Electrical and Electronic Engineering - Research Publications
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ItemAdvanced Planning of PV-Rich Distribution Networks - Deliverable 3: Traditional Solutions(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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ItemAdvanced Planning of PV-Rich Distribution Networks - Deliverable 2: Innovative Analytical Techniques(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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ItemAdvanced Planning of PV-Rich Distribution Networks - Deliverable 1: HV-LV modelling of selected HV feeders(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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ItemResidential battery controller for solar PV impact mitigation: A practical and customer-friendly approach(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.
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ItemActive Management of PV-Rich Low Voltage Networks( 2017)The increased penetration of residential-scale photovoltaic (PV) systems in European-style low voltage (LV) networks (i.e., long feeders with high number of connected customers) is leading to technical issues such as voltage rise and thermal overload of the most expensive network assets (i.e., transformer, cables). As these issues significantly limit the ability of LV networks to accommodate higher PV penetrations, Distribution Network Operators (DNOs) are required to proceed with expensive and time-consuming investments in order to reinforce or replace these assets. In contrast to this traditional approach of network reinforcement, which potentially leads to massive capital expenditure, the transition towards active LV networks where controllable elements, existing (i.e., PV systems) and likely to be adopted (i.e., battery energy storage systems, LV on-load tap changer transformers), can be managed in real-time, poses an attractive alternative. Although several active network management schemes have been recently proposed to increase the hosting capacity of PV-rich LV networks, they are mostly based on managing voltage issues only; and, in general, aim to solve technical issues separately. Integrated solutions aiming at managing simultaneously voltage and thermal issues are required, as recent studies demonstrate that both issues can coexist in PV-rich LV networks. More importantly the majority of studies, which commonly neglect the characteristics of real LV networks (e.g., unbalanced, three-phase, radial, multiple feeders with several branches, different types of customers), use complex optimisation techniques that require expensive communication infrastructure and extensive or full network observability (currently not available in LV networks). However, considering the extensiveness of LV networks around the world, practical, cost-effective and scalable solutions that use limited and already available information are more likely to be adopted by the industry. Considering the above gaps in the literature, this Thesis contributes by proposing innovative and scalable active network management schemes that use limited network monitoring and communication infrastructure to actively manage (1) Residential-scale PV systems, (2) Residential-scale Battery Energy Storage (BES) systems and (3) LV on-load tap changer (OLTC)-fitted transformers. The adoption of the proposed active network management schemes, which makes use of already available devices, information and requires limited monitoring (i.e., secondary distribution substation), allows making the transition towards active LV networks more practical and cost-effective. In addition, to tackle the challenges related to this research (i.e., lack of realistic LV network modelling with high resolution time-series analyses), this Thesis, being part of the industrial project Active Management of LV Networks (funded by EDF R&D) and having access to French data, contributes by considering a fully modelled typical real residential French LV network (three-phase four-wire) with different characteristics and number of customers. Moreover, realistic (1-min resolution) daily time-series household (from real smart meter data) and PV generation profiles are considered while a stochastic approach (i.e., Monte Carlo) is adopted to cater for the uncertainties related to household demand as well as PV generation and location.
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ItemDeliverable 2.4 Benefits of adopting Storage Devices in LV networks(Electricity of France (EDF), 2016)This report corresponds to the final Deliverable 2.4 “Benefits of adopting Storage Devices in LV Networks” part of Work Package 2 “Control of Low Carbon Technologies” of the iCASE project “Active Management of LV Networks” jointly funded by EDF R&D and EPSRC. It presents a quantitative assessment on the benefits from adopting storage devices in combination with PV systems in LV networks considering different PV penetrations and locations.
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ItemDeliverable 2.3 Benefits of controlling EVs and PV in combination with other technologies(Electricity of France (EDF), 2016)This report corresponds to Deliverable 2.3 “Benefits of controlling EVs and PV in combination with other technologies” part of Work Package 2 “Control of Low Carbon Technologies” of the iCASE project “Active Management of LV Networks” jointly funded by EDF R&D and EPSRC. It presents a quantitative assessment of the benefits of adopting different control strategies (local, centralised or combination) with the additional control of other technologies (i.e., OLTC) that could help managing voltages and congestion. This assessment considers different control strategies, locations and penetrations of low carbon technologies, different control cycles and LV networks.
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ItemDeliverable 2.2 Benefits of controlling EVs and PV(Electricity of France (EDF), 2016)This report corresponds to Deliverable 2.2 “Benefits of controlling EVs and PV” part of Work Package 2 “Control of Low Carbon Technologies” of the iCASE project “Active Management of LV Networks” jointly funded by EDF R&D and EPSRC. It presents a quantitative assessment of the benefits of adopting control of EVs and PV systems considering different control strategies, penetration of low carbon technologies, and penetrations per feeder, and types of LV networks.
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ItemDeliverable 2.1 EVs and PV: Literature review and initial modelling(Electricity of France (EDF), 2015)This report corresponds to Deliverable 2.1 “EVs and PV: Literature review and initial modelling” part of Work Package 2 “Control of Low Carbon Technologies” of the iCASE project “Active Management of LV Networks” jointly funded by EDF R&D and EPSRC. It presents a summary of the literature review and current pilot projects investigating the control of EV and PV systems.
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ItemDeliverable 1.3 Monitoring Aspects and Benefits of OLTC in LV Networks(Electricity of France (EDF), 2015)This report corresponds to Deliverable 1.3 “Monitoring Aspects and Benefits” part of Work Package 1 “On-load tap changing LV transformers” of the iCASE project “Active Management of LV Networks” jointly funded by EDF R&D and EPSRC. It presents a quantitative assessment of the benefits from adopting different levels of monitoring considering location, sampling and data latency.