Subsidy-Free Renewable Energy Trading : A Meta Agent Approach

Longoria, Genaro and Davy, Alan and Shi, Lei (2020) Subsidy-Free Renewable Energy Trading : A Meta Agent Approach. IEEE Transactions on Sustainable Energy, 11 (3). pp. 1707-1716. ISSN 1949-3029

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Can we automate the energy exchange of a power trader? To address this challenge, we present the Meta Agent Learner (MAL). The MAL is a tiered and multi-policy energy trader. It comprises data analytics (DA), a deep sequence-to-sequence recurrent neural network (DS2S) and reinforcement learning (RL). The DA phase draws knowledge out of the sheer flow of data. The DS2S phase creates wisdom and provides the intelligence for decision making. The RL phase senses and learns from the market to act strategically. We demonstrate the MAL in a scenario of a price-taker wind farm with a hydro plant. The testbed is real data from the NordPool and East Denmark (DK2). More specifically, electricity consumption, wholesale and balancing prices, cross border energy exchange, and weather conditions. The MAL optimizes the combined production of the wind farm and hydro pumped storage. Runs the hydro plant such that spillage of wind power is avoided or stores cheap market electricity. The performance is benchmarked with three traders.

Item Type: Article
Additional Information: Funding Information: Manuscript received February 15, 2019; revised June 10, 2019; accepted August 15, 2019. Date of publication August 28, 2019; date of current version June 19, 2020. This work was supported by the Science Foundation Ireland via the CONNECT Research Centre under Grant 13/RC/2077. Paper no. TSTE-00197-2019. (Corresponding author: Genaro Longoria.) G. Longoria and A. Davy are with the Telecommunications Software and Systems Group, Waterford Institute of Technology, X91 P20H Waterford, Ireland (e-mail:; Publisher Copyright: © 2010-2012 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2100/2105
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Date Deposited: 19 Oct 2022 23:08
Last Modified: 28 Jul 2023 22:40

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