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.

An energy study of the Carnarvon power system using load demand, irradiance and photovoltaic generation data acquired from the power system operator, for calender year 2018, has been performed. This study utilised the HOMER Pro simulation system to determine photovoltaic and battery capacities to be applied to the Carnarvon power system to operate, without fuelled prime mover driven synchronous generation (FSG) input, over a 24 hour cycle given adequate irradiation. This goal is realised by using a metric that balances competing mechanisms in deciding PV and battery storage capacities. The metric calculation resulted in capacities of 35MW for PV and 90MWhr for battery storage, resulting in a renewable energy fraction of 96.2 %, 589 hours of FSG operation and levelised cost of energy (LCoE) of 47.6c/kWhr. Power ratio and storage ratio give dimensionless quantities for PV and storage sizes relative to load demand. These values can estimate PV and battery capacities for similar systems in similar environments to achieve comparable outcomes. The determined capacity of 35MW of PV is assumed to be all roof-top installed. This is based on roof-top potential estimates, made in other work, of 70MW for Carnarvon.

Facilitating data sharing while upholding privacy is essential for driving meaningful insights, promoting informed decision-making, and fostering responsible data-driven solutions in today’s energy management. This is especially important in the context of load forecasting, where accurate predictions of energy consumption patterns are needed for efficient energy management. The existing models guarantee the protection of the load profile data of all households for low-aggregate level load forecasting. However, in the real world, some customers may be willing to fully share their data and others may not. Moreover, persuading some customers to give consent for direct use and full disclosure of their load profile data is not always possible. Accordingly, this paper introduces the concepts of full and limited consent levels- the former refers to the willingness of an individual to fully disclose their ground truth, otherwise, their privacy must be protected under the limited consent level while releasing the aggregate forecast. Moreover, this study contributes to the research gap on the intricate balance between utility and privacy in the domain of probabilistic low-aggregate load forecasting particularly when a community comprises various proportions of customers with full/limited consent levels. In doing so, a differential privacy (DP) model for short-term probabilistic load forecasting is used so that the user, such as grid operators or retail providers, receives the forecast retaining the 95% confidence interval (CI) of the unperturbed/original forecast. Furthermore, a Bayesian neural network (BNN) is utilized as the forecasting engine.

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.

The application of short-term forecasts in remote PV diesel networks is an approach to enable higher PV penetrations and fuel savings without deteriorations in network stability. In this simulation study, two different strategies for the integration of short-term forecasts in combination with battery storage into remote PV diesel networks are analyzed. Method 1 combines conventional battery control strategies with predicted spinning reserve by gensets. The resulting additional fuel savings range from 3.9% to 5.6% compared to conventional control. Further benefits are reduced battery throughput compared to battery only and increased network stability compared to forecast-only or battery-only control strategies. Method 2 uses forecasts to reduce PV power fluctuations by curtailment. Additional fuel savings range from 0.6% to 1.3%, while battery throughput is halved compared to method 1.