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Real-Time Load-Side Control of Electric Power Systems

Citation

Zhao, Changhong (2016) Real-Time Load-Side Control of Electric Power Systems. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9RN35TJ. http://resolver.caltech.edu/CaltechTHESIS:05232016-160307020

Abstract

Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems.

(1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control.

(2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:power systems, control
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Awards:Demetriades-Tsafka-Kokkalis Prize in Benign Renewable Energy Sources or Related Fields, 2016
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Low, Steven H.
Thesis Committee:
  • Low, Steven H. (chair)
  • Doyle, John Comstock
  • Kostina, Victoria
  • Murray, Richard M.
  • Wierman, Adam C.
Defense Date:6 May 2016
Non-Caltech Author Email:zhchangh1987 (AT) gmail.com
Record Number:CaltechTHESIS:05232016-160307020
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:05232016-160307020
DOI:10.7907/Z9RN35TJ
ORCID:
AuthorORCID
Zhao, Changhong0000-0003-0539-8591
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:9739
Collection:CaltechTHESIS
Deposited By: Changhong Zhao
Deposited On:25 May 2016 23:35
Last Modified:18 May 2017 22:03

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