Application of scenario reduction to LDC and risk based generation expansion planningIndustrial and Manufacturing Systems Engineering Conference Proceedings and Posters
Document TypeConference Proceeding
Publication VersionAccepted Manuscript
Link to Published Versionhttp://dx.doi.org/10.1109/PESGM.2012.6345655
Conference Title2012 IEEE Power and Energy Society General Meeting
Conference DateJuly 22-26, 2012
AbstractAbstract: Two-stage stochastic mixed-integer programming models are formulated for minimizing expected cost or Conditional Value-at-Risk (CVaR) of a long-term power generation expansion planning problem incorporating load duration curves. The multivariate stochastic processes, such as electricity demands and fuel prices, are modeled as geometric Brownian motion (GBM) processes. Scenario paths for their future evolution are generated by statistical extrapolation of long-term historical trends. The size of the scenario set is controlled by using increasing length time periods in a tree structure. Nevertheless, some method of scenario thinning is necessary to achieve manageable solution times. To mitigate the computational complexity of the forward selection heuristic for scenario reduction, a combined heuristic scenario reduction method named Forward Selection in Wait-and-see Clusters (FSWC) is applied to the large scenario set. Numerical results for a twenty year generation expansion planning case study indicate substantial computational savings to achieve similar solutions as those obtained by forward selection alone.
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Citation InformationYonghan Feng and Sarah M. Ryan. "Application of scenario reduction to LDC and risk based generation expansion planning" San Diego, CA(2012)
Available at: http://works.bepress.com/sarah_m_ryan/90/