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

Conference paper (2022)

Authors

T.J.T. van der Heijden Water Resources - CEG
P. Palensky Intelligent Electrical Power Grids - EEMCS
N.C. van de Giesen Water Resources - CEG
E. Abraham Water Resources - CEG
Research Group
Water Resources (CEG) (TU Delft)
Copyright: © 2022 T.J.T. van der Heijden, P. Palensky, N.C. van de Giesen, E. Abraham
DOI: https://doi.org/10.1109/POWERCON53406.2022.9929940
Quantile regression Demand response Day ahead market Deep neural network Scenario generation Probabilistic forecasting Probabilistic electricity price forecasting Non-parametric bayesian networks
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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Water Management

Research Group

Water Resources

Abstract

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.