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With the rising extension of renewable energies, the intraday electricitymarkets have recorded a growing popularity amongst traders as well as electricutilities to cope with the induced volatility of the energy supply. Throughtheir short trading horizon and continuous nature, the intraday markets offerthe ability to adjust trading decisions from the day-ahead market or reducetrading risk in a short-term notice. Producers of renewable energies utilizethe intraday market to lower their forecast risk, by modifying their providedcapacities based on current forecasts. However, the market dynamics are complexdue to the fact that the power grids have to remain stable and electricity isonly partly storable. Consequently, robust and intelligent trading strategiesare required that are capable to operate in the intraday market. In this work,we propose a novel autonomous trading approach based on Deep ReinforcementLearning (DRL) algorithms as a possible solution. For this purpose, we modelthe intraday trade as a Markov Decision Problem (MDP) and employ the ProximalPolicy Optimization (PPO) algorithm as our DRL approach. A simulation frameworkis introduced that enables the trading of the continuous intraday price in aresolution of one minute steps. We test our framework in a case study from theperspective of a wind park operator. We include next to general tradeinformation both price and wind forecasts. On a test scenario of Germanintraday trading results from 2018, we are able to outperform multiplebaselines with at least 45.24% improvement, showing the advantage of the DRLalgorithm. However, we also discuss limitations and enhancements of the DRLagent, in order to increase the performance in future works.

A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator