Application of reinforcement learning-based algorithms in CO2 allowance and electricity markets

Climate change is one of the most important challenges faced by the world this century. In the U.S., the electric power industry is the largest emitter of CO₂, contributing to the climate crisis. Federal emissions control bills in the form of cap-and-trade programs are currently idling in the U.S. Congress. In the mean time, ten states in the northeastern U.S. have adopted a regional cap-and-trade program to reduce CO₂ levels and also to increase investments in cleaner technologies. Many of the states in which the cap-and-trade programs are active operate under a restructured market paradigm, where generators compete to supply power. This research presents a bi-level game-theoretic model to capture competition between generators in cap-and-trade markets and restructured electricity markets. The solution to the game-theoretic model is obtained using a reinforcement learning based algorithm.