Hydrogen infrastructure strategic planning using multi-objective optimization

Increasingly, hydrogen is being promoted as an alternative energy carrier for a sustainable future. Many argue that its use as a transportation fuel in fuel cell vehicles offers a number of attractive advantages over existing energy sources, especially in terms of well-to-wheel greenhouse gas emissions. Following this interest, several of the leading energy companies, like BP, have started investigating strategies for its introduction. The challenge of developing a future commercial hydrogen economy clearly still remains, though: what are the energy efficient, environmentally benign and cost effective pathways to deliver hydrogen to the consumer? Establishing what these “best” pathways may be is not trivial, given that a large number of technological options exist and are still in development for its manufacturing, storage, distribution and dispensing. Cost, operability, reliability, environmental impacts, safety and social implications are all performance measures that should be considered when assessing the different pathways as viable long-term alternatives. To aid this decision-making process, we present a generic optimization-based model for the strategic long-range investment planning and design of future hydrogen supply chains. By utilizing Mixed Integer Linear Programming (MILP) techniques, the model is capable of identifying optimal investment strategies and integrated supply chain configurations from the many alternatives. Realizing also that multiple performance criteria are of interest, the optimization is conducted in terms of both investment and environmental criteria, with the ultimate outcome being a set of optimal trade-off solutions representing conflicting infrastructure pathways. Since many agree that there is no one single template strategy for investing in a hydrogen infrastructure across the globe, emphasis is placed on developing a generic model such that it can be readily applied to different scenarios, geographical regions and case studies. As such, the model supports BP's strategic hydrogen infrastructure planning using high-level optimization programming, and is coined bpIC-H2. The features and capabilities of the model are illustrated through the application to a case study.