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Titlebook: Decision Making Under Uncertainty; Energy and Power Claude Greengard,Andrzej Ruszczynski Conference proceedings 2002 Springer Science+Busin

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書目名稱Decision Making Under Uncertainty
副標題Energy and Power
編輯Claude Greengard,Andrzej Ruszczynski
視頻videohttp://file.papertrans.cn/265/264203/264203.mp4
叢書名稱The IMA Volumes in Mathematics and its Applications
圖書封面Titlebook: Decision Making Under Uncertainty; Energy and Power Claude Greengard,Andrzej Ruszczynski Conference proceedings 2002 Springer Science+Busin
描述In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. We live in a world of uncertainties, and decisions must be made from information which may be incomplete and may contain uncertainty. The key mathematical question addressed in this volume is "how to make decision in the presence of quantifiable uncertainty." The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete. The major tools used in studying these problems are mathematical modeling and optimization techniques; especially stochastic optimization. These articles are meant to provide an insight into this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research.
出版日期Conference proceedings 2002
關(guān)鍵詞Stochastic Optimization; Stochastic Programming; electricity; electricity markets; mathematical modeling
版次1
doihttps://doi.org/10.1007/978-1-4684-9256-9
isbn_softcover978-1-4419-3014-9
isbn_ebook978-1-4684-9256-9Series ISSN 0940-6573 Series E-ISSN 2198-3224
issn_series 0940-6573
copyrightSpringer Science+Business Media New York 2002
The information of publication is updating

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On Supply Function Bidding in Electricity Markets,upply function to offer to the spot market, so as to maximise expected return. We give conditions under which a supply function exists that optimises return for every demand realisation. We also analyse the case in which the behaviour of the competition can be modelled by an appropriate probability
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Qualitative Implications of Uncertainty in Economic Equilibrium Models,resentations of the supply technologies, known as process models. Econometric models measure the consequences of peoples’ decisions and are typically used to estimate demand because it is impossible to represent each individual decision and its consequences. Process modeling is an outgrowth of input
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0940-6573 to this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research.978-1-4419-3014-9978-1-4684-9256-9Series ISSN 0940-6573 Series E-ISSN 2198-3224
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Maximilian E. Tschuchnig,Michael Gadermayroblems that are solved by dynamic programming and a specialized descent algorithm, respectively. The optimal stochastic multipliers are used in Lagrangian heuristics to construct approximately optimal first stage decisions. Numerical results are presented for realistic data from a German power utili
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