Day Ahead Market price scenario generation using a Combined Quantile Regression Deep Neural Network and a Non-parametric Bayesian Network

Day Ahead Market price scenario generation using a Combined Quantile Regression Deep Neural Network and a Non-parametric Bayesian Network: A framework for risk-based Demand Response

van der Heijden, T.J.T. (TU Delft Water Resources)
Palensky, P. (TU Delft Intelligent Electrical Power Grids)
van de Giesen, N.C. (TU Delft Water Resources)
Abraham, E. (TU Delft Water Resources)

2022

In this manuscript we propose a methodology to generate electricity price scenarios from probabilistic forecasts. Using a Combined Quantile Regression Deep Neural Network, we forecast hourly marginal price distribution quantiles for the DAM on which we fit parametric distributions. A Non-parametric Bayesian Network (BN) is applied to sample from these distributions while using the observed rank-correlation in the data to condition the samples. This results in a methodology that can create an unbounded amount of price-scenarios that obey both the forecast hourly marginal price distributions and the observed dependencies between the hourly prices in the data. The BN makes no assumptions on the marginal distribution, allowing us to flexibly change the marginal distributions of hourly forecasts while maintaining the dependency structure.

Probabilistic electricity price forecasting
scenario generation
deep neural network
non-parametric bayesian networks
quantile regression
probabilistic forecasting
day ahead market
demand response

http://resolver.tudelft.nl/uuid:06a286c4-f46f-400d-9185-e6b8412a95b6

https://doi.org/10.1109/POWERCON53406.2022.9929940

IEEE

2023-05-04

978-1-6654-1776-1

Proceedings of the 2022 IEEE International Conference on Power Systems Technology (POWERCON)

2022 IEEE International Conference on Power Systems Technology (POWERCON), 2022-09-12 → 2022-09-14, Kuala Lumpur, Malaysia

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 T.J.T. van der Heijden, P. Palensky, N.C. van de Giesen, E. Abraham