Multi-year stochastic generation capacity expansion planning under environmental energy policy

We present a multi-year stochastic generation capacity expansion planning model to investigate changes in generation building decisions and carbon dioxide (CO2) emissions under environmental energy policies, including carbon tax and a renewable portfolio standard (RPS). A multi-stage stochastic mixed-integer program is formulated to solve the generation expansion problem. The uncertain parameters of load and wind availability are modeled as random variables and their independent and identically distributed (i.i.d.) random samples are generated using the Gaussian copula method, which represents the correlation between random variables explicitly. A multi-stage scenario tree is formed with the generated random samples, and the scenario tree is reduced for improved computation performance. A rolling-horizon method is applied to obtain one generation plan at each stage.