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dc.contributor.advisorYang, Fuwen
dc.contributor.authorQiu, Quanwei
dc.date.accessioned2020-12-21T06:42:49Z
dc.date.available2020-12-21T06:42:49Z
dc.date.issued2020-12-02
dc.identifier.doi10.25904/1912/4030
dc.identifier.urihttp://hdl.handle.net/10072/400460
dc.description.abstractMore and more renewable energy sources are being integrated into microgrids—and while this causes many control challenges for microgrids, it can also yield numerous economic and environmental benefits. Therefore, it is necessary to develop proper control schemes for microgrids to address the different control issues in their hierarchical structure while adapting to the different time scales of the three control levels. Conversely, because model predictive control (MPC) has significant advantages—the inclusion of forecasts, the simplicity of the algorithm, and the flexibility to handle hard constraints—it has attracted significant attention in industrial control systems. Motivated by these factors, this research focuses on implementing MPC techniques in microgrids, which are solely supplied by photovoltaic (PV) generators, to address different control problems. For primary control of the microgrid hierarchy, which is mainly responsible for the inner control of the local distributed generation units, MPC can be applied to control of the power converters that serve as interfaces between the sources and the loads. Therefore, in this control level, a novel output-feedback MPC technique based on ellipsoidal set-membership state estimation is designed for a direct current to direct current (DC-DC) converter, considering the unknown-but-bounded external disturbances. A long-horizon finite-states (FS) MPC strategy is designed for the direct current to alternating current (DC-AC) inverter to reduce the sampling and switching frequency through a multi-step implementation approach and a control sequence rearrangement method. For secondary control, which is in charge of the compensation for the frequency and voltage deviations and is usually communication-based, the distributed MPC strategy can be used to realize the desired cooperative control among the geographically dispersed units. Thus, a novel distributed model predictive controller is developed to enhance system performance. It takes into account the fact that the distributed controllers’ communication network might be subject to switching topology due to the disconnection and reconnection of controllers, random failures, and recoveries of the links between controllers. A Markov chain with a time-varying probability transition matrix is used to describe the stochastic topology evolution of the control network. Tertiary control is used to coordinate the power flow between the microgrid and the utility grid and offers economic operations for microgrids. Since the integration of renewable energy sources causes low inertia and power fluctuation in microgrids, battery energy storage is essential to addressing these issues. To coordinate the charging/discharging schedule of the battery storage units, a networked MPC strategy can be adopted to realize the communication between different microgrid components and make use of the forecasts for PV power generation and load demand. The multi-microgrid system is considered subject to partial fault because of non-functional generators, batteries, or even transmission lines in this research. Hence, both the connection status of the electrical grid and the communication network are incorporated into the system modeling. In addition, the set-membership estimation is adopted to deal with the possible state unavailability caused by non-functional batteries or communication failures. In the theoretical section of this thesis, different sufficient conditions are established to ensure the stability of the investigated systems, and the optimal control inputs are obtained by solving the corresponding optimization problems. For easy implementation with MATLAB solvers, all the constraints and conditions of the optimization problems are transformed into linear matrix inequalities. Different recursive MPC algorithms are designed to control the target systems, and some extended algorithms are also developed to assist with the computation to determine the optimal solutions. In the demonstration section of this thesis, the designed controllers are all implemented in the numerical simulations or Simulink tests to verify their effectiveness, and an experimental test based on Raspberry Pi is conducted to demonstrate the wireless communication employing the designed networked model predictive controller.
dc.languageEnglish
dc.language.isoen
dc.publisherGriffith University
dc.publisher.placeBrisbane
dc.subject.keywordsrenewable energy
dc.subject.keywordsmicrogrids
dc.subject.keywordsmodel predictive control
dc.titleNetworked Model Predictive Control for Microgrids with Distributed PV Generators
dc.typeGriffith thesis
gro.facultyScience, Environment, Engineering and Technology
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorZhu, Yong
gro.identifier.gurtID000000023069
gro.thesis.degreelevelThesis (PhD Doctorate)
gro.thesis.degreeprogramDoctor of Philosophy (PhD)
gro.departmentSchool of Eng & Built Env
gro.griffith.authorQiu, Quanwei


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