Electrical Transmission Research Articles

Harnessing stimuli‐responsive biomaterials for advanced biomedical applications

AbstractCell behavior is intricately intertwined with the in vivo microenvironment and endogenous pathways. The ability to guide cellular behavior toward specific goals can be achieved by external stimuli, notably electricity, light, ultrasound, and magnetism, simultaneously harnessed through biomaterial‐mediated responses. These external triggers become focal points within the body due to interactions with biomaterials, facilitating a range of cellular pathways: electrical signal transmission, biochemical cues, drug release, cell loading, and modulation of mechanical stress. Stimulus‐responsive biomaterials hold immense potential in biomedical research, establishing themselves as a pivotal focal point in interdisciplinary pursuits. This comprehensive review systematically elucidates prevalent physical stimuli and their corresponding biomaterial response mechanisms. Moreover, it delves deeply into the application of biomaterials within the domain of biomedicine. A balanced assessment of distinct physical stimulation techniques is provided, along with a discussion of their merits and limitations. The review aims to shed light on the future trajectory of physical stimulus‐responsive biomaterials in disease treatment and outline their application prospects and potential for future development. This review is poised to spark novel concepts for advancing intelligent, stimulus‐responsive biomaterials.

Life Cycle Analysis of a Battery Energy Storage System

For Latvian government and transmission system operator energy security and independence has been an issue for a period of time, even more when political situation changed, motivating to act faster in order to swich off from BREL system completely. Therefore, three Baltic states have made an agreement on building more stable electricity transmission system within Baltic region and to connect with European transmission systems, building new stations with synchronous compensators and energy storage systems. In order to build a system, that allows to include more energy from renewable resources and secure possible disbalances in system, Latvian TSO operator is building a new battery energy storage system. This paper is aimed to look at what are the costs of this battery energy storage system from climate perspective to Latvia and its society in order to gain energy security and possibility to include more energy from renewable resources.

Enhancing the Evaluation of District Heating System Resilience: A Literature Review

Ensuring an uninterrupted supply of energy, electricity, and heat is becoming an increasingly pressing necessity. Energy provides both social and economic growth therefore, the energy systems should be resilient to different external conditions. Resilience is the ability of a system to recover from adversity [1] and in connection with the critical infrastructure - the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. Several factors influence the resilience of the energy supply system: energy security, reliability, robustness, flexibility, and resilience. If energy resilience has not been well considered or even ignored in the planning, design, and operation stages in district energy communities can lead to system fragility and vulnerability. Therefore, the research assesses the different methods for resilience assessment in energy systems by focusing on district heating. During climate change mitigation and adaptation energy supply system resilience is unclear. Based on current studies, the energy resilience factor for most is considered in the context of the electricity transmission system, and the heat transmission resilience study is limited. During the research of the literature, it has been concluded that there is no consistent quantitative and quality approach to resilience because there is no consistent treatment of the concept of resilience. The available literature sources about energy resilience are limited in their scope and usability and hence are not amenable for use outside the scope where they have been developed. The metric and formula used for calculating resilience, if any, and the input data required for such calculations are also dependent on the research scope. There is a need for a fundamental generic quantitative and quality approach for resilience that would be usable and useful across various scopes or sectors in a consistent manner, which can be used for the development of resilient systems and effective resilience strategies for district heating systems.

Dynamic knowledge graph approach for modelling the decarbonisation of power systems

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

Exploring dynamic behaviors of diverse electrical soliton pulses in lossy nonlinear electrical transmission lines: Insights from analytical method and linear stability analysis technique

Exploring dynamic behaviors of diverse electrical soliton pulses in lossy nonlinear electrical transmission lines: Insights from analytical method and linear stability analysis technique

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

AbstractBattery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO2eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

On reliability and quality of functioning of power grids (as exemplified by the branch of PJSC Rossetti Volga – Samara Distribution Networks)

The analysis of the electric energy transmission through the distribution electric networks of the branch of PJSC Rosseti Volga — Samara Distribution Networks over the period of 2018 – 2022 was carried out. The structure of the company is considered, the characteristics of the main elements of the power grids under study are given, the balance of the electric energy transmission in and out of the company's networks is estimated. Based on the information published in public media on the accident rate in the company's power grids, assessment was made of the number of emergency situations that occurred over the period under study, assessment of the duration of interruptions in power supply to consumers, as well as the amount of undersupplied electric energy caused by these interruptions. The influence of the seasonal component on the number of emergencies during the period under study is considered. Based on the 2022 data, the analysis of the main causes of damage to components of power grids and the classification of failures are presented, their main characteristics over the period in question are considered. The percentage ratio of the number of failures for the most characteristic reasons to the total number of shutdowns over the period under study was established, and the classification of the number of emergency shutdowns according to the degree of undersupply of electrical energy was made. General scientific methods, numerical methods of analysis, methods of the electrical circuit theory were used in the study. To visualize the results of the analysis, the technologies of the MATLAB graphics editor were used. The results obtained may be of interest to the heads of power grid companies, as well as researchers and engineers engaged in research in the field of power supply reliability.

Lessons from the History and Prospects for the Development of Electric Power Transmission

Lessons from the History and Prospects for the Development of Electric Power Transmission

Dual-layer optimization planning for offshore wind farms considering power system security

The vigorous development of offshore wind farms (OWFs) has further promoted the utilization of wind energy. Owing to the demand for low-carbon environmental protection, the increase in the scale of OWF constructions has also had a significant impact on the power system, which cannot be ignored. Specifically, the uncertainty and volatility of wind-power generation should be considered in power system optimization planning schemes. Therefore, this study proposes a probabilistic power-flow analysis index construction method that employs the static-security region theory to optimize the OWFs–grid-connection topology, including the number, location, and capacity of the public connection points connected to the onshore power grid. This method can be used to calculate the probability indicators of power grid insecurity for OWFs–grid-connected systems with different network topologies. Furthermore, a dual-layer multi-objective programming model that considers the economics and stability for integrated planning of electrical collector and multiterminal direct current transmission systems is established. The proposed model comprises two inner-layer models and one outer-layer model. The inner-layer models are used to construct the collection and transmission topologies of the OWFs–grid-connected system that minimize the probability of grid insecurity and the planning cost of the outer-layer model. Finally, the effectiveness of the proposed OWFs planning model was verified using an improved IEEE 24-bus system that can be used for topology planning of electrical collector and transmission systems while ensuring grid stability.

A probabilistic extension of existing site-specific liquefaction triggering and liquefaction manifestation methods for regional scale evaluations

Liquefaction hazard analyses are needed for regional-scale applications, such as risk analysis and upgrades of electrical transmission, water conveyance, and highway systems. In this paper, we expand well-established site-specific liquefaction triggering and manifestation analysis methods for regional scale application by explicitly addressing the uncertainty in groundwater conditions, soil behavior classification, and cyclic resistance. The uncertainty inherent to regional-scale geotechnical properties is quantified by considering the inputs into conventional liquefaction triggering and manifestation analyses as random variables. With this flexible and extensional formulation, the geostatistical properties needed for regional scale analyses may be estimated by either averaging borehole information or spatially interpolating geotechnical properties across broad geologic deposits. We demonstrate the utility of the method by analyzing the liquefaction hazard throughout the south San Francisco Bay area, California, U.S.A. The resulting estimates of the probability of liquefaction manifestation tend to be lower than previously published estimates, largely owing to the geologic structure of the area and the uncertainty in liquefaction surface manifestation once liquefaction is triggered at depth. Uncertainties in groundwater conditions, soil behavior classification, penetration resistance, and liquefaction manifestation are quantified by the regional scale analyses, which provide a complete and systematic evaluation of the liquefaction hazards, localize the uncertainties in subsurface conditions, and facilitate supplemental investigations at areas of high uncertainty to fill data gaps.

Deep Learning‐Assisted Design of Bilayer Nanowire Gratings for High‐Performance MWIR Polarizers

AbstractOptical metamaterials have revolutionized imaging capabilities by manipulating light‐matter interactions at the nanoscale beyond the diffraction limit. Bilayer nanowire grating configurations exhibit significant potential as exceptional elements for high‐performance polarimetric imaging systems. However, conventional computational approaches for predicting electromagnetic responses are time‐consuming and labor‐intensive, and thereby, the practical implementation remains challenging through an iterative design, analysis, and fabrication process. Here, a deep learning‐based design process is presented utilizing an artificial neural network (ANN) trained on finite element method (FEM) simulations that enables the prediction of bilayer nanowire gratings‐based electromagnetic responses. The study validates predictions through nanoimprinted bilayer nanowire gratings, demonstrating the reliability of the ANN's predictions. Furthermore, the research identifies critical geometric parameters significantly influencing transverse magnetic (TM) and transverse electric (TE) transmission. The ANN model effectively tailors design for specific mid‐wavelength infrared (MWIR) wavelengths, which may provide a practical tool for rapidly designing and optimizing metamaterial for high‐performance polarizers.

Economic evaluation of decarbonizing the electricity sector in the Dominican Republic

This paper presents an in-depth analysis of decarbonizing the electricity sector in the Dominican Republic, pivotal for addressing climate change and fostering economic growth. Employing the robust-decision making methodology, we studied multiple scenarios via computational models, capturing inputs from stakeholders and evaluating each scenario across 1,000 futures to capture deep uncertainty. Four scenarios were examined: baseline, reference, natural gas, and renewable. The renewable scenario emerged as the most advantageous, proposing the replacement of coal-fired power generation with renewable sources, primarily solar and wind, coupled with batteries. A significant investment, averaging US$3.3 billion, is necessary for this shift toward renewable energy; however, these investments are overcompensated by savings in operational costs. Crucially, this transition promises substantial benefits by 2050: an estimated cumulative average net economic gain of US$2.7 billion, an 8% reduction in average generation costs in 2050, the creation of 160,000 direct jobs, and the avoidance of circa 140 million tons of CO2. The findings underscore the feasibility and economic viability of transitioning to a 55% renewable energy generation by 2050. The study offers a critical roadmap for policymakers, highlighting renewable energy expansion, transmission grid strengthening, and strategic coal generation replacement, thus offering a comprehensive blueprint for the nation's energy transition.

Operative Benefits of Residential Battery Storage for Decarbonizing Energy Systems: A German Case Study

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators.

Simulation Of Electrical Load With An Over Load System Shedding

A break in one of the circuits in a double circuit transmission network system can cause the system to overload. Where excess load on the transmission network system can cause quite dangerous impacts. The Over Load Shedding system is a protection system for the electric power transmission network which aims to prevent widespread blackouts by removing certain predetermined loads. By using the Arduino Mega 2560 as a control center, security against load abnormalities can be carried out and can be monitored automatically using Scada, apart from that, PMT taping for load balancing can also be done using Scada. The Arduino Mega 2560 will read the current on each phase wire using the ACS712 current sensor to detect the load of one of the circuits to be aware of whether it is overloaded or not. This simulator tool has carried out several measurements and tested how to operate it. This tool has a fairly high level of work accuracy, reaching 85% success in operation. So it is hoped that this tool can provide knowledge to the wider community about over load shedding systems in maintaining the electric power transmission system properly and easily to understand.

Stability Analysis via Impedance Modelling of a Real-World Wind Generation System with AC Collector and LCC-Based HVDC Transmission Grid

Stability Analysis via Impedance Modelling of a Real-World Wind Generation System with AC Collector and LCC-Based HVDC Transmission Grid

A conductive piezoelectric hydrogel combined with perampanel and wireless electrical stimulation for spinal cord injury repair

After experiencing spinal cord injury (SCI), the interruption of electrical signal transmission impedes both nerve regeneration and functional recovery. Despite its potential as a treatment for spinal cord injury, intrathecal electrical stimulation (ES) is hindered by the risk of infection and the need for a subsequent operation to remove the tethered electrical interface, thereby severely limiting its effectiveness and clinical applicability. Herein, we constructed a conductive piezoelectric hydrogel (SFBP/PER) with ultrasound-triggered wireless ES capability and satisfied conductivity for SCI regeneration. The fabrication process involved the integration of perampanel and polypyrrole-modified BaTiO3 nanoparticles into silk fibroin, followed by enzymatic crosslinking at physiological temperature. We demonstrate that the SFBP/PER hydrogel exhibits anti-glutamate excitotoxicity and antioxidative capabilities in vitro. In vivo, experiments revealed that the SFBP/PER hydrogel significantly enhanced recovery of SCI rats by mitigating oxidative stress, apoptosis, and inflammation, reducing lesion cavities, enhancing remyelination, and promoting the regeneration of functional neurons. This study presents a novel approach utilizing a conductive piezoelectric hydrogel system for in vivo ES in tissue engineering applications.

Analysis of Electric Power Transmission Lines Through Graph Theory: Protecting Environmental Preservation Areas Through Strategic Planning

Electrical networks consist of generation plants, transmission and distribution substations of electrical en-ergy. Power is transmitted through towers and lines that span hundreds or even thousands of kilometers. To ensure the health, stability of ecosystems and our quality of life, we must protect the environmental areas where transmission lines intersect. Given the global challenges related to sustainability, the conservation of natural resources and the mitigation of climate change, these studies are imperative. Through graph algorithms, especially vertex degree search and depth-first search, we identify alternative routes, avoiding sensitive regions such as conservation areas within the 440kV transmission networks of São Paulo, Brazil. By reallocating energy transport, we can ensure net-work maintenance and reduce environmental impacts from potential accidents. The results highlight the sustainable planning of transmission lines, aligning energy needs with Brazilian environmental conservation efforts, including the Rio State Park Peixe, Aguapeí State Park, Itapetinga State Park and Itaberaba State Park. Combining existing environmental laws with the promotion of sustainable technologies and practices, promoting more efficient use of resources while protecting the environment.

Building Blocks for an Energy Transition

The present need for an energy transition in the wake of a global climate catastrophe led to the “EU green deal” which requires a transition of the energy system in all countries. This study aimis survey aims to identify strategies within such an energy transition based on global trend analyses and reports on available technologies for implementation. Based on a literature analysis of over 300 pieces (of feasibility studies and technology implementation reports with a focus on Central Europe) and a statistical analysis of the levels of “energy intensity” (E/GDP) covering three decades and all countries worldwide, the diagnosis is made that steady improvements in several dedicated technological focus areas can be made and were made and could still be increased in the future, as required by climate targets. Across several distinct fields of energy economy, the concrete options for such improvements are portrayed: Energy infrastructure, Smart grids, Transmission grid management, Electricity storage, Heat storage, and Industrial waste heat; and linked with the diagnosed long-term trends emerging from the “Global Change Data Base” GCDB, allowing for a sectorial analysis of the so-called energy intensity, which was not yet delivered until now as time series in the literature. The main findings show which economic sectors and clusters of technologies can be most appropriate to achieve climate targets while safeguarding social aspects of sustainability.

A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis

Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.

Deep fuzzy nets approach for energy efficiency optimization in smart grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.