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

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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.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE International Conference on Power Systems Technology (POWERCON)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-6654-1775-4
ISBN (Print)978-1-6654-1776-1
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Power Systems Technology (POWERCON) - Kuala Lumpur, Malaysia
Duration: 12 Sept 202214 Sept 2022

Conference

Conference2022 IEEE International Conference on Power Systems Technology (POWERCON)
Country/TerritoryMalaysia
City Kuala Lumpur
Period12/09/2214/09/22

Bibliographical note

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.

Keywords

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

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