Multi-year stochastic generation capacity expansion planning under environmental energy policy. (1st December 2016)

• Record Type: Journal Article
• Authors: Park, Heejung; Baldick, Ross
• Is Part Of: Applied energy. Volume 183(2016)
• Page Start: 737
• Page End: 745
• Publication Date: 2016-12-01
• Subjects: Generation planning -- Stochastic optimization -- Wind power -- Multi-stage stochastic program -- Greenhouse gas emissions -- Scenario reduction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.08.164
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A methodology for policy assessment was developed using a multi-stage stochastic program. Carbon tax and renewable portfolio standard are applied as energy environmental policies. Correlated wind and load samples are generated via Gaussian copula. A Scenario tree is constructed with i.i.d. random samples and reduced by GAMS/SCENRED2. A long-term stochastic generation capacity expansion model is presented. Abstract: 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.