• Energy Research
• 2025-2025close
• other engineering and technologies close
• FR close
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Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater cost savings. Energy Peer-to-Peer (P2P) systems have thus been shown to be beneficial for prosumers and consumers through reductions in energy cost while also being attractive to grid or service providers. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities; such challenges include the inherent complexity of matching together geographically distributed peers and the significant computations required to calculate the local matching preferences. Hence dedicated algorithms are required to be able to perform a cost-efficient matching of thousands of peers in a computational-efficient fashion. We define and analyze in this work a precise mathematical modelling of the geographical peer matching problem and several heuristics solving it. Our experimental study, based on real-world energy data, demonstrates that our solutions are efficient both in terms of cost savings achieved by the peers and in terms of communication and computing requirements. Our scalable algorithms thus provide one core building block for practical and data-efficient peer-to-peer energy sharing communities within large-scale optimization systems.

Comprehensive occupancy information in smart buildings has become more imperative in order to develop new control strategies in energy management systems. Several techniques can be used to collect occupancy information considering accurate sensing techniques, such as passive infrared (PIR), carbon dioxide (CO2) and different types of cameras (i.e., thermal, or optical cameras). Recent studies show the usefulness of integrating occupancy information into energy management systems to reduce energy consumption while maintaining the occupants’ comfort. The purpose of this work is to elaborate a comprehensive review on occupancy detection systems in smart buildings. This study presents a set of comparison standards including methods, occupancy resolution, type of buildings and sensors. A classification of different approaches, which can be implemented and integrated into the building management system for detecting indoor occupancy, is introduced. Summary and discussions are given by highlighting the usefulness of machine learning for enabling predictive control of active systems in smart buildings.

Enhancing the performance of traditional vapor compression cooling cycles is an important aspect in the quest to minimize global energy consumption, to own sustainable energy systems soon, and to preserve the environment. This study performed a comparative analysis of the performance of a water cooler with different working fluids to replace R143a and improve system performance. A mathematical model derived from energy and exergy analysis is developed for the evaluation of the effect of operating conditions on the system COP, exergetic losses, and exergetic efficiency. The evaluation has been conducted for evaporation and condensation temperatures ranging between -30°C to 15°C and 25°C to 55°C, respectively. Results showed that the cycle with R510A has the maximum COP. The average system COP with R510A, RE170, and R152a are 19.54%, 13.53%, and 9.36 % higher than that with R134a, respectively. The highest value of exergy loss takes place in the compressor. At different working fluids, exergy losses decrease as evaporation temperatures increase and condensation temperatures decrease. The system with R510A has the minimum exergy losses. The average exergy losses for systems with R510A, RE170, and R152a are 34.62%, 28.33%, and 18.64% lower than that of R134a, respectively. The system with R510A has higher exergy efficiency and R134a has the minimum values of exergy efficiency. Generally, the water cooler provided better performance with R510A and RE170 than with R152a and R134a. Therefore, R510A can be considered as the best replacement for R134a and R152a.

International audience

This presented research delves into the challenges and approaches encompassed in allotting available resources for the maintenance and renovation of crucial infrastructure assets, focusing intently on buildings in Old Cairo earmarked for relocation to the new administrative capital, manipulating an integral empirical assessment and advanced modeling techniques. Amidst intensifying imperatives for robust infrastructure against a backdrop of financial constraints and hastened asset deterioration, this study proposes a cutting-edge, thorough approach to fund distribution. The developed approach incorporates asset hierarchy and classification, condition assessment via pilot survey, Markov chain model for degradation anticipation, stakeholder perception analysis through questionnaires for comparative analysis, and constructing an optimization model for fund allocation. The findings unveiled the dire condition of the selected infrastructure assets and the pressing requisite for strategic maintenance and renovation interventions. The developed approach offered a pragmatic evidence-based approach concerning decision-makers, improving resource allocation efficacy and fostering urban development schemes. This paper calls for protractible presented methodologies to a broad spectrum of assets and geographies and integrating incipient technologies for dynamic asset management approaches.

This paper delves into the potential of Fault Current Limiters (FCLs) as a transformative technology within power systems. FCLs assume a pivotal role in streamlining network expansion efforts, sustaining fault current levels, and augmenting the overall performance of power systems. The classification of FCLs is outlined, encompassing superconducting FCLs (SFCLs), Solid-State FCLs (SFCLs), and non-superconducting FCLs (Non-SFCLs). The core of this study lies in an exhaustive review of relevant literature, with a keen focus on optimal allocation strategies for FCLs. The primary objective of this paper is to function as an all-encompassing reference for both researchers and engineers, providing optimal FCL allocation studies. This paper discusses various techniques for the optimal allocation of FCLs within power systems. These allocation methods are categorized based on multiobjective functions such as cost, fault current reduction, stability, protection coordination, reliability, and power quality. This search presents an overview of the FCL survey structured around key components, including objective functions, design variables, constraints, optimization methods, network types, FCL types, and research contributions. This work aims to empower professionals in the field with a robust understanding of FCL allocation, ultimately contributing to the efficient and sustainable evolution of power systems. FCLs represent a promising technology for enhancing the performance and reliability of power systems, and this paper serves as a comprehensive resource for those interested in optimizing their allocation within these systems.

The growing global demand for clean and sustainable energy sources has sparked interest in hybrid energy systems that combine multiple renewable energy technologies. This review paper explores the integration of cow dung biogas, solar thermal, and kinetic energy for power production. The synergistic utilization of these energy sources holds significant potential for addressing the energy challenges faced by various communities. This paper provides an overview of each technology, discusses the benefits and challenges of integration, and highlights successful case studies. Furthermore, it discusses this hybrid energy generation system's potential future developments and implications.

International audience

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.