Structural Misalignment in Financial Transmission Rights

TL;DR

This paper introduces a geometric framework to analyze structural misalignments in FTR markets, revealing how modeling choices and outages can cause underfunding and inefficiency.

Contribution

It develops a novel geometric approach to diagnose FTR underfunding and quantify the impact of modeling assumptions and outages on market efficiency.

Findings

FTR underfunding can arise from model misalignment independent of bidding behavior.

Uniform transmission derates and outages contribute to structural underfunding.

Multi-interval FTRs have intrinsic hedging inefficiencies with variable shadow prices.

Abstract

Financial Transmission Rights (FTRs) enable electricity market participants to hedge congestion risk in Day Ahead Market (DAM) operations, but for the market to be solvent, Independent System Operators (ISOs) must ensure that FTR payouts do not exceed the collected DAM merchandising surplus that funds them. We show that FTR underfunding (or conversely, hedging efficiency) can arise structurally from misalignment between the network models used in the FTR auction and the DAM, independent of bidding behavior. We develop a geometric framework in which both DAM merchandising surplus and the maximum supportable FTR payout are expressed as support functions of network-feasible injection polytopes. The resulting dual representation assigns nonnegative weights to transmission element-contingency constraints, enabling constraint-level attribution of model misalignment. Using this framework, we derive sharp implications for canonical FTR network modeling choices like uniform transmission element derates, and for structural sources of underfunding like unplanned DAM outages. We further show that multi-interval FTR products impose an intrinsic hedging inefficiency when DAM shadow prices vary over time, even under perfect model alignment. These results provide ISOs with rigorous tools to diagnose underfunding and quantify the efficiency cost of conservative FTR network modeling choices.