Energy Arbitrage

Energy arbitrage leverages price fluctuations in electricity markets to profit from buying energy at low prices and selling it at higher prices, often facilitated by energy storage systems like batteries. Current research heavily utilizes reinforcement learning (RL) algorithms, including deep Q-learning, soft actor-critic, and proximal policy optimization, to optimize trading strategies across day-ahead and balancing markets, often incorporating risk management and handling uncertainties in renewable energy generation. This field is crucial for integrating renewable energy sources, improving grid stability, and enhancing the economic viability of energy storage technologies, with practical applications ranging from large-scale grid management to individual building energy systems.