Power Transmission Infrastructure Research Articles

Sustainable Concrete Technology in the Field of Transmission Infrastructure: A Review

The increasing demand for energy and the imperative shift towards sustainable infrastructure have propelled research efforts towards developing eco - friendly materials for critical applications such as power transmission projects. This abstract provides a comprehensive overview of the current state of sustainable concrete technologies tailored specifically for power transmission infrastructure. The study delves into the environmental impact of conventional concrete used in such projects and explores innovative approaches to enhance sustainability. The review encompasses various aspects, including the incorporation of alternative binders such as fly ash, slag, and silica fumes, as well as the optimization of mix designs to reduce carbon footprint. Special attention is given to the performance, durability, and long - term resilience of sustainable concrete in comparison to traditional counterparts. Moreover, the document discusses the economic feasibility of implementing sustainable concrete in power transmission projects, considering factors such as material cost, construction practices, and life - cycle analysis. The abstract concludes with insights into ongoing research trends and potential future developments in sustainable concrete technologies, emphasizing the need for collaborative efforts between academia, industry, and policymakers to promote environmentally conscious practices in the realm of power transmission infrastructure, taking into account desired power transmission best performance.

Effect of temperature on seasonal wind power and energy potential estimates in Nordic climates

AbstractA major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.

2D-convolutional neural network based fault detection and classification of transmission lines using scalogram images

The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.

From Curtailed Renewable Energy to Green Hydrogen: Infrastructure Planning for Hydrogen Fuel-Cell Vehicles

Problem definition: Hydrogen fuel-cell vehicles (HFVs) have been proposed as a promising green transportation alternative. For regions experiencing renewable energy curtailment, promoting HFVs can achieve the dual benefit of reducing curtailment and developing sustainable transportation. However, promoting HFVs faces several major hurdles, including uncertain vehicle adoption, the lack of refueling infrastructure, the spatial mismatch between hydrogen demand and renewable sources for hydrogen production, and the strained power transmission infrastructure. In this paper, we address these challenges and study how to promote HFV adoption by deploying HFV infrastructure and utilizing renewable resources. Methodology/results: We formulate a planning model that jointly determines the location and capacities of hydrogen refueling stations (HRSs) and hydrogen plants as well as electricity transmission and grid upgrade. Despite the complexity of explicitly considering drivers’ HFV adoption behavior, the bilevel optimization model can be reformulated as a tractable mixed-integer second-order cone program. We apply our model calibrated with real data to the case of Sichuan, a province in China with abundant hydro resources and a vast amount of hydropower curtailment. Managerial implications: We obtain the following findings. (i) The optimal deployment of HRSs displays vastly different spatial patterns depending on the HFV adoption target. The capital city, a transportation hub, is excluded from the plan under a low target and only emerges as the center of HFV adoption under a high target. (ii) Promoting the HFV adoption can overall help reduce hydropower curtailment, but the effectiveness depends on factors such as the adoption target and the grid upgrade cost. (iii) Being a versatile energy carrier, hydrogen can be transported to various locations, which allows for strategic placement of HRSs in locations distinct from hydrogen plant sites. This flexibility offers HFVs greater potential cost savings and curtailment reduction compared with other alternative fuel vehicles (e.g., electric vehicles) under current cost estimates. Funding: W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106 and 72188101] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0381 .

Earthing System Analysis for Steel Tower Carrying 33kV Line

This study examines the critical analysis of earthing systems connected to steel towers that carry 33 kV power lines. Ensuring the safety and dependability of power transmission infrastructure becomes crucial in light of the growing demand for energy. By utilizing sophisticated computational methodologies and simulation approaches, this research carefully investigates how well the earthing system performs in various operational circumstances and fault scenarios. A thorough modeling technique is used in the analysis, which considers a number of variables including fault currents, tower design, soil resistivity, and grounding electrode configurations. In order to give a comprehensive understanding of the behavior of the system and its consequences for operational reliability, the study simulates many situations, including normal operation and fault occurrences. By using sophisticated numerical simulations and sensitivity analysis, the research pinpoints important factors affecting the earthing system's efficiency and suggests creative optimization techniques. These optimization techniques could involve changing the location of the grounding electrode, improving the materials used in the conductor, or putting additional safety precautions in place. The researcher's conclusions have important ramifications for the engineering community since they provide practical advice on how to strengthen the security and robustness of power transmission networks. The study adds to the continuous efforts to improve the efficiency and dependability of electrical grids by addressing potential weaknesses in the architecture of the earthing system. This thorough study advances the field of earthing system engineering by offering a solid platform for next studies and real-world implementations. Through this work, we can better understand the dynamics of earthing systems and optimization methodologies, which will help build more resilient and sustainable power transmission infrastructure that can adapt to society's changing energy needs.

The transmission line foreign body detection algorithm based on weighted spatial attention.

The secure operation of electric power transmission lines is essential for the economy and society. However, external factors such as plastic film and kites can cause damage to the lines, potentially leading to power outages. Traditional detection methods are inefficient, and the accuracy of automated systems is limited in complex background environments. This paper introduces a Weighted Spatial Attention (WSA) network model to address the low accuracy in identifying extraneous materials within electrical transmission infrastructure due to background texture occlusion. Initially, in the model preprocessing stage, color space conversion, image enhancement, and improved Large Selective Kernel Network (LSKNet) technology are utilized to enhance the model's proficiency in detecting foreign objects in intricate surroundings. Subsequently, in the feature extraction stage, the model adopts the dynamic sparse BiLevel Spatial Attention Module (BSAM) structure proposed in this paper to accurately capture and identify the characteristic information of foreign objects in power lines. In the feature pyramid stage, by replacing the feature pyramid network structure and allocating reasonable weights to the Bidirectional Feature Pyramid Network (BiFPN), the feature fusion results are optimized, ensuring that the semantic information of foreign objects in the power line output by the network is effectively identified and processed. The experimental outcomes reveal that the test recognition accuracy of the proposed WSA model on the PL (power line) dataset has improved by three percentage points compared to that of the YOLOv8 model, reaching 97.6%. This enhancement demonstrates the WSA model's superior capability in detecting foreign objects on power lines, even in complex environmental backgrounds. The integration of advanced image preprocessing techniques, the dynamic sparse BSAM structure, and the BiFPN has proven effective in improving detection accuracy and has the potential to transform the approach to monitoring and maintaining power transmission infrastructure.

Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

AbstractUrban power systems rely on intricate wire networks, known as the power grid, which form the essential electric infrastructure in cities. While these networks transmit electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO‐LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real‐world systems.

Research and Analysis of the Cost of Right of Way for Conventional Overhead Transmission Lines and Power Transmission Cable

The study explores the pros and cons of adding these advanced conductors to transmission lines. This study examines the costs of procuring RoW for standard overhead transmission lines and power transmission cables. The report details the costs and considerations of obtaining land for power transmission infrastructure. Research has two main parts. The document begins with transmission asset owner’s RoW acquisition methods and rules. It explains the regulatory framework and mechanisms for securing land rights for power transmission infrastructure. The second section analyses costs in detail. It compares the economics of installing overhead transmission lines with power transmission cables, focusing on subsurface XLPE cables. The study examines how RoW acquisition affects adjacent land prices and the economic effects on TAOs and surrounding communities. The research also evaluates transmission line dismantling costs, including waste metal value, considering metal price changes. RoW costs persist after the transmission line's tenure. The paper also examines a replacement model that estimates RoW costs for switching from overhead transmission lines to underground XLPE cables for outmoded transmission infrastructure. This analysis seeks to illuminate RoW Acquisition's financial elements for power transmission decision-makers.

Long-term energy transition planning: Integrating battery system degradation and replacement for sustainable power systems

The transition towards sustainable energy systems has gained significant importance globally due to high levels of environmental pollution caused by the use of fossil fuels. This study addresses a critical aspect of the global transition towards sustainable energy systems. We emphasize the impact of considering energy storage system degradation and replacement in long-term energy transition planning. The importance of this lies in mitigating the challenges posed by the intermittency and variability of renewable energy sources, such as wind and solar, without failing to consider the negative effect that excessive use of batteries as a storage energy system would have. We propose an optimization model that integrates power systems generation and transmission infrastructure with strategic energy storage system implementation to optimize their size, operation, and location for maximum efficiency and benefits. This paper offers a comprehensive approach to take into account the environmental effect of the depth of the discharge and replacement of battery systems over a long-term planning horizon considering hourly operation of representative time periods. The electricity system of Baja California, Mexico, a region heavily reliant on fossil fuels, is presented as a case study to show the applicability of the model. Results demonstrate that continuous replacement of dispatchable generators (natural gas power plants) for non-dispatchable generators (variable renewable power plants) may cause an unexpected increase of indirect greenhouse gas emissions from the continuous replacement of batteries when degradation is not considered. The results of the case study show an average emissions factor of 0.325 Ton CO2eq/MWh, approximately 24 % lower than government expectations, using the proposed MILP model.

Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

Abstract The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.

Complementary potential of wind-solar-hydro power in Chinese provinces: Based on a high temporal resolution multi-objective optimization model

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector.

Modelling of a High-Temperature Superconductor HVDC Cable Under Transient Conditions

The carbon neutrality goal of achieving net-zero by 2050 has sparked significant interest in offshore wind farms. With the offshore wind farms installed > 100 km away from the seashore, underground power transmission infrastructure is a necessity. Considering the power cable length and low net effective losses, a high-voltage direct-current (HVDC) system is chosen for this study. Also being lossless in DC operation, the HVDC cables are considered to be made out of high-temperature superconductor (HTS) material. But, unlike copper/aluminium, HTS material has a sharp transient behaviour as a function of the operating current, temperature and field. Thus, under transient conditions, as the fault current ramps up, this can lead to an increase in the HTS operating temperature, causing either degradation or permanent damage to the HVDC cable. In this paper, an HTS HVDC cable model has been developed in MATLAB/Simscape coupling both electrical and thermal models. For this study, a 100 km long coaxial 100 kV/1 GW DC HTS power cable is considered and modelled both as a lumped element and distributed element model with 100 elements to compare and evaluate the cable parameters along the length. The parameters include temperature distribution, resistance, critical current, and losses at different spots throughout the length of the cable. To simulate the transient condition, a line-to-ground (LG) fault is considered and the current distribution between the copper former and HTS tapes is studied. Using this cable model, the maximum temperature of the HTS and coolant both in the superconducting state and transient state are evaluated and presented. In comparison to the distributed model, the lumped model displayed different thermal and electrical values.

Administration strategy of energy management in smart grid: system view and optimization path

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

The role of power transmission infrastructure in income inequality: Fresh evidence from China

Power infrastructure plays a critical role in poverty and economic growth, but its distributional effects remain actively debated, especially in developing countries. This paper empirically examines the effects of power transmission infrastructure on income inequality in China. We find that power transmission infrastructure has a significant income distribution effect. This paper also analyzes its impact mechanism and suggests that the distributional effects mainly work through improving agricultural productivity and rural residents' income. With further research, the empirical results show that the fruit of power transmission infrastructure development is unevenly distributed within different groups, and the income inequality within urban and rural sectors has become even worse. The distributional effects also differ across regions, and the power-importing areas benefit more than exporting regions. These findings suggest insightful references for developing countries’ power infrastructure planning and construction strategy.

Digitalization as a problem or solution? Charting the path for research on sustainable information systems

Digitalization has permeated all aspects of human lives, economies, and societies. This transformation has been driven by the rapid growth in computing power, storage capabilities, and data transmission infrastructures. These changes have enabled innovations, such as cloud computing, artificial intelligence, smartphones, digitalized homes, (semi) autonomous vehicles, quantum computing, and more. Digitalization has further resulted in faster, more effective service delivery by many organizations. The phenomenon of digitalization relies on an increasingly finite supply of resources, such as crude oil, silicon, and energy. Over the past 150 years, humans have consumed as many natural resources as they have consumed in the past 20,000 years. In part, this increasing clip of consumption has been driven by digitalization, as novel, technology-based solutions, such as blockchain, supplant older, slower low-tech solutions, such as books and ledgers, to process data and create value. Digitalization’s demand for resources may be leading us to an environmental abyss. Consider cryptocurrencies such as Bitcoin, whose electricity consumption is approximately equal to the energy needs of small nations such as Malaysia or Sweden. Such consumption evokes the question, is, “is more digitalization really better, or given the harm to the planet, is this one context where less is more?”. In this paper, we develop a research agenda for understanding the full cost of digitalization and its impact on sustainability. We do so in three parts; first, we offer a crisp definition of sustainability; second, we offer a concise review of the digitalization and sustainability literature; and third, we offer suggestions for research that advances our understanding of how digitalization impacts sustainability.

Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

Autonomous power line detection and tracking system using UAVs

A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection operations.This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons; (2) a localisation system for pylon navigation and cable grasping; and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

Measuring dynamics of solar energy resource quality: Methodology and policy implications for reducing regional energy inequality

Solar energy utilization has been widely recognized as the most promising solution for achieving the global and regional carbon neutral targets. While existing literature on assessment of the solar energy resource mostly focuses on the current availability of energy quantity, the dynamic quality characteristics of reliable energy supplies are always neglected. This research presents a holistic spatiotemporal assessment methodology to quantify the solar energy resource quality (SERQ) from a quantity and quality coupled perspective. Specifically, we used the time-series daily meteorological reanalysis data, annual land use/cover, and nighttime light data during 1992–2018 to investigate the dynamic properties of SERQ at the 0.125° × 0.125° grid cell level across China. The validation process based on exiting solar power farms was conducted to analyze the applicability of proposed method in site selection. Our findings revealed that the annual solar energy potential in China was estimated at 66 PWh with the high resource quality level (SERQI>0.6). Furthermore, the outcomes of resource quality evaluation are more sensitive to the power load accessibility index. The involvement of energy quality dimension would reduce regional energy inequality in China. Thus, we highlight the necessity of improving the long-distance power transmission and energy storage infrastructures. Mapping the SERQ at the country level could provide investors and decision makers with useful insights to develop appropriate energy planning and incentive policies.

Concurrent 22. Presentation for: Central Queensland hydrogen project

Presented on Thursday 19 May: Session 22 Renewable hydrogen is being heavily promoted around the world for energy storage, and to support the decarbonisation of energy systems. Queensland is well placed for hydrogen export to Asia and is actively working to make this a reality. Stanwell Corporation, together with its project partners Iwatani, APA Group, Kawasaki Heavy Industries, Marubeni and Kansai Electric Power Company, is working to develop a commercial world-scale project to produce and export hydrogen from Gladstone to Japan, supporting Japan’s Net Zero by 2050 commitment. The size of the hydrogen production system (3 GW) is significant and as a first of a kind in Queensland raises several technical, regulatory and social challenges. To highlight the scale of the project, the renewable power generation to be installed to support this project will have a capacity equivalent to the current Queensland daily power demand. There are several challenges, one being that solar and wind energy resources by their nature are variable. Identifying and defining the mix of renewable energy to achieve a reliable power supply, and integrating this with the electrolyser systems, will be key to the success of the project. The upgrades required to existing power transmission infrastructure are significant, while additional water supply in the Gladstone region will be required. Finally, recent developments in Gladstone have created a boom–bust cycle and the social acceptance of this development requires ongoing engagement to address community concerns. To access the presentation click the link on the right. To read the full paper click here

Central Queensland hydrogen project

Renewable hydrogen is being heavily promoted around the world for energy storage, and to support the decarbonisation of energy systems. Queensland is well placed for hydrogen export to Asia and is actively working to make this a reality. Stanwell Corporation, together with its project partners Iwatani, APA Group, Kawasaki Heavy Industries, Marubeni and Kansai Electric Power Company, is working to develop a commercial world-scale project to produce and export hydrogen from Gladstone to Japan, supporting Japan’s Net Zero by 2050 commitment. The size of the hydrogen production system (3 GW) is significant and as a first of a kind in Queensland raises several technical, regulatory and social challenges. To highlight the scale of the project, the renewable power generation to be installed to support this project will have a capacity equivalent to the current Queensland daily power demand. There are several challenges, one being that solar and wind energy resources by their nature are variable. Identifying and defining the mix of renewable energy to achieve a reliable power supply, and integrating this with the electrolyser systems, will be key to the success of the project. The upgrades required to existing power transmission infrastructure are significant, while additional water supply in the Gladstone region will be required. Finally, recent developments in Gladstone have created a boom–bust cycle and the social acceptance of this development requires ongoing engagement to address community concerns.