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It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management. [ABSTRACT FROM AUTHOR]

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. [ABSTRACT FROM AUTHOR]

Large-scale renewable energy plants, flexible AC (alternating current) and high voltage DC (direct current) transmission systems, and modern consumer devices utilize power electronics that tend to increase harmonic emissions. Furthermore, such emissions are nowadays known to exceed the traditional 2 kHz range typically considered for harmonic analysis. However, the accuracy of such harmonic measurements in medium and high voltage networks is questionable due to the lack of accuracy specifications for the respective instrument transformers that are being used in the measurement chain. Therefore, the motivation of this study is to review the existing techniques for measuring high-frequency voltage harmonics, i.e., those in the range 2–9 kHz, in medium-, high-, and extra high-voltage electricity networks, where most large-scale power electronic converters are being connected. Different transducer types are compared in terms of measurement accuracy. The reviewed literature indicates that some transducers can introduce errors due to their nonlinearities. The study also identifies the limitations of calibrating these transducers at frequencies above 2 kHz due to the unavailability of suitable sources capable of generating the required test waveforms. Furthermore, the study emphasizes the necessity for establishing accuracy limits for harmonic measurements above 2 kHz. [ABSTRACT FROM AUTHOR]

In recent years, integration of solar photovoltaic (PV) systems into distribution networks has been increasing rapidly, as it has become the most promising renewable energy source (RES) in the transition of power generation from centralised to decentralised systems. With the power electronic (PE) interfaces that use high-frequency internal switching, all renewable energy sources are considered to be harmonic emitters, especially near switching frequencies, i.e., above 2 kHz. This paper evaluates the behaviour of high-frequency harmonics in the 2–20 kHz range due to the parallel operation of multiple solar PV inverters connected to a low-voltage (LV) network. The circulation current component that flows within the installation due to the low impedance paths at higher frequencies is analysed. Summation of high-frequency harmonic currents is observed with phase diversity analysis. The circulating current component can become 14 times higher than the grid current component at higher frequencies. [ABSTRACT FROM AUTHOR]