Martina Calais

The accelerating deployment of photovoltaic (PV) systems intensifies the urgency to address various challenges in their performance prediction, operation and maintenance, and long-term reliability. Digital Twin (DT) technology – leveraging advancements in Industry 4.0 – offers great potential to tackle such challenges, by serving a wide range of PV applications and use cases. Nevertheless, the adoption of Digital Twins for PV systems (PVDTs) is still in its early stages, with limited published research work in this area. This paper presents a systematic literature review (SLR) of 61 peer-reviewed PVDT studies, aiming to map recent research trends, identify gaps, and provide recommendations guided by the review results. The works presented in the reviewed articles were categorized based on predefined review criteria, and were examined against a set of proposed PVDT eligibility criteria, stemming from commonly accepted generalized DT definitions and taxonomies. The review reveals that most reported implementations lack essential features, mainly bidirectional data flows and self-adaptability, with only 3.3 % of papers meeting all the eligibility criteria. Key identified trends include a dominance of data-driven models for power prediction, and limited utilization for life cycle assessments and design optimizations. Based on the review findings, the paper further introduces a general DT taxonomy tailored to PV applications and guided by the identified trends and gaps. This study emphasizes the need for unified and standardized PVDT definitions, comprehensive multi-domain modelling approaches, and integration of sustainability metrics to guide future research and industrial adoption.

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich.

The integration of solar photovoltaic (PV) into electricity networks introduces technical challenges due to varying PV output. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (MGs) with high penetration of solar PV generation. PV plants and optionally controllable distributed energy resources (DERs) in MGs can be operated in an optimized way based on nowcasting, which is also called very short-term solar irradiance forecasting up to 60 min ahead. This study presents an extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs. Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs. An online survey has been conducted to identify the limitations, benefits and challenges of deploying nowcasting in MGs. The survey outcomes show that the incorporation of nowcasting tools in MG operations is still limited, though the possibility of increasing solar PV penetration levels in MGs if nowcasting tools are incorporated is acknowledged. Additionally, recent nowcasting tools, such as sky camera-based tools, require further validation under various conditions for more widespread adaptation by power system operators.

Deployment of distributed energy resources has rapidly increased during the last few years. The uptake of renewable energy and especially photovoltaic (PV) systems are of interest to utilities in remote and rural areas where the use of conventional power generation is costly. Investigating the effects of such PV systems on isolated power systems at different penetration levels is a relevant research topic. This study reports on the data acquisition system deployed in a remote town in Western Australia and presents some of the findings and observations extracted from the captured real data. It highlights the maximum PV output variations and investigates the underlying factors. The impact of the PV systems on the voltage across the network is also analysed in this study. The studies show that inverter tripping events have led to larger PV output variations in shorter intervals while the cloud movements have contributed to variations in longer intervals.

There is an urgent need for educational institutions to produce graduates with appropriate skills to meet the growing global demand for professionals in the sustainable energy industry. For universities to stay at the forefront of meeting this global demand from industry, universities need to ensure their curricula and pedagogies stay relevant. The use of benchmarking is a key means of achieving this and ensuring any gap between university curricula and the practical needs of industry is minimized. The aim of this paper is to present an approach to benchmarking a sustainable energy engineering undergraduate degree with respect to curriculum frameworks recommended by industry and pedagogy standards required and recommended by academia and education research. The method uses the Murdoch University renewable energy engineering degree major as a case study. The results show that the learning outcomes of the renewable energy engineering units, in general, align well with the recommended learning outcomes for a complete sustainable energy degree, as prescribed by the Australian Government Office for Learning and Teaching. In addition, assessment task and marking criteria for the capstone unit of the major were at Australian Universities’ standard. A similar approach to benchmarking can be adopted by developers of new or existing sustainable energy engineering degrees in order to align with curriculum frameworks and pedagogy standards required by industry and academic peers.

One of the primary technical challenges of integrating high levels of PV generation into standalone off-grid power supply systems is their variable power output characteristics. In dealing with this issue, the integration of reliable PV forecasting techniques and preferably energy storage, are highly effective. Applying a short-term PV forecasting method, together with a compensatory controllable resource, can help in the management of system operation. This study incorporates the development of an energy flow modelling tool that has been used to analyse the benefits of 1-min ahead PV forecasting and battery storage for different system configurations. Based on the five days of 1-min ahead forecasting results analysed, it is found that PV forecasting enables the prosumer to install more than double the PV capacity, compared to the allowed installed PV capacity when no forecasting is employed. This additional PV capacity saves around 24–25% (on average) of diesel fuel per day for the diesel-PV-battery configuration. The outcomes evidently indicate that incorporating 1-min ahead PV forecasting enables a significant increase of PV hosting capacity of the system, without compromising the reliability of the system.

Australia and most other countries are adopting renewable energy generation as the dominant means of reducing dependence on fossil fuels. This has been made more feasible by the exponential take-up of solar photovoltaic (PV) systems and their concurrent production scale-up and cost decline. Rooftop solar PV, combined with battery storage, seems likely to be the dominant means of providing household electricity needs. In response to the technical challenges from rooftop PV, network utilities have implemented various low cost options to cope with PV’s impact on network voltages. However, if we want this clean energy technology to fully utilise the available roof space and eventually meet residential electricity needs, additional hardware, control and commercial options will need to be adopted by both network utilities and their customers to overcome the technical barriers, especially voltage rise. This paper presents the authors’ evaluations of options to mitigate voltage rise, including operating solar inverters with reactive power absorption (var absorbing), dependent only on solar power output or operating the solar inverters in a volt–var response mode (voltage droop control) where the inverter adjusts its reactive power (Q) in response to changes in its terminal voltage – Q(V). This paper also considers the fulltime Q(V) option, where an inverter’s reactive power capacity is independent of solar conditions – statcom mode. The network utility option of using line drop compensation (LDC – used on long rural MV feeders) on urban MV feeders during daylight hours is assessed to lessen voltage rise on LV feeders with low net loading or reverse power flow due to high solar PV generation. The paper concludes that a combination of solar inverters performing fast fulltime voltage droop control outside a voltage deadband (statcom mode) and HV/MV substation transformers with slow acting daytime LDC mitigates voltage rise, whilst limiting feeder reactive power requirements.

The integration of a high share of photovoltaic (PV) power generation in remote electricity networks is often limited by the networks’ capabilities to accommodate PV power fluctuations caused by passing clouds. Increasing the share of PV penetration in such networks is accompanied by an increased effort to achieve integration. In the absence of solar forecasting, sufficient spinning reserve must always be provided to cover unforeseen reductions. The expected ramp rates are magnified in small and centralised PV systems and can be in the order of a few seconds. In this study, we investigate the use of a low-cost sky camera for very short-term solar forecasting. Almost 2 months of sky camera data have been recorded in Perth, Western Australia and processed for to provide high-resolution irradiance forecasts based on visible sky images. For performance validation, the capability to provide reliable forecasts under constant clear sky conditions is investigated. During these times, PV generation is expected to be high and reliable, which provides an opportunity to reduce the online spinning reserve often enabling power station operation with one less operating diesel generation. For networks with disconnected diesel generators, we assume that clouds that could reduce the PV generation output have to be predicted at least 2 min before their arrival to have enough time for a diesel generator to start and synchronize with the grid. Therefore, we define an irradiance threshold discriminating between the persistent state of constant clear sky (stays clear) and the non-persistent state (cloud shading event) based on a 2–5 min time horizon. In a binary evaluation, we achieve an overall accuracy of 97% correct forecasts and low 3% false alarms of cloud events indicating a high potential for fuel savings. Focusing on the rare (2% of the time) but more critical non-persistent conditions, we found 8 out of 84 cloud events have not been predicted in advance. Reasons for erroneous forecasts and suggestions for model improvements are provided.