Based on the ideas of choice architecture (Thaler and Sunstein 2008), Weinmann et al. (2016) propose a framework for the development of digital nudges. The five steps of the framework include the definition of digital context and goals, understanding of the users decision process, selection and implementation of the nudge, and last, testing in an experimental setting. In the following we follow first four steps of the digital nudging framework to implement a feedback nudge on CO2 emissions. The main focus is on estimating the CO2 saving potential of postponed charging. This is the basis for the implementation of the nudge (i.e., feedback on avoided CO2 emissions depending on the offered temporal charging flexibility) and allows to test the effectivity of the nudge in an experimental setting.
Digital context and goals
Our goal is to design an information system for smart charging that provides direct feedback on CO2 emissions avoided through a smart charging algorithm. This means that the user can plug her BEV into a charging station and enter her charging flexibility via an app or via an interface of the charging station. To simplify matters, we only consider temporal charging flexibility. This means that the charging flexibility is determined by the time at which the user wants her BEV to be charged to full capacity. Providing energy flexibility as defined in Ludwig et al. (2017) by reducing the amount of energy that is required at the end of the charging process is not considered.
There are different possibilities, when the user can enter her temporal flexibility. On the one hand, she can set a default for her temporal flexibility. This could happen when logging into a charging system for the first time. Alternatively, systems could provide an affordance for entering temporal flexibility at the beginning of each charging process. In both cases, information about the CO2 savings could nudge BEV users towards providing more temporal flexibility.
Understand decision process
Various factors determine the acceptance of the smart charging. In a survey, Will and Schuller (2016) examined which factors supported the acceptance of smart charging. BEV users are more likely to accept smart charging if it does not restrict their mobility behaviour, contributes to grid stability, or fosters the integration of renewable energies. Monetary incentives were not considered important by the BEV users in the survey. Following these results, there are two non-monetary incentives that could lead users to provide more temporal flexibility. To show users the load status of their distribution grid, the gird must be equipped with intelligent measurement technology. In addition, network bottlenecks in the distribution network are a local problem that must be determined individually for each user. In contrary, CO2 emissions can be compared globally and are better understood by BEV users, as there is a medium-high self-reported understanding global warming in the throughout population (McCright and Dunlap 2011). It can therefore be assumed that CO2 emissions play a driving role in the decision on providing flexibility. However, most electricity consumers in general and BEV users in particular do not receive any feedback as to when electricity should be used in order to reduce CO2 emissions.
Various works show an effect of feedback on the energy conservation in households. Grønhøj and Thøgersen (2011) find that visible and salient feedback on displays leads to energy savings of 8.1%. In addition, Asensio and Delmas (2016) show that feedback about energy consumption is particularly effective when it is framed as a health issue. In contrary, if the feedback only mentions the cost savings, the energy savings are lower and only made over a short period of time. Likewise, normative feedback can help households save energy. Schultz et al. (2007) show at influence of social norms by communicating information about their income and energy consumption of their neighbors to households.
Direct feedback is also successfully used in vehicles (BEV and car with internal combustion engine) to encourage drivers to adopt a more efficient and thus environmentally friendly driving style (Magana and Munoz-Organero 2011).
Select nudge
The previous findings let us assume that feedback on CO2 savings could also be an important driver to provide charging flexibility. Feedback mechanisms have been successfully used both in household and in vehicles to encourage users to behave in an environmentally friendly manner.
Nudges or design elements can be divided into three categories (Münscher et al. 2016): First, the decision information can be changed (for example, if the information is put in a different light by a framing energy conservation as an health issue). Second, the structure of the decision can be changed (for example, if the order of dishes on a menu is changed). Third, the decision can be supported by reminder or similar tools.
An information system that gives feedback about the CO2 saving potentials for a certain charging flexibility can be described within this taxonomy as follows: The information system changes the decision information by translating the information. By communicating the CO2 savings it reframes the problem to an environmental context (besides, money can be saved through load shifting.) Furthermore, it changes the decision information by making the impact of the own behavior (i.e., providing flexibility) visible in the decision environment (i.e., the potential CO2 savings).
Implement nudge
The nudge is to be implemented as follows: The BEV user enters her charging flexibility into the charging system and receives feedback on the possible CO2 saving potentials with the given amount of temporal flexibility. For this, an algorithm must predict the CO2 saving potentials during the charging process and report them back to the user. In this paper, we limit the algorithm to a post-hoc analysis of historical data.
Similar algorithms for smart charging have already been implemented elsewhere. Gottwalt et al. (2013) develop a mixed-integer optimization program to coordinate BEV charging to maximize the use of renewable energy in a generation portfolio. However, they do not consider greenhouse gas emissions and only consider local generation. Sortomme and El-Sharkawi (2011) optimize the charging to ensure grid stability. However, the authors are not aware of any algorithms that focus on decision support and incentives for the BEV users.