Abstract-This paper presents a methodology for the development of bidding strategies for electricity producers in a competitive electricity marketplace. Initially, the problem is modeled as a two level optimization problem where, at the first level, a market participant tries to maximize his expected profit under the constraint that, at the second level, an independent system operator dispatches power solving an optimal power flow problem that minimizes total system cost. It is assumed that each supplier bids a linear supply function and chooses his bidding strategy based on probabilistic estimates of demand and rival behavior. Monte Carlo simulation is used to calculate the expected profit and Genetic Algorithms are employed to find the optimal strategy. Subsequently, the formulation is expanded to account for different market participants' risk profiles. It is shown that risk aversion may influence the optimal bidding strategy of an individual.

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This paper presents a Q-Learning algorithm for the development of bidding strategies for market participants in FTR auctions. Each market participant is represented by an autonomous adaptive agent capable of developing its own bidding behavior based on a Q-learning algorithm. Initially, a bilevel optimization problem is formulated. At the first level, a market participant tries to maximize his expected profit under the constraint that, at the second level, an independent system operator tries to maximize the revenues from the FTR auction. It is assumed that each FTR market participant chooses his bidding strategy, for holding a FTR, based on a probabilistic estimate of the LMP differences between withdrawal and injection points. The market participant expected profit is calculated and a Qlearning algorithm is employed to find the optimal bidding strategy. A two-bus and a five-bus test system are used to illustrate the presented method.

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