Stochastic generation capacity expansion planning reducing greenhouse gas emissions

With increasing concerns about greenhouse gas emissions, a least-cost generation capacity expansion model to control carbon dioxide (CO₂) emissions is proposed in this paper. The mathematical model employs a decomposed two-stage stochastic integer program. Realizations of uncertain load and wind are represented by independent and identically distributed (i.i.d.) random samples generated via the Gaussian copula method. Two policies that affect CO₂ emissions directly and indirectly, carbon tax and renewable portfolio standard (RPS), are investigated to assess how much CO₂ emissions are expected to be reduced through those policies.