Adaptive Trading in Continuous Intraday Electricity Markets for a Storage Unit

被引:15
|
作者
Bertrand, Gilles [1 ]
Papavasiliou, Anthony [1 ]
机构
[1] UCLouvain, CORE, Louvain La Neuve 1348, Belgium
基金
美国国家科学基金会;
关键词
Markov processes; Markov decision processes; policy function approximation; reinforcement learning; continuous intraday market; WIND POWER; STOCHASTIC OPTIMIZATION; TUTORIAL;
D O I
10.1109/TPWRS.2019.2957246
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The increasing integration of renewable resources in electricity markets has increased the need for producers to correct their trading position close to real time in order to avoid volatile real-time prices. The closest option to delivery time in European markets is to trade in the continuous intraday market. This market is therefore an attractive trading outlet for assets that target at extracting value from their flexibility. Trading in this market is challenging due to the multistage nature of the problem, its high uncertainty and the fact that decisions need to be reached rapidly, in order to lock in profitable trades. We model the trading problem of a storage unit in the Markov Decision Process framework. We present an approach based on policy function approximation for tackling the problem. We provide relevant parameters for defining our policy, and demonstrate the effectiveness of our approach by comparing it to the rolling intrinsic policy on real historical data. Our proposed approach outperforms the rolling intrinsic policy, which is commonly employed in practice for storage units, by increasing profitability by on out-of-sample testing for a storage with perfect round-trip efficiency and by for a storage unit with a round-trip efficiency of 81%.
引用
收藏
页码:2339 / 2350
页数:12
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