A study of supply function equilibria in electricity markets /

Deregulation is a growing trend and the electricity industry has not escaped its reaches. With worldwide experiences spanning only thirty years, there is substantial interest in analyzing current and future market designs so that market power cannot be used to increase the price of electricity significantly.This thesis analyzes market power in electricity markets through the notion of Nash equilibrium (NE) and, more specifically, through Supply Function Equilibrium (SFE). We will examine how SFE can be modified to incorporate capacity constraints on generators and generating companies (gencos) controlling more than one generator for a Poolco electricity market with marginal pricing.A genco's supply function is assumed to be of the form gi=l-aibi . Gaming is done either with ai or bi only, while keeping the other parameter at true cost. Gaming with both variables cannot be analyzed since the problem would have too many degrees of freedom. For each possible generator output level (minimum output, maximum output, or in between), analytical methods are employed to determine all candidate Nash equilibria. Then, simulations are performed over the range of possible genco offers to determine whether these candidates meet the complete set of Nash equilibrium criteria, specifically whether any genco can or cannot improve its profit by gaming.For various inelastic demand levels, study cases indicate that there are either no Nash equilibria or only one. In the multi-unit genco case, the price of electricity is found to be higher than in the case where each genco owns only one generator, illustrating the effect of market concentration on the price. Whether capacity constraints are considered or not, the price of electricity appears to be higher if gencos are allowed to game with bi instead of ai.The inclusion of capacity constraints on generators and the consideration of the multi-unit genco case will allow for better genco modeling in a Poolco market with marginal pricing. In turn, this will lead to more accurate analysis of the effects of current and possible rules and regulations on the price of electricity