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Abstract We apply an approach that theoretically connects processes at different scales within a sociotechnical sustainability transitions frame, focusing on individual actors. Drawing on interviews with actors involved in the electric mobility transition in Lower Saxony, Germany, we analyse actors’ narratives as revelatory of situated or ‘conjunctural’ knowledge, as viewed from a strong structuration perspective of sociotechnical change. The purpose is to shed a particular type of light on structure-agency processes. Strong structuration is an extended version of Giddens’ social theory that takes account of actors’ subjective experience of their situation, viewing this as both shaped by - and shaping of - structure, in the sense of rules that are internal and external to individuals. Here we show how actors’ narratives of their experiences and positions in the sociotechnical system reveal themes relevant to the on-going structuration electric vehicles in the case study region of Lower Saxony, Germany. We identify general ‘meta-narratives’ that span more than one individual, as well as personal narratives that are specific to individuals. In addition, the analytic value of narratives in transition literature is discussed.

Abstract We apply an approach that theoretically connects processes at different scales within a sociotechnical sustainability transitions frame, focusing on individual actors. Drawing on interviews with actors involved in the electric mobility transition in Lower Saxony, Germany, we analyse actors’ narratives as revelatory of situated or ‘conjunctural’ knowledge, as viewed from a strong structuration perspective of sociotechnical change. The purpose is to shed a particular type of light on structure-agency processes. Strong structuration is an extended version of Giddens’ social theory that takes account of actors’ subjective experience of their situation, viewing this as both shaped by - and shaping of - structure, in the sense of rules that are internal and external to individuals. Here we show how actors’ narratives of their experiences and positions in the sociotechnical system reveal themes relevant to the on-going structuration electric vehicles in the case study region of Lower Saxony, Germany. We identify general ‘meta-narratives’ that span more than one individual, as well as personal narratives that are specific to individuals. In addition, the analytic value of narratives in transition literature is discussed.

Electric vehicles offer the potential of climate-friendly driving and have received more and more attention in diffusion research recently. However, the effect of the availability of home charging options on the market potential of electric vehicles has not been targeted in detail in the diffusion process so far, although it represents a beneficial differentiation to conventional vehicles. Therefore, we present an agent-based simulation to address the buying behavior of German consumers regarding electric and plug-in hybrid electric vehicles using empirical data primarily derived from a choice-based conjoint study. We explicitly consider individual possibilities of home charging in our model, which appears to have an important influence on the diffusion of both, electric and plug-in hybrid electric vehicles. However, its importance decreases with faster charging times at public charging stations. Furthermore, our findings reveal that in the longer term, technological progress in charging time, range, and charging station density of electric vehicles is presumably cannibalizing plug-in hybrid electric vehicles’ market shares more than that of conventional vehicles. However, with still lower technological capabilities of electric vehicles, a governmental subsidy can initially promote plug-in hybrid electric vehicles, but electric vehicles will benefit later from that promotional effect.

Electric vehicles offer the potential of climate-friendly driving and have received more and more attention in diffusion research recently. However, the effect of the availability of home charging options on the market potential of electric vehicles has not been targeted in detail in the diffusion process so far, although it represents a beneficial differentiation to conventional vehicles. Therefore, we present an agent-based simulation to address the buying behavior of German consumers regarding electric and plug-in hybrid electric vehicles using empirical data primarily derived from a choice-based conjoint study. We explicitly consider individual possibilities of home charging in our model, which appears to have an important influence on the diffusion of both, electric and plug-in hybrid electric vehicles. However, its importance decreases with faster charging times at public charging stations. Furthermore, our findings reveal that in the longer term, technological progress in charging time, range, and charging station density of electric vehicles is presumably cannibalizing plug-in hybrid electric vehicles’ market shares more than that of conventional vehicles. However, with still lower technological capabilities of electric vehicles, a governmental subsidy can initially promote plug-in hybrid electric vehicles, but electric vehicles will benefit later from that promotional effect.

Users charging the batteries of their electric vehicles in an uncoordinated manner can present energy systems with a challenge. One possible solution, smart charging, relies on the flexibility within each charging process and controls the charging process to optimize different objectives. Effective smart charging requires forecasts of energy requirements and parking duration at the charging station for each individual charging process. We use data from travel logs to create quantile forecasts of parking duration and energy requirements, approximated by upcoming trip distance. For this task, we apply quantile regression, multi-layer perceptrons with tilted loss function, and multivariate conditional kernel density estimators. The out-of-sample evaluation shows that the use of local information from the vehicle's travel data improves the forecasting accuracy by 13.7% for parking duration and 0.56% for trip distance compared to the data generated at the charging stations. In addition, the analysis of a case study shows that using probabilistic forecasts can control the interruption of charging processes more efficiently compared to point forecasts. Probabilistic forecasting leads up to 7.0% less interruptions, which can cause a restriction in drivers' mobility demand. The results show that charging station operators benefit from leveraging the driving patterns of electric vehicles. Thereby, smart charging and the application of the proposed models as benchmarks models for the related forecasting tasks is an improvement for the operators.

Users charging the batteries of their electric vehicles in an uncoordinated manner can present energy systems with a challenge. One possible solution, smart charging, relies on the flexibility within each charging process and controls the charging process to optimize different objectives. Effective smart charging requires forecasts of energy requirements and parking duration at the charging station for each individual charging process. We use data from travel logs to create quantile forecasts of parking duration and energy requirements, approximated by upcoming trip distance. For this task, we apply quantile regression, multi-layer perceptrons with tilted loss function, and multivariate conditional kernel density estimators. The out-of-sample evaluation shows that the use of local information from the vehicle's travel data improves the forecasting accuracy by 13.7% for parking duration and 0.56% for trip distance compared to the data generated at the charging stations. In addition, the analysis of a case study shows that using probabilistic forecasts can control the interruption of charging processes more efficiently compared to point forecasts. Probabilistic forecasting leads up to 7.0% less interruptions, which can cause a restriction in drivers' mobility demand. The results show that charging station operators benefit from leveraging the driving patterns of electric vehicles. Thereby, smart charging and the application of the proposed models as benchmarks models for the related forecasting tasks is an improvement for the operators.

Adopting electric vehicles (EVs) and implementing variable electricity tariffs increase peak demand and the risk of congestion in distribution grids. To avert critical grid situations and sidestep expensive grid expansions, Distribution System Operators (DSOs) must have intervention rights, allowing them to curtail charging processes. Various curtailment strategies are possible, varying in spatio-temporal differentiation and possible discrimination. However, evaluating different strategies is complex due to the interplay of economic factors, technical requirements, and regulatory constraints - a complexity not fully addressed in the current literature. Our study introduces a sophisticated model to optimize electric vehicle charging strategies to address this gap. This model considers different tariff schemes (Fixed, Time-of-Use, and Real-Time) and incorporates DSO interventions (basic, variable, and smart) within its optimization framework. Based on the model, we analyze the ´rexibility demand and total electricity costs from the users' perspective. Applying our model to a synthetic distribution grid, we îond that ´rexible tariffs offer consumers only marginal economic benefits and increase the risk of grid congestion due to herding behavior. All curtailment strategies effectively alleviate congestion, with variable curtailment featuring spatio-temporal differentiation, approaching optimality regarding ´rexibility demand. Notably, applying curtailment from the users' perspective does not lower cost savings significantly.

Adopting electric vehicles (EVs) and implementing variable electricity tariffs increase peak demand and the risk of congestion in distribution grids. To avert critical grid situations and sidestep expensive grid expansions, Distribution System Operators (DSOs) must have intervention rights, allowing them to curtail charging processes. Various curtailment strategies are possible, varying in spatio-temporal differentiation and possible discrimination. However, evaluating different strategies is complex due to the interplay of economic factors, technical requirements, and regulatory constraints - a complexity not fully addressed in the current literature. Our study introduces a sophisticated model to optimize electric vehicle charging strategies to address this gap. This model considers different tariff schemes (Fixed, Time-of-Use, and Real-Time) and incorporates DSO interventions (basic, variable, and smart) within its optimization framework. Based on the model, we analyze the ´rexibility demand and total electricity costs from the users' perspective. Applying our model to a synthetic distribution grid, we îond that ´rexible tariffs offer consumers only marginal economic benefits and increase the risk of grid congestion due to herding behavior. All curtailment strategies effectively alleviate congestion, with variable curtailment featuring spatio-temporal differentiation, approaching optimality regarding ´rexibility demand. Notably, applying curtailment from the users' perspective does not lower cost savings significantly.