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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Statistical Reliability Of Wind Power Scenarios And Stochastic Unit Commitment Cost, Didem Sari, Sarah M. Ryan Nov 2018

Statistical Reliability Of Wind Power Scenarios And Stochastic Unit Commitment Cost, Didem Sari, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the cost of implemented solutions, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. The statistical reliability of wind power scenario sets can be assessed by approaches extended from ensemble forecast verification. We examine the ...


Reliability Of Wind Power Scenarios And Stochastic Unit Commitment Cost, Didem Sari, Sarah M. Ryan Jan 2017

Reliability Of Wind Power Scenarios And Stochastic Unit Commitment Cost, Didem Sari, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Technical Reports and White Papers

Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the expected cost, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. Reliability of wind power scenario sets can be assessed by statistical verification approaches. In this study, we examine the relationship between the statistical ...


Scenario Construction And Reduction Applied To Stochastic Power Generation Expansion Planning, Yonghan Feng, Sarah M. Ryan Jan 2013

Scenario Construction And Reduction Applied To Stochastic Power Generation Expansion Planning, Yonghan Feng, Sarah M. Ryan

Industrial and Manufacturing Systems Engineering Publications

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduction heuristic in the context of long-term power generation expansion planning. We generate two different sets of scenarios for future electricity demands and fuel prices by statistical extrapolation of long-term historical trends. The cardinality of the first set is controlled by ...


Modeling And Solving A Large-Scale Generation Expansion Planning Problem Under Uncertainty, Shan Jin, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff Nov 2011

Modeling And Solving A Large-Scale Generation Expansion Planning Problem Under Uncertainty, Shan Jin, Sarah M. Ryan, Jean-Paul Watson, David L. Woodruff

Industrial and Manufacturing Systems Engineering Publications

We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independently minimizing the expected cost and the Conditional Value-at-Risk; i.e., the risk of significantly larger-than-expected operational costs. We introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees that accurately represent the uncertainty space. Using a realistic problem instance ...