Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning

TL;DR

This paper analyzes the Coordinated Transaction Scheduling (CTS) mechanism in multi-area power markets using game theory and real data, revealing how market factors influence equilibrium outcomes and how bidders learn to optimize their strategies.

Contribution

It provides a game-theoretic analysis of CTS, incorporating market liquidity, forecast accuracy, fees, and virtual transactions, supported by empirical verification using real data.

Findings

Market liquidity significantly impacts equilibrium outcomes.

Bidders can learn Nash equilibria through simple algorithms.

Empirical data supports theoretical equilibrium predictions.

Abstract

Tie-line scheduling in multi-area power systems in the US largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effect of market liquidity, market participants' forecasts about inter-area price spreads, transactions fees and coupling of CTS markets with up-to-congestion virtual transactions. Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from equilibrium analysis.