Evaluating cost and emission reduction potentials with stochastic PPA portfolio optimization for green hydrogen production in a decarbonized glassworks

Abstract

The decarbonization of heavy industries demands large volumes of green hydrogen. To produce green hydrogen via electrolysis, the EU’s Renewable Energy Directive II imposes rules to ensure the use of renewable electricity. Hydrogen producers can use portfolios of power purchase agreements (PPAs) to buy renewable electricity. These portfolios must meet hydrogen demand cost-effectively, and battery storage can help by shifting excess renewable generation. However, high uncertainty around future electricity prices and demand complicates optimal portfolio design. Current literature lacks comprehensive models that evaluate such portfolio optimization under uncertainty for real-world case studies including battery storage. This work addresses the gap by introducing a stochastic mixed-integer linear programming model tailored to industrial applications. We demonstrate the model using a real-world glass manufacturing site in Germany. Our findings show that portfolio optimization alone can reduce the levelized cost of hydrogen (LCOH) by 6.24% under EU rules. Adding a battery further cuts costs, achieving an LCOH of 11.8 €₂₀₂₄ kg⁻¹. Exploring different temporal matching schemes reveals that weekly matching reduces LCOH by 2 €₂₀₂₄kg⁻¹ while maintaining a high share of renewable energy. The model offers a flexible tool for optimizing PPA portfolios in various industrial settings.