution to anticipate or avert congestion. For example by storing energy generated by solar panels in a smart way and waiting to charge vehicles until there is less demand. However, such ‘smart charging’ demands (increased) flexibility of end users. This study will provide insights into the willingness of Dutch city residents to engage with smart charging.
Findings will be presented from 75 qualitative semi-structured interviews conducted in five neighborhoods in a large city in the Netherlands, differing in, for example, socio-economic positions. The interviews are coded using the COM-B framework, allowing us to identify both barriers and facilitating factors in the capabilities, opportunities and motivations for residents of Dutch cities for the use of (shared) electric vehicles and smart charging of private electric vehicles.
Insights can provide entry points for the development of tailored interventions to stimulate the use of electric vehicles and smart charging of electric vehicles, and can inform policy making on potential ways to stimulate electric vehicle use and smart charging.