(538f) An Interactive Rolling Horizon Strategy for Envisaging the Role of Cost-Optimal Hydrogen Supply Chains in the Future UK Energy System

Hydrogen is envisaged to have an important role in the future energy mix of the transport sector [1]. Despite its widely known advantages as an energy carrier with the potential of reducing greenhouse gas (GHG) emissions and increasing energy supply security, the problem of building an infrastructure capable of producing, storing and delivering hydrogen in a cost-effective manner still remains an unsolved key barrier for current market penetration.

Recent literature reviewing the area of hydrogen supply chain optimization [2] shows that assumptions made around hydrogen demand, along with inaccurate representation of the delivery infrastructures, such as pipelines,  discount rate factors and remaining infrastructure may be preventing hydrogen of becoming a more realistic and attainable option.

Additionally to the lack of representation of spatial demand variations, mainly due to expensive data collection procedures involved, the hydrogen supply side is hardly ever included into these modeling frameworks. However, the availability of resources for the supply chain is obviously the most important factor determining the infrastructure design, operation, cost and ultimately its own existence.

In this sense, energy systems models, despite showing a more aggregated picture of the infrastructure deployment, have the ability to provide more accurate representations of the demand satisfaction levels and resource supply details. Therefore, it is the aim of this work to bridge the existing gap between process systems engineering (PSE) and energy systems mathematical programming frameworks by sourcing the hydrogen supply chain economic optimization model with the demand and supply insights obtained from an existing energy systems model for the UK economy.

The proposed approach builds on previous work developed by the authors on hydrogen supply chains [3,4] and expands it to account for a proper modeling of the hydrogen pipelines, carbon sequestration infrastructure, discount factor and remaining infrastructure.  On the other hand, the spatial demand variations and supply availabilities modeling make use of the authors precedent work on the UK MARKAL Energy Systems model for investigating spatial hydrogen infrastructure development [5].

The rolling horizon strategy consists of an iterative procedure were a series of batch runs are performed, in first instance for the more detailed hydrogen supply chain optimization model to feed economic and technological parameters of an initial period into the energy systems model. The later will provide the demand and supply parameters for consecutive runs of the infrastructure model until equilibrium is reached for the given and future periods in a sequential process.

The optimization problem is mathematically formulated as a multi-period four echelon supply chain convex mixed-integer linear program (MILP) that optimizes the total discounted cost of the network considering the remaining infrastructure. The constraints include environmental calculations and endogenous demand and supply constraints that are updated in an explicit manner via the rolling-horizon strategy. Also hydrogen and carbon transportation pipelines are modeled. In this formulation, the binary variables represent the existing transportation links between locations, integer variables represent the number of production, storage and transportation technologies to be built or used, while the continuous variables correspond to operating conditions such hydrogen flows. Results for the UK hydrogen supply chain evolution over time will be presented and analysed.

[1] Martín del Campo, J. S., Rollin, J., Myung, S., Chun, Y., Chandrayan, S., Patiño, R., Adams, M. W. and Zhang, Y.-H. P. High-Yield Production of Dihydrogen from Xylose by Using a Synthetic Enzyme Cascade in a Cell-Free System . Angew. Chem. Int. Ed.2013; 52: 4587–590. 

[2]Agnolucci P and McDowall W. Designing future hydrogen infrastructure: Insights from analysis at different spatial scales. Int. J. Hydrogen Energ 2013;38:5181-191

[3] Sabio N, Kostin A, Guillén-Gosálbez G, Jimenez L. Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimisation and principal component analysis. Int J Hydrogen Energ 2012;37:5385-405.

[4] Almansoori A. and Shah, N. Design and operation of a future hydrogen supply chain: Multi period model. Int. J. Hydrogen Energ 2009;34:7883-897

[5] Strachan N, Balta-Ozkan N, Joffe D, McGeevor K, Hughes N. Soft-linking energy systems and GIS models to investigate spatial hydrogen infrastructure development in a low-carbon UK energy system. Int. J. Hydrogen Energ 2009;34(2):642-57