Algorithmic trading of real-time electricity with machine learning
研究了在实时不平衡电力市场中，利用强化学习算法为三种市场参与者（非实物交易者、燃气发电机、电池储能系统）寻找盈利机会，并用英国数据回测了利润和风险。
Algorithmic trading is becoming the dominant approach in many electricity spot and futures markets. This paper focuses on the emerging interest in the less documented real-time imbalance markets, by developing reinforcement learning agents to find profit-making opportunities algorithmically. We develop a repeatable experimental setting to compare different market participants and explore the applications of Q-learning with neural networks for three types of market participants: a non-physical trader, a gas generator, and a battery electricity storage system. We backtest all three agents using British data across summer and winter months to compare their profits, risks and various experimental design considerations.