第21回統計的機械学習セミナー / The 21st Statistical Machine Learning Seminar

Date&Time
2014年10月22日(水)13:45-15:45
/ 22 October, 2014 (Wed) 13:45-15:45

Admission Free,No Booking Necessary

Place
統計数理研究所 セミナー室2(3階 D304)
/ Seminar Room 2(D304)@ The Institute of Statistical Mathematics
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Speaker1
Gerald Gamrath (ZIB, Germany)
Title
The SCIP Optimization Suite - Concepts, Developments, and Applications
Abstract
We present the SCIP Optimization Suite, a tool for modeling and solving optimization problems.  It is built around the constraint integer programming framework SCIP, which is one of the fastest MIP solvers available in source code.  We start with a discussion of the concepts of SCIP and how they allow to solve a wide range of optimization problems including pseudo-boolean optimization, scheduling, and non-convex MINLP.  Then, we report on current developments in the SCIP Optimization Suite and present several real-world applications in which SCIP is used.  Thereby, we elaborate on how these applications and the challenges they bear push forward the development of SCIP and our research on mixed-integer programming.
Hereby, we lay the focus on a supply chain management project in which we regard instances coming from a wide range of applications from all kinds of industry branches which regularly test the boundaries of our computational possibilities.
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Speaker2
Inken Gamrath (ZIB, Germany)
Title
Optimizing battery load schedules
Abstract
As the influence of renewable energy grows, also the flexible storage of energy gains in importance.  For the consumers in a power grid, it may be advantageous to store and release energy at suitable times.  One aspect in this context is the construction of storage schedules which provide charging and discharging periods while considering the storage and power grid properties.  In this talk, we present a model that allows for optimizing a storage schedule for this purpose.  Given predicted demand load curves for a certain future time period and determined energy prices, the nonlinear model provides a storage schedule.  The model accounts for technical constraints, such as the charging and discharging losses due to physical properties of the storage technology, as well as economical and operational ones.
Since environment parameters can change suddenly, our aim is to  find good solutions in a short time in order to allow ad hoc reoptimization.  We present a case study where batteries are used as storage devices, describe different variants to cope with nonlinearities and to solve the model, compare them with respect to quality and efficiency, and show exemplary computations.
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