Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration.

Increasing penetration of distributed energy resources is manifesting as voltage regulation challenges in many LV networks. Appropriate regulation of supply voltage magnitude is essential to ensure efficacy and efficiency in the operation of electricity supply networks and consumer equipment. While the theoretical impacts of supply voltage magnitude on the performance of consumer equipment, which include additional energy consumption and decreased equipment lifespan as voltage magnitude increases, are relatively well known, this has not been translated into quantitative impacts. This paper applies the outcomes of previous impact of supply voltage magnitude studies by the authors, in conjunction with domestic load models, to develop algorithms to estimate the quantitative impacts of supply voltage magnitude on consumers. The paper then applies these algorithms to calculate quantitative economic impacts that can be associated with the magnitude of the supply voltage. The outcomes of this research suggest that the per-annum impact of supply voltage magnitude on consumer equipment loss of life is potentially an order of magnitude greater than the resultant increased energy consumption based on case studies using Australian data.

Rapid evolution of electricity distribution networks challenges the maintenance of up-to-date information in electricity utility databases. This hinders the ability of utilities to understand phase connectivity and topology of users in their distribution networks. Extensive research has been conducted to develop smart meter data-driven phase identification and topology determination approaches as alternatives to the conventional, time-consuming, and expensive approach of manual inspection. However, the majority of such approaches are challenged by low levels of smart meter penetration in distribution networks, entailing further investigation. The objective of this paper is to contribute to this challenge by proposing an alternative smart meter data-driven approach of user phase identification, verification, and topology determination and testing the method on a real Australian distribution network under varied levels of smart meter penetration. This paper first presents a smart meter data-driven user phase identification tool using k-means clustering. Then, a smart meter data-driven user phase verification and topology determination approach is introduced by analyzing voltage-to-power sensitivities obtained from linear regression. Four distinct linear regression models are developed and compared to recognize relevant parameters and input variables leading to the most reliable sensitivities. The overall process proposed in this study demonstrated high accuracy at original smart meter penetration of 75% of the case study DN. The performance at reduced smart meter penetrations of 50% and 25% is also examined and discussed in the paper.

The widespread integration of rooftop solar photovoltaic systems into electricity distribution networks often leads to poor voltage regulation at user connection points, potentially breaching system voltage standards. Therefore, it is important for distribution network service providers to thoroughly assess such real and potential impacts to ensure compliant and safe operation of their power systems. The conventional approach of non-linear power flow-based voltage estimation using model-based methods is complex and time-intensive. Consequently, there is an increasing research interest towards model-free voltage estimation methods as a reliable alternative. This paper proposes and compares two distinct model-free voltage estimation approaches that can be utilised for effective hosting capacity estimation of residential solar photovoltaic systems in low voltage distribution networks. One approach utilises linear regression based on linearised power flow equations, while the other employs neural networks to capture non-linear power flow dynamics. The study developed 16 linear regression models and 648 neural network models utilising historical data collected from residential smart electricity metres in a real low voltage distribution network and compared their efficacy against conventional model-based, non-linear power flow simulations. Results indicate that the proposed model-free voltage estimation approaches can estimate voltages at user connection points in a similarly accurate but faster manner compared to the model-based approach. Observations show that the proposed linear regression-based voltage estimation approach is superior to the proposed neural network-based voltage estimation approach in terms of interpretability and practicality.

As the penetration of distributed energy resources (DERs) continues to increase, there is conjecture concerning the power quality implications of the inverters used to interface these DERs with low-voltage (LV) electricity supply networks. As a power electronics converter, inverters are a known source of harmonic emissions. Using a combination of large-scale field measurements, laboratory evaluations of inverter performance, and power system modelling, this study applies an empirical data-driven approach to investigate the impact of small-scale solar PV inverters on LV harmonic distortion magnitudes. This multi-facetted approach, involving field data analysis, laboratory assessments of inverter performance, and power system simulation to evaluate the impact of small-scale DER on harmonic distortion in LV networks, is novel in comparison to other studies, which only utilise one or two of the analysis methods of simulation, laboratory evaluation, or analysis of field measurements but not all three. The analysis of field measurement data collected over the past decade does not indicate any significant changes in harmonic distortion magnitudes that can be attributed to the increasing penetration of DERs. Power system modelling, which incorporates data obtained from laboratory inverter performance evaluations, indicates that, even at very high levels of penetration, the harmonic current emissions from solar PV inverters are only sufficient to add modest levels of harmonic distortion to LV networks, a 0.25% increase in THD for 40% penetration and a 0.62% increase in THD for 100% penetration, providing an explanation for the findings of the field data analysis.