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Renewable generations are growing fast and they are becoming the major generation technology in power systems. Amongst all renewables, wind and solar resources have the highest growth rate. However, fluctuation and uncertainty of these sources are the main barriers in their utilization and can affect reliability of power system. Therefore, these generators must be treated differently in adequacy studies and their reliability contribution should be evaluated. South Australia (SA) accounts for more than half of Australia's wind share and its solar capacity is rapidly increasing as well. Thus, not only the reliability benefit of wind is required to be investigated in this power system, but also the contribution of solar energy and its impacts should be analysed as well. The reliability benefit of wind and PV has been studied in the literature, but there is a need to evaluate their combined contribution in reality and investigate the influence of their correlations. Therefore, this paper evaluates the reliability contribution of wind and solar power in South Australia and the impact of solar energy on wind load carrying capability. State sampling Monte Carlo technique is utilized for reliability assessment and Fuzzy C-means clustering method is employed to create a multistep load model. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

To achieve clean energy targets, more renewable generators – mainly wind and photovoltaic (PV) – are being integrated into electricity networks. However, wind and PV are non-synchronous generators to the grid and they traditionally have neither enough inertia response nor governor support to control major frequency excursions under most situations. Currently in South Australia, the combined generation of wind and rooftop PV has nearly reached the level of average demand with minor support through interconnection import. In such a situation it is likely that there may be fewer synchronous machines online for frequency regulation. Under such a scenario, a contingency could produce a more severe frequency excursion, violating in place frequency standards. Further, many distributed PV systems in South Australia may have a default under-frequency protection setting, which could be higher than the under-frequency load shedding (UFLS) threshold. Hence, a moderate generation/import trip may cause a massive secondary PV trip, which may result in an unacceptably low frequency, triggering further load shedding. This will certainly have an adverse impact on network security. A similar issue of secondary PV trip is not restricted to South Australia and may occur in other regions and countries as well. In this paper, a network which loosely represents the Australian National Electricity Market (NEM) is selected and modified to investigate and analyze network frequency response under high non-synchronous machine penetration. This network contains all necessary models for a comprehensive frequency response study, such as synchronous machine, governor, stabilizer and wind generator. The relevant functions such as load frequency relief, PV tripping and load shedding which are essential for frequency response evaluation are established in the approved and well-accepted platform – PSS®E. The results show that low inertia and secondary PV tripping can become serious issues for network frequency regulation and in some situations they can even cause system oscillation and stability concerns. [ABSTRACT FROM AUTHOR]