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.

Although it is commonly assumed that power swing is symmetrical and will have identical effects on all three-phase voltages and currents, there is another type of power swing, which is asymmetrical and mainly occurs during single-pole tripping (SPT), the impact of which on power systems integrated with the inverter-based resources (IBRs) has not been investigated so far. Since the likelihood of the occurrence of asymmetrical power swing is increasing in today's heavily loaded transmission lines, this article investigates the impact of IBR integration on the performance of distance protection during single-pole tripping. Furthermore, a new scheme is proposed to detect earth faults that occur during asymmetrical power swings to enhance the performance of power swing blocking functions. To do so, the superimposed component of the zero-sequence current during SPT is extracted. Then, the moving average window is used to calculate the average of both zero-sequence current and its superimposed component. Furthermore, to enhance the security of the proposed scheme, a new technique based on the phase angle shift between the three-phase currents is devised to detect the asymmetrical power swing conditions. This article shows that as the penetration level of IBRs increases, the standard reclosing dead-time needs to be revisited otherwise it can create an unstable power swing condition, even during SPT. Moreover, this article shows that the proposed earth fault detection scheme can efficiently detect all single-line-to-ground faults occurring during SPT.

Power generation through solar photovoltaics has shown significant growth in recent years. However, high penetration of solar PV creates power system operational issues as a result of solar PV variability and uncertainty. Short-term PV variability mainly occurs due to the intermittency of cloud cover. Therefore, to mitigate the effects of PV variability, a sky-image-based, localized, global horizontal irradiance forecasting model was introduced considering the individual cloud motion, cloud thicknesses, and the elevations of clouds above the ground level. The proposed forecasting model works independently of any historical irradiance measurements. Two inexpensive sky camera systems were developed and placed in two different locations to obtain sky images for cloud tracking and cloud-based heights. Then, irradiance values for onsite and for a PV site located with a distance of 2km from the main camera were forecasted for 1 minute, 5 minutes, and 15 minutes ahead of real-time. Results show that the three-level cloud categorization and the individual cloud movement tracking method introduced in this paper increase the forecasting accuracy. For partially cloudy and sunny days, the forecasting model for 15min forecasting time interval achieved a positive skill factor concerning the persistent model. The accuracy of determining the correct irradiance state for a 1min forecasting time interval using the proposed model is 81. The average measures of RMSE, MAE, and SF obtained using the proposed method for 15min forecasting time horizon are 101Wm^-2, 64Wm^-2, and 0.26, respectively. These forecasting accuracy levels are much higher than the other benchmarks considered in this paper.

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed.

This article presents two low bandwidth distributed model predictive control (MPC) based algorithms for the coordinated control of residential energy storage (ES) to mitigate overvoltage and reduce peak demand along LV radial distribution feeders. Each ES unit consists of a low level controller (LLC) that utilises MPC with three distinct objective functions that can optimise for financial benefit, voltage regulation and peak shaving. These modes are activated via binary signals sent by the high level controller (HLC). The HLC, operating at the feeder level, determines if any network violations such as overvoltage or overloading are predicted to occur. Using inter-bus $\boldsymbol{P}$ , $\boldsymbol{|V|}$ and $\boldsymbol{\Delta P}$ data, the HLC determines the minimum ES units to dispatch to mitigate these issues. The result is a very low bandwidth distributed control architecture with low computational complexity, that can achieve acceptable network operating conditions while dispatching the minimum number of ES units. The proposed distributed MPC algorithms were shown to outperform decentralised control strategies for voltage regulation and peak shaving from the Australian Standards and also had significantly less computational requirements than a centralised MPC controller to achieve the same network overvoltage and peak load objectives.