Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Karimi-Ghartemani, Masoud

Committee Member

Abdelwahed, Sherif

Committee Member

Fu, Yong

Committee Member

Iqbal, Umar

Date of Degree

5-1-2020

Original embargo terms

Complete embargo for 2 years

Document Type

Dissertation - Open Access

Major

Electrical Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Distributed generators (DGs) with integration of renewable resources (RRs) such as photovoltaic (PV) and wind turbine have been widely considered to reduce the dependency on conventional power generation systems along with enhancement of the quality and sustainability of the power system. Recently, DC microgrid has gained popularity in many real-world applications such as rural electrification due to its simplicity and low power losses. However, the power variability of renewable resources and continuous change in load demand imposes risks of power mismatch in standalone DC systems that increase the chances of stability and reliability issues. Therefore, complementary generation and/or storage systems are coupled with standalone DC microgrid to mitigate the power fluctuations and maintain a power balance in the system. This dissertation presents a power management strategy (PMS) based on model predictive control (MPC) for a standalone DC microgrid. A control scheme for a standalone DC microgrid system with RRs, storage, and load is desired to have the capability of effective power management that maximizes the extraction of energy from renewable generators, minimizes the transients in the system during disturbances, and protects the storage from over/under charging conditions. As a part of the proposed MPC, an optimization problem is formulated to meet the voltage performance in the system with respect to operating conditions and constraints. The proposed PMS uses the ARIMA prediction method to forecast the load and environmental parameters. The predicted parameters are utilized to estimate the future performance of the system by solving the dynamic model of the system, and a cost function is optimized to generate suitable control sequences. This research also presents detailed mathematical models of the considered systems. This dissertation presents an extensive simulation-based analysis of the proposed approach. With the proposed control, maximum utilization of the renewable generators has been achieved, and the DC bus voltage is regulated at nominal value with minimum transients under various load/environmental disturbances. Moreover, the research investigates the proposed MPC based on ARIMA prediction by comparing the performance of different types of prediction methods. The dissertation also measures the effectiveness of the proposed MPC by comparing its performance with a conventional PI controller.

URI

https://hdl.handle.net/11668/16763

Comments

Power Management||Model Predictive Control||DC Microgrid||Photovoltaic||Wind Turbine||Battery Storage System||Maximum Power Point Tracking

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