Equilibrium analysis of electricity markets with day-ahead market power mitigation and real-time intercept bidding

Electricity markets are cleared by a two-stage, sequential process consisting of a forward (day-ahead) market and a spot (real-time) market. While their design goal is to achieve efficiency, the lack of sufficient competition introduces many opportunities for price manipulation. To discourage this phenomenon, some Independent System Operators (ISOs) are planning to sequentially implement system-level market power mitigation policies that replace noncompetitive bids, based on an estimation of generator costs, a.k.a. default bids. However, without fully accounting for all participants' incentives (generators and loads), the application of such a policy may lead to unintended consequences. In this paper, we model and study the interactions of generators and inelastic loads in a two-stage settlement where the system operator imposes default bids, based on an estimation of generators' cost function in the day-ahead market. We show that such policy, when accounting for generator and load incentives, leads to a generalized Stackelberg-Nash game where load decisions (leaders) are performed in day-ahead market and generator decisions (followers) are relegated to the real-time market. Furthermore, the use of conventional supply function bidding for generators in real-time, does not guarantee the existence of a Nash equilibrium. This motivates the use of intercept bidding, as an alternative bidding mechanism for generators in the real-time market. An equilibrium analysis in this setting, leads to a closed-form solution that unveils several insights. Particularly, it shows that, unlike standard two-stage markets, loads are the winners of the competition in the sense that their aggregate payments are less than that of the competitive equilibrium. Moreover, heterogeneity in generators cost has the unintended effect of mitigating loads' market power. Finally, an analysis on the effect of overestimation of generation cost and real-time demand uncertainty shows a tendency for generators to benefit from uncertainty in both cases. Numerical studies validate and further illustrate these insights.