Multistage stochastic optimization of a mono-site hydrogen infrastructure by decomposition techniques

TL;DR

This paper presents a multistage stochastic optimization model for managing a hydrogen infrastructure with renewable sources, using decomposition techniques to handle uncertainties and improve cost efficiency.

Contribution

It introduces a novel multistage stochastic optimization framework with decomposition methods for hydrogen infrastructure management considering renewable energy constraints.

Findings

Solution achieves a small duality gap, indicating high effectiveness.

Renewable energy sources are prioritized to meet subsidy eligibility.

Decomposition method efficiently solves complex stochastic optimization problem.

Abstract

The development of hydrogen infrastructures requires to reduce their costs. In this paper, we develop a multistage stochastic optimization model for the management of a hydrogen infrastructure which consists of an electrolyser, a compressor and a storage to serve a transportation demand. This infrastructure is powered by three different sources: on-site photovoltaic panels (PV), renewable energy through a power purchase agreement (PPA) and the power grid. We consider uncertainties affecting on-site photovoltaic production and hydrogen demand. Renewable energy sources are emphasized in the hydrogen production process to ensure eligibility for a subsidy, which is awarded if the proportion of nonrenewable electricity usage stays under a predetermined threshold. We solve the multistage stochastic optimization problem using a decomposition method based on Lagrange duality. The numerical results indicate that the solution to this problem, formulated as a policy, achieves a small duality gap, thus proving the effectiveness of this approach.