demand response

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Visible to the public Learning with Abandonment

Submitted by rodrigka on Tue, 01/22/2019 - 5:50pm. Contributors:

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.

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Visible to the public Advanced peak demand forcast and battery dispatch algorithms to integrate storage-based demand response with BAS

Submitted by Christoph Meinrenken on Mon, 02/01/2016 - 1:26pm. Contributors:

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.

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Visible to the public Active Regression for Cyber-Physical Systems

Submitted by zhangbao on Mon, 02/01/2016 - 5:36am. Contributors:

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.

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Visible to the public Boolean Microgrid

Submitted by Panganamala Kumar on Mon, 02/01/2016 - 5:36am. Contributor:

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).