Towards an Aggregator that Exploits Big Data to Bid on Frequency Containment Reserve Market
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A4 Artikkeli konferenssijulkaisussa
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Date
2017-11-01
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Language
en
Pages
6
7514-7519
7514-7519
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Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
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.Description
Keywords
demand response, energy market, frequency containment reserve, machine learning, price forecasting, regression analysis, smart grid
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Citation
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