Hydrogen Network Expansion Planning considering the Chicken-and-egg Dilemma and Market Uncertainty

TL;DR

This paper develops a risk-averse, distributionally robust framework for hydrogen network expansion planning that addresses the chicken-and-egg dilemma and market uncertainty, providing strategic insights for early-stage hydrogen markets.

Contribution

It introduces a novel decision-dependent demand model and tailored solution algorithms for hydrogen network planning under market uncertainty and demand ambiguity.

Findings

Considering demand uncertainty leads to earlier investments.

The approach improves planning robustness under market ambiguity.

Case study validates the model's practical relevance.

Abstract

Green hydrogen is thought to be a game changer for reaching sustainability targets. However, the transition to a green hydrogen economy faces a critical challenge known as the `chicken-and-egg dilemma', wherein establishing a hydrogen supply network relies on demand, while demand only grows with reliable supply. In addition, as the hydrogen market is in the early stage, predicting demand distributions is challenging due to lack of data availability. This paper addresses these complex issues through a risk-averse framework with the introduction of a distributionally robust hydrogen network expansion planning problem under decision-dependent demand ambiguity. The problem optimizes location and production capacity decisions of the suppliers considering the moments of the stochastic hydrogen demand as a function of these investment decisions. To obtain tractable representations of this problem, we derive two different reformulations that consider continuous and discrete hydrogen demand support sets under different forms of decision dependencies. To efficiently solve the reformulations, we develop a tailored algorithm based on the column-and-constraint generation approach, and enhance the computational performance through solving the master problems to a relative optimality gap, decomposing the subproblems, and integrating pre-generated columns and constraints. To validate the effectiveness of our approach, we investigate a real case study leveraging data from the "Hydrogen Energy Applications in Valley Environments for Northern Netherlands (HEAVENN)" project. The results reveal that considering the chicken-and-egg dilemma under uncertain hydrogen market conditions leads to earlier and more diverse investments, providing critical insights for policymakers based on the degree of decision dependency.