SEMINAR: Optimization, Inference, and Learning for District Energy Systems

SEMINAR: Optimization, Inference, and Learning for District Energy Systems

Seminar Abstract 

We discuss how Optimization, Inference, and Learning (OIL) methodology is expected to re-shape future demand-response technologies acting across interdependent energy, i.e. power, natural gas and heating/cooling, infrastructures at the district/metropolitan/distribution level.  We describe hierarchy of deterministic and stochastic planning and operational problems emerging in the context of physical flows over networks associated with the laws of electricity, gas-, fluid- and heat-mechanics. Then we proceed to illustrate development and challenges of the physics-informed OIL methodology on examples of:

a)      Graphical Models approach applied to a broad spectrum of the energy flow problems, including online reconstruction of the grid(s) topology from measurements;

b)      Direct and inverse dynamical problems for timely delivery of services in the district heating/cooling systems;

c)       Ensemble Control of the phase-space cycling energy loads via Markov Decision Process (MDP) and related reinforcement learning approaches.

Seminar Speaker(s) 

Michael Chertkov
Staff Scientist, Los Alamos National Laboratory
More information about this speaker

Dr. Chertkov's areas of interest include statistical and mathematical physics applied to energy and communication networks, machine learning, control theory, information theory, computer science, fluid mechanics and optics. Dr. Chertkov received his Ph.D. in physics from the Weizmann Institute of Science in 1996, and his M.Sc. in physics from Novosibirsk State University in 1990. After his Ph.D., Dr. Chertkov spent three years at Princeton University as a R.H. Dicke Fellow in the Department of Physics. He joined Los Alamos National Lab in 1999, initially as a J.R. Oppenheimer Fellow in the Theoretical Division. Since 2001 he is a technical staff member at LANL. Since 2012 Dr. Chertkov is advising SkolkovoTech -- new graduate school in Moscow/Russia. He also has an adjunct professor affiliation with the Department of Industrial & Operations Engineering of the U of Michigan, Ann Arbor. Dr. Chertkov has published more than 180 papers. He is an editor of the Journal of Statistical Mechanics (JSTAT), associate editor of IEEE Transactions on Control of Network Systems, member of the Editorial Board of Scientific Reports (Nature Group), a fellow of the American Physical Society (APS) and a senior member of IEEE.

Date 

Nov 3, 2017 -
12:00pm to 1:00pm

Location 

90-3122

Host