A multi-objective decision-making framework for renewable energy transportation

The mismatch in renewable energy generation potential, levelized cost and demand across different geographies highlight the potential of a future global green energy economy, through the trade of green fuels. This potential and need call for modeling frameworks to make informed decisions on energy investments, operations and regulations. In this work, we present a multi-objective optimization framework for modeling and optimizing energy transmission strategies considering different generation locations, transportation modes and often conflicting objectives of cost, environmental impact and transportation risk. An illustrative case study on supplying renewable energy to Germany demonstrates the utility of the framework across diverse options and trade-offs. Sensitivity analysis reveals that the optimal energy carrier and transmission strategy depend on distance, demand and existing infrastructure that can be re-purposed. The framework is adaptable across geographies and scales to offer actionable insights to guide investment, operational and regulatory decisions in renewable energy and hydrogen supply chains. • MO Optimization framework for hydrogen and renewable energy transport is developed. • Model supports investment, operations, and policy decisions in energy systems. • Analysis shows optimal energy carrier depends on distance, demand and infrastructure. • Ammonia emerges as a favorable carrier for long-distance energy transportation. • Case study on Germany illustrates trade-offs among conflicting objectives.