Dynamic electricity pricing and smart-grids are key elements of the vital transformation towards a decarbonized power production. Integrating volatile produced renewable electricity at large-scale will require electricity users who adapt their consumption to the available electricity supply. While the technical prerequisites for the implementation of dynamic tariffs are in place, the lack of consumer acceptance remains a central obstacle. Therefore, the BeSmart project identifies and develops scientifically sound practical solutions to raise the social acceptance of dynamic retail pricing schemes and thereby to unlock the potential of demand-side flexibility in low-carbon electricity markets. To analyze the choice of electricity tariffs by consumers, a model of behaviorally biased electricity retail contract choices was developed. This model is now numerically applied in a scenario-based electricity market analysis.
Update on the project´s process
Although the Covid19 pandemic made it very difficult to conduct the representative household survey, the data collection was successfully completed in May 2021. In the coming months, the data will be analyzed in detail and subsequently incorporated into the economic models and simulations. This integration of input parameters into the modelling is necessary to derive proposals for the design of legal regulations.
The theoretical analysis of dynamic electricity tariffs shows that even for households with high electricity consumption (5,000 kWh), an average short-term price spread of at least 30 cents/kWh must be realized to yield enough savings for households to overcome their transaction costs. In contrast, large technical consumption units can achieve significantly higher savings through automated price response and time flexibility and therefore offer greater potential for the introduction of dynamic tariffs. However, our first descriptive analyses of the household survey reveal that dynamic pricing is also rated significantly less attractive than a static pricing model when it comes to electric car charging.
Preliminary results of the project
For households to benefit from dynamic tariffs, they need to shift their electricity consumption from periods with high electricity prices into times with lower prices. Figure 1 illustrates the response of a household and a heat pump, which is combined with a thermal storage, to dynamic price signals. Since heat pumps typically are charged with reduced grid fees and surcharges, a second tariff for the heat pump (solid line) is considered in addition to the household tariff (dashed line). In the upper figure, the light grey area shows the standard load profile "H0", which represents the electricity consumption of a typical household. The dark grey area represents the electricity consumption of the heat pump, which always consumes electricity when the thermal storage needs charging. The bottom figure illustrates the price-induced load shift of the household in response to the dynamic tariff. Similarly, the heat pump avoids expensive price zones, by the optimized charging of the thermal storage in periods of lower prices. This modelling allows the quantification of financial savings and the analysis of different tariff characteristics (cf. Central results).
Freier, Julia; von Loessl, Victor: Dynamic electricity tariffs: Designing reasonable pricing schemes for private households (working title).
Gräper, Gerrit; von Wangenheim, Georg: Choosing within and between dynamic electricity tariffs (working title).
Groh, Elke; Ziegler, Andreas: On the attractiveness of dynamic tariffs in electric car charging: A micro-econometric analysis for Germany (working title).
Groh, Elke; von Loessl, Victor: An econometric analysis of the determinants of the individual data privacy risk assessment of smart meters and e-mobility charging apps (working title).
von Loessl, Victor; Wetzel, Heike: Smart home technologies and preferences for dynamic electricity tariffs: Empirical evidence from a stated choice analysis (working title).