In power systems, electrical consumers can become a signiﬁcant source of ﬂexibility, by adjusting their consumption according to grid’s needs while respecting their operational constraints. Consumers’ ﬂexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Speciﬁcally, the nonlinear autoregressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers’ price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.
|Title of host publication||Proceedings of Probabilistic Methods Applied to Power Systems 2020|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||16th International Conference on Probabilistic Methods Applied to Power Systems - Virtual platform, Liege , Belgium|
Duration: 18 Aug 2020 → 21 Aug 2020
|Conference||16th International Conference on Probabilistic Methods Applied to Power Systems|
|Period||18/08/2020 → 21/08/2020|
- Demand response
- Smart grid
- Neural network
- Electricity prices
De Zotti, G., Binder, H., Hansen, A. B., Madsen, H., & Relan, R. (2020). Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs. In Proceedings of Probabilistic Methods Applied to Power Systems 2020 IEEE.