Electric Operation Research Articles

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%.

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing.

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal-oxide-semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Fluorescence by incoherently pumped polar quantum dot placed in a low-frequency monochromatic field

Abstract We studied spectral properties of high-frequency fluorescent radiation from a two-level quantum system with broken inversion spatial symmetry, which can be implemented as a model of a one-electron two-level asymmetric polar semiconductor quantum dot whose electric dipole moment operator has permanent unequal diagonal matrix elements. The case of the excitation of this system by incoherent pumping of some sort supplemented with an additional driving monochromatic field, which frequency is much lower than the optical transition frequency of the quantum dot, was considered. An analytical expression for the fluorescence spectrum as a function of the amplitude and frequency of the monochromatic driving field, as well as of the pumping intensity, was derived. We also discussed feasible practical applications of the effects under study in the field of quantum optics and optoelectronics for the generation of high-frequency radiation, possessing stationary equidistant spectrum, and for the control of the radiative properties of such nanodevices as spasers (dipole nanolasers).

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

AbstractThe short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.

Transitioning the grid for climate change: power transmission futures and grid justice

Abstract Amid the ongoing international boom in renewable power generation, debates over the future of the grid are gaining increasing attention in the United States and beyond. Climate change poses parallel but entangled questions for the large-scale movement of electricity. On the one hand, grid operation is a profoundly altered undertaking in renewables-dominated grids, bringing new management challenges around multi-directional flows, variability, bids for long-distance renewable power transmission, and more. On the other hand, electricity operators and users simultaneously face new climate-related disruptions, repair needs, and risks. In this paper, we explore an important set of energy and climate justice debates emerging around these combined decarbonization and grid resilience challenges, particularly concerns related to high-voltage transmission in the United States and other countries with developed but aging grids. We consider questions of transmission grid 1) access, 2) ownership, 3) siting, and 4) scale/rescaling. We illustrate these issues via the exemplary case of New York State, as the state has become a high-profile focus for debates around grid congestion, rising financial sector ownership of transmission projects, and siting justice issues, as well as for varying progressive alternatives in campaigns for both large-scale public power and decentralized ‘non-wires’ solutions.

Effect of Morphology and Concentration of CeO2 Nanoceria on the Thermal Stability of Polyvinyl Alcohol Films

Objective: To determine the thermal stability of polyvinyl alcohol films under different concentrations of CeO2 nanoceria. Method: Cerium Oxide doped polyvinyl alcohol (PVA) nano-composites were synthesized by the traditional solution casting method for various concentrations of nano CeO2 by solution combustion method. The thermal properties of prepared PVA-2.5 wt% CeO2, PVA-7.5 wt% CeO2 , PVA-12.5 wt% CeO2, and PVA-25 wt% CeO2 composite films were elucidated by using techniques such as Differential Scanning Calorimetry, Thermogravimetry analysis and Differential Thermal analysis. Findings: The glass transition temperature of the pure PVA is found to be 114°C, whereas it varies between 110°C-120°C for composite films. The melting temperature of the composite films is the same as that of pure PVA except in the case of 7.5wt% CeO2 and 25wt% CeO2 composites. The reduced melting point of these composites elicits a decrease in crystallinity. A 12.5 wt % and 25 wt% CeO2-PVA nano-composite films exhibit an increase in the final degradation temperature, a high specific heat capacity, low mass loss, and a larger residual weight, indicating their potential applicability in thermal coating and thermal sensing applications. Polyvinyl alcohol (PVA) films are being used routinely as a dielectric layer for electrical applications due to the good physiochemical and dialectic properties of the material. An incorporation of CeO2 nanoceria improves not only the thermal but also the mechanical properties of PVA and extends its application for electrical and optical operation. Here in the present study, different concentrations of CeO2 were incorporated with the PVA and analysed for the thermal properties. The study showed that 12.5 wt % and 25 wt% CeO2-PVA nano-composite enhanced PVA film thermal stability. Keywords: PVA-CeO2 Nanocomposites; Differential Scanning Calorimetry; Thermogravimetry Analysis and Differential Thermal Analysis; Physical properties and thermal properties

Unveiling the role of external electric field on the charge transport and excited-state properties of high T1 host for blue OLED devices

Density functional theory (DFT) and time-dependent DFT (TDDFT) calculations were performed to understand the effect of the external electric field (EEF) on the reorganization (λ) and the lowest triplet excited-state (T1) energies of high T1 blue host materials. Depending on the direction and the strength of the external electric field (EEF), the positive and negative changes of hole and electron λ(h and λ e) values were found in these materials. More importantly, λ e seems to be more sensitive than λ h values under the EEF. It is also noticed that the calculated T1 energies are meaningfully changed in the application of EEFx and EEFy. In contrast, the effect of EEFz on the T1 energies can be negligible. From the results of theoretical investigation, the obvious evidence related to the influence of EEF on the charge transport and excited-state properties of high T1 blue host materials were obtained. In the present work, we expect that our theoretical study will provide new insight into understanding the influence of EEF as a key player in manipulating essential properties of the high T1 blue host material during the electrical operation.

Regional Operation of Electricity-Hythane Integrated Energy System Considering Coupled Energy and Carbon Trading

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure.

Regional electricity cooperation model for cost-effective electricity management with an emphasis on economic efficiency

Effectively reducing the total electricity cost while ensuring its overall efficiency is vital for the sustainable development of power systems. Herein, we develop a regional cooperation optimization model to enhance the existing power cost model. This is achieved by incorporating power trading scenarios and pollution considerations, leveraging the distinct characteristics of power generation costs from various sources. The developed regional cooperation optimization model simultaneously accounts for production efficiency and overall power costs, ensuring equitable distribution and compensation of benefits among cooperating entities. In addition, the key findings indicate that the interregional cooperation optimization model significantly reduces the total cost of electricity operations in China. Specifically, during the 11th five-year plan period, the total electricity cost in China exhibited a noteworthy decline of USD 50,876 million owing to multi-objective cooperative optimization, constituting 25.99% of the total electricity cost compared with the preoptimization period. This study identifies the eastern and western regions as principal contributors to the cost savings achieved by the interregional cooperative optimization model. Simultaneously, recognizing their significant contributions, it is recommended that the eastern and western regions provide additional cost compensation to other regions to ensure the long-term viability of the cooperative alliance.

Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression

Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression

A fully nanofiber-based ultrathin electronic patch for self-powered and multifunctional on-skin applications

On-skin electronics that simultaneously ensure biocompatibility and stable electrical operation on soft tissue is still challenging. This study introduces a dual-layered and natural material based nanofiber platform designed for multifunctional on-skin operation (from energy harvesting to transdermal drug delivery). The ultrathin electronic patch consists of polyaniline (PANI)-coated cellulose nanofibers and silk fibroin nanofibers. The top PANI/cellulose nanofiber layer, exhibiting the resistivity of 0.315 Ω·m and over 90 % light absorbance in full visible range, functions as a flexible electrode. The silk nanofiber layer offers the skin-compatibility and strong adhesion to biological tissues, along with the capability of drug-loading. The nanofiber mesh structure withstands mechanical deformation, including compression, stretching, and bending, while its porous nature enhances breathability and hygiene. The fully nanofiber-based electronic patch platform is flexible, adheres well to the skin, and conforms to curved surfaces. By integrating the conductive properties of PANI with the biocompatibility of silk fibroin, this dual-layered membrane enhances the efficacy of transdermal drug delivery while offering multifunctional capabilities such as energy harvesting in a triboelectric nanogenerator (TENG), programmable drug delivery, and thermal therapy. This innovative approach paves the way for advanced biomedical applications by combining multiple functionalities in a single platform.

Effect of SF6 circuit breaker parameters on switching overvoltages evaluated based on nominal modes of converter transformers

In this work, we set out to investigate the influence of operating currents of SF6 circuit breakers on the magnitude of overvoltages during switching of converter transformers. The experience of electrical equipment operation is analyzed with a focus on the problems associated with the natural aging of insulation, overloads, and switching overvoltages. The methods of equipment diagnostics were used. According to statistical data, at present, about 37% of short circuits between phases and 42% of single-phase earth faults in electrical networks of industrial enterprises occur due to switching overvoltages. The conducted tests showed that the overvoltages emerging during switching by SF6 circuit breakers of converter transformers, which are used in aluminum smelters, may reach a three-fold rated voltage of the network. This threatens the insulation of transformer windings, cable line, and the switch itself. It was established that an increase in operating currents of an LF2 circuit breaker, not exceeding 27.8% of the rated current of the switch, leads to an increase in overvoltages during switching of converter transformers. At a further increase in the operating currents of the circuit breaker by more than 27.8% of the rated current of the circuit breaker, a sharp decrease in the value of switching overvoltages is observed. Therefore, the operating currents of SF6 circuit breakers were established to affect the level of switching overvoltages of converter transformers. The underlying mechanism of this phenomenon was determined, which should be taken into account when improving the reliability of power supply of converter transformers and, consequently, the reliability of aluminum production. The feasibly of resistive-capacitive dampers as the most effective means for limiting overvoltages during switching of converter transformers was confirmed.

Optimal scheduling strategy of household electrical equipment based on scenario dynamic modeling

Household electricity is highly unpredictable, necessitating a deep understanding of its impact on energy scheduling for optimal resource allocation and economic gains. This issue’s complexity can lead traditional optimization methods to converge on suboptimal solutions. In this study, the dynamic Copula (DC) model is used to construct the dynamic correlation between power consumption characteristics, and the Monte Carlo (MC) method is used to generate multiple power consumption scenarios to cope with the uncertainty of user behavior. Then, the optimal scenario set is selected through the average distribution error (ADE) to participate in the subsequent scheduling. In addition, based on the established equipment operation characteristic model, the electricity cost and load peak-to-average ratio (PAR) are comprehensively considered. The Improved Dynamic search multi-objective particle swarm optimization (IDSMOPSO) is introduced to optimize the running time of the equipment. Taking the electricity consumption of a family in Xi’an as an example, the results show that the algorithm is significantly better than the other two improved algorithms in performance. Meanwhile, the electricity cost of the family was significantly reduced by 17.09 %, and the PAR value was also reduced by 31.59 %, which realized the economic operation of household electricity.

Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis.

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

Application of artificial intelligence technologies at substantiation of effective control algorithms for the electrical engineering complex of urban electric transport traction electrical equipment

Background. Studies on the current state and development trends of urban ground public electric transportation in Russia highlight the urgent need for innovative technologies. These innovations should focus on designing and operating new types of rolling stock, traction electrical equipment, and promising types of electric traction. At the same time, special attention should be paid to building autonomous control systems for electric transportation using artificial intelligence technologies. Aim. This study aims to explore the application of neural networks to develop algorithms for effectively controlling the electrical engineering complex of traction electrical equipment in urban ground rail electric transport. Materials and Methods. The research utilized data from studies on the traction electrical equipment modes of rolling stock. These studies were conducted through both computer simulations and field experiments under real operating conditions of urban electric transport using different control algorithms. By applying probability theory and mathematical statistics, the analysis identified the correlations between operational and energy parameters of rolling stock movement and the operational mode of its traction electrical equipment. Results. The identified correlation dependencies informed the design of an effective network architecture, including its size and complexity, as well as the composition of their training samples. This led to the development of an original, simplified algorithm for determining effective control parameters for the electrical complex of traction electrical equipment during the movement of a vehicle on a given section of track. Conclusion. The research concluded that using “simple” neural networks for calculating the parameters of effective control of traction electrical equipment operation modes in urban electric transport provides higher speed and sufficient accuracy compared to complex neural network models. These results are valuable for developers of intelligent control systems for streetcar transportation.

Electric polarization evolution equation for antiferromagnetic multiferroics with the polarization proportional to the scalar product of the spins

The spin current model of electric polarization in multiferroics is justified via the quantum hydrodynamic method and the mean-field part of the spin-orbit interaction. The spin current model is applied to derive the electric polarization proportional to the scalar product of the spins of the nearby ions, which appears to be caused by the Dzylaoshinskii-Moriya interaction. The symmetric tensor spin structure of the polarization is discussed as well. We start our derivations for the ferromagnetic multiferroic materials and present further generalizations for the antiferromagnetic multiferroic materials. We rederive the operator of the electric dipole moment, which provides the macroscopic polarization obtained via the spin current model. Finally, we use the quantum average of the found electric dipole moment operator to derive the polarization evolution equation for the antiferromagnetic multiferroic materials. The possibility of spiral spin structures is analyzed.

A mixed-methods examination of fixed-object crashes among electric utility company fleet vehicles

IntroductionOne of the most common and serious types of crashes among all motor-vehicle users involves collisions with fixed objects. This type of crash occurs frequently among utility vehicle workers while driving for work. The overarching objective of this research was to improve the safety of electric utility company vehicle operators by determining the circumstances under which utility vehicles are involved in crashes with fixed objects and to provide recommendations to help drivers and utility fleet company management prevent these types of crashes. MethodsThe study incorporated a mixed methods approach and gathered information from structured interviews with safety managers at electric power companies and an analysis of utility vehicle crashes using data supplied by electric power companies and a statewide database in Michigan. ResultsMany factors were found to contribute to fixed-object crashes including engagement in secondary tasks and job productivity pressure. Information was organized and evaluated to develop 11 recommendations for countermeasure strategies that are directed at various levels within the system in which the drivers operate. DiscussionThis study was the first to explore fixed-object crashes among electric utility fleet vehicles. The findings are unique in that they provide insight into the safety practices in an industry that has received limited academic attention and highlight the need to place greater emphasis on road safety practices in the workplace. Practical applicationCountermeasures generated for this study are applicable to companies in which fleet driver safety is a primary concern.

Insulation Detection Algorithm for Gloves Based on Improved YOLOv8n

With the rapid development of China's industry, the safety problems in the process of electrical operation are highly concerned by the electrical industry. In order to reduce the incidence of electrical safety accidents, it is very necessary to wear insulating gloves for electric power construction, which is also of great significance to ensure the safety of electric power production. However, the small target of insulating gloves and the complexity of the electrician operation site, which undoubtedly increases the difficulty of identifying the algorithm model of insulating gloves. For this problem, insulation glove target detection algorithm based on YOLOv8n improvement model. Through many experiments, YOLOv8n was selected as the basic framework of the insulating glove detection algorithm. In order to make the model more accurately locate and identify the targets of interest, the efficient coordinate attention mechanism Coordinate Attention was inserted into backbone to improve the accuracy of the model. The introduction of PConv in necbricks reduces redundant computation and memory access for a more efficient extraction of spatial features. After the improved YOLOv8n algorithm, the mAP is increased from the initial 96.4% to 98.6%, which provides a good reference value for the subsequent research of the target detection algorithm of insulating gloves.