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

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

doi:10.1109/IECON.2017.8217316

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 proceeding › Conference article in proceedings › Scientific › peer-review

15 Citations (Scopus)

387 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 language	English
Title of host publication	Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Pages	7514-7519
Number of pages	6
ISBN (Print)	9781538611272
DOIs	https://doi.org/10.1109/IECON.2017.8217316
Publication status	Published - 1 Nov 2017
MoE publication type	A4 Conference publication
Event	Annual Conference of the IEEE Industrial Electronics Society - Beijing, China Duration: 29 Oct 2017 → 1 Nov 2017 Conference number: 43 http://iecon2017.csp.escience.cn/

Publication series

Name	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
ISSN (Print)	1553-572X

Conference

Conference	Annual Conference of the IEEE Industrial Electronics Society
Abbreviated title	IECON
Country/Territory	China
City	Beijing
Period	29/10/2017 → 01/11/2017
Internet address	http://iecon2017.csp.escience.cn/

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IECON.2017.8217316

Giovanelli_etal_IECON_2017_7514
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Accepted author manuscript, 298 KB

National Institute of Informatics
Giovanelli, C. (Visiting researcher)
15 Jan 2017 → 7 Jul 2017
Activity: Visiting an external institution types › Visiting an external academic institution

Cite this

Giovanelli, C., Liu, X., Sierla, S., Vyatkin, V., & Ichise, R. (2017). Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market. In Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 7514-7519). (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society). IEEE. https://doi.org/10.1109/IECON.2017.8217316

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title = "Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market",

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.",

keywords = "demand response, energy market, frequency containment reserve, machine learning, price forecasting, regression analysis, smart grid",

author = "Christian Giovanelli and Xin Liu and Seppo Sierla and Valeriy Vyatkin and Ryutaro Ichise",

year = "2017",

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Giovanelli, C, Liu, X, Sierla, S , Vyatkin, V & Ichise, R 2017, Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market. in Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 7514-7519, Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 29/10/2017. https://doi.org/10.1109/IECON.2017.8217316

Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market. / Giovanelli, Christian; Liu, Xin; Sierla, Seppo et al.
Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. p. 7514-7519 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Giovanelli, Christian

AU - Liu, Xin

AU - Sierla, Seppo

AU - Vyatkin, Valeriy

AU - Ichise, Ryutaro

N1 - Conference code: 43

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Y1 - 2017/11/1

N2 - 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.

AB - 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.

KW - demand response

KW - energy market

KW - frequency containment reserve

KW - machine learning

KW - price forecasting

KW - regression analysis

KW - smart grid

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DO - 10.1109/IECON.2017.8217316

M3 - Conference article in proceedings

SN - 9781538611272

T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

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BT - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE

T2 - Annual Conference of the IEEE Industrial Electronics Society

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ER -

Giovanelli C, Liu X, Sierla S , Vyatkin V, Ichise R. Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market. In Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE. 2017. p. 7514-7519. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society). doi: 10.1109/IECON.2017.8217316

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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Activities

National Institute of Informatics

Cite this