Electricity Transmission Infrastructure Research Articles

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.

Optimal design and analysis of a grid‐connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar

AbstractThis study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond.

Transmission Necessary to Scale Clean Energy

It is imperative to invest concurrently and quickly in electric transmission infrastructure if the United States has any chance of meeting its decarbonization goals. More renewable energy, distributed resources located far from load centers, grid hardening, and faster grid recovery after severe weather events depend on it.

Deep learning based real time Demand Side Management controller for smart building integrated with renewable energy and Energy Storage System

Electric Power Consumption (EPC) and Renewable Energy Generation (REG) are inconsistent and this fickle nature affects the utility grid's power quality and system stability. The electric transmission and distribution infrastructure must be upgraded to meet the consumer's peak demand. Hence, proposing a Demand Side Management (DSM) program in smart grid to reduce utility grids Peak to Average Ratio (PAR) and end-users electricity tariff. Renewable energy with Energy Storage System (ESS) in the DSM controller is used to enhance the end user's economic and environmental features. This article proposes a Recurrent Neural Network (RNN) based Long Short Term Memory (LSTM) framework for Science Block (SCB) every minute and 5 min of EPC and REG forecasting to develop the DSM program. This proposed deep learning model performance, is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-squared. The results show that the proposed DSM program benefits end electricity users and smart grid operators.

Multi-stage deep learning networks for automated assessment of electricity transmission infrastructure using fly-by images

The modern electric power transmission system is a geographically extensive network which can span hundreds of kilometres, crossing harsh terrain and making manual inspection of its components costly. This paper proposes a novel condition assessment methodology for transmission overhead lines that is significantly more cost effective compared to traditional foot-patrol visual inspections. The proposed methodology utilizes a multi-stage ensemble deep learning network to automatically classify tower conditions based on high resolution aerial images. While aerial inspections allow relatively quick inspection of transmission routes, they are not usually used for condition assessment due to the high capturing altitude of images which would require time consuming manual processing to identify defects on hundreds to thousands of images. The proposed methodology automatically isolates transmission poles, disaggregates components, detects defects and determines the health index of concrete structures and insulators. The method involves pushing images through layers of automatic detection, region of interest (RoI) extraction and patching. A number of recent object detectors were tested on real-world data to evaluate their performance and an ensemble model is composed to improve the reliability of the detection. Results indicate that the multi-layer approach with output-based ensemble modelling, can effectively detect critical defects, although incipient fault conditions remain uncertain.

The Impact Analysis of Electricity Prices on the Energy Intensity of the Kazakhstani Economy and Sustainable Development

The independent Kazakhstan is highly inefficient electricity production, transmission and distribution infrastructure, while electricity prices both for industry and residential consumers is significantly lower that world average level due to abundance of oil, gas and coal resources. As a result of resource usage inefficiency, the country loss of 3-5% of national growth domestic product and competitiveness on international economic arena. The aim of this study is to determine the possibilities of ensuring economic growth in Kazakhstan by improving the energy efficiency of the economy. The paper proposes to identify the relationship between economic growth and energy efficiency in Kazakhstan by applying the correlation-regression and comparative analysis of the dynamics of the national gross product and its specific energy intensity, which will determine and substantiate the priority direction of increasing energy efficiency and sustainable development in Kazakhstan. The result of correlation-regression analysis reveals that an increase in the price of electricity supply for industry by 2.5 KZT tenge per 1 kW leads to an improvement in the energy intensity of GDP by 0.012 kg per 47,000 KZT (or 100 USD), and an increase in the price of electricity supply for the population by 2.5 KZT per 1 kW leads to an improvement in energy intensity GDP by 0.005 kg per 47,000 KZT. In discussion, the article argues that country is mostly focuses on development of renewable energy sector, while considering the situation that Kazakhstan is geographically large country with high energy losses during the transmission and distribution electricity system, the application of the Smart Grid technologies seems as a prerequisite for improvement of energy efficiency and economic growth in Kazakhstan.

Preserving and Presenting Heritage through Sustainable Energy Tourism: The Case of Kobarid in Slovenia

Energy tourism, which is quite recent despite the fact that the practice of tourists visiting power plants, very often for educational purposes, has a long tradition in Slovenia due to power plants on the Drava River. Particularly, the oldest Fala power plant is an area where the technical field of electric power production and transmission overlaps with tourism. The article that employs the methods of participant observation, interviews with some stakeholders and content analysis focuses on some possibilities of including electric power production and transmission infrastructure into various tourist and educational programmes, including through storytelling, which is a useful tool also when it comes to presenting sustainable and socially responsible project design, considering the needs of all stakeholders involved in the process and, consequently, raising awareness and responsibility towards the environment. Based on a case study of the Kobarid substation, which is a modern sustainably designed power facility built in a Natura 2000 protected area, this article focuses on the possibilities of creating new energy tourism products by employing storytelling, new media and new technologies.

Forecasting riverine erosion hazards to electricity transmission towers under increasing flow magnitudes

Flooding and erosion will pose increasing challenges to urban settlements and critical infrastructure, such as roads and power grids in the future. Improved projections on the impact of climate change to critical infrastructure are essential to assist future planning. This paper uses hydro-sedimentary modelling to predict river erosion threats to electricity transmission infrastructure in an urbanised river valley under multiple increasing flow magnitude scenarios. We use a coupled hydrodynamic and landscape evolution model, CAESAR-Lisflood, to simulate river channel changes along a reach of the River Mersey, UK from the present day to 2050. A range of synthetic flow scenarios, based on recent hydrological records, was used in the model ranging from ‘no change’ up to a flow with 50% higher magnitude. The results revealed: (1) riverbank erosion will pose significant threats to several transmission towers located along the river, requiring intervention to avoid destabilisation by the moving channel; (2) the total area of floodplain erosion and deposition ≥ 0.5 m deep was positively related to increasing projected flow magnitudes. However, through running a ‘low’ and ‘high’ erosion version of the model, the simulations revealed these threats were most sensitive to the calibration of the erosion component of the model, illustrating the challenges and uncertainty in forecasting long-term river channel change; and (3) how long-term simulations can assist in adaptation planning for electricity transmission towers. Further reach- and catchment-scale modelling will be necessary to determine the timings of large floods more accurately, which produce the most significant erosion and deposition events, and to evaluate the efficacy of protections to transmission towers.

Protection of electricity transmission infrastructure from sinkhole hazard based on electrical resistivity tomography

Sinkhole formation induced by limestone quarrying has the potential to cause severe environmental problems that impact agricultural activities and damage property. Moreover, sinkholes present a stability hazard to critical infrastructure such as power transmission towers. Field investigations in northeast Thailand found surface expressions of potentially dangerous sinkholes adjacent to a limestone quarry. 2D electrical resistivity tomography (ERT) profiles were deployed across previously mapped fractures that are located near power lines in order to detect the presence of subsurface structures and cavities in the carbonate bedrock. A 3D ERT survey was conducted near the base of power transmission tower T1 to precisely determine variations in subsurface material and structures. The ERT results were combined to delineate possibly hazardous cavities and continuous fractures. The surficial collapse features were generally observed to be in alignment with interpreted subsurface cavities and fractures. Water-table declines, quarry dewatering, and quarry blasting are possible triggers for the formation of the sinkholes. Additional sinkhole collapses could intensify along specific air-filled cavity zones in the near surface. Professional evaluations and regulatory action may be necessary to limit further quarry dewatering activities and prevent additional sinkhole collapses and further risk to the power infrastructure.

Aeroelastic modeling to study the wind-induced response of a self-supported lattice tower

The results from a 1:50 scale aeroelastic model of a self-supported steel lattice tower subjected to simulated hurricane winds are presented. The lattice tower considered is a typical structure that is used as part of a tower-insulator-conductor system for electrical transmission infrastructure. The aeroelastic tests were conducted at the NSF Wall of Wind Experimental Facility (WOW EF) at the Florida International University (FIU). The tower was tested at various wind speeds ranging from 50 m/s to 92 m/s (equivalent full-scale speeds) for varying wind directions. Two system identification (SID) techniques were utilized to evaluate along-wind aerodynamic damping and compare with theoretical estimates. The SID techniques were also utilized to evaluate crosswind aerodynamic damping. A buffeting analysis was conducted to estimate the response of the tower and compare it to measured values at the WOW. Drag and moment coefficients were calculated from the measured responses, and the dynamic amplification factors (DAF) as well as gust effect factors were computed. The analysis required consideration of the variation of the turbulence intensity along the height of the tower in the buffeting analytical equations. The drag coefficients are shown to agree with values proposed in the current standards. However, there might be a need to introduce base moment coefficients in lattice tower design. The resonance contribution is shown to reach a maximum of 18% of the peak response of the tower.

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities

Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

Transmission Network Investment Using Incentive Regulation: A Disjunctive Programming Approach

A well-planned electric transmission infrastructure is the foundation of a reliable and efficient power system, especially in the presence of large scale renewable generation. However, the current electricity market designs lack incentive mechanisms which can guarantee optimal transmission investments and ensure reliable integration of renewable generation such as wind. This paper first proposes a stochastic bilevel disjunctive program for optimal transmission investment based on the newly proposed theoretical H-R-G-V incentive mechanism. The upper level is a profit-maximization problem of an independent transmission company (Transco), while the lower level is a welfare maximization problem. The revenue of the Transco is bounded by a regulatory constraint set by the regulator in order to induce socially optimal investments. The application of the H-R-G-V mechanism allows the regulator to ensure social maximum transmission investments and helps to reduce transmission congestion and wind power spillage. The transmission investment under the H-R-G-V mechanism is modeled as a stochastic bilevel disjunctive program. To solve the developed mathematical model we first propose a series of linearization and reformulation techniques to recast the original model as a stochastic mixed integer linear problem (MILP). We exploit the disjunctive nature of the reformulated stochastic MILP model and further propose a Bean decomposition algorithm to efficiently solve the stochastic MILP model. The proposed decomposition algorithm is also modified and accelerated to improve the computational performance. The computational performance of our MILP modeling approach and modified and accelerated Bean decomposition algorithm is studied through several examples in detail. The simulation results confirm a promising performance of both the modeling approach and its solution algorithm.

The Synoptic and Mesoscale Evolution Accompanying the 2018 Camp Fire of Northern California

AbstractThe Camp Fire event was associated with dry, northeasterly winds that descended the western slopes of the Sierra Nevada of Northern California during the early morning hours of 8 November 2018. The downslope winds peaked around sunrise, with strong winds pushing the fire rapidly toward Paradise, California. Similar to recent central/Northern California wildfires associated with downslope winds, the synoptic pattern was characterized by building sea level pressure over the Intermountain West and a trough along the coastal zone, with both the synoptic evolution and low-level winds skillfully forecast by operational models. The maximum wind gusts along the western Sierra Nevada slopes ranged from 10–20 kt (1 kt ≈ 0.51 m s−1) at sheltered locations to 50–60 kt at exposed sites on the mid- to upper slopes of the barrier. The highest winds were not climatologically exceptional, and low-level temperatures were cooler than normal over and to the east of the Sierra Nevada, near normal over the western slopes, and warmer than normal over coastal California. Drier-than-normal conditions prevailed during the ∼3 days preceding and during the event, as a result of downslope winds. The origin of the fire can be traced to strong winds interacting with a failing electrical transmission infrastructure, with highly flammable surface fuels fostering rapid fire movement between the ignition source and Paradise.

Modeling China’s interprovincial electricity transmission under low carbon transition

Interprovincial electricity transmission is expected to play an important role towards low carbon transition to facilitate new and renewable energy development in West China to meet the increasing electricity demand in East China. To analyze changes in interprovincial electricity transmission and transmission infrastructure construction under China’s Nationally Determined Contributions and the 2-degree target, this study develops an interprovincial electricity transmission model with multi-voltage levels and integrates it with an improved 30-province energy system model to simulate reference scenario (REF) and three low carbon scenarios from a whole energy system perspective, CP30 (emissions peak in 2030), CPE (emissions peak in advance) and C2D (2-degree target). In 2050, electricity transmission is expected to increase to 2526.5 TWh, 3299.3 TWh, 3714.2 TWh and 4002.0 TWh under the REF, CP30, CPE and C2D scenarios, respectively, and the ultrahigh-voltage transmission demand is expected to reach 595.5 GW, 843.3 GW 917.0 GW and 1198.0 GW respectively. The overall pattern of electricity transmission will become increasingly complex as long-distance and large-capacity transmission becomes more prominent. Inner Mongolia, Northwest China and Southwest China will be the major sources of electricity transmissions, and the Beijing-Tianjin-Hebei region, Guangdong, Central China and East China will be the major importers. Ultrahigh-voltage lines will form large-capacity and long-distance electricity transmission paths connecting the power generation bases and load centers. These findings suggest that there will be a significant increase in the demand for electricity transmission and infrastructure construction in the future and that the policies promoting ultrahigh-voltage lines development should be enhanced.

Assessing wind and solar resources. Case study: department of Norte de Santander, Colombia, stage 1

Renewable sources used to produce energy has gained momentum due to its environmental benefits and economic feasibility. Colombia has been part of the global tendency to develop non-conventional renewable technologies such as wind and solar and integrate those technologies into the energy matrix of the country. However, the lack of understanding of the wind and solar resources in some areas of the country including the department of Norte de Santander, Colombia, has limited the deployment of these technologies and the development of new projects. This research proposes an approach to assess wind and solar resources in the department of Norte de Santander, Colombia. Also, this paper discusses the physics behind the generation of energy from wind and solar sources and the contributions required from different backgrounds to help in the wide deployment of solar and wind energy. In this first stage, a rough technical concept is developed, and the proposed sites are selected based on publicly available maps of low resolution presenting average irradiance and wind speed, and proximity to electricity transmission infrastructure. The areas selected have a global horizontal irradiance ranging from 4.0 kWh/m2 to 5.0 kWh/m2 and wind speeds between 4.0 m/s and 6.0 m/s at 10.0 m height on average in a year.

Water-carbon trade-off for inter-provincial electricity transmissions in China

Electricity transmissions have been utilized in China to balance its spatially mismatched development needs and natural resources endowments. However, it has led to spatial shifts of negative environmental impacts induced by electricity generations, including water consumption and carbon emissions. This study calculates that, from 2010 to 2016, carbon emissions and water consumption to produce the transmitted electricity have grown from 507 Mt and 2.7 km3 respectively to 642 Mt and 6.5 km3. Applying a structural decomposition model coupled with a Quasi-Input-Output model to quantify the driving factors of such increases, we find that GDP increase has played the dominant role in driving the increase of both factors. Our results also highlight the potential conflicts between carbon reduction and water conservation in developing future electricity transmission infrastructure systems. Changing the electric power sector's energy portfolio and the transmission structure by increasingly utilizing hydropower productions have both contributed to national total carbon emissions reductions, but at the expenses of increased national water consumption. As a result, on a national level, in 2016, due to the inter-provincial differences in energy portfolios and technologies, electricity transmissions have led to 155.27 Mt of net carbon reduction and 3.4 km³ of additional net water consumption compared to the counterfactual scenario where no electricity transmission were facilitated. The future expansion of electricity transmission network needs to consider such trade-offs, particularly within the contexts of global climate change mitigation and emergent challenges posed by water stresses.

Optimum Renewable Energy Investment Planning in Terms of Current Deficit: Turkey Model

In this study, a methodology was suggested for wind and solar energy investment plans through linear optimization model for the countries with an energy-based current deficit problem. The originality of the study is that it is a renewable energy investment model based on the functioning of the balance of payments for current deficit reduction, which has not previously been encountered in the literature. While creating the model, without causing external economic imbalance, certain parameters were taken into consideration such as profit transfers for the foreign direct investments, interest payments for the domestic investments, import rates for the wind and solar energy systems, energy electric power production values, electric power load balance, electricity transmission infrastructure, CO2 emission, future electric power demand projection, and import source rates in the electric power production. It was proven that the model, for the 2019–2030 period in Turkey, not only is an opportunity for decreasing the current deficit but also ensures reaching the CO2 emission reduction target. Additionally, through the investments in wind and solar energy, it was calculated that fossil-based electric power production will decrease by 80%, and a CO2 reduction will be provided, which is equivalent of 100 million tonnes GWh natural gas. As a more general result, an optimization model was created which provides a solution for countries coping with energy-based current deficit in economic terms, energy-based air pollution in environmental terms, and renewable energy technology insufficiency.

Assessment of peak power demand reduction available via modulation of building ventilation systems.

Peak power demand strains electrical grids and increases cost of electricity generation, transmission and distribution infrastructure. Many studies have examined ways of reducing peak power demand, including modification of room air temperature setpoints or reduction of lighting levels. However, very few or no studies have examined the peak power reduction resource offered by temporary curtailment of building ventilation systems. For this reason, we conducted a simulation campaign in which we examined the resource offered by temporary ventilation curtailment in commercial buildings of different use types across the United States and in residences in the state of California, with the essential constraint that any changes resulted in air quality acceptable to occupants through additional ventilation to compensate for the curtailment. To do this, we employed previously validated building models implemented in the airflow and contaminant transport tool CONTAM and building thermal and systems modeling tool EnergyPlus, in some cases co-simulated. Results show savings are highly dependent on building type and climate but range from 0–2 W/ft2 and up to 40% of total peak building power demand. Depending on building type, this power shed can be conducted for 1.5–8 h before acute exposure or odor concerns are expected, assuming a safety factor of 2. This reduction is of the same magnitude as that offered by thermal control strategies such as setpoint increase, or from lighting reduction strategies.

Climate change impacts and costs to U.S. electricity transmission and distribution infrastructure

This study presents a screening-level analysis of the impacts of climate change on electricity transmission and distribution infrastructure of the U.S. In particular, the model identifies changes in performance and longevity of physical infrastructure such as power poles and transformers, and quantifies these impacts in economic terms. This analysis was evaluated for the contiguous U.S, using five general circulation models (GCMs) under two greenhouse gas emission scenarios, to analyze changes in damage and cost from the baseline period to the end of the century with three different adaptation strategies. Total infrastructure costs were found to rise considerably, with annual climate change expenditures increasing by as much as 25%. The results demonstrate that climate impacts will likely be substantial, though this analysis only captures a portion of the total potential impacts. A proactive adaptation strategy resulted in the expected costs of climate change being reduced by as much as 50% by 2090, compared to a scenario without adaptation. Impacts vary across the contiguous U.S. with the highest impacts in parts of the Southeast and Northwest. Improvements and extensions to this analysis would help better inform climate resiliency policies and utility-level planning for the future.