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Towards an aggregator that exploits big data to bid on frequency containment reserve market
School of Electrical Engineering, Department of Electrical Engineering and Automation, Aalto University.
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo.
Department of Electrical Engineering and Automation, Aalto University.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, School of Electrical Engineering Aalto University.ORCID iD: 0000-0002-9315-9920
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2017 (English)In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 7514-7519Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 7514-7519
Series
IEEE Industrial Electronics Society, ISSN 1553-572X
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-68269DOI: 10.1109/IECON.2017.8217316ISI: 000427164807066Scopus ID: 2-s2.0-85046627496ISBN: 9781538611272 (electronic)OAI: oai:DiVA.org:ltu-68269DiVA, id: diva2:1196485
Conference
43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017
Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2018-05-22Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Output format
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