Consider a demand response provider that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform. For example, the platform will want to personalize the thermostat control for the user, but faces the risk that the user unsubscribes forever if they are mistreated. We propose a general thresholded learning model for scenarios like this, and discuss the structure of optimal policies.

Abstract: Large scale applications of cyber physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort.

Abstract: One defining feature of cyberphysical systems is the fact that human users are closely intertwined with the physical system. Yet both the system and users themselves are often do not explicitly know how users would behave. A natural question arises: How do we design cyberphysical systems that effectively learn about their users, and optimize system behavior accordingly? This poster presents the idea of active regression as a vehicle to learn about users.

Abstract: In this project, we are working on the broad problem of maximizing the penetration of renewables in smart grid. To that end, we are developing strategies for optimal demand response, operation, and real-time pricing. In particular, we propose theory for optimal operation for the independent system operator (ISO), and for the load serving entity (LSE).

Abstract: This year, we continued our research effort on designing optimal decentralized monitoring and control mechanisms for networked infrastructure systems providing demand response services by first focusing on the example of intelligent electric transportation systems. The results are highlighted in this poster. Electric Vehicles (EV) are emerging as one of the primary solutions to make electricity demand elastic.

Abstract:  This project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level.

Abstract: The main goal of this project is to design algorithms and architectures for large scale integration of electrical vehicles in power grids. In particular the following topics will be explored:

Abstract: Large-scale critical infrastructure systems, including energy and transportation networks, comprise millions of individual elements (human, software and hardware) whose actions may be inconsequential in isolation but profoundly important in aggregate. The focus of this project is on the coordination of these elements via ubiquitous sensing, communications, computation, and control, with an emphasis on the electric grid.