• Energy Research
• 2021-2025close
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In recent years, the occurrence and damages of the blackouts in power systems saw an increase. Many of them are due to the impacts of climate change. However, tracing all blackouts that happened in the world and associating them with specific causes is quite challenging due to the huge amount of information over the Internet. In this paper, we proposed a framework for automatically analyzing the information collected from Internet based on the Ontological Approach coupled with Artificial Intelligence (AI) tailored for Natural Language Processing, which permits information extraction from reports to better understand the major entities of a power outage, their role, and the connections among them. However, our test shows that, despite the promising results of Named Entity Recognition in other fields, the lack of a standard ontology for blackout analysis is both the proof and the cause for the missed chance of exploiting this AI branch.

The growing global awareness of social and environmental issues has led investors to prioritize sustainable investments. This study examines the relationship between ten Islamic and conventional sector indices, which include stocks screened for environmental, social, and governance (ESG) factors, across three energy-related fields: renewable energy, energy efficiency, and oil equipment and services. Using a quantile cross-spectral approach, this research finds a high level of long-term synchronization among all energy-related sectors and indices. However, as markets shift from bearish to bullish, the current relationship between energy-related sectors and Islamic and conventional indices weakens. To support the findings in this research and investigate potential financial implications, this investigation applies a causality-in-quantiles approach. The research in this study shows a bidirectional causal relationship between conventional and Islamic indices, particularly in the energy efficiency sector during bearish market conditions. At lower tails, ESG market indices are found to cause renewable energy products, suggesting a hedging mechanism. The findings of this study assert that investments that do not possess any ESG shortcomings are imperative for effectively bolstering renewable energy companies and fostering opportunities for energy efficiency. These conclusions hold considerable ramifications for energy policymakers as they strive to devise more robust financial mechanisms that can provide substantial support to the burgeoning green sector.

This article concerns a field study about the use of non-invasive manual lighting and shading control to save energy in listed buildings. The system was chosen to limit cabling and masonry work. The test room consists of an individual office located in a historical building in Southern Italy. The room was retrofitted with two roller shades (semi-transparent and blackout) and six LED-based pendants provided with step-dimming and three Correlated Colour Temperature options. Shading and lighting could be remotely controlled from the desk by six subjects who took part in the test for two weeks each. Behavioural interventions and a set back to default setting at the end of the working day were adopted to improve the test subjects’ energy behaviour. The results show that energy for lighting could be reduced between 15% and 71% compared to European benchmark, with wide range accounting for variability of individual preference and weather conditions. The savings are due to the computer-based work, the communication and engagement campaign, as well as the default settings. The findings suggest that simple manually controlled systems are energy and economic viable solution for listed buildings, since the system accommodates users’ needs, and proper training is provided to the users.

Abstract The growing integration of renewable energy sources in the energy grid presents intermittency and negative pricing challenges, necessitating large-scale energy storage solutions. Pumped thermal energy storage (PTES) can address these issues by storing and delivering substantial energy whenever required. High-temperature heat pump development is crucial to deploying PTES for storing heat at sink temperatures that are well above the ambient temperature(>450 °C) to ensure a reasonable round-trip efficiency (RTE). Currently, however, it is not a technological possibility for heat pumps to achieve these temperatures even with the support of freely available heat (200 °C to 400 °C) as source temperatures. This study explores a potential layout of the TI-PTES system that exploits commercially available equipment by storing heat below the ambient temperature while still being able to utilize the freely available heat source (Solar, Waste heat, biomass, etc.) to support the overall RTE. The charging phase employs a well-established CO2-refrigeration cycle to accumulate energy below the ambient temperature in cold thermal storage. While the discharging phase runs a trans-critical CO2 power cycle between the freely available heat source and the cold thermal storage. Overall, offering a practically implementable model for the PTES system with market-available components. The study investigates the design of this innovative system presenting the relevance of different operating and machine parameters as well as the contribution of freely available heat sources to the overall performance. Finally, benchmarking the technology with other long-duration energy storages.

Abstract The scope of the work is to depict a solid framework to interpret the capacity development of solar PV in Italy. To this aim the pivotal roles of public incentives is addressed and a vis a vis analysis of the regulatory framework and corresponding quantitative data is provided. The Italian regulatory framework did not provide an adequate focus on operational requirements for developing solar PV and allowed the speculative behavior of many investors. This is demonstrated by the correlation index between installed capacity and level of incentive equal to 0.988. Additionally, LCOE is calculated and from 2009 onward it is half of the average incentive level. Furthermore, the role of “followers” assumed by large power generators in the first phase of the PV development determined the absence of an industrial leadership. Oppositely, in a second phase, large utilities became more active on the PV market since they realized the impact on power market price. The linear correlation index between market price and PV capacity is found to be equal to −0.68, therefore price decreases at the increase of PV installed capacity and this is detrimental for large power utilities, thus they were pushed to be active in the PV sector.

The present work introduces the conceptual design of the optics for a spectral-splitting and light-guiding photovoltaic (PV) four-terminal (4T) module with appealing features for bifacial operation. The geometry here described performs low-gain 2D concentration in the visible (VIS) spectrum and can couple a Si solar cell to a wide-bandgap (WG) one. We analyze and discuss the conditions of applicability of the solution from the optical viewpoint. The performance analysis has been performed using commercial ray-tracing software (ZEMAX OpticStudio), by admitting direct and isotropically diffused solar irradiation at standard AM1.5G spectrum.

The aim of this study is to generate energy by capturing the movements of plants and trees through a bio-mimetic approach, whether in open spaces due to atmospheric forces or within enclosed environments, even from subtle motions and vibrations. The proposed mechanism involves using a triboelectric converter comprising a dielectric layer sandwiched between two cylindrical bars that slide against each other, strategically positioned to harness the broader movements of branches or plant stems. Additionally, several layers of polymeric piezoelectric material replicate the motions of leaves. This innovative system aims to extract energy that can power electronic devices and sensors. It is worth noting that the conceived device involves harnessing energy from small vibrations generated by rubbing leaves (in the frequency range 5-25 Hz) together and larger displacements, like those of a tree trunk, typically at lower frequencies than previously considered «5 Hz). The potential applications of this energy harvesting technology are diverse, spanning various sectors, e.g. sustained harvesters for autonomous measurement systems in agriculture. Additionally, this technology can be discreetly integrated into historically significant gardens and spaces. The study's findings highlight the effectiveness of this approach for these specific purposes.

Abstract Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also, we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling t 2 index.