Paper Type
Research-in-Progress Paper
Description
Information systems in future smart grids will expand the capabilities of the power system through new services and control options. In this context, dynamically updated time-of-use (TOU) rates can be a building block for creating effective and robust pricing schemes in future retail electricity markets: On the one hand, they are better suited to match market dynamics and uncertainties than static, linear tariffs; on the other hand they mitigate the complexity arising from hourly real-time prices. Hence, the proper design of these dynamic rates requires managing the trade-off between complexity and efficiency. \ \ To this end, careful tuning of the rate complexity with respect to variability (number of time zones) and dynamics (frequncy of rate adjustments) is necessary. This challenges calls for efficient decision support that allows energy retailers to identify and implement promising rate designs. A framework to determine, analyse and compare a set of rate designs featuring different structural and dynamic design options is presented in this paper. This approach is illustrated using an exemplary scenario based on empirical electricity price data.
EVALUATING TIME-OF-USE DESIGN OPTIONS
Information systems in future smart grids will expand the capabilities of the power system through new services and control options. In this context, dynamically updated time-of-use (TOU) rates can be a building block for creating effective and robust pricing schemes in future retail electricity markets: On the one hand, they are better suited to match market dynamics and uncertainties than static, linear tariffs; on the other hand they mitigate the complexity arising from hourly real-time prices. Hence, the proper design of these dynamic rates requires managing the trade-off between complexity and efficiency. \ \ To this end, careful tuning of the rate complexity with respect to variability (number of time zones) and dynamics (frequncy of rate adjustments) is necessary. This challenges calls for efficient decision support that allows energy retailers to identify and implement promising rate designs. A framework to determine, analyse and compare a set of rate designs featuring different structural and dynamic design options is presented in this paper. This approach is illustrated using an exemplary scenario based on empirical electricity price data.