Abstract
This work develops a high-spatial resolution multi-stage stochastic programming (MS) model for power grid capacity expansion that co-optimizes generation, transmission, and energy storage system investments under uncertainty. Traditional two-stage stochastic programming (TS) models determine all investments in a single stage, limiting their ability to adapt to changing conditions such as evolving capital costs, policies, or supply chain disruptions. In contrast, the proposed MS formulation introduces sequential decision stages where partial information is revealed over time, allowing for adaptive, scenario-contingent investments. We compare TS and MS formulations using a modified IEEE 24-bus case study to quantify the Value of the Multistage Solution, which measures the economic benefit of allowing investment decisions to adapt over time as uncertainty is progressively resolved. Results show that while MS models are computationally more challenging, they achieve lower expected costs and yield flexible investment strategies that better respond to geopolitical and market uncertainties.