Bidding Equilibria in Electricity Markets: ERCOT, PJM, and the Mean Field Limit

Abstract

As electricity markets grow larger and more uncertain with increasing renewable penetration, a central question is whether standard equilibrium models of strategic bidding remain valid at scale. This thesis evaluates that question in two steps. First, I apply a static supply-function equilibrium (SFE) framework to two U.S. wholesale electricity markets with distinct designs: ERCOT and PJM. In ERCOT, the model overpredicts strategic withholding but captures the qualitative direction of price movements. In PJM, the model breaks down more fundamentally, producing nearly load-invariant equilibrium prices despite substantial variation in observed outcomes. Second, I examine the computational limitations of finite-player Nash equilibrium. Scaling experiments show that best-response algorithms fail to converge beyond approximately N ≈ 20 firms, making the approach infeasible at realistic market sizes. To address this, I implement a mean field game (MFG) approximation, which replaces the high-dimensional fixed-point problem with a representative agent formulation. Calibrated to PJM data, the mean field equilibrium closely matches the finite-player Nash solution, consistent with theoretical predictions. However, both models fail to reproduce observed price dynamics in similar ways. This suggests that discrepancies between model predictions and real market outcomes arise primarily from omitted operational features, such as transmission constraints, unit commitment, and intertemporal coupling, rather than from the modeling of strategic interaction itself.