Wang, Jingdi (2022) Droop Based Control Strategy for an Isolated DC Nanogrid with Boost Type Interfaces. Masters thesis, Concordia University.
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Abstract
A Nanogrid can integrate Renewable Energy Sources (RESs) easily and power loads locally. DC distribution is more efficient than AC. An isolated DC Nanogrid increases the resilience of a single building, as it can operate in the off-grid mode. Energy storage (ES) units can balance the fluctuating power from RESs and variable load demand.
The considered isolated DC Nanogrid consists of one Photovoltaic (PV) unit, two Energy Storage (ES) units, and multiple loads. Hierarchical control is often used. Decentralized current-mode droop control on the primary level achieves power-sharing. State of Charge (SoC) control avoids overcharging and deep discharging for ES units, working on the slope of droop control on the secondary level. Also, voltage regulation on the secondary level maintains the bus voltage at its nominal value by modifying the “no-load” voltages of droop control for the PV unit and ES units respectively.
Boost-type Class C converters are used as interfaces for ES units. They allow the ES units to have a lower voltage than the bus voltage. The stability issue related to the Right Half Plane (RHP) zero of the Class C converter is analyzed. Based on a worst-case scenario, the controllers are designed to guarantee the system can be stable under considered circumstances. A new design approach for the output capacitor of the converter is introduced to stabilize the system with a random droop factor.
The control strategy is verified in simulations of analog and digital control in different scenarios.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
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Item Type: | Thesis (Masters) |
Authors: | Wang, Jingdi |
Institution: | Concordia University |
Degree Name: | M.A. Sc. |
Program: | Electrical and Computer Engineering |
Date: | 15 July 2022 |
Thesis Supervisor(s): | Lopes, Luiz and Eicker, Ursula |
ID Code: | 991702 |
Deposited By: | Jingdi Wang |
Deposited On: | 21 Jun 2023 14:40 |
Last Modified: | 21 Jun 2023 14:40 |
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