Scheduling, revenue sharing, and user behavior for aggregated demand response
Abstract/Contents
- Abstract
- The modern electric grid can trace its origins back over 100 years. Naturally, many changes and improvements have occurred during this time as new policies and new technologies are incorporated. Although these changes have, in the main, made distributing electrical energy more reliable and economical, the essential role of the grid has not changed significantly - generate sufficient power to match the quantity of power consumed. It has long been acknowledged that the ability for the operator to dynamically adjust both the quantity of power consumed as well as the quantity of power generated would be a major aid in ensuring the electric grid continues to operate economically. Attempting to adjust the consumption of energy in response to challenges faced by the operator is an approach known as demand response and it is the focus of this thesis. Although the concept of demand response is not new, substantial questions remain to be answered. Questions such as how demand response programs could be implemented for residential users, how participants in these programs should be compensated, and how the actions of such residential users can be modeled. This dissertation seeks to answer these questions by establishing and analyzing analytical models. We analyze demand response programs from the point of view of a ``Load Serving Entity, '' or ``aggregator, '' which engages in the energy markets by coordinating and controlling the participants in the program. We describe a novel procedure for direct control of non-preemptive loads by an aggregator. This program is based on a greedy algorithm which schedules the underlying loads in a manner that seeks to reduce the error between the final aggregate load profile and a predefined target profile. Algorithms are presented for two scenarios -- firstly, the case where the loads are known in advance, and secondly where the load demands are stochastic. The aggregator will realize revenue from participating in the energy markets, a portion of which must be returned to the individual users as compensation for being a part of the program. The problem of fairly compensating these users is challenging, and we propose a solution by modeling the scenario in a game theoretic setting and utilizing the concept of the Shapley Value in order to calculate a fair compensation. Finally, we analyze data from an existing demand response program, and model user behavior using a discrete choice modeling framework in order to determine what factors influence the participants response to a program.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | O'Brien, Gearoid |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | El Gamal, Abbas A |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Rajagopal, Ram |
| Thesis advisor | Van Roy, Benjamin |
| Advisor | Rajagopal, Ram |
| Advisor | Van Roy, Benjamin |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Gearóid O'Brien. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Gearoid O'Brien
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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