[Crib-list] Computational Engineering Seminars-Andy Philpott 4 PM TODAY; Christoph SchwabTHURS/25 at 4 (fwd)

[Shirley Entzminger]

Distinguished Seminar Series in Computational Science and Engineering

Andy Philpott Today at 4 PM in 56-114

Christoph Schwab Next Thursday (9/25)   INFINITE DIM NUMERICAL ANALYSIS

Abstracts & Info Below

Andy Philpott, "Stochastic optimization in electricity systems"
Electric Power Optimization Centre, University of Auckland
Thursday September 18, 2014 | 4:00 PM | 56-114
cce.mit.edu/events

Abstract:

Methods for optimization under uncertainty are becoming increasingly 
important in models of electricity systems. Recent interest has been 
driven by the growth in disruptive technologies (e.g. wind power, solar 
power and energy storage) which contribute to or ameliorate the volatility 
of demand that must be met by conventional electricity generation and 
transmission. In such systems, enough capacity and ramping plant must be 
on hand to deal with sudden increases in net demand. On the other hand, in 
systems dominated by hydro power with uncertain inflows, stochastic 
optimization models have been studied for many years. Rather than planning 
capacity, these models construct generation policies to minimize some 
measure of total thermal fuel costs and risks of future energy shortages. 
Such models are indispensible in benchmarking the performance of 
hydro-dominated electricity markets. We present two classes of model that 
show how uncertainty is incorporated in these two settings. In the first 
class, net demand is modeled as a time-inhomogeneous Markov chain, and we 
seek least-cost investments in thermal generation and transmission 
capacity that will cover random demand variations. We solve the investment 
problem using a combination of Benders and Dantzig-Wolfe decomposition. 
The investment solutions are contrasted with those from conventional 
screening-curve models. The second class of model is a stochastic dynamic 
program that treats reservoir inflows as random. This is solved by DOASA, 
our implementation of the stochastic dual dynamic programming method of 
Pereira and Pinto (1991), which has been the subject of some recent 
interest in the stochastic programming community. We give some examples of 
the application of DOASA to the New Zealand electricity system by the 
electricity market regulator. (Joint work with Golbon Zakeri, Geoff 
Pritchard, Athena Wu)

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Christoph Schwab, "Infinite-Dimensional Numerical Analysis"

Seminar for Applied Mathematics, ETH Zurich

Thursday September 25, 2014 | 4:00 PM | 56-114

Abstract:

Spurred by the emerging engineering discipline of Uncertainty 
Quantification and the `big-data, sparse information' issue, engineering 
and life-sciences have seen an explosive development in numerics of 
direct-, inverse- and optimization problems for (deterministic or 
stochastic) differential equations on high- or even infinite-dimensional 
state- and parameter-spaces, and for statistical inference on these 
spaces, conditional on given (possibly large) data. One objective of this 
talk is a (biased...) survey of several emerging computational 
methodologies that allow efficient treatment of high- or 
infinite-dimensional inputs to partial differential equations in 
engineering, and to illustrate their performance by computational 
examples. We address in particular Multilevel Monte-Carlo (MLMC) and 
Multilevel Quasi-Monte-Carlo (MLQMC) Methods, adaptive Smolyak and 
generalized polynomial chaos (gpc), of Galerkin and collocation type, and 
tensor compression techniques. A second objective is to indicate elements 
of a mathematical basis for these methods that has emerged in recent years 
that has allowed to prove dimension-independent rates of convergence. The 
reates are shown to be limited only by the order of the method and by 
certain sparsity measures for the uncertain inputs' KL, gpc or ANOVA 
decompositions. Examples include stochastic elliptic and parabolic PDE, 
their Bayesian inversion, control and optimization, reaction rate models 
in biological systems engineering, shape inversion in acoustic and 
electromagnetic scattering, and nonlinear hyperbolic conservation laws. 
Despite favourable scaling, massively parallel computation is, as a rule, 
required for online simulations of realistic problems. Scalability and 
Fault Tolerance in an exascale compute environment become crucial issues 
in their practical deployment. Acknowledgements: Grant support by Swiss 
National Science Foundation (SNF), ETH High Performance Computing Grant, 
and the European Research Council (ERC).