Demand Response in Electricity Markets

In this paper agent-based simulation is employed to study the effect of demand-side bidding in the exercise of monopoly power by generators. The energy market is formulated as a stochastic game, where each stage game corresponds to an hourly energy auction. Each hourly energy auction is cleared using Locational Marginal Pricing. Generators and consumers are modeled as adaptive agents capable of learning through the interaction with their environment, following a Reinforcement Learning algorithm. The SA-Q-learning algorithm, a modified version of the popular Q-Learning, is used. Test results on a two- node power system with two generator-agents and two consumer- agents, lead to some useful conclusions.

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