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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20110101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120308T140000
DTEND;TZID=UTC:20120308T140000
DTSTAMP:20260418T182806
CREATED:20170124T102149Z
LAST-MODIFIED:20170124T102149Z
UID:610-1331215200-1331215200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: The Long-run Impact of Wind Power on Electricity Prices and Generating Capacity (joint work with Nicholas Vasilakos)
DESCRIPTION:Title: The Long-run Impact of Wind Power on Electricity Prices and Generating Capacity (joint work with Nicholas Vasilakos)Speaker: Prof. Richard Green – Alan and Sabine Howard Professor of Sustainable Energy Business at Imperial College Business SchoolAffiliation: Imperial College LondonLocation: Room 344A Huxley BuildingTime: 2:00pm \nAbstract. This paper uses a market equilibrium model to calculate how the mix of generating capacity would change if large amounts of intermittent renewables are built in Great Britain\, and what this means for operating patterns and the distribution of prices over time. If generators bid their marginal costs\, we find that the changes to the capacity mix are much greater than the changes to the pattern of prices. Thermal capacity falls only slightly in response to the extra wind capacity\, and there is a shift towards power stations with higher variable costs (but lower fixed costs). The changes to the pattern of prices\, once capacity has adjusted\, are relatively small. In an oligopolistic setting\, strategic generators will choose lower levels of capacity. If wind output does not receive the market price\, then mark-ups on thermal generation will be lower in a system with large amounts of wind power. \nAbout the speaker. Richard Green is the Alan and Sabine Howard Professor of Sustainable Energy Business at Imperial College Business School.  He was previously Professor of Energy Economics and Director of the Institute for Energy Research and Policy at the University of Birmingham\, and Professor of Economics at the University of Hull.  He started his career at the Department of Applied Economics and Fitzwilliam College\, Cambridge.  He has spent time on secondment to the Office of Electricity Regulation and has held visiting appointments at the World Bank\, the University of California Energy Institute and the Massachusetts Institute of Technology. He has been studying the economics and regulation of the electricity industry for over 20 years.  He has written extensively on market power in wholesale electricity markets and has also worked on transmission pricing.  More recently\, the main focus of his work has been on the impact of low-carbon generation (nuclear and renewables) on the electricity market\, and the business and policy implications of this.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-the-long-run-impact-of-wind-power-on-electricity-prices-and-generating-capacity-joint-work-with-nicholas-vasilakos/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120321T140000
DTEND;TZID=UTC:20120321T140000
DTSTAMP:20260418T182806
CREATED:20170124T102149Z
LAST-MODIFIED:20170124T102149Z
UID:609-1332338400-1332338400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Payment Rules through Discriminant-Based Classifiers
DESCRIPTION:Title: Payment Rules through Discriminant-Based ClassifiersSpeaker: Prof. David ParkesAffiliation: School of Engineering and Applied Sciences at Harvard UniversityLocation: Room 217-218 Huxley BuildingTime: 2:00pm \nAbstract. In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex-post regret\, we are able to adapt techniques of statistical machine learning to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi- dimensional types and situations where computational efficiency is a concern. Specifically\, given an outcome rule and access to a type distribution\, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex-post regret\, and that penalizing classification errors is effective in preventing failures of ex-post individual rationality.  \nAbout the speaker. David C. Parkes is the Gordon McKay Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He was the recipient of the NSF Career Award\, the Alfred P. Sloan Fellowship\, the Thouron Scholarship\, the Harvard University Roslyn Abramson Award for Teaching. Parkes received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001\, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. At Harvard\, Parkes founded the Economics and Computer Science research group. His research interests include mechanism design\, electronic commerce\, market design\, and social computing. Parkes is editor of Games and Econonic Behavior\, and on the editorial boards of JAAMAS\, ACM TEAC and INFORMS J. Computing. Parkes also serves as the Chair of the ACM SIG on Electronic Commerce and was the Program Chair of ACMEC’07 and AAMAS’08\, and General Chair of ACMEC’10.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-payment-rules-through-discriminant-based-classifiers/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120322T150000
DTEND;TZID=UTC:20120322T150000
DTSTAMP:20260418T182806
CREATED:20170124T102149Z
LAST-MODIFIED:20170124T102149Z
UID:608-1332428400-1332428400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Lifting Methods for Generalized Semi-Infinite Programs
DESCRIPTION:Title: Lifting Methods for Generalized Semi-Infinite ProgramsSpeaker: Dr. Boris HouskaAffiliation: Centre for Process Systems Engineering at Imperial CollegeLocation: Room 217 Huxley BuildingTime: 3:00pm \nAbstract. In this talk we present numerical solution strategies for generalized semi-infinite optimization problems (GSIP)\, a class of mathematical optimization problems which occur naturally in the context of design centering problems\, robust optimization problems\, and many fields of engineering science. GSIPs can be regarded as bilevel optimization problems\, where a parametric lower-level maximization problem has to be solved in order to check feasibility of the upper level minimization problem. In this talk we discuss three strategies to reformulate a class lower-level convex GSIPs into equivalent standard minimization problems by exploiting the concept of lower level Wolfe duality. Here\, the main contribution is the discussion of the non-degeneracy of the corresponding formulations under various assumptions. Finally\, these non-degenerate re-formulations of the original GSIP allow us to apply standard nonlinear optimization algorithms. \nAbout the speaker. Boris Houska studied mathematics and physics at the university of Heidelberg in 2003-2008. He obtained his Ph.D. in 2011 in Electrical Engineering at the Optimization in Engineering Center (OPTEC) at K.U. Leuven. Since 2012 he is a postdoctoral researcher at the Centre of Process Systems Engineering at Imperial College. His research interests include numerical optimization and optimal control\, robust optimization\, as well as fast MPC algorithms.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-lifting-methods-for-generalized-semi-infinite-programs/
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