Dissertation Announcement for Salem Batiyah — 03/16/2020 at 8:00 AM
February 24, 2020
Dear Faculty, Graduate and Undergraduate Students,
You are cordially invited to my Ph.D. dissertation defense.
Dissertation Title: Predictive Control of Standalone DC Microgrid with Energy Storage Under Load and Environmental Uncertainty
Date and time: Monday, March 16, 2020, 08:00 AM
Venue: Simrall-228 (Conference Room)
Candidate: Salem Batiyah
Degree: Doctor of Philosophy, Electrical, and Computer Engineering
Committee:
Dr. Masoud Karimi-Ghartemani
Associate Professor of Electrical and Computer Engineering
(Major Professor)
Dr. Sherif Abdelwahed
Professor of Electrical and Computer Engineering at Virginia Commonwealth University
(Co-Major Professor/ Dissertation Director)
Dr. Yong Fu
Professor of Electrical and Computer Engineering
(Committee Member)
Dr. Umar Iqbal
Assistant Professor of Electrical and Computer Engineering
(Committee Member)
Abstract: “Distributed generators (DGs) with an integration of renewable resources such as solar 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 with renewable resources has gained popularity in many real-world applications such as electrical shipboard and rural electrification due to their 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 power fluctuations and maintain power balance.
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 PV, wind, battery, and load is desired to have the capability of effective power management that maximizes the extraction of energy from PV and wind, minimizes the transients in the system during disturbances, and prevents the battery from over/under charging conditions. In this study, the PMS objective is achieved by controlling the renewable generators at their maximum power point (MPP) using maximum power point tracking (MPPT) algorithms and operating the battery to regulate the DC bus voltage at a given reference value. The proposed PMS effectively manages power flow in the system under various weather and load conditions with minimal transients. There are two scenarios presented in this dissertation. In the scenario I, two PV systems, a wind turbine, a battery, and a load are interconnected to form a standalone DC microgrid. The proposed MPC based PMS operates the PV and the wind at MPP while the BES absorbs/supplies the power to regulate the DC bus voltage. In this scenario, the over/under charging conditions of the battery have been ignored. In scenario II, a PV system, a battery, and a load form a standalone DC system. In this scenario, the proposed PMS operates the PV at its MPP during normal operation to maximize the utilization of the PV, and the battery absorbs/supplies power to compensate for the power mismatch in the system. The proposed PMS in this scenario also includes a strategy to protect the battery from the over-charging condition by executing the power-curtailment of the PV when the PV power exceeds the load demand and the battery is fully charged.
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 predict 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. Therefore, this study presents detailed mathematical models of both study systems in the scenario I and II.
This study 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 study investigates the proposed MPC based on ARIMA prediction by comparing the performance of different types of prediction methods. The study also presents the effectiveness of the proposed MPC based strategy by comparing its performance with a conventional PI controller.”
Thank you,
Salem Batiyah