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.
Dynamic electricity tariffs must offer sufficiently large price ranges to allow attractive financial savings. At the same time, microeconomic modelling of risk-averse tariff decision-makers shows that consumers' desire for cost protection, which is evident based on the analysis of the stated choice experiment, should be met by implementing price caps on annual average prices. In addition, the results indicate that households systematically overestimate their electricity consumption and costs. This can lead to an overestimation of the potential welfare gains associated to dynamic electricity tariffs. Nevertheless, uniform corrective measures (for example switching premiums) could be adequate to correct biased tariff choices in a sufficiently efficient manner. Thereby, an optimal utilization level of dynamic tariffs can be achieved. Based on the published results, the policy advice that BeSmart gives regarding the design and dissemination of dynamic tariffs can be used in practice.
Illustration of project results
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).
- Sufficient cost savings can be achieved by dynamising all consumption-dependent electricity price components.
- Ceilings for annual average prices efficiently safeguard against cost risks and at the same time preserve the incentive effect of dynamic tariffs.
- Acceptance problems can and should be addressed with the help of various approaches (e.g. better provision of information, cost insurance or automated load shifting).
- Time-of-day tariffs with many price zones represent a possible trade-off between efficient prices and household preferences.
- Systematic errors in tariff decisions can be efficiently corrected by means of uniform financial incentives.
Freier, J., von Loessl, V. (2022):
Dynamic electricity tariffs: Designing reasonable pricing schemes for private households. Energy Economics 112: 106146.
Gambardella, C. (2022):
Biased Beliefs and Retail Rate Choice: Welfare Effects in the German Electricity Market (Discussion Paper).
Gräper, G., von Wangenheim, G. (2022):
How electricity tariffs optimally induce consumption behavior and the selection of incentive contracts (Arbeitstitel).
Gerhardt, M., Groh, E. D., Ziegler, A. (2022):
The relevance of life-cycle CO2 emissions for vehicle purchase decisions: A stated choice experiment (Arbeitstitel).
Nakai, M., von Loessl, V., Wetzel, H. (2022):
Preferences for Dynamic Electricity Tariffs: A Comparison of Households in Germany and Japan. MAGKS Discussion Paper No. 13-2022.