Search Results (6)

Hybrid Renewable Energy Systems are pivotal for the sustainable energy transition, yet their design and operation present complex optimization challenges due to diverse components, stochastic resources, and multifaceted objectives. This systematic review formalizes the HRES optimization problem space and identifies critical research gaps. Employing the PRISMA 2020 guidelines, it comprehensively analyzes the literature (2015–2025) from Scopus, IEEE Xplore, and Web of Science, focusing on architectures, mathematical formulations, objectives, and solution methodologies. The results reveal a decisive shift from single-objective to multi-objective optimization (MOO), increasingly incorporating environmental and emerging social criteria alongside traditional economic and technical goals. Metaheuristic algorithms (e.g., NSGA-II, MOPSO) and AI techniques dominate solution strategies, though challenges persist in scalability, uncertainty management, and real-time control. The integration of hydrogen storage, vehicle-to-grid (V2G) technology, and multi-vector energy systems expands system boundaries. Key gaps include the lack of holistic frameworks co-optimizing techno-economic, environmental, social, and resilience objectives; disconnect between long-term planning and short-term operation; computational limitations for large-scale or real-time applications; explainability of AI-based controllers; high-fidelity degradation modeling for emerging technologies; and bridging the “valley of death” between simulation and bankable deployment. Future research must prioritize interdisciplinary collaboration, standardized social/resilience metrics, scalable and trustworthy AI, and validation frameworks to unlock HRESs’ potential. Full article

Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil fuel vehicles. Additionally, electric vehicles are highly efficient, with an efficiency of around 90%, in contrast to fossil fuel vehicles, which have an efficiency of about 30% to 35%. The higher energy efficiency of electric vehicles contributes to lower operational costs, which, alongside regulatory incentives and shifting consumer preferences, has increased their strategic importance for many vehicle manufacturers. In this paper, we present a thematic literature review on electric vehicles charging station location planning and scheduling. A systematic literature review across various data sources in the area yielded ninety five research papers for the final review. The research results were analyzed thematically, and three key directions were identified, namely charging station deployment and placement, optimal allocation and scheduling of EV parking lots, and V2G and smart charging systems as the top three themes. Each theme was further investigated to identify key topics, ongoing works, and future trends. It has been found that optimization methods followed by simulation and multi-criteria decision-making are most commonly used for EV infrastructure planning. A multistakeholder perspective is often adopted in these decisions to minimize costs and address the range anxiety of users. The future trend is towards the integration of renewable energy in smart grids, uncertainty modeling of user demand, and use of artificial intelligence for service quality improvement. Full article

Vehicle-to-Grid (V2G) technology enables electric vehicles (EVs (Unless otherwise specified, Electric Vehicles (EVs) in this study refer to the totality of BEVs, PHEVs, and other battery-equipped vehicles that have the potential to participate in V2G)) to interact with renewable energy sources, positioning it as a key driver of energy system decentralization. While V2G holds significant potential for enhancing grid stability and economic efficiency, its large-scale deployment requires a robust economic assessment. However, existing research predominantly focuses on technical feasibility, lacking comprehensive economic evaluations due to the complexity of V2G system architectures. To bridge this gap, we propose the BSTP (Business-Stakeholders-Technology-Policy) V2G economic evaluation framework and the VRR (Value Realization Rate) methodology, employing a Semi-Systematic Co-Design Approach. This framework systematically characterizes the evolution of V2G business models, the interactions among key stakeholders, the influence of technological and policy factors, and the criteria for economic feasibility assessment. Furthermore, we identify a “Big Models, No Trials” issue in V2G economic research, where large-scale theoretical models lack empirical validation. To address this challenge and ensure the practical applicability of our framework, we define six core challenges that must be resolved for a rigorous economic evaluation of V2G. Our findings provide a structured foundation for future research and policy development, offering insights that could accelerate the transition to decentralized energy systems. Full article

The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers. Full article

The rapid and accurate testing of SARS-CoV-2 infection is still crucial to mitigate, and eventually halt, the spread of this disease. Currently, nasopharyngeal swab (NPS) and oropharyngeal swab (OPS) are the recommended standard sampling techniques, yet, these have some limitations such as the complexity of collection. Hence, several other types of specimens that are easier to obtain are being tested as alternatives to nasal/throat swabs in nucleic acid assays for SARS-CoV-2 detection. This study aims to critically appraise and compare the clinical performance of RT-PCR tests using oral saliva, deep-throat saliva/posterior oropharyngeal saliva (DTS/POS), sputum, urine, feces, and tears/conjunctival swab (CS) against standard specimens (NPS, OPS, or a combination of both). In this systematic review and meta-analysis, five databases (PubMed, Scopus, Web of Science, ClinicalTrial.gov and NIPH Clinical Trial) were searched up to the 30th of December, 2020. Case-control and cohort studies on the detection of SARS-CoV-2 were included. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2). We identified 1560 entries, 33 of which (1.1%) met all required criteria and were included for the quantitative data analysis. Saliva presented the higher accuracy, 92.1% (95% CI: 70.0–98.3), with an estimated sensitivity of 83.9% (95% CI: 77.4–88.8) and specificity of 96.4% (95% CI: 89.5–98.8). DTS/POS samples had an overall accuracy of 79.7% (95% CI: 43.3–95.3), with an estimated sensitivity of 90.1% (95% CI: 83.3–96.9) and specificity of 63.1% (95% CI: 36.8–89.3). The remaining index specimens could not be adequately assessed given the lack of studies available. Our meta-analysis shows that saliva samples from the oral region provide a high sensitivity and specificity; therefore, these appear to be the best candidates for alternative specimens to NPS/OPS in SARS-CoV-2 detection, with suitable protocols for swab-free sample collection to be determined and validated in the future. The distinction between oral and extra-oral salivary samples will be crucial, since DTS/POS samples may induce a higher rate of false positives. Urine, feces, tears/CS and sputum seem unreliable for diagnosis. Saliva testing may increase testing capacity, ultimately promoting the implementation of truly deployable COVID-19 tests, which could either work at the point-of-care (e.g. hospitals, clinics) or at outbreak control spots (e.g., schools, airports, and nursing homes). Full article

The Vehicle-2-grid (V2G) technology enabling bidirectional charging between electric vehicles and the energy grid system for frequency regulation and load balancing has the potential of significantly improving the financial viability of electric mobility. This paper has identified that the introduction of V2G offers a plethora of potentially beneficial business models, which primarily focus on providing stability services to the energy grid and optimizing the economic benefits of owning an EV. Within these overarching categories, it is likely that several niche business models will emerge, as the current V2G concepts include the integration of intermittent renewable energy into the grid, reduction of peak load, charging optimization, and regulation of participating capacity. Most important is the balancing of the five market factors in order to create a profitable business case, as this is what makes V2G move from a potential revenue generator to a profitable business. Full article