EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

Smart Grid Seminar: Safety-Constrained Learning Algorithms for Demand Management

Topic: 
Safety-Constrained Learning Algorithms for Demand Management
Thursday, October 24, 2019 - 1:30pm
Venue: 
Y2E2 111
Speaker: 
Mahnoosh Alizadeh (UC Santa Barbara)
Abstract / Description: 

In the first part of the talk, we study the problem of designing optimal control actions (e.g., real-time prices) for demand management in power distribution systems given unknown customer response functions. Without exact response information or a market mechanism that extracts this information from customers, the design of demand management initiatives can present economic uncertainty and grid reliability concerns for the aggregator. This highlights the need for safety-constrained learning heuristics that can be applied in power and more broadly safety-critical systems. We showcase "safety-aware" bandit heuristics for designing control actions that constrain the probability of violation of power grid constraints during the learning process. We then highlight the effect of such safety constraints on the growth of regret for special classes of stochastic bandit optimization problems.

In the second part of the talk, we consider the problem of joint routing, battery charging, and pricing problem faced by a profit-maximizing transportation service provider that operates a fleet of autonomous electric vehicles. To accommodate for the time-varying nature of trip demands, renewable energy availability, and electricity prices, a dynamic pricing and control policy is required. We highlight several such policies, including one trained through deep reinforcement learning to develop a near-optimal policy. We also determine the optimal static policy to serve as a baseline for comparison with our dynamic policy and for determining the capacity region of the system. While the static policy provides important insights on optimal pricing and fleet management, we showcase how in a real dynamic setting, it is inefficient to utilize a static policy.


 

We invite you to join us in this quarter's Stanford Smart Grid Seminar. The theme of the seminar series is on smart grids and energy systems, scheduled for Thursdays, and with speakers from academic institutions and industry. The seminar room is Room 111 in Y2E2 Building.

The speakers are renowned scholars or industry experts in power and energy systems. We believe they will bring novel insights and fruitful discussions to Stanford. This seminar is offered as a 1 unit seminar course, CEE 272T/EE292T. Interested students can take this seminar course for credit by completing a project based on the topics presented in this course. Please discuss with the faculty in charge before signing up for credit.

Smart Grid Seminar Organization Team:

  • Ram Rajagopal, Associate Professor, Civil & Environmental Engineering, and Electrical Engineering
  • Liang Min, Managing Director, Bits and Watts Initiative
  • Chin-Woo Tan, Director, Stanford Smart Grid Lab
  • Mohammad Rasouli, Postdoctoral Scholar, Civil and Environmental Engineering

Bio:

Mahnoosh Alizadeh is an assistant professor of Electrical and Computer Engineering at the University of California Santa Barbara. She received the B.Sc. degree in Electrical Engineering from Sharif University of Technology in 2009 and the M.Sc. and Ph.D. degrees from the University of California Davis in 2013 and 2014 respectively, both in Electrical and Computer Engineering. From 2014 to 2016, she was a postdoctoral scholar at Stanford University. Her research is focused on the design of network control and optimization algorithms for societal-scale cyber-physical systems, with a particular focus on renewable energy integration in the power grid and electric transportation systems. She is a recipient of the NSF CAREER award.