Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market

Christian Giovanelli, Xin Liu, Seppo Sierla, Valeriy Vyatkin, Ryutaro Ichise

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)
180 Downloads (Pure)

Abstract

The increased penetration of distributed and volatile renewable generation requires the demand-side to be actively involved in energy balancing operations. This paper proposes a solution in which big data and machine learning methods are employed to enhance the capabilities of a Virtual Power Plant to participate and intelligently bid into a demand response energy market. The energy market being investigated consists of the frequency containment reserve market. First, we define the core decision-making required to overcome the uncertainties in the frequency containment reserve market participation for a Virtual Power Plant. Then, we focus on forecasting the frequency containment reserve prices for the day-ahead. We analyze the price data, and identify and collect the relevant features for the prediction of the prices. In addition, we select several regression analysis methods to be utilized for the prediction. Finally, we evaluate the performance of the implemented methods by executing several experiments, and compare the the performance with the performance of a state of the art autoregression method.
Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages7514-7519
Number of pages6
ISBN (Print)9781538611272
DOIs
Publication statusPublished - 1 Nov 2017
MoE publication typeA4 Article in a conference publication
EventAnnual Conference of the IEEE Industrial Electronics Society - Beijing, China
Duration: 29 Oct 20171 Nov 2017
Conference number: 43
http://iecon2017.csp.escience.cn/

Publication series

NameProceedings of the Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
ISSN (Print)1553-572X

Conference

ConferenceAnnual Conference of the IEEE Industrial Electronics Society
Abbreviated titleIECON
CountryChina
CityBeijing
Period29/10/201701/11/2017
Internet address

Keywords

  • demand response
  • energy market
  • frequency containment reserve
  • machine learning
  • price forecasting
  • regression analysis
  • smart grid

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  • National Institute of Informatics

    Christian Giovanelli (Visiting researcher)

    15 Jan 20177 Jul 2017

    Activity: Visiting an external institution typesVisit abroad

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