Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning
This work addresses the challenge of revenue generation for energy storage operators in volatile markets, representing an incremental advance by applying reinforcement learning to a known bottleneck.
The paper tackles the problem of designing optimal arbitrage strategies for energy storage in real-time markets, which is difficult due to price uncertainty, and uses reinforcement learning with a novel reward function to achieve significant performance improvements over existing algorithms.
In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning. Real-time price arbitrage is an important source of revenue for storage units, but designing good strategies have proven to be difficult because of the highly uncertain nature of the prices. Instead of current model predictive or dynamic programming approaches, we use reinforcement learning to design an optimal arbitrage policy. This policy is learned through repeated charge and discharge actions performed by the storage unit through updating a value matrix. We design a reward function that does not only reflect the instant profit of charge/discharge decisions but also incorporate the history information. Simulation results demonstrate that our designed reward function leads to significant performance improvement compared with existing algorithms.