Electrolyzers Bidding in Electricity Markets under Green Hydrogen Regulations and Uncertainty

TL;DR

This paper proposes an uncertainty-aware bidding strategy for electrolyzers in electricity markets, optimizing profit under renewable generation uncertainty and analyzing regulatory impacts on incentives.

Contribution

It introduces a linear programming approach to develop bid curves that incorporate renewable uncertainty for electrolyzers under green hydrogen regulations.

Findings

Incorporating renewable uncertainty increases electrolyzer profit by about 4%.

The approach reveals potential distortions in temporal-matching incentives.

The method provides a systematic way to maximize expected profit under regulatory constraints.

Abstract

Hydrogen produced through electrolysis offers a pathway to decarbonize hard-to-abate sectors by replacing gray hydrogen derived from natural gas reforming when produced using renewable power. However, grid-connected electrolyzers may inadvertently increase power-system emissions, resulting in hydrogen whose life-cycle intensity is similar to or higher than that of gray hydrogen. To address the high cost barrier of electrolytic hydrogen, both the E.U. and U.S. have introduced subsidy schemes conditional on low associated emissions. One key requirement is temporal matching, under which a subsidy applies only to the hydrogen volume that, ex-post, can be shown to match renewable generation over each one-hour interval. This requirement exposes the electrolyzer to uncertainty in the subsidy-eligible volume and thus the value of the produced hydrogen. This paper develops an uncertainty-aware day-ahead bid curve for a grid-connected electrolyzer. We formulate a linear program that maximizes expected profit across scenarios of renewable production and derive the bid curve from its Karush-Kuhn-Tucker conditions. A case study demonstrates that incorporating renewable uncertainty into the bid curve increases electrolyzer profit by approximately 4%, although it does not improve ex-post temporal matching. This finding highlights a potential distortion in the incentive effects of temporal-matching regulations when uncertainty is taken into account.