Giovanna Adinolfi

The decarbonization of the electricity grid is one of the actions that can help reduce fossil fuel emissions, and thus their impact on global warming in the future. This decarbonization will be achieved mainly through the integration and widespread diffusion of renewable power sources. This is also going to be supported by the shift from the paradigm of production–transmission–distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will very likely reduce the reliability of the grid, unless appropriate countermeasures are taken/implemented, hopefully at the same time that the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might possibly affect some of the central renewable (wind, PV, hydrogen) and ancillary technologies that will be used to establish future renewable-based power systems. Accordingly, this paper presents a literature survey, also extending the focus to related datasets that can be used for deeper investigation. It is highlighted that the gaps mainly refer to a lack of a common taxonomy that prevents the establishment of structured knowledge in the scope of renewable-based power systems, a lack of contributions to anomalies/faults specific to wind turbines, and a lack of datasets related to electrolyzers, fuel cells, DC/x conversion, and monitoring and communication systems. Further, in the case of monitoring and communication systems, the scientific literature is both very dated, therefore not considering possible new aspects that would be currently worthy of investigation, and not oriented toward the particular domain addressed, thus considering peculiar aspects that are left out.

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

Power systems comprise different electrical, electronic, electromechanical and electrochemical components. Adequacy, security, resilience and reliability represent essential requirements for grids functioning mode. The evaluation of such aspects can constitute a delicate task in the presence of heterogeneous components. Focusing on reliability assessment, several Reliability Prediction Models are available. They are suitably applied according to the type of component under evaluation. The lack of homogeneity of these models forbids the comparison of performance and identification of unreliable systems and grid section. This paper aims to face the mentioned issue proposing a unique reliability assessment methodology able to characterize different equipment connected to radial/meshed/ring grids and subjected to different stressing and ageing factors. It is customized for electrical lines, transformers, circuit breakers, converters and renewables plants. Component and systemic key indices are calculated. Furthermore, a novel “load feeding reliability“ indicator is evaluated for providing information about the supply reliability of a specific load. This index is meaningful for the identification of unreliable grids, microgrids and systems. Such an approach can contribute to improve power systems design, planning and control. The proposed method is integrated in a software application implemented for grid reliability assessment. The obtained results are reported for an urban grid including an underground transportation area.

In this paper, specific control logics defined for generators, controllable loads and electric energy storage systems are illustrated in order to improve the reliability of AC/DC hybrid microgrids in their grid-off operation following failure events such as short-circuits, line breaks, sudden disconnections of generating units or loads, etc. After defining specific control schemes, these were modelled in a digital environment and finally tested with dynamic simulations. Finally, the paper highlights the role of virtual synchronous machine control for the self-healing capacity of islanded hybrid microgrids.

Integration of DC grids into AC networks will realize hybrid AC/DC grids, a new energetic paradigm which will become widespread in the future due to the increasing availability of DC-based generators, loads and storage systems. Furthermore, the huge connection of intermittent renewable sources to distribution grids could cause security and congestion issues affecting line behaviour and reliability performance. This paper aims to propose a planning tool for congestion forecasting and reliability assessment of overhead distribution lines. The tool inputs consist of a single line diagram of a real or synthetic grid and a set of 24-h forecasting time series concerning climatic conditions and grid resource operative profiles. The developed approach aims to avoid congestions criticalities, taking advantage of optimal active power dispatching among “congestion-nearby resources”. A case study is analysed to validate the implemented control strategy considering a modified IEEE 14-Bus System with introduction of renewables. The tool also implements reliability prediction formulas to calculate an overhead line reliability function in congested and congestions-avoided conditions. A quantitative evaluation underlines the reliability performance achievable after the congestion strategy action.

This paper presents the design and hardware implementation of open-source hardware dedicated to smart converter systems development. Smart converters are simple or interleaved converters. They are equipped with controllers that are able to online impedance match for the maximum power transfer. These conversion systems are particularly feasible for photovoltaic and all renewable energies systems working in continuous changing operating conditions. Smart converters represent promising solutions in recent energetic scenarios, in fact their application is deepening and widening. In this context, the availability of a hardware platform could represent a useful tool. The platform was conceived and released as an open hardware instrument for academy and industry to benefit from the improvements brought by the researchers’ community. The usage of a novel, open-source platform would allow many developers to design smart converters, focusing on algorithms instead of electronics, which could result in a better overall development ecosystem and rapid growth in the number of smart converter applications. The platform itself is proposed as a benchmark in the development and testing of different maximum power point tracking algorithms. The designed system is capable of accurate code implementations, allowing the testing of different current and voltage-controlled algorithms for different renewable energies systems. The circuit features a bi-directional radio frequency communication channel that enables real-time reading of measurements and parameters, and remote modification of both algorithm types and settings. The proposed system was developed and successfully tested in laboratory with a solar module simulator and with real photovoltaic generators. Experimental results indicate state-of-art performances as a converter, while enhanced smart features pave the way to system-level management, real-time diagnostics, and on-the-flight parameters change. Furthermore, the deployment feasibility allows different combinations and arrangements of several energy sources, converters (both single and multi-converters), and modulation strategies. To our knowledge, this project remains the only open-source hardware smart converter platform used for educational, research, and industrial purposes so far.

In last years new energetic scenario are emerging. They are particularly applicable in residential contexts due to DER sources application and predictable energetic needs. In this paper residential multi-DER microgrids are considered with their devices and systems. A software tool is proposed to support designers in the identification and sizing of multi-DER domestic microgrids. Functioning conditions, sources and loads input data can be defined to configure the microgrid. Photovoltaic, fuel cells and micro wind generators are taken into account to plan the microgrid according to dwellings needs. Specific control techniques are implemented to minimize the active power exchange with the AC electric grid and to maximize the consumption of auto generated power. The application of Smart Converters to DER generators permits an online maximum power achievement. A case study is presented with particular attention to a cooling dominated building microgrid.

Silicon carbide (SiC) power MOSFETs are available only for high-power and medium-to-high-voltage applications, generally above 600 V, because for lower blocking voltages, they comparatively provide lower advantages in terms of efficiency. There are applications,however, for which ruggedness and reliability are as important as efficiency, such as power optimizers for photovoltaic (PV) modules, which fall within the low power, low voltage category of dc-dc converters. These circuits, which maximize the energy produced by each single PV module, operate in continuously changing and stressing conditions yet having to assure high performances in terms of efficiency as well as of temperature insensitivity and long-term reliability. The aim of this paper is to predict the basic characteristics of a 4H-SiC MOSFET tailored for this kind of applications and, therefore, characterized by a breakdown voltage BVDS of 150 V and currents of the order of 10 A. The study, based on numerical simulations, shows that, besides the expected higher ruggedness, the static characteristics would be comparable to those of silicon MOSFETs rated for a comparable BVDS, with RON in the order of 100 kΩ·μm ² , while advantages would result in terms of dynamic characteristics, and in particular in terms of switching times.