Electrical and Electronic Engineering - Research Publications

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    Advanced Planning of PV-Rich Distribution Networks - Deliverable 3: Traditional Solutions
    Procopiou, A ; Petrou, K ; Ochoa, L (Department of Electrical and Electronic Engineering, The University of Melbourne, 2020)
    This document investigates the adoption of traditional solutions such as change of off-load and on-load tap changer positions and/or network augmentation to increase the hosting capacity of PV-rich distribution networks considering the new Victorian Volt-Watt and Volt-var settings which mandates that both power quality response modes are enabled. Studies are performed on four fully modelled and significantly different HV feeders (i.e., urban and rural) considering time-series seasonal analyses with growing penetrations of solar PV. Findings show that enabling both Volt-Watt and Volt-var functions with the Victorian settings provides significant benefits to both DNSPs and customers. Voltage rise issues and curtailment are dramatically reduced, making it possible to host 20% of customers without the need for other solutions. Adopting traditional solutions can help increase the solar PV hosting capacity to 40% (excluding HV feeders with long SWER lines). However, beyond 40%, traditional solutions were found to have limited effectiveness in mitigating network issues.
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    Advanced Planning of PV-Rich Distribution Networks - Deliverable 2: Innovative Analytical Techniques
    Procopiou, A ; Ochoa, L (Department of Electrical and Electronic Engineering, The University of Melbourne, 2019-10-22)
    This document presents a smart meter-driven analytical technique proposed by The University of Melbourne to estimate PV hosting capacity in distribution networks. Two significantly different HV feeders, urban and rural, are modelled in detail with growing PV penetrations in a horizon of 5 years to create a large realistic smart meter data set. The analytical technique is then applied to this data set for different PV penetrations. The findings show that the proposed analytical technique provides adequate estimations of PV hosting capacity, making it a faster and simpler alternative to model-based approaches.
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    Advanced Planning of PV-Rich Distribution Networks - Deliverable 1: HV-LV modelling of selected HV feeders
    Procopiou, A ; Ochoa, L (Department of Electrical and Electronic Engineering, The University of Melbourne, 2019-06-10)
    This document first presents the process adopted by The University of Melbourne in collaboration with AusNet Services to select the HV feeders that will be modelled and used throughout the project. Then, the selected HV feeders are fully modelled along with their corresponding LV networks using the software OpenDSS.
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    Residential battery controller for solar PV impact mitigation: A practical and customer-friendly approach
    Petrou, K ; Procopiou, A ; Ochoa, L ; Langstaff, T ; Theunissen, J (CIRED, 2019)
    The rapid adoption of residential solar photovoltaic (PV) systems combined with the falling prices of residential battery energy storage (BES) systems is paving the way for a future in which customers could locally supply most of their energy needs. However, as off-the-shelf (OTS) residential BES systems operate for the sole benefit of the customer, there are no guarantees that they will be able to charge during periods of peak solar generation; thus being unable to mitigate overvoltage and asset congestion issues resulting from reverse power flows. This work proposes a practical adaptive decentralized (AD) controller that, throughout the day, constantly adapts the charging and discharging power rate of the BES system so that reverse power flows are significantly reduced whilst still reducing customer grid imports. Its performance is assessed on a real Australian medium voltage feeder with realistically modelled low voltage networks and smart meter data. Results highlight that the proposed AD controller overcomes the limitations of the OTS by mitigating technical issues while still bringing similar reductions in electricity imports.