Dynamic network energy management

Abstract/Contents

Abstract

This dissertation considers the application of convex optimization to solve a range of problems associated with the management and operation of dynamic, networked energy devices. Due to dynamic device and network constraints, the generation, transmission and consumption of energy must be closely and globally coordinated across both time and space to achieve efficient operation of the network. The near instantaneous speed of power transmission requires coordinated, real-time reactions to unexpected disturbances and deviations from planned behavior in order to avoid damage to both the network and attached devices. Lastly, the number of devices connected to next generation energy networks is expected to dramatically increase over present levels. Current methods for managing energy networks are increasingly ill-equipped to deal with these growing challenges [23]. We present new techniques that allow for scalable, efficient, real-time operation of energy networks. In the first part of this thesis, we ignore the effect of network constraints and focus on the dynamically coupled nature of energy management for the operation and configuration of a portfolio of storage devices. We show how recent advances in convex optimization [57] can be used to operate the portfolio in real-time and also select the configuration of the portfolio via efficient Monte Carlo simulation. In the second part of this thesis, we consider more general networks of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its own dynamic constraints and objective, that are connected by AC and DC lines. The problem is to minimize the total network objective subject to the device and line constraints over a time horizon. This is a large optimization problem with variables for consumption or generation for each device, power flow for each line, and voltage phase angles at AC buses in each period. We develop a decentralized method for solving this problem called proximal message passing. The method is iterative: At each step, every device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing its own objective augmented by a term determined by the messages it has received. We show that this message passing method converges to a solution when the device objective and constraints are convex. The method is completely decentralized and needs no global coordination other than iteration synchronization; the problems to be solved by each device can typically be solved extremely efficiently and in parallel. The proximal message passing method is fast enough that even a serial implementation can solve substantial problems in reasonable time frames. We report results for several numerical experiments, demonstrating the method's speed and scaling, including the solution of a problem instance with over 30 million variables in 5 minutes for a serial implementation; with decentralized computing, the solve time would be less than one second.

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	Kraning, Matt
Associated with	Stanford University, Department of Electrical Engineering.
Primary advisor	Boyd, Stephen P
Thesis advisor	Boyd, Stephen P
Thesis advisor	El Gamal, Abbas A
Thesis advisor	Lall, Sanjay
Advisor	El Gamal, Abbas A
Advisor	Lall, Sanjay

Subjects

Genre	Theses

Bibliographic information

Statement of responsibility	Matt Kraning.
Note	Submitted to the Department of Electrical Engineering.
Thesis	Thesis (Ph.D.)--Stanford University, 2014.
Location	electronic resource

Access conditions

Copyright

© 2014 by Matthew Stephen Kraning

License

This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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