Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty

TL;DR

This paper models utilities' bidding strategies in day-ahead electricity markets with load uncertainty, showing that prediction-based bidding forms a unique, efficient, and fault-immune equilibrium under realistic conditions.

Contribution

It introduces a novel game-theoretic model demonstrating that prediction-based bidding is a unique, efficient, and robust equilibrium in electricity markets with load uncertainty.

Findings

Prediction-based bidding is a unique pure strategy Nash Equilibrium.

The equilibrium incurs no efficiency loss and is fault-immune.

Simulations confirm theoretical results with real-world data.

Abstract

We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration. We model the interactions among utilities as a non-cooperative game, in which each utility aims at minimizing its per-unit electricity cost. We investigate utilities' optimal bidding strategies and show that all utilities bidding according to (net load) prediction is a unique pure strategy Nash Equilibrium with two salient properties. First, it incurs no loss of efficiency; hence, competition among utilities does not increase the social cost. Second, it is robust and (0, $N-1$) fault immune. That is, fault behaviors of irrational utilities only help to reduce other rational utilities' costs. The expected market supply-demand mismatch is minimized simultaneously, which improves the planning and supply-and-demand matching efficiency of the electricity supply chain. We prove the results hold under the settings of correlated prediction errors and a general class of real-time spot pricing models, which capture the relationship between the spot price, the day-ahead clearing price, and the market-level mismatch. Simulations based on real-world traces corroborate our theoretical findings. Our study adds new insights to market mechanism design. In particular, we derive a set of fairly general sufficient conditions for the market operator to design real-time pricing schemes so that the interactions among utilities admit the desired equilibrium.