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The Formulation of a Novel Control Framework for Regulation of an Active Low Voltage Network
The low voltage (LV) Network has become more complex due to the addition of loads like Electric Vehicles (EVs) and generation from Renewable Energy Sources (RES). These additions will result in power quality issues arising from excess supply or load unbalance. As LV networks and power systems were n...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2023
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| Summary: | The low voltage (LV) Network has become more complex due to the addition of loads like Electric Vehicles (EVs) and generation from Renewable Energy Sources (RES). These additions will result in power quality issues arising from excess supply or load unbalance. As LV networks and power systems were not designed with these entities in mind, scalable and flexible mitigation strategies will be needed to tackle these problems. This general conclusion was determined in the literature. This dissertation presents the implementation of a novel framework established to solve these problems. The proposed framework consists of a Multi-Agent Control System (MAS) to coordinate the various independent entities (agents) and a Thévenin Equivalent Impedance (TEI) based estimator to measure real-time load unbalance towards determining optimal currents in real-time and adjust supply/demand optimally to minimize losses. A test network was developed to compare systems making use of the same MAS-generated charging scheme (for the ESS and EV) but with different modes of phase power injection, BPI (Balanced Power Injection), and OPI (Optimal Power Injection). The study reveals OPI minimizes transmission losses by exploiting the transmission lines with lower impedance. Also, the impact of OPI on the network voltage is minimal as seen in phase voltage unbalance rate (%PVUR) figures, this means the extra unbalance introduced by OPI is negligible. Based on the findings the study concludes that the integration of MAS and OPI (guided by TEI parameter estimations) is a feasible combination and an improvement on the existing frameworks as it minimizes losses. |
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