Clean Energy Transition Under Technological and Climate Change Uncertainty

The deployment of renewable energy is increasingly impeded by the lack of grid-scale storage. The appeal of future cost-competitive batteries has led to a strategy of waiting for further cost reduction, which introduces risk and costs over time. While other forms of energy storage may be feasible in the interim, there is an urgent need for grid operators to understand how and where these types of storage might be deployed. This project seeks to formulate a long-term sequential capacity expansion tool under technological uncertainty for energy storage. The proposed planning approach aims to find a middle ground between rushed highly expensive investments and postponing all decisions for future.

Climate change uncertainty was considered in the tool development, to ensure the power system’s resiliency. Specifically, the proposed framework is designed as sequential programming that quantifies and updates the available understanding of technological and climate change uncertainty and evaluates the trade-off between the increased risks of postponing the storage investments and the cost-saving as a result of making more informed decisions. To solve such a complex sequential optimization, advanced surrogate-based computational methods will be developed. The completion of this plan required an interdisciplinary approach combining key methodologies from the mathematical programming, uncertainty quantification, and power systems engineering disciplines, thereby helping expand the frontiers of multiple research areas into a converging framework.