Electric Power Research Institute

A pulse-modulated microwave air discharge operating at 2.475 GHz was applied for the conversion of nitrogen ( N2) and oxygen ( O2) into nitrogen oxides ( NOx), including NO, NO2, and N2O4. The effects of pulse modulation frequency, duty cycle, flow rate and O2 content were investigated for better understanding and optimization of the NOx productivity and the corresponding energy cost (EC). The results indicated that high-frequency pulses (10–100 kHz) had a minimal effect on NOx productivity and EC. However, the duty cycle significantly influenced EC, leading to a reduction of approximately 21% when decreased to 40% compared to full load. Meanwhile, the reduction in duty cycle considerably decreased the plasma temperature, lowering it from around 4900 K at 100% to around 2700 K at 40%. The flow rate also had a significant effect on both NOx productivity and EC; higher duty cycles and larger flow rates increased productivity, while lower duty cycles and higher flow rates achieved the lowest EC, with a minimum value of 2.98 MJ mol⁻¹. As the O2 content increases from 10 % to 90 %, NOx productivity initially rises and then declines, while EC follows opposite trend. The maximum NOx productivity and minimum EC occur at O2 content of approximately 40 % or 50 %, with the lowest EC reaching 3.03 MJ mol⁻¹. This study demonstrates the effective reduction of NOx EC under various conditions by adjusting duty cycle, flow rate, and O2 content, providing insights for improving nitrogen fixation efficiency in future research.

Combining the benefits of a low profile, high gain, high efficiency, and wideband operation in a planar antenna presents a significant challenge for antenna designers. Low-profile wideband antennas often suffer from low gain. This study introduces a compact wideband artificial magnetic conducting surface (AMCS) positioned behind a wideband omnidirectional antenna to enhance its gain across the operational frequency range. This integration allows the radiating structure to achieve both high gain and wideband functionality in a single design. In this research, a wideband planar monopole printed antenna is developed to function as an omnidirectional radiator, delivering excellent impedance matching and radiation efficiency across the frequency range of 3.9–7.2 GHz (60% bandwidth) in free space. The free-standing antenna dimensions are 30 mm × 20 mm (0.39 λo × 0.3 λo), where λo corresponds to the lowest operating frequency of the antenna). It exhibits a gain ranging from 2 dBi to 4.5 dBi over this frequency band. To improve gain, a wideband AMCS is designed, consisting of just 3 × 3 unit cells with overall dimensions of 9 × 9 cm (1.1 λo × 1.1 λo). The AMCS is placed parallel to the planar antenna at a distance of 1.75 cm behind it. The gain of the AMCS-backed antenna reaches up to 9 dBi without compromising bandwidth or impedance matching. Furthermore, the radiation efficiency remains above 98% across the operational band of 3.6–7.2 GHz (66% bandwidth). The wideband antenna and AMCS are fabricated to experimentally validate the performance of the AMCS-based antenna. Measurements of impedance matching, gain, and radiation efficiency demonstrate close alignment with simulation results, confirming the effectiveness of the proposed design.

Current pipe jacking projects predominantly use C50 concrete pipes, which are prone to cracking and failure under jacking forces during complex construction. This study investigates the mechanical performance and failure mechanisms of C150 ultra-high performance fibre reinforced concrete (UHPFRC) pipes through full-scale trilateral loading test and numerical simulation. Key outcomes include crack load (232 kN/m), damage load (452 kN/m), and maximum displacements (64.3 mm horizontal, 71.6 mm vertical). Radial loading induced four primary cracks along the inner/outer left-right surfaces of the pipe, accompanied by extensive secondary crack networks. Damage progression began with concrete spalling at the top/bottom due to crack propagation, followed by reinforcement yielding and structural collapse. A predictive model for crack load and ultimate bearing capacity was developed, validated by experimental data. ABAQUS finite element simulations demonstrated excellent correlation with experiments, accurately reproducing macroscopic damage patterns. Notably, UHPFRC pipes exhibited 2.55-fold higher radial bearing capacity compared to C50 counterparts. This research establishes UHPFRC as a superior material for pipe jacking applications, offering enhanced structural integrity and reliability.

This paper presents experimental observations and simulation results on the interaction between plasma and gas flow in pin-to-plate corona discharge under different voltage polarities. Schlieren experiments reveal that during a single discharge process, only a unidirectional electro-hydrodynamic (EHD) force toward the plate electrode is observed in positive corona discharge, while an additional scope of EHD force toward the pin electrode is found in negative corona discharges. Similarly, this phenomenon can be observed in a 2D fluid model simulation that disregards any disturbance to helium gas during transient discharging development. The evolution of EHD force follows a similar pattern as the discharge itself, which can be divided into three stages. The EHD force is scarcely observed during the dissipation stage. During the development stage, an EHD force toward the pin electrode is generated as the streamer approaches the plate electrode. In the stable stage following a complete discharge, EHD forces toward both electrodes are observed, with a spatial distribution structure of centrosymmetry presented in positive corona discharges. However, in negative cases, the EHD force toward the pin electrode is significantly smaller than that toward the other electrode. Through the analysis of spatial and temporal distribution of charged particles, it is determined that O 2 + ions play a predominant role in generating the EHD force directed toward the pin electrodes in negative corona discharges. The reversal of local net charge polarity caused by Penning ionization and attachment reactions producing negative ions further enhances this effect in negative corona discharges, thereby highlighting the polarity differences.

The wind industry’s expansion in North America due to the need to provide clean energy is leading to increased regulatory concern for bats, particularly those that are endangered due to white-nose syndrome. The projected growth of installed wind capacity overlaps extensively with the ranges of several endangered and potentially regulated bat species. Wind energy operators in the US can comply with the Endangered Species Act (ESA) by submitting a Habitat Conservation Plan (HCP) and Incidental Take Permit (ITP) to the U.S. Fish and Wildlife Service. HCP documents include wind project overviews, estimates for incidental take (e.g., unavoidable fatalities), outline minimization and compensatory mitigation measures to avoid take, and often include estimated cost information for actions to implement the HCP/ITP. However, the lack of insight into specific cost data, combined with the lengthy ITP application process, has potentially led to the perception that ESA compliance imposes a costly regulatory burden on the private sector, deterring motivation for voluntary compliance. Resulting from the absence of routine reporting practices, it is not known how much it costs for companies to comply with ESA listings, nor is there a standardized database of compliance costs or a method for estimating them. This analysis of 25 publicly available project-specific HCPs published through 2022 establishes one approach to conceptualizing these costs and determined the median total cost for an HCP to be approximately $4.68 million (USD), with a notable discrepancy between the median costs for compensatory mitigation cost ($1.64 million) and fatality monitoring ($3.15 million). This analysis also created a general linear model that can be used to estimate potential project-specific costs, and overall provides better insight into the costs of complying with the ESA by identifying variables that might affect compliance costs, and estimating future costs for the wind industry.

This study enhances the accuracy of controlled closing for 800 kV filter field circuit breakers by analyzing the factors affecting the closing time. A multi-factor prediction model was developed using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) neural network algorithm, incorporating ambient temperature, dwell time, the number of actions, and working hours. On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. Results show an average prediction error within ±0.21 ms, with the maximum error tolerance meeting controlled closing requirements. The prediction error decreased as more variables were integrated, confirming that ambient temperature (inverse correlation), dwell time (positive correlation), working hours, and action frequency significantly influence closing times. For instance, lowering the ambient temperature by 40 °C reduced the closing time by ∼1 ms, while increasing the dwell time to 168 h raised it by 2.5 ms. The model enables real-time prediction of closing times using dynamic operational parameters, improving phase-selection precision by 18% compared with conventional methods. This data-driven approach provides theoretical and technical support for optimizing controlled closing in ultra-high voltage systems.

Accurate power network estimation is critical for the efficient operation and management of distribution networks, especially in the context of integrating renewable energy sources and maintaining grid stability. This paper presents a novel two-stage approach for topology identification and line parameter estimation in distribution networks. The proposed method integrates variational mode decomposition into feature-enhancing techniques to extract meaningful features from noisy, volatile nodal data, effectively addressing the challenges posed by measurement disturbance. Additionally, a graph convolutional network based model is introduced to accurately capture both local and global dependencies within the network topology, enhancing the scalability and robustness of the estimation process. Experimental results demonstrate a significant improvement in accuracy, achieving a 62.3% reduction in identification errors compared to mainstream methods. The proposed framework effectively handles networks under different scales, offering a robust and scalable solution for distribution network analysis and real-time operational applications.

The task scheduling problem based on directed acyclic graphs (DAGs) has been proven to be NP-complete in general cases or under certain restrictions. In this paper, building upon existing scheduling algorithms, we introduce a static task scheduling algorithm based on directed acyclic graphs. By incorporating the proportion of task transmission delay as a guiding metric in the optimization process, processors can be prioritized for tasks with high latency, thereby improving computational efficiency. We first validate the theoretical feasibility of the algorithm using a theoretical case study and illustrate the algorithmic effectiveness using two real case studies, direct current (DC) model and alternating current (AC) model respectively. The research indicates that the scheduling algorithm proposed in this paper achieves an average scheduling length improvement of over 1.2% compared to the Heterogeneous Earliest-Finish-Time algorithm (HEFT) in topologies with high latency tasks. Additionally, the experiments show that the HEFT algorithm consumes 39.85us and the EMT-DM algorithm consumes 38.29us during simulation using DC, and the HEFT algorithm consumes 31.23us and the EMT-DM algorithm consumes 26.51us during simulation using AC, both of which are improved compared to the HEFT algorithm.

The influence of service exposure on creep crack growth behavior on compact-tension specimens of P91 steel was investigated at 625 °C. The initial microstructure of ex-service material consisted of brittle Laves phase and higher geometrically necessary dislocation density owing to service damage, as compared to the virgin material. The ex-service material exhibited a comparatively higher crack growth rate and crack extension under similar test conditions. Similar creep fracture mode was observed in both the materials.

With the rapid advancement of big data technologies, Location-Based Services have significantly enhanced user convenience while simultaneously exacerbating the challenge of data explosion. The advent of cloud computing has alleviated issues related to data silos and information overload, thereby facilitating the comprehensive realization of data value. However, given that cloud servers cannot be considered fully trustworthy, malicious servers may potentially execute unauthorized queries, return falsified or manipulated query results, or compromise user query privacy. In this paper, we propose a Secure and Verifiable Location Range Query (SVLRQ) scheme that enables users to perform secure and efficient range queries on location data while maintaining the capability to verify query results. Specifically, the SVLRQ scheme introduces Order-Revealing Encryption for location information encryption and constructs a pyramid hash tree data structure to ensure both verifiability and privacy preservation. Our SVLRQ scheme achieves security, verifiability, and privacy preservation compared to existing solutions. We provide rigorous theoretical analysis and formal proofs regarding the scheme’s security and correctness. Experimental results demonstrate the scheme’s strong practical applicability. Notably, when operating on a database of size $2\times 10^5$, the query time of existing solutions (2.32 ms and 0.38 ms) is approximately 116 times and 19 times greater, respectively, than that of our proposed scheme (0.02 ms).

With the acceleration of urbanization, the safe and stable operation of dense urban cable channels is of great importance to the guarantee of urban power and communication systems. Cable channels face many sources of risk that bring great challenges to urban power supplies. Most existing risk assessment methods are based on accurate mathematical models, which require clear and deterministic boundaries of assessment indicators. These methods have difficulty in dealing with the fuzziness and uncertainty of cable channel risk factors, such as the challenge of determining the degree of aging of cable insulation or the degree of influence of external environmental factors that cannot be simply quantified. This paper presents a risk assessment model of a dense urban cable passage based on fuzzy mathematics. The model combines a membership function with a fuzzy comprehensive evaluation method to analyze and classify the risk factors of a dense urban cable passage. Eight risk factors were identified, including external damage, facility defects, and non-standard cable laying, and the importance of each factor was evaluated by constructing a membership matrix based on historical data and expert scoring methods. A typical dense cable trench and cable tunnel in actual operation in a region of China Southern Power Grid are analyzed, and the risk level is calculated by MATLAB 2021a programming. The results show that the model can effectively assess the level of risk and clearly show the impact of individual risk factors on the overall risk. For example, in the cable trench risk assessment, the model accurately identifies that external damage and cable overheating risk factors lead to moderate risk, and the remaining six factors are low risk. In the cable tunnel assessment, the corresponding risk level of each risk factor is also accurately determined. This indicates that the evaluation method based on fuzzy mathematics can not only quantify the uncertainty of risk factors but also improve the rationality of the evaluation results and provide a scientific decision basis for the safety management and maintenance of cable channels. The model has significant advantages over traditional evaluation methods.

Utilities provide essential services that underpin social welfare and economic activity. There is a robust literature on how to best organize the provision of these activities, but no consensus has emerged on the presence and extent of economies of scope in the utility sector. We consider how the scope of a utility relates to residential prices. In contrast to the existing literature, our study focuses on utilities within the US and that provide services across distinct sectors (water and electricity), and we directly consider price implications. Specifically, we draw on surveys in 2017 and 2021 of around 400 water utilities – 14 percent of which also provide electricity – to generate empirical evidence on where joint water-electric utilities tend to operate and their association with retail prices for water and electricity. We find significant evidence that water charges are 18 percent lower among the joint utilities than water only utilities. Ancillary data and analysis suggest electricity rates are also lower, indicating significant economies of scope are available. Our evidence addresses canonical gaps related to questions of the boundary of the firm and economies of scope in this important supply node of the water-energy nexus that can inform considerations of restructuring the provision of these services.

The typhoon–rainstorm–flood disaster chain poses a significant flooding risk to urban distribution network (DN) equipment, often leading to power system outages. The increasing frequency and severity of this disaster chain in East Asia, driven by global warming, population growth, and land‐use changes, highlight the need for improved disaster preparedness. Traditional studies focusing on individual meteorological disasters, such as typhoons or floods, may be insufficient for developing efective mitigation strategies. To address this gap, this study proposes a novel risk analysis method for enhancing the disaster defence strategy of DNs. First, a hybrid deep learning model is developed to forecast a 48‐h rainstorm time series following a typhoon's landfall. Second, a one‐dimensional pipe network and a two‐dimensional surface‐coupled urban flood model are constructed to predict flood depth based on the typhoon–rainstorm time series. Third, an influence factor set is established from environmental and societal perspectives, and spatial correlation analysis is applied to assess DN outage risk. To validate the proposed method, Typhoon Talim (2023), which made landfall in China, is used as a case study. The results demonstrate that the model effectively captures disaster‐causing mechanisms and accurately identifies high‐risk areas. This research provides a theoretical foundation for outage risk prevention in developing countries, particularly in mitigating the impacts of the typhoon–rainstorm–flood disaster chain.

In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm (CoDMOIECO). Specifically, a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based on NBI and angle are proposed., enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them. Additionally, new update strategies for the clan leader, male elephants, and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence. Finally, a new CoD-based environmental selection method is proposed, introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors, significantly improving both the convergence and distribution of the algorithm. Experimental comparisons on the ZDT, DTLZ, and WFG function sets with four benchmark multi-objective algorithms—MOEA/D, CAMOEA, VaEA, and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.

Electricity and hydrogen have emerged as viable alternatives to traditional fossil fuels, playing a crucial role in clean and sustainable transportation solutions. The rapid growth of hydrogen vehicles (HVs) and electric vehicles (EVs) has significantly increased the demand for electricity–hydrogen hybrid charging stations (HCSs). Compared to the existing literature that predominantly focuses on optimal energy management from a system-level perspective, this paper explores power management in multiple HCSs and multienergy trading between HCSs and vehicles. In the proposed energy trading mechanism, the EVs and HVs are enabled to strategically submit their offer prices to maximize their utilities. Based on these prices, the aggregator allocates electricity and hydrogen and determines the final payments for the vehicles, aiming to maximize social welfare within the system, subject to the operational constraints of the HCSs. The theory of the Vickrey–Clarke–Groves (VCG) mechanism is employed to design the energy trading mechanism. Furthermore, we introduce the concept of information rents to address potential budget imbalances for the aggregator, enhancing the economic stability of the system. We also provide theoretical proofs for the properties of the proposed mechanism, which include truthfulness, individual rationality, and social welfare maximization. Simulation results demonstrate the effectiveness of the proposed mechanism and verify its three properties.

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

Data informs policy and drives decisions. With an increasing number of regulations requiring entities to use local climate data in their planning, it’s more important than ever to understand the strengths and limitations of data we use. While they have been shown to capture long-term statistics on global or regional levels, the ability of gridded climate datasets to capture trends and extreme events is not common knowledge. Four widely used gridded datasets, ERA5, ERA5-Land, MERRA-2, and PRISM, were assessed for their ability to capture extreme heat, extreme cold, and heavy precipitation events, as well as trends in annual maximum and minimum temperatures and total precipitation, over the contiguous US (CONUS). Spatial patterns are evident in each dataset, with the largest differences between observations and the gridded data across the western US for temperature and along the Gulf Coast for heavy precipitation. In general, gridded datasets captured extreme heat better than extreme cold or heavy precipitation, and trends in annual maximum temperature better than trends in annual minimum temperatures and annual total precipitation. All dataset capture extreme heat days comparably, but PRISM generally performed best for extreme cold and the bias-adjusted MERRA-2 dataset generally performed best for heavy precipitation days.

As one of the weakest points in high-voltage direct current cables and accessories, the accumulation of space charges at the crosslinked polyethylene (XLPE)/ethylene propylene diene monomer (EPDM) interface coated with silicone oil is crucial to insulating properties. The physical mechanisms underlying this charge accumulation and dissipation phenomenon remain unclear, particularly at the molecular level. Thus, the interfacial space charge accumulation and dissipation behavior at EPDM/XLPE, EPDM/non-polar dimethyl silicone oil (PDMS)/XLPE, and EPDM/polar fluorinated silicone oil (PMTFS)/XLPE interfaces was measured using pulsed electroacoustic (PEA) method, and molecular simulation techniques were employed to calculate the electronic properties across those interfaces. It was found that the transformation law of the interfacial charge polarity does not completely align with the Maxwell–Wagner (MW) model, which is related to the contact type of the interface (with or without silicone oil and the type of silicone oil) and the voltage polarity. The presence of a high interfacial potential barrier is an important factor behind the fact that the transformation law of the interfacial charge polarity does not align with the MW model. The high hole potential barrier (greater than 1 eV) of EPDM/XLPE and EPDM/PMTFS is the reason why the interfacial charges of EPDM/XLPE and EPDM/PMTFS/XLPE remain always positive as the applied negative voltage and temperature increases. Due to the low potential barrier of the EPDM/PDMS/XLPE interface, the polarity of the interfacial charge is always consistent with the polarity of the voltage applied to the medium with a greater conductivity. At 40 °C and 60 °C, EPDM/XLPE and EPDM/PMTFS/XLPE positive interface charge accumulation is significantly reduced compared to that observed at room temperature under a negative voltage, which is attributed to the enhanced charge injection and migration of XLPE with rising temperature. This study provides theoretical insights for finding an effective coating material to reduce charge accumulation at the cable accessory interface.

The growing demand for advanced rubber composites highlights the need for enhanced performance and durability. This study explores the synergistic reinforcement effect of graphene nanosheets (GNs) and zinc dimethacrylate (ZDMA) on ethylene–butene–terpolymer (EBT) composites, focusing on their microscopic reinforcement mechanisms. In situ‐modified GNs and ZDMA are employed for co‐crosslinking. The impact of varying GNs contents and ZDMA ratios is analyzed, with a focus on their effects on the material's mechanical, thermal, and dynamic properties. GNs significantly enhance tensile strength, elongation at break, and hysteresis heat generation, while ZDMA further improves strength and thermal stability by optimizing the crosslinking network. Their synergy markedly boosts the performance of EBT composites, making them ideal for high‐strength, high‐temperature applications. The study provides design guidelines for advanced automotive rubber components and lays a foundation for optimizing EBT composites.