Mobility resilience to compounding disruptions: Power outages and social events

• We survey 19 Utah water and wastewater organizations on their power resilience. • Most have backup power and some have renewable energy. • The most prevalent renewables are photovoltaic solar, cogeneration, and hydropower. • However, ee find more cases of failed renewables than successful ones. • Feasibility studies seem to miss operation of renewable energy. Drinking water and wastewater services are vulnerable to power disruptions because they rely on the electric grid. Power outages can lead to water outages, creating cascading failures that compromise sanitation, firefighting, healthcare, and daily routines. However, there are few studies of water and wastewater power resilience at the state level. In this study we investigate the extent of backup power and renewable energy integration as well as the motivations and barriers around their power resilience. We collect our data through semi-structured interviews with staff at 19 urban and rural water and wastewater facilities throughout the state of Utah, USA. We find that all facilities experience power outages, 89% have backup power, and 69% are satisfied with their backup power. We also find that 19% of facilities have functioning renewable energy technologies while 30% have inoperable renewables. The high rate of failed renewables corresponds to circumstances after installation that feasibility studies did not consider, including monitoring, compatibility, permitting, and inconsistent generation. In addition, 96% of organizations have explored renewables, 7% plan to install more renewables, and 30% plan to procure more backup power. The primary motivations for power resilience are providing uninterrupted service and saving money. The primary barriers are financial and operational difficulties associated with the technologies. We recommend that water and wastewater facilities ensure continuity of service through backup generators, water storage, and other alternatives; more carefully select renewables, with particular attention to their operability after installation; and commit to raising funds—the most-cited barrier—for power resilience projects.

Voltage support is an essential requirement for inverter-based resources to maintain the stability and reliability of modern power grids during faults. Short-circuit events are among the most severe disturbances, as they can lead to equipment failure and power outages. Therefore, various fault ride-through (FRT) strategies have been developed to support the grid voltage during such events. As most faults are asymmetric, they cause asymmetry in the grid impedance and unbalanced voltages. However, existing control strategies only consider voltage unbalance, whereas they largely overlook the asymmetry in the grid impedance. Consequently, even during asymmetric short-circuit events, the equivalent grid impedance at the point of common coupling is still assumed to be symmetric. This work proposes a generalized optimal voltage-support FRT control that accounts for grid asymmetry. First, an analytical derivation of the grid impedance matrix is presented. Then, a generalized voltage-support strategy is formulated that explicitly accounts for impedance asymmetry. Numerical simulations on a modified version of the CIGRÉ low-voltage benchmark microgrid and experimental results from a laboratory system validate the proposed strategy.

Accurate thermal field prediction is the cornerstone of thermal design of high-frequency transformer (HFT). The leakage flux induced power loss (LFPL), which includes leakage flux eddy current loss (LFECL) and leakage flux core loss (LFCL), can increase the hotspot temperature of nanocrystalline core HFT. This paper proposes a coupled thermal field prediction method for nanocrystalline core HFT considering LFPL and analyzes the thermal field characteristics. Firstly, the LFPL density distributions in nanocrystalline core are analyzed with finite element method, demonstrating high LFPL density on surface ribbons. Then, a coupled thermal field prediction method for nanocrystalline core HFT considering LFPL is proposed. The total thermal field is calculated by summation of thermal fields with LFECL, LFCL, and main flux core loss and winding loss. The thermal fields with LFPL are calculated with unidirectional magnetic-thermal coupled method, in which LFPL densities are calculated by proposed equivalent frequency domain simulation method and coupled to Heat Transfer module. The temperature dependences of power losses are considered for high-accuracy thermal field prediction. With the proposed method, the temperature calculation errors are reduced to below 3°C. Finally, the thermal field characteristics are analyzed. Finite element simulation results show that hotspot temperatures will be greatly increased by LFPL. The proposed method can help improve the thermal design of nanocrystalline core HFT.

Enhanced maximum power point tracking for photovoltaic systems: A Modified African Vulture Optimization (MAVO) algorithm approach

Ultra-wideband polarization-incident angle insensitive nano-scale metamaterial absorber for solar energy harvesting and infrared detection

Optimal dispatch of distributed energy resources in microgrids via virtual power plants for grid resiliency and flexibility using homeostatic PSO algorithm

Hybrid sensitivity-driven enhanced modified ant lion optimizer framework for multi- distributed generation allocation in radial distribution networks

In segmented dynamic wireless power transfer (DWPT) systems, the presence of cross-coupling mutual inductance (MI) leads to the generation of induced circulating current (ICC) in the inactive rails (IAR), resulting in power loss, magnetic field leakage, increased inverter capacity, and reduced efficiency. To address this problem, a parameter configuration method based on resonance tuning coefficient λ is proposed. This method effectively suppresses ICC, reduces inverter capacity, and improves system efficiency. Additionally, it is found that the abnormal drop in inverter current during λ tuning is caused by multiple resonance points at the inverter input impedance. For scenarios involving multiple power supply rails (PSRs), it is revealed that under conventional parameter configuration method (CPCM), the ICC exhibits hyperbolic-sine-type damped diffusion behavior. Building on this, a general circuit analysis model is proposed for different operating modes of PSR, with circuit parameters summarized and a design flowchart provided. Finally, a 12-meter DWPT platform is built to validate the proposed method, achieving maximum output power of 63.39 kW and system efficiency of 94.15%.

The accelerating transition toward sustainable transportation has led to a rapid deployment of Plug-in Hybrid Electric Vehicles (PHEVs), introducing significant operational challenges for active distribution networks, particularly in terms of voltage regulation and network flexibility. High and spatially concentrated charging demand, combined with stochastic vehicle behavior, can substantially reduce voltage headroom and compromise grid integrity if not properly managed. To address these challenges, this paper proposes a novel Active Distribution Network Management (ADNM) framework based on the Voltage Network Flexibility Index (VNFI) for coordinated PHEV charging and discharging. The VNFI is employed as an actionable steering signal to identify voltage-critical buses and time periods, enabling flexibility-aware scheduling decisions under strict network-security constraints. Stochastic PHEV arrival, departure, and energy demand are modeled using probabilistic distributions, and the proposed framework is implemented and validated through a high-fidelity MATLAB–OpenDSS co-simulation on a modified IEEE 33-bus distribution system. Numerical results demonstrate that the VNFI-driven coordination improves the voltage flexibility margin by up to 44.2% and reduces total power losses by 29.4% compared with a conventional TOU-based charging strategy. Moreover, even under 100% PHEV penetration, the maximum voltage deviation remains within 0.055 p.u., confirming the robustness and scalability of the proposed approach. The results highlight the effectiveness of VNFI-based management in transforming PHEVs into flexibility resources for future smart grid operations. • The study examines the operational challenges posed by electric vehicles (EVs) on distribution systems, focusing on voltage control and power losses. • A probabilistic model for aggregating plug-in hybrid electric vehicles (PHEVs) is proposed, based on parameters derived from the National Household Travel Survey (NHTS). • The concept of voltage flexibility is introduced, with the evaluation of a newly proposed index to assess it. • A smart charging/discharging approach is developed for optimal PHEV scheduling within active distribution networks (ADNs), addressing power demand throughout different hours. • A nonlinear optimization method for mixed integers is used to formulate and solve the scheduling problem. • The proposed method is tested on an IEEE 33-bus distribution system, demonstrating its effectiveness and validating the proposed index against previously reported strategies.

Hybrid PSO-MPC-based dynamic tuning of battery management parameters for enhanced lithium-ion battery performance in electric vehicles

Assessment of UHI mitigation strategies on indoor and outdoor thermal comfort under future extreme heat and power outage conditions, case study: educational building in Shahrood, Iran

A comprehensive review of one year that investigates the revolutionary possibilities of integrating IoT (Internet of Things) and ML (Machine Learning) for renewable energy forecasting in Indian healthcare systems, with a focus on the rural village of Vellore, Tamil Nadu. Increased patient mortality due to unpredictable power supplies from various regions around the world. Over 300 articles indexed by SCI (Science Citation Index) from 2014 to 2025 and real case studies address power outages using IoT for real-time monitoring (solar irradiance: 1000 W/m², wind speed: 5–7 m/s) and ML models (LSTM (Long Short Term Memory), CNN-LSTM (Convolutional Neural Network-Long Short Term Memory), XG Boost (Extreme Gradient), RF (Random Forest), ANN (Artificial Neural Network) for forecasting on weekly, monthly, and yearly timescales, expanding on previous research's short and medium term emphasis. A 100-kW hybrid solar-wind system (20 kW PV, 10 kW wind) with edge-cloud optimizes demand (4000–4250 kWh/day for 200 beds) and reduces peak load (200–210 kW) by 15–20%, achieving 85–95% accuracy and 15–35% efficiency. Case studies demonstrate a 5–7-year ROI (Return on Investment), 15–40% CO₂ reduction, and 95% uptime (>99.9% with IoT). NB (Narrow band)-IoT/5G microgrids, subsidies (0.05–0.07 $/kWh), and IoT-ML to raise accuracy by 10–20% are proposed to improve healthcare resilience and global sustainability. This pioneering study lays the groundwork for hospital renewable energy infrastructure assessments. • For rural hilly microgrids, the first hybrid quantum-classical framework tailored to India (QPSO + QAOA + Q-GIS). • 30% lower levelized energy costs (₹8.00 → ₹5.60/kWh) compared to traditional PSO/GA/MILP. • Annual CO 2 emissions were reduced by 25% (from 500 to 375 tons), while the percentage of renewable energy rose to 92%. • In difficult hilly terrain, Q-GIS finds 40% more suitable sites (ANOVA p < 0.05). • Supports SDGs 7 and 13 and India's 500-GW non-fossil target with NISQ-era simulation (Qiskit + PuLP).

Economic and performance evaluation of an automated PV cleaning system with water recycling in Islamabad, Pakistan

SynGenLib: Synchronous generator library for active power loss and reactive power capability calculations

• CFD was used to analyze the flow field characteristics, such as the velocity field, pressure field, viscous force field, and turbulent kinetic energy distribution. • As the angle of the sector-shaped opening in the meshing area decreases, the windage of the gear pair decreases. • The oil drainage groove should be designed at a position where the included angle between the side of meshing out and the meshing center of the gear pair is 45°. • When the sector-shaped opening of the groove was small, the windage of the gear was also small, and the windage loss was the smallest when the groove opening angle was 10°. To reduce the windage loss caused by hydrodynamic behavior, this study optimizes the parameters of the windshield based on the analysis of the drag reduction mechanism of the windshield. CFD was used to analyze the flow field characteristics to study the influence of the windshield on the motion state of the fluid around the gears and to clarify the drag reduction mechanism of the windshield. The control variable method is adopted to study the influence of the opening at the meshing area of the gear pair and the oil drainage groove on the drag reduction effect of the windshield to optimize the design parameters of the windshield. The research results show that: the drag reduction effect is optimal when the windshield covers the three surfaces of the spiral bevel gear, and the windage power loss is minimized when the gap between the windshield and the gear surface is 1 mm. Without affecting the meshing motion of the gear pair, the smaller the meshing opening, the smaller the windage of the gear pair. When an oil drainage groove with a 10° sector-shaped opening is designed at the position where the included angle between the meshing-out side of the gear pair and meshing center is 45°, the drag reduction effect of the windshield is the best.

Optimal UPFC deployment for voltage stability enhancement: Application of weighted voltage stability indices and hybrid optimization

Controlling power loss of ozonation via ultrasonic cavitation in strongly alkaline and high-salt solutions: Mass transfer and numerical simulation.

Modular multilevel matrix converter (M3C) is a topology that can directly realize the AC-AC power conversion and is widely used in high-power motor drive applications. However, when the motor frequency is close to the grid frequency, the voltage fluctuation problem of the sub-module capacitors worsens. The traditional method of reducing capacitor voltage fluctuations is to inject common mode voltage and circulating current, resulting in the increase of voltage and current stress of the bridge arms. In this paper, a new topology of M3C combined with cascaded H-bridge is proposed, which can effectively reduce fluctuations in the capacitor voltage of the M3C bridge arm submodule when the input and output frequencies are close or equal, while it still has the advantages of the traditional M3C such as easy modular design, low output harmonics, and strong scalability. Firstly, a mathematical model of the combined M3C topology is established. With the model, the cause of the capacitor voltage fluctuations of the M3C bridge arm is analyzed, and its mathematical expression is derived when the motor frequency is close or equal to the grid frequency. Then, a capacitive compensation method using cascaded H-bridges is proposed to provide the reactive power required by the motor to reduce fluctuations in the capacitor voltage of the M3C bridge arm submodule under the close or equal frequency working condition. Compared with the conventional methods, the combined M3C achieves smaller voltage fluctuations of the capacitors, eliminates the need for common-mode voltage and circulating current injection, and achieves lower power loss. Finally, the correctness and effectiveness of the proposed topology and control strategy are verified by simulations and experiments.

Environmental benefits and impacts forecasting for three-phase induction motors operations in marine applications: A multiple linear regression approach