The end-of-life recycling of crystalline silicon photovoltaic (PV) modules and the utilisation of waste is of fundamental importance to future circular-economy societies. In the present work, the wet-chemistry synthesis route – a low-temperature dissolution–precipitation process – was explored to produce aluminosilicate minerals from waste c-Si solar cells. Nanostructured crystals were produced in an alkaline medium by increasing the reaction temperature from room temperature to 75 °C. The morphology of the produced crystals varied from nanolayered aggregates to rod-shaped crystals and was found to be dependent on the temperature of the reaction medium. Chemical and phase composition studies revealed that the synthesised compounds consisted of structurally different phases of aluminosilicate minerals. The purity and elemental composition of produced crystals were evaluated by energy dispersive spectroscopy (EDS) and micro X-ray fluorescence (μXRF) analysis, confirming the presence of Al, O, and Si elements. These results give new insights into the processing of aluminosilicate minerals with sustainable attributes and provide a possible route to reducing waste and strengthening the circular economy.

Traditional reliability evaluation of lighting sources often assesses only 50% of a lamp's volume, which can lead to performance disparities and misapplications due to their limited reflection of real-world scenarios. To address the limitations, it is essential to adopt advanced asset management approaches that enhance awareness and provide a more comprehensive evaluation framework. This paper delves into the nuances of human-centric and integrative lighting asset management in Swedish public libraries, employing a qualitative field study to ascertain the alignment of current practices with these advanced lighting principles. Expanding library services to 20 high-latitude locations (>55° N) in Sweden, our research employed field observations, stakeholder interviews, and questionnaires, coupled with a thorough gap analysis, to understand the current landscape and stakeholder perceptions. Our findings reveal a dichotomy between the existing conditions of library lighting and the stakeholders' experiences and expectations. Despite the intention to create conducive environments, there is a clear disconnect, with overt problems and covert challenges affecting user satisfaction and efficacy of lighting management. Managers, staff, and users reported varied concerns, including eye strain and discomfort, indicative of substantial room for improvement. The study advocates for a paradigm shift in not only lighting asset management but also reliability evaluation of lighting sources, moving toward continuous improvement, and enhanced awareness and training on human-centric and integrative lighting principles.

Low or even zero carbon dioxide emissions will be an essential requirement for energy supplies in the near future. Besides transport and electricity generation, industry is another large carbon emitter. Hydrogen produced by renewable energy provides a flexible way of utilizing that energy. Hydrogen, as an energy carrier, could be stored in a large capacity compared to electricity. In Sweden, hydrogen will be used to replace coal for steel production. This paper discusses how the need for electricity to produce hydrogen will affect the electricity supply and power flow in the Swedish power grid, and whether it will result in increased emissions in other regions. Data of the Swedish system will be used to study the feasibility of implementing the hydrogen system from the power system viewpoint, and discuss the electricity price and emission issues caused by the hydrogen production in different scenarios. This paper concludes that the Swedish power grid is feasible for accommodating the additional electricity capacity requirement of producing green hydrogen for the steel industry. The obtained results could be references for decision makers, investors, and power system operators.

The deterministic techniques in use for transmission-grid planning and operation have resulted in a high reliability, but they have certain limitations that could be removed by applying stochastic techniques. This paper presents a detailed review of one such stochastic technique: operational risk assessment. A general procedural model of operational risk assessment is introduced, based on one simple equation, highlighting the important elements. The research trends are presented for each of these elements: contingency definition and calculating the probability of a contingency case; contingency filtration methods; defining and calculating the severity factor. Next to an overview of the state-of-the-art, this paper contains a detailed discussion section, where the most important research gaps are identified. Emphasis is especially on bridging the gap between research and practical applications of operational risk assessment. The paper closes with a future outlook on operational risk assessment and its applications.

Radiated electromagnetic emission of photovoltaic systems, for example, adversely impacting radiocommunication, can pose a major barrier against further increase in photovoltaic penetration. This is particularly the case near sensitive infrastructure and activities such as hospitals, airports, search and rescue, and military. To understand the impact of each component and installation detail, we performed systematic radiated electromagnetic emission measurements on comparable commercial photovoltaic systems in the frequency range 150 kHz to 30 MHz. Our measurements indicate that string inverters are unlikely to interfere with radiocommunication when installed according to recommended standards, rules, guidelines, and regulations. It was shown that module-level power optimizers are the main cause of high levels of radiated emissions. The frameless bifacial module showed higher levels of radiated emissions than the monofacial module with frame. Changes in cable management and earthing have less impact on radiated emissions than the choice of solar inverter concept and module type.

We are currently experiencing a truly global energy crisis. Globally, high fuel prices are responsible for 90% of the average cost increase in electricity generation, with natural gas alone accounting for 50%. The Russian invasion of Ukraine in February 2022 has amply demonstrated how geopolitics can have a profound impact on energy systems, but above all, it has revealed the reality of our pattern of energy consumption and generation. As seen in Figure 1.1, fossil fuels supplied 84.3% of global energy use, while the share of wind and solar power (after decades of heavy government subsidies) was only 3.3%, even though there was almost enough installed wind and solar power capacity to supply all their electricity if they were to generate electricity at rated output continuously. Fossil fuel support in 51 countries across the world nearly doubled to $697.2 billion in 2021, up from $362.4 billion in 2020 [1]. However, many developing countries found themselves in a situation where they cannot afford fossil fuels while renewable energy is scarce. This has resulted, on the one hand, in 75 million people who had recently gained access to electricity no longer being able to afford it, and on the other hand, in 100 million people no longer being able to cook with clean fuels, being forced to rely on wood, cow dung, and other unsafe and unhealthy sources. This implies that "much more energy will be needed over the next few generations to extend decent lives for the majority of the world's growing population" [2]. In these countries, access to affordable energy is considered at least as important as controversial climate change. This reality is shown particularly in the countries selected in Figure 1.1(b). Particularly in coal consumption, its decrease takes place mostly in the developed world, particularly in Europe and North America. However, this decline is compensated by growth in coal consumption in developing economies, particularly in Asia. More on this interesting topic at [3].

The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers' failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in terms of application, type of data, and learning technique. This work shows that the majority of the deep learning applications to power quality are based on unrealistic synthetic data and supervised learning without proper labelling. Some applications with deep learning have already been solved by previous machine learning methods or expert systems. The main barriers to implementing deep learning to power quality are related to lack of novelty, low transparency of the deep learning methods, and lack of benchmark databases. This work also discusses that even with automatic feature extraction by deep learning methods, power quality expert knowledge is still needed to implement and analyse the results. The main research gaps identified in this work are related to the applications of semi-supervised learning, explainable deep learning and hybrid approaches combining deep learning with expert systems. Suggestions for overcoming the present limitations are: providing a stronger collaboration among the grid stakeholders and academy to keep track of power quality events; proper labelling and enlarging of datasets in deep learning methods; explaining the end-to-end decision making of deep learning methods; providing open-access databases for comparison purposes.

This paper utilizes the Unscented Transform together with Cornish-Fisher expansion to estimate the 2%-value of switching overvoltages when considering the impact of trapped charge and corona attenuation. Simulations were performed using a piecewise linear corona model for twin- and triple-conductor lines. The proposed method was compared to Monte Carlo methods through simulations in PSCAD. The method is shown to be able to estimate the 2%-value with comparable accuracy to methods used in industry today, but with only one fifth of the number of calculations.

The ability of the distribution network to host electric vehicle (EV) charging might be limited by the harmonic voltages due to their harmonic emission. Network harmonic impedance seen from the point of connection of the charging station plays an important role in harmonic voltage calculation. In this paper, the hosting capacity is estimated for a fast charging station close to a distribution transformer considering different scenarios in terms of network modelling. Data from a typical Swedish distribution network is used, together with a one-month measurement of the emission from state-of-the-art EV charging. The hosting capacity using harmonic voltage limits is compared with the hosting capacity using the transformer rating. A stochastic approach is used for both. This paper shows that harmonic hosting capacity studies are needed; it shows that details of the distribution network must be included to get an accurate estimation of the harmonic hosting capacity; it also shows that a stochastic approach is needed for estimating the harmonic hosting capacity.