Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs

Giulia De Zotti, Hanne Binder, Anders Bavnhøj Hansen, Henrik Madsen, Rishi Relan

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of Probabilistic Methods Applied to Power Systems 2020
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Print)978-1-7281-2822-1
Publication statusPublished - 2020
Event16th International Conference on Probabilistic Methods Applied to Power Systems - Virtual platform, Liege , Belgium
Duration: 18 Aug 202021 Aug 2020
http://aimontefiore.org/PMAPS2020/

Conference

Conference16th International Conference on Probabilistic Methods Applied to Power Systems
LocationVirtual platform
CountryBelgium
CityLiege
Period18/08/202021/08/2020
Internet address

Keywords

  • Demand response
  • Smart grid
  • Neural network
  • Electricity prices

Cite this

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.