30 | Graduate programs
PhD STUDENTS
(continued)
Last Name
First Name
Advisor
Previous Institution
Bilgin
Enes
Caramanis
Bilkent University
I am working with Michael Caramanis on regulating electricity markets and integration of smart grids into these markets. Particularly, we are trying to develop alternative pricing methods for wholesale power markets and mechanisms to control the demand in smart grids. Hence, we aim to increase efficiency in energy consumption and create more room for green energy resources while maintaining the security for power systems.
Chen
Yin
Paschalidis
Shanghai Jiao Tong University
Dissertation: From Networks to Proteins: Modeling and Optimization with Markovian Models. Placement: Procter & Gamble German Innovation Center
Cizelj
Igor
Belta
University of Zagrebe
I work in the Hybrid and Networked Systems (HyNeSs) Lab. My research interests include robotics, probability theory, motion planning and control. More specifically, my interests lie in the modeling and optimization of planning and control algorithms for safety-critical systems, with a large emphasis on robotic applications. Currently I am focused on probabilistically safe vehicle control in threat rich environment. In the near future I wish to address the following problem: what probabilistic guarantees can we have, if any, when the vehicle motion is modeled as a stochastic system and the properties of the environment can be detected only locally. Foster
Justin
Caramanis
Bowdoin College
I work in Michael Caramanis’ Lab where our research focuses on sustainable power systems, environmental policy analysis, and electricity market design. We are working towards the development of an applied science base incorporating demand response and distributed generation, which holds promise for dramatic global effects on sustainable energy when implemented in both developed and developing countries. Currently, we are focused on the market-based coordination of plug-in electric vehicles and renewable electricity generation, in particular, wind, that will contribute to the broad adoption of both technologies. In addition, we are developing transmission topology control policies, i.e., appropriate changes in transmission line status, which can redistribute power flow and significantly lower congestion costs.wish to address the following problem: what probabilistic guarantees can we have, if any, when the vehicle motion is modeled as a stochastic system and the properties of the environment can be detected only locally. Geng
Yanfeng
Cassandras
University of Science and Technology of China
We propose a “smart parking” system for an urban environment based on a dynamic resource allocation (DRA) approach. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the user’s objective function that combines proximity to destination with parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our first approach is to solve a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. Simulation results show that using this “smart parking” idea can achieve significant improvement over state-of-the-art guidance-based systems. Then we generalized the DRA problem and solved it with Approximate Dynamic Programming (ADP) method. Simulations show that we can obtain near-optimal allocation results. We are now implementing this idea in a BU garage.
Annual Report 2010–2011