Search Results (855)

This study presents a simulation-based design of a silicon single-photon avalanche diode (SPAD) optimized for picosecond-resolved photon detection. Utilizing COMSOL Multiphysics, we implement a dead-space-aware impact ionization model to accurately capture history-dependent avalanche behavior. A guard ring structure and tailored doping profiles are introduced to improve electric field confinement and suppress edge breakdown. Simulation results show that the optimized device achieves a peak electric field of 7 × 10⁷ V/m, a stable gain slope of −0.414, and consistent avalanche triggering across bias voltages. Transient analysis further confirms sub-20 ps response time under −6.5 V bias, validated by a full-width at half-maximum (FWHM) of ~17.8 ps. Compared to conventional structures without guard rings, the proposed design exhibits enhanced breakdown localization, reduced gain sensitivity, and improved timing response. These results highlight the potential of the proposed SPAD for integration into next-generation quantum imaging, time-of-flight LiDAR, and high-speed optical communication systems. Full article

The transition of electric systems from a centralized, fossil-based model toward a decentralized, renewable-powered architecture is reshaping the way electricity is generated, managed and consumed. As distributed energy resources (DERs) proliferate, grid management becomes increasingly complex, especially at the distribution level. In this context, flexibility emerges as a key enabler for more stable and efficient grid operation, while also facilitating greater integration of DER and supporting the electrification of energy demand. Local flexibility markets (LFMs) are gaining importance as structured mechanisms that allow grid operators to procure flexibility services from prosumers, aggregators and other actors. However, to ensure widespread participation, it is essential to develop digital tools that accommodate users of different profiles, regardless of their size, technical background or market experience. The REEFLEX project addresses this challenge by designing and developing 14 interoperable flexibility tools tailored to diverse stakeholder needs. To ensure that these tools are aligned with real market conditions and user expectations, REEFLEX conducted extensive stakeholder-centric surveys. This paper presents the methodology and key findings of those surveys, providing insights into user perceptions, technical requirements and adoption barriers. Results are contextualized within existing literature and other funded initiatives, highlighting implications for the design of inclusive and scalable flexibility markets. Full article

This paper investigates the factors influencing the power consumption of electric vehicle (EV) charging infrastructure and develops a methodology for determining the design electrical loads of EV charging stations (EVCSs). A comprehensive review of existing research on demand factor (DF) calculations for EVCSs is presented, highlighting discrepancies in current approaches and identifying key influencing factors. To address these gaps, a simulation model was developed in Python 3.11.9, generating minute-by-minute power consumption profiles based on EVCS parameters, EV fleet characteristics, and charging behavior patterns. In contrast with state-of-the-art methods that often provide limited reference values or scenario-specific analyses, this study quantifies the influence of key factors and demonstrates that the average number of daily charging sessions, EVCS power rating, and the number of charging ports are the most significant determinants of DF. For instance, increasing the number of sessions from 0.5 to 4 per day raises DF by 2.4 times, while higher EVCS power ratings reduce DF by 32–56%. This study proposes a practical generalized algorithm for calculating DF homogeneous and heterogeneous EVCS groups. The proposed model demonstrates superior accuracy (MAPE = 6.01%, R² = 0.987) compared with existing SOTA approaches, which, when applied to our dataset, yielded significantly higher errors (MAPE of 50.36–67.72%). The derived expressions enable efficient planning of distribution networks, minimizing overestimation of design loads and associated infrastructure costs. This work contributes to the field by quantifying the impact of behavioral and technical factors on EVCS power consumption, offering a robust tool for grid planners and policymakers to optimize EV charging infrastructure deployment. Full article

This study examined the temporal changes and dispersion of potential sources of the six criteria air pollutants, namely, particulate matter with an aerodynamic diameter of less than 2.5 and 10 μm (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃), in eastern Xinjiang, China, during the COVID-19 period in summer 2020 (16 July to 29 August ). Compared to the same periods in 2019 and 2021, the mean concentrations of all pollutants, except for SO₂ and O₃, and the air quality index (AQI) were lower in 2020 (relative changes: NO₂ 48.3–54.4%, PM₁₀ 35.8–49.6%, PM_2.5 19.3–43.5%, CO 16.5–34.8%, AQI 17.2–29.4%), which can be attributed to the reduced anthropogenic activities. Compared to the period before the lockdown in 2020 (16 June to 15 July), the mean NO₂ concentration showed the largest decrease during the lockdown (47.9%), followed by PM_2.5 (32.7%), PM₁₀ (37.6%), and CO (15.4%). In contrast, there were only minimal changes in O₃, with the mean concentrations falling slightly by 7.56%, and the mean concentration of SO₂ increased by 10.4%. The decrease in NO_x and the dry climate could have hindered O₃ formation, while vital industrial activities in eastern Xinjiang probably maintained SO₂ emissions. In the subsequent recovery period (30 August to 28 September), the mean NO₂ concentration increased the most at 59.3%, which was due to the rapid resumption of traffic-related emissions. During the lockdown in 2020, the diurnal profiles of PM_2.5, PM₁₀, NO₂, and CO concentrations showed lower peak concentrations in the morning (09:00–11:00) and evening (20:00–22:00), demonstrating a significant reduction in traffic-related emissions. The lower O₃ and higher SO₂ peak concentrations may have resulted from lower NO_x levels and higher electricity consumption due to the “stay-at-home” policy. The analysis of the distribution of potential sources showed that O₃ generally originated from widespread source areas, while the other pollutants mainly originated from local emissions. During the lockdown period, the source areas of PM_2.5 and PM₁₀ were more dispersed, with an enhanced contribution from long-range transport. Full article

Continuous decrease in inertia and sensitivity to load/generation fluctuation are significant challenges for present-day power networks. The primary reason for these issues is the increased penetration capabilities of renewable energy sources. An imbalanced load with significant power output has a substantial impact on the frequency and voltage characteristics of electrical networks. Various load frequency control (LFC) technologies are widely used to address these issues. Existing LFC approaches in the literature are inadequate in addressing system uncertainty, parameter fluctuation, structural changes, and disturbance rejection. As a result, the purpose of this work is to suggest a better LFC approach that makes use of a combination of a one plus tilt fractional filtered derivative (1+TDF^λ) cascaded controller and a fractional order proportional–integral–derivative (PI^λD^μ) controller, which is referred to as the recommended 1+TDF^λ/PI^λD^μ controller. Drawing inspiration from the dynamics of religious societies, including the roles of followers, missionaries, and leaders, and the organization into religious and political schools, this paper proposes a new application of the efficient divine religions algorithm (DRA) to improve the design of the 1+TDF^λ/PI^λD^μ controller. A triple-area test system is constructed to analyze a realistic power system, taking into account certain physical restrictions such as nonlinearities as well as the impact of PV and wind energy integration. The effectiveness of the presented 1+TDF^λ/PI^λD^μ controller is evaluated by comparing their frequency responses to those of other current controllers like PID, FOPID, 2DOF-PID, and 2DOF-TID^μ. The integral time absolute error (ITAE) criterion was employed as the objective function in the optimization process. Comparative simulation studies were conducted using the proposed controller, which was fine-tuned by three recent metaheuristic algorithms: the divine religions algorithm (DRA), the artificial rabbits optimizer (ARO), and the wild horse optimizer (WHO). Among these, the DRA demonstrated superior performance, yielding an ITAE value nearly twice as optimal as those obtained by the ARO and WHO. Notably, the implementation of the advanced 1+TDF^λ/PI^λD^μ controller, optimized via the DRA, significantly minimized the objective function to $0.4704\times {10}^{-4}$. This reflects an approximate enhancement of $99.5\%$ over conventional PID, FOPID, and 2DOF-TID^μ controllers, and a $99\%$ improvement relative to the 2DOF-PID controller. The suggested case study takes into account performance comparisons, system modifications, parameter uncertainties, and variations in load/generation profiles. Through the combination of the suggested 1+TDF^λ/PI^λD^μ controller and DRA optimization capabilities, outcomes demonstrated that frequency stability has been significantly improved. Full article

In recent years, home energy management systems (HEMSs) have emerged as critical components within the concept of smart cities and grids. Within HEMSs, load flow analysis represents one of the fundamental tools for smart grid studies, forming the basis for a wide range of advanced applications, including state estimation, fault diagnosis, and optimal power flow computation. To achieve high levels of load flow accuracy and reliability, HEMSs must incorporate detailed models of all electrical elements found in modern residential units, including appliances, wiring, and energy resources. This paper proposes a load flow solution for smart home networks, featuring detailed models of wiring, appliances, and on-site generation systems. Moreover, a detailed appliance model derived from smart meter data, capable of representing both static-load devices and complex appliances with time-varying consumption profiles, is introduced. Additionally, a measurement-based validation of residential electrical wiring model is presented. The proposed models and calculation procedures have been verified by comparing the simulated results with the literature, yielding a deviation of approximately 0.7%. Analyses of a large-scale network have shown that this approach is up to six times faster compared to state-of-the-art procedures. The developed solution demonstrates practical applicability for use in industry-grade smart power management software. Full article

This paper presents a case study of the Honduran electricity system and evaluates the technical impact of integrating distributed generation through modeling and simulation using Pandapower, (version 3.1.0) an open-source Python tool. A multi-criteria methodology was applied to select connection nodes considering the voltage sensitivity (∆V/MW), loss factor, available thermal capacity (headroom), and hosting capacity. The analysis focused on voltage stability, power losses, and line loading under various distributed generation scenarios. This methodology prioritized buses with critical voltages and significant loads. The case study model included official data from the Honduran National Dispatch Center. The simulations included a redispatch scheme for conventional generators to maintain power balance in all scenarios (20–100% distributed generation profiles), using GEN (controllable output) and SGEN (fixed output) components. The results show that with 50% distributed generation relative to local demand, voltages at critical buses improved by up to 0.14 p.u. Total active losses decreased by 9%, and reactive losses decreased by 44%. Additionally, indirect improvements were observed in non-intervened buses, as well as load relief in lines and transformers. These results confirm that strategic distributed generation injections combined with redispatch can improve supply quality and operational efficiency in weak and radial network topologies. The proposed methodology is scalable and able to be replicated in other power systems, providing technical input for energy planning and renewable energy integration in developing countries. Full article

High penetration of distributed photovoltaics (DPV) in distribution networks can lead to voltage violations, increased network losses, and renewable energy curtailment, posing significant challenges to both economic efficiency and operational stability. To address these issues, this study develops a coordinated planning framework for DPV and energy-storage systems (ESS) that simultaneously achieves cost minimization and operational reliability. The proposed method employs a cluster partitioning strategy that integrates electrical modularity, active and reactive power balance, and node affiliation metrics, enhanced by a net-power-constrained Fast-Newman Algorithm to ensure strong intra-cluster coupling and rational scale distribution. On this basis, a dual layer optimization model is developed, where the upper layer minimizes annualized costs through optimal siting and sizing of DPV and ESS, and the lower layer simultaneously suppresses voltage deviations, reduces network losses, and maximizes PV utilization by employing an adaptive-grid multi-objective particle-swarm optimization approach. The framework is validated on the IEEE 33-node test system using typical PV generation and load profiles. The simulation results indicate that, compared with a hybrid second-order cone programming method, the proposed approach reduces annual costs by 6.6%, decreases peak–valley load difference by 22.6%, and improves PV utilization by 28.9%, while maintaining voltage deviations below 6.3%. These findings demonstrate that the proposed framework offers an efficient and scalable solution for enhancing renewable hosting capacity, and provides both theoretical foundations and practical guidance for the coordinated integration of DPV and ESS in active distribution networks. Full article

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article

In modern power systems, FACTS tools are essential for addressing voltage variation along with fault ride-through (FRT) challenges within the electrical power systems, particularly for wind generation integration. Several prominent publications emphasize that the squirrel cage induction generator (SCIG) currently comprises about 15% of operational wind turbines. This research proposes the use of FACTS devices to boost voltage stability and FRT capability. The implementation of these devices leads to improved efficiency in the electrical power system. This study considers many events, including an ideal wind profile, turbulent wind profile, symmetrical faults, and unsymmetrical faults, to support the proposed selection. Furthermore, the proposed approach is evaluated by comparison between a fixed capacitor, static synchronous compensator (STATCOM), and Static VAR Compensator (SVC) to guarantee the achievement of voltage stability, reactive power consumption, and FRT capacity under various wind speed profiles and fault conditions. An overall evaluation is conducted to compare them in all examined circumstances and to highlight their advantages and effects. The simulation findings demonstrate the efficacy and primacy of FACTS in enhancing the functioning of an integrated wind system, which is built upon a grid-connected SCIG, as well as enhancing the power system performance. The MATLAB/Simulink toolbox is used to design the models of SCIG, SVC, and STATCOM. Full article

This research considers the production of Potassium Nitrate product, a water-soluble nitrogen–potassium (NK) fertilizer containing 13.7% nitrogen and 46% potassium oxide. Potassium Nitrate (NOP) is produced as a fertilizer grade. The current system incurred high energy consumption, elevated emissions of greenhouse gases, resource degradation, and excessive production costs. Consequently, this research aims to implement the four steps of Cleaner Production (CP) to assess the environmental impacts of Potassium Nitrate products and their main manufacturing processes, and identify the best solution that achieves environmental goals. Environmental assessment was then used to calculate the unit indicators for raw materials, energy, waste generation, product, and packaging. The results showed that the integrated indicator was 5.18, with the energy profile being the most influential factor. Solar thermal and photovoltaic (PV) cell systems were suggested to reduce the high consumption of heavy fuel oil (HFO), including a solar thermal system to support the steam boilers and photovoltaic cells to support the electrical generator. The two alternatives were assessed based on multiple criteria using feasibility analysis and the Analytical Hierarchical Process (AHP). The solar thermal system, comprising 250 evacuated tube collectors, was preferable and resulted in savings of HFO by 121 tons/year, which led to a reduction in gaseous emissions by 375.6 metric tons of CO₂ and 21.685 kg of N₂O per year. Such improvements can also result in significant cost reductions. In conclusion, applying the CP methodology supported decision-makers in deciding the best system to enhance energy efficiency and reduce environmental nuisance at NOP plants. Full article

Equivalent energy consumption minimization methods of energy management systems have been implemented as a rule-based strategy to enhance electric propulsion system efficiency. This study compares the efficiencies of different systems by applying variable- and constant-speed generators with battery hybrid systems, measuring fuel consumption. In the same scenario, the variable-speed operation showed a notable improvement of 10.36% compared to the conventional system. However, in the verification of hybrid system efficiency, onshore charged energy cannot be considered a reduction in fuel consumption. Instead, when converting onshore energy usage into equivalent fuel consumption for comparative analysis, both hybrid constant- and variable-speed operation modes achieved efficiency enhancements ranging from 5.5% to 9.79% compared to the conventional, nonequivalent constant-speed operation mode. Conversely, the nonequivalent variable-speed operation mode demonstrated an efficiency that was 5.41% higher than that of the hybrid constant-speed operation mode. In contrast, the battery-integrated variable-speed operation mode indicated a system efficiency approximately equal to that of the nonequivalent variable-speed operation mode. For vessels with load profiles characterized by prolonged periods of idling or low-load operations, a battery-integrated hybrid system could be a practical solution. This study demonstrates the necessity of analyzing load profiles, even when aiming for the optimal operational set points of the generator engine. Full article

This study evaluates the environmental benefits of retrofitting conventional diesel-powered waterbuses in Venice with a series hybrid electric powertrain comprising three generator sets and dual electric propulsion motors. Using real-world operational profiles recorded during typical passenger service, a quasi-static simulation model was developed to assess energy and emission performance. Real-world speed and torque data were collected from a conventional waterbus during regular passenger service to accurately reflect real operational conditions, including driver behavior and the sea state. These profiles were used as inputs to a quasi-static simulation model to assess the hybrid system’s energy efficiency and emission performance. Dynamic programming was applied to derive emissions-optimal control strategies, targeting trade-offs between nitrogen oxides (NOx) and unburned hydrocarbons (HC). The results demonstrate emission reductions of up to 31% in NOx and 15% in HC, confirming the strong potential of hybridization for urban maritime transport. The paper also examines component-level behavior under optimal control and discusses practical considerations for implementing these strategies in real-time applications. These findings support the strategic value of hybrid retrofitting and fleet renewal for reducing the environmental footprint of passenger ferries and improving air quality in sensitive coastal urban environments. Full article

The increasing integration of renewable energy sources and the widespread adoption of electric vehicles have introduced considerable uncertainty into the operation of large-scale power systems. Traditional deterministic unit commitment models are insufficient for managing such variability in a reliable and cost-effective manner. This study proposes a two-stage stochastic unit commitment model that captures uncertainties in solar photovoltaic generation, electric vehicle charging demand, and load fluctuations using a mixed-integer linear programming framework with recourse. The model is applied to Thailand’s national power system, comprising 171 generators across five regions, to assess its scalability for sustainable large-scale planning. Results indicate that the stochastic model significantly enhances system reliability across most demand profiles. Under the Winter Weekday group, the number of lacking scenarios decreases by 76.92 percent and the number of missing periods decreases by 78.57 percent, while the average and maximum lack percentages are reduced by 56.32 percent and 72.61 percent, respectively. Improvements are even greater under the Rainy Weekday group, where lacking scenarios and periods decline by more than 92 percent and the maximum lack percentage falls by over 98 percent, demonstrating the model’s robustness under volatile solar output and load conditions. Although minor anomalies are observed, such as slight increases in average and maximum lack percentages in the Summer Weekday group, these are minimal and likely attributable to randomness in scenario generation or boundary effects in optimization. Overall, the stochastic model provides substantial advantages in managing uncertainty, achieving notable improvements in reliability with only modest increases in operational cost and computational time. The findings confirm that the proposed approach offers a robust and practical framework for supporting sustainable and resilient power systems in regions with high variability in both generation and demand. Full article

Multiple differentiation protocols have emerged in recent years, producing neurons with diverse morphologies, gene and protein expression profiles, and functionality. Many of these differentiation techniques require months of culture and the use of expensive growth factors. Most importantly, the derived neurons usually do not exhibit any electrical activity. This limits the value of the protocol as a tool for engineering and investigating neural networks. Here, we describe an efficacious method for differentiating mouse embryonic stem cells into functional neurons. CGR8 cells were neurally induced via the simultaneous application of retinoic acid and purmorphamine. The derived cells expressed neuronal (TUJ1 and NeuN) and synaptic (GAD2, PSD-95, Synaptophysin, and VGLUT1) markers. During whole-cell recordings, neurons exhibited inward and outward currents, likely caused by fast-inactivating voltage-gated potassium channels. Upon current injection, miniature action potentials were also recorded. The efficient generation of diverse subtypes of functional neurons can be a useful tool in fundamental investigations of neural network activity and translational studies. Full article