Toward distributed control for autonomous electrical energy systems

In this thesis we study the problem of enabling autonomous electrical energy systems (AEESs) by means of distributed control. We first propose a modular modeling approach that represents a general electrical energy system (EES) as a negative feedback configuration comprising a planar electrical network subsystem and a subsystem of single-port components. The input-output specifications of all components are in terms of power and voltage. This mathematical modeling supports the basic physical functionality of balancing power supply and demand at the acceptable Quality of Service (QoS). These input-output specifications are met by the controllable components equipped with the newly proposed distributed control. We show that these controllers enable stable and feasible system-level closed-loop dynamics. Moreover, an interactive algorithm for autonomous adjustments of their controller set points based on the information exchange with neighboring components is introduced. This serves as a proof-of-concept illustration of how components adjust their power and voltage toward a system-level equilibrium. Such process is the basis for autonomous reconfigurable operation of small microgrids. As the first step toward scaling up the proposed concepts, we consider the problem of enhanced automatic generation control (E-AGC) for systems with highly dynamic load variations, including effects of intermittent renewable generation. Further work is needed to fully generalize this approach for control design of large-scale EES. In addition to theoretical results, we also report the results of several numerical and hardware tests. These show the effectiveness of the proposed approach in fairly complex scenarios, including unplanned large faults and hard-to-predict fast-varying power disturbances.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Miao, Xia,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Marija D. Ilić.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128321

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128321