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Evaluating Renewable Energy Policies Using a Multi-agent Reinforcement Learning Model

Even as governments combat greenhouse emissions through a range of initiatives, it has yet to be clarified how renewable energy policy, energy market structure, and number of energy producers impact social welfare. We model a deregulated market for electricity as a blind single-price call auction and construct a multi-agent system with reinforcement learning that facilitates more realistic market evaluations and observation of equilibrium processes. We validate our simulation by comparing its results with the results from theoretical analysis in a simplified market.