Smart Deployment of Smart Technology: Field Experimentation on the Demand-side of Electricity Markets by Prof. Meredith Fowlie

Location and Date: 
Thursday,August 24, 2017, 2:00 pm,LT 201
Electricity consumers have an important role to play in advancing (or
hindering!) our progress
towards cleaner and more reliable electric
power systems. Globally, governments are looking to accelerate the
adoption of more efficient technologies and enable smarter, more efficient
electricity demand-response. Systematic assessment of how these
interventions are
actually working is essential because real-world
outcomes can look quite different from ex-ante engineering estimates.

Professor Fowlie will provide an overview of how randomized field
experiments are being
used to evaluate the impacts of energy
efficiency programs and smart grid deployment.  The potential for field
experimentation in an Indian energy context will be highlighted.


Prof. Meredith Fowlie holds the Class
of 1935 Endowed Chair in Energy at UC  Berkeley. She is an Associate
Professor in the Agriculture and Resource  Economics department,?an
affiliated faculty of the Energy and Resources  Group,?a research
affiliate at the Energy Institute at Haas, and  Research Associate at the
National Bureau of Economic Research
in the  Energy and Environmental
Economics group.

Fowlie has worked  extensively on
the economics of energy markets and the environment. Her  research
investigates real-world applications of market-based  environmental
regulations, the economics of energy efficiency, the  demand-side of
energy markets, energy use in emerging economies.?Her  work has appeared
in The American Economic Review,
the Journal of  Political Economy,
The Review of Economics and Statistics, and other  academic journals.?

She received a PhD in Economics from UC  Berkeley in 2006, an
M.Sc. from Cornell in 2000, and a B.Sc.?from  Cornell in 1997. Before
joining the faculty at UC Berkeley she was an  Assistant Professor of
Economics and Public Policy at the University of  Michigan.