Stochastic Models and Algorithms for the Optimal Operation of a Dispersed Generation System under Uncertainty

Edmund Handschin (), Frederike Neise (), Hendrik Neumann () and Rüdiger Schultz ()
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Edmund Handschin: University of Dortmund, Institute of Energy Systems and Energy Economics
Frederike Neise: University of Duisburg-Essen, Department of Mathematics
Hendrik Neumann: University of Dortmund, Institute of Energy Systems and Energy Economics
Rüdiger Schultz: University of Duisburg-Essen, Department of Mathematics

A chapter in Mathematics – Key Technology for the Future, 2008, pp 205-233 from Springer

Abstract: Abstract Due to the impending renewal of generation capacities and present decisions concerning energy policy, dispersed generation systems become more and more important. The optimal operation of such a system and corresponding trading activities are substantially influenced by uncertainty and require powerful optimization techniques. We present expectation-based as well as risk-averse stochastic mixedinteger linear optimization models using risk measures and dominance constraints. Two case studies show the benefit of stochastic optimization in power generation and the superiority of tailored solution methods over standard solvers.

Keywords: Risk Measure; Forecast Error; Stochastic Optimization; Optimal Operation; Spot Market (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-77203-3_15

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DOI: 10.1007/978-3-540-77203-3_15

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