• 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.

The purpose of this study is to enhance control strategies for selective catalytic reduction (SCR) and ammonia slip catalyst (ASC) systems, aiming to effectively reduce NOx emissions from automotive engines during realistic driving cycles. Despite the effectiveness of these after-treatment systems (ATS), their dynamic and non-linear characteristics present significant challenges in achieving precise control. Therefore, this research proposes a hybrid approach that combines backward induction (BI) as the primary optimization technique with model predictive control (MPC) framework for real-time application. The article introduces a reduced-state control-oriented model of the SCR + ASC system, which is embedded into the BI algorithm to calculate optimal control actions within a finite horizon. Additionally, it is proposed an alternative approach for adapting the grid of model states within the BI algorithm, effectively reducing the computational cost. This adjustment enables the algorithm to operate in real-time with near-optimal results, as confirmed by experimental validation. Lastly, the study explores how different degrees of knowledge regarding system disturbances impact the strategy’s performance, examining three distinct scenarios: constant prediction horizon, probabilistic description, and full knowledge of the prediction horizon.

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.