Using heat demand prediction to optimise Virtual Power Plant production capacity

Vincent Bakker, Albert Molderink, Johann L. Hurink, Gerardus Johannes Maria Smit

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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Abstract

In the coming decade a strong trend towards distributed electricity generation (microgeneration) is expected. Micro-generators are small appliances that generate electricity (and heat) at the kilowatt level, which allows them to be installed in households. By combining a group of micro-generators, a Virtual Power Plant can be formed. The electricity market/network requires a VPP control system to be fast, scalable and reliable. It should be able to adjust the production quickly, handle in the order of millions of micro-generators and it should ensure the required production is really produced by the fleet of microgenerators. When using micro Combined Heat and Power microgenerators, the electricity production is determined by heat demand. In this paper we propose a VPP control system design using learning systems to maximise the economical benefits of the microCHP appliances. Furthermore, ways to test our design are described.
Original languageUndefined
Title of host publicationProceedings of the Nineteenth Annual Workshop on Circuits, Systems ans Signal Processing (ProRISC)
Place of PublicationUtrecht
PublisherTechnology Foundation
Pages11-15
Number of pages5
ISBN (Print)978-90-73461-56-7
Publication statusPublished - 27 Nov 2008
Event19th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2008 - Veldhoven, Netherlands
Duration: 27 Nov 200828 Nov 2008
Conference number: 19

Publication series

Name
PublisherSTW Technology Foundation
Number2008/16200

Conference

Conference19th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2008
CountryNetherlands
CityVeldhoven
Period27/11/0828/11/08

Keywords

  • EWI-14764
  • Artificial Neural Networks
  • Distributed Generation
  • METIS-255063
  • IR-65257
  • Algorithm design
  • Weather Sensitive Short-term Load Forecasting

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