A Multiscale Modeling and Optimization Framework for the Design of Energy Systems with Embedded Life Cycle Considerations

Abstract

The role of dynamic energy storage in future energy systems driven by a mix of intermittent renewable power generation and dense energy carriers (DECs) cannot be understated. Reliable energy storage could pay dividends in terms on grid resilience and energy security as well. Nevertheless, in the context of promoting a systematic energy transition towards net-carbon neutrality by 2050, the long-term environmental impact of energy storage from the perspective of materials utilization and supporting sustainable power generation mandates a thorough evaluation. To this end, the article presents a modeling and optimization framework developed in the energiapy package to analyze the at-scale life-cycle impact of different technology pathways. The mixed integer programming (MIP) framework is applied towards the design of future integrated energy systems consisting of both renewable power generation through solar or wind power, DECs production, and battery energy storage. The trade-offs between the levelized cost of energy and the environmental impacts, the potential to exploit synergies between value chains, and the comparison of different cost and technology scenarios are elucidated upon.