UC Berkeley

Planning the long-term expansion of a power sector requires anticipating future technologies, fuel costs, and new carbon policies. Many state-of-the-art models rely on exogenous data for cost and performance projections where the inherent uncertainty is either ignored or addressed only with sensitivity analysis and scenarios. For the few models accounting for uncertainty, the transition from the research field to policy making has not occurred because of important practical barriers in the latter field: higher reliance on time-tested models, impossibility to constantly adopt new models, run-time issues. To streamline this process, we present a new modular two-step methodology, based on mean-variance optimization, to help policy makers adjust for risks on costs their findings from current cost-minimizing tools, while sparing them the hurdles of adopting a new model. To illustrate this, we refine the SWITCH-China least-cost power expansion pathway by minimizing its cost uncertainty.