Abstract

This talk addresses a centralized generation expansion planning problem, accounting for both long- and short-term uncertainties. The long-term uncertainty (demand growth) is modeled via a set of scenarios, while the short-term uncertainty (wind power) is considered using moment-based ambiguity sets. In the expansion stage, the optimal units to be built are selected among discrete options. In the operational stage, a detailed representation of unit commitment constraints is considered. To make this problem tractable, we solve it in linear decision rules, and use a tight relaxation approach to covexify the unit commitment constraints. The resulting model is a distributionally robust chance-constrained optimization, which eventually recasts as a mixed-integer second-order cone program. We consider the IEEE 118 -bus test system as a case study, and explore the performance of the proposed model using an out-of-sample analysis.