PresentacióN Frances

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BONJOUR professeur Stephany

Je m´apelle Alma Delfina Villaverde García.

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Why Do We Ignore Risk in Power Economics?

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A survey of our 2015 HICSS article (reference below), which is largely a survey of demand response technology developed at the University of Florida. Presented at the Workshop on Electricity Markets and Optimization 27th of November 2014. Aalborg University, Denmark @inproceedings{barbusmey14, Address = {Kauai, Hawaii}, Author = {Barooah, Prabir and Bu\v{s}i\'{c}, Ana and Meyn, Sean}, Booktitle = {Proc. {48th Annual Hawaii International Conference on System Sciences (HICSS)}}, Note = {(invited)}, Publisher = {University of Hawaii}, Title = {Spectral Decomposition of Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid}, Year = {2015}}

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https://netfiles.uiuc.edu/meyn/www/spm_files/Q2009/Q09.html Abstract: Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It has proven to be effective for models with finite state and action space. This paper establishes connections between Q-learning and nonlinear control of continuous-time models with general state space and general action space. The main contributions are summarized as follows. * The starting point is the observation that the "Q-function" appearing in Q-learning algorithms is an extension of the Hamiltonian that appears in the Minimum Principle. Based on this observation we introduce the steepest descent Q-learning (SDQ-learning) algorithm to obtain the optimal approximation of the Hamiltonian within a prescribed finite-dimensional function class. * A transformation of the optimality equations is performed based on the adjoint of a resolvent operator. This is used to construct a consistent algorithm based on stochastic approximation that requires only causal filtering of the time-series data. * Several examples are presented to illustrate the application of these techniques, including application to distributed control of multi-agent systems.

Q-Learning and Pontryagin's Minimum Principle

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State estimation and Mean-Field Control with application to demand dispatch

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En vedette (6)

Why Do We Ignore Risk in Power Economics?

Why Do We Ignore Risk in Power Economics?

Why Do We Ignore Risk in Power Economics?

Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid: Elim...

Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid: Elim...

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Demand-Side Flexibility for Reliable Ancillary Services

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Q-Learning and Pontryagin's Minimum Principle

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State estimation and Mean-Field Control with application to demand dispatch

State estimation and Mean-Field Control with application to demand dispatch

State estimation and Mean-Field Control with application to demand dispatch

Panel Lecture for Energy Systems Week

Panel Lecture for Energy Systems Week

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PresentacióN Frances

1. BONJOUR professeur Stephany

2. Je m´apelle Alma Delfina Villaverde García.

3. Combiens d´ans as-Tu?

4.

5.

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