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

TL;DR

This paper tackles the challenge of expanding hydrogen networks under the chicken-and-egg dilemma and market uncertainty by formulating a two-stage distributionally robust expansion problem in which demand means depend on investment decisions. It introduces monolithic reformulations for generic decision-dependent moments and two specific moment-function variants (location- and capacity-based), together with a tailored column-and-constraint generation algorithm and acceleration techniques. Computational results on synthetic instances and a HEAVENN-based Northern Netherlands case show that incorporating decision-dependent uncertainty yields earlier and more diverse investments, while the proposed algorithmic enhancements achieve substantial speedups over benchmarks. The work provides practical guidance for policymakers and energy planners on when and where to invest in hydrogen infrastructure under uncertainty, and demonstrates scalable methods for large-scale DR problems with decision-dependent features.

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.

Figures (6)

• Figure 1: Performance of C&CG+ compared to existing approaches of the literature (C&CG, Benders) for a 0.1% optimality gap and a 5% optimality gap
• Figure 2: Map of the Northern Netherlands with supply, port and demand nodes marked
• Figure 3: Capacity expansion plans
• Figure 4: Capacity expansions under different levels of decision dependency
• Figure 5: Capacity expansion plans with import costs competitive to production cost

Theorems & Definitions (7)

• Theorem 1
• Theorem 2
• Remark 1
• Theorem 3
• Remark 2
• Theorem 4
• Theorem 5