Natural gas is the largest source of electricity generation in Texas. In 2021, it accounted for almost half of the power generated in the U.S. state. Wind ranked second, but by a wide margin, representing some 21 percent of Texas's electricity generation that year.

Texas has suffered major disruptions to its electricity supply due to storm Uri sweeping the United States from February 14, 2021. The state's largest independent system operator, the Electric Reliability Council of Texas (ERCOT) was unable to deliver electricity to over four million customers, as extreme cold temperatures resulted in many power plants having to be shut off. ERCOT manages roughly 90 percent of Texas' electric load, serving 26 million customers. Between February 15 and 17, the difference between day-ahead forecasts for electricity demand and electricity generation was particularly pronounced.

Texas is the leading electricity-consuming state in the United States. In 2022, the state consumed roughly 475 terawatt-hours of electricity. California and Florida followed in second and third, each consuming approximately 250 terawatt-hours.

Profitability and investment risk of Texan power system winterization

This data repository contains interim and final results of the paper “Profitability and investment risk of Texan power system winterization” published in Nature Energy. Code used to generate these results can be found at github

Abstract

A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure.

Date and time format

Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in input/ is in UTC, all other input datasets are in local Texas time (GMT-6). In interim, temperatures/temppop/, temperatures/temp_gas_powerplant.csv, temperatures/temp_gas_outages.csv, temperatures/temp_coal_powerplant.csv, temperatures/temp_coal_outages.csv and the wind power simulation output (windpower/) is in UTC. All other datasets are in local Texas time.

Data

cache/

Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R)

figures/

Figures shown in the manuscript

raw_data: includes raw data for reproducing the figures in the main part of the manuscript

outage_model: figures representing the outage function as derived with our model

input/

Input data from external sources (with exception of orcd not included due to licensing issues)

ERA5_windspeeds_USA: available from the CDS. Download with scripts/download_era5_USA.py

gas_production: available from the Texas Railroad Commission in PDF format here. We extracted the data manually.

Load: available from ERCOT here

orcd: Scarcity prices as regulated by ERCOT. Manually extracted from J. Zarnikau et al.

outages: Outage Events from ERCOT with geo locations provided by Edgar Virguez here resulting from unit outage data provided by Ercot

population: population density data provided by arcgis here

powerplants: locations of power plants in Texas provided by the Energy Information Administration here

shp: shapefile of Texas state boundaries provided by arcgis here

temperatures: available from the CDS here. Can be downloaded with script scripts/download_era5_TX_temp.py

USWTDB: US wind turbine data base provided by the US Geological Service here. We used version: uswtdb_v3_3_20210114

GWA2: Global Wind Atlas Version 2.1 accessible here

interim/

Intermediary files from the analysis

bootstrap_year.csv: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb

bootstrap_year2020.csv: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb

turbine_data.csv: turbine data for Texan wind turbines Generated by scripts/prepare_TX_turbines.py Columns:

capacity: turbine capacity (kW)

height: turbine height (m)

lon: longitude coordinate (°)

lat: latitude coordinate (°)

sp: specific power (W/m²)

ind: running index

interim/load/

Temperature dependent estimates of electricity load for Texas.

load_est70_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns:

load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh)

temp: population weighted temperature (°C)

load_est10_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns:

load: observed load in period 2012-2021 as published by ERCOT (MWh)

load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh)

load_est9_LR24temptrend2021_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns:

load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh)

load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 forecast) were used, except the predicted year, i.e. this is a replication of the observed load with our model for validation purposes. (MWh)

load_est17_crossvalidation_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns:

load: observed load 2004 - 2021/01 and load forecast 2021/02 as published by ERCOT (MWh)

load_est: load estimated for cross validation for years 2004-2021, training years 2012-2020, trained with each year in traning period except modelled year with variable load level, i.e. this is a replication of the observed load with our model for validation purposes(MWh)

load_est_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns:

load: observed load 2004 - 2021 as published by ERCOT (MWh)

load_est: years 2004-2021 predicted with a model which was trained for the years 2012-2020 considering time trend, i.e. this is a replication of the observed load without outages with our temperature dependent model with our model for validation purposes (MWh)

interim/outages

outages.feather Outage by minute of all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns:

station: name of power plant

unit: name of generation unit

fullname: concatenated string of station and name

dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)

Longitude: Longitude of location of power plant

Latitude: Latitude of location of power plant

reduction: hourly reduction of capacity due to outage in this minute (MW)

cap_available: available capacity in this minute (MW)

cap_max: maximum capacity of unit (MW)

outages-hourly.feather Hourly outages at all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns:

station: name of power plant

unit: name of generation unit

fullname: concatenated string of station and name

dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)

Longitude: Longitude of location of power plant

Latitude: Latitude of location of power plant

reduction: hourly reduction of capacity due to outage in this time step (MW)

cap_available: hourly available capacity in this minute (MW)

cap_max: maximum capacity of unit (MW)

outages_reduction.csv Hourly outages per fuel (MW). We use these outages for COAL and GAS only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns:

NG: natural gas power plants

WIND: wind power plants

SOLAR: solar power plants

ESR: energy storage resource

HYDRO: hydropower plants

NUCLEAR: nuclear power plants

outages_reductionNorth.csv Hourly outages for the Northern part of Texas (latitude > 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.

outages_reductionSouth.csv Hourly outages for the Southern part area of Texas (latitude <= 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.

interim/temperatures

temppop Generated by scripts/calc_temppopC.py

contains population weighted temperatures for Texas, one file for each year (°C).

temp_coal_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns:

t2m: temperature weighted by coal power plants experiencing outages in February 2021 (°C)

temp_coal_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns:

t2m: temperature weighted by all coal power plants (°C)

temp_gas_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns:

t2m: temperature weighted by gaspower plants experiencing outages in February 2021 (°C)

temp_gas_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns:

t2m: temperature weighted by all gas power plants (°C)

temp_gasfields.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns:

t2m: temperature weighted by all gasfields (°C)

tempWP_NSsplit.csv Generated by notebooks/wp_temp_NSsplit.ipynb Columns:

t2mSouth: temperatures weighted by all wind power plants in the South (°C)

t2mNorth: temperatures weighted by all wind power plants in the North (°C)

interim/thresholds

thresh_total63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns:

total available capacity of

Texas is the U.S. state with the highest electricity generation from coal. In 2021, Texas utility scale coal power plants produced some 88.8 terawatt hours of electricity. West Virginia was the second largest coal power producer that year, at 59.6 terawatt hours. Although it topped this ranking, coal is only the third-largest electricity generating source in Texas, with natural gas by far the main contributor to the state's power mix.

The dataset of the synthetic Texas 123-bus backbone transmission (TX-123BT) system.The procedures and details to create TX-123BT system are described in the paper below:Jin Lu, Xingpeng Li et al., “A Synthetic Texas Backbone Power System with Climate-Dependent Spatio-Temporal Correlated Profiles”.If you use this dataset in your work, please cite the paper above.***Introduction:The TX-123BT system has similar temporal and spatial characteristics as the actual Electric Reliability Council of Texas (ERCOT) system.TX-123BT system has a backbone network consisting of only high-voltage transmission lines distributed in the Texas territory.It includes time series profiles of renewable generation, electrical load, and transmission thermal limits for 5 years from 2017 to 2021.The North American Land Data Assimilation System (NLDAS) climate data is extracted and used to create the climate-dependent time series profiles mentioned above.Two sets of climate-dependent dynamic line rating (DLR) profiles are created: (i) daily DLR and (ii) hourly DLR.***Power system configuration data:'Bus_data.csv': Bus data including bus name and location (longitude & latitude, weather zone).'Line_data.csv': Line capacity and terminal bus information.'Generator_data.xlsx': 'Gen_data' sheet: Generator parameters including active/reactive capacity, fuel type, cost and ramping rate.'Solar Plant Number' sheet: Correspondence between the solar plant number and generator number.'Wind Plant Number' sheet: Correspondence between the wind plant number and generator number.***Time series profiles:'Climate_5y' folder: Include each day's climate data for solar radiation, air temperature, wind speed near surface at 10 meter height.Each file in the folder includes the hourly temperature, longwave & shortwave solar radiation, zonal & Meridional wind speed data of a day in 2019.'Hourly_line_rating_5y' folder: Include the hourly dynamic line rating for each day in the year.Each file includes the hourly line rating (MW) of a line for all hours in the year.In each file, columns represent hour 1-24 in a day, rows represent day 1-365 in the year.'Daily_line_rating_5y' folder: The daily dynamic line rating (MW) for all lines and all days in the year.'solar_5y' folder: Solar production for all the solar farms in the TX-123BT and for all the days in the year.Each file includes the hourly solar production (MW) of all the solar plants for a day in the year.In each file, columns represent hour 1-24 in a day, rows represent solar plant 1-72.'wind_5y' folder: Wind production for all the wind farms in the case and for all the days in the year.Each file includes the hourly wind production (MW) of all the wind plants for a day in the year.In each file, columns represent hour 1-24 in a day, rows represent wind plant 1-82.'load_5y' folder: Include each day's hourly load data on all the buses.Each file includes the hourly nodal loads (MW) of all the buses in a day in the year.In each file,columns represent bus 1-123, rows represent hour 1-24 in a day.***Python Codes to run security-constrainted unit commitment (SCUC) for TX-123BT profilesRecommand Python Version: Python 3.11Required packages: Numpy, pyomo, pypower, pickleRequired a solver which can be called by the pyomo to solve the SCUC optimization problem.*'Sample_Codes_SCUC' folder: A standard SCUC model.The load, solar generation, wind generation profiles are provided by 'load_annual','solar_annual', 'wind_annual' folders.The daily line rating profiles are provided by 'Line_annual_Dmin.txt'.'power_mod.py': define the python class for the power system.'UC_function.py': define functions to build, solve, and save results for pyomo SCUC model.'formpyomo_UC': define the function to create the input file for pyomo model.'Run_SCUC_annual': run this file to perform SCUC simulation on the selected days of the TX-123BT profiles.Steps to run SCUC simulation:1) Set up the python environment.2) Set the solver location: 'UC_function.py'=>'solve_UC' function=>UC_solver=SolverFactory('solver_name',executable='solver_location')3) Set the days you want to run SCUC: 'Run_SCUC_annual.py'=>last row: run_annual_UC(case_inst,start_day,end_day)For example: to run SCUC simulations for 125th-146th days in 2019, the last row of the file is 'run_annual_UC(case_inst,125,146)'You can also run a single day's SCUC simulation by using: 'run_annual_UC(case_inst,single_day,single_day)'* 'Sample_Codes_SCUC_HourlyDLR' folder: The SCUC model consider hourly dynamic line rating (DLR) profiles.The load, solar generation, wind generation profiles are provided by 'load_annual','solar_annual', 'wind_annual' folders.The hourly line rating profiles in 2019 are provided by 'dynamic_rating_result' folder.'power_mod.py': define the python class for the power system.'UC_function_DLR.py': define functions to build, solve, and save results for pyomo SCUC model (with hourly DLR).'formpyomo_UC': define the function to create the input file for pyomo model.'RunUC_annual_dlr': run this file to perform SCUC simulation (with hourly DLR) on the selected days of the TX-123BT profiles.Steps to run SCUC simulation (with hourly DLR):1) Set up the python environment.2) Set the solver location: 'UC_function_DLR.py'=>'solve_UC' function=>UC_solver=SolverFactory('solver_name',executable='solver_location')3) Set the daily profiles for SCUC simulation: 'RunUC_annual_dlr.py'=>last row: run_annual_UC_dlr(case_inst,start_day,end_day)For example: to run SCUC simulations (with hourly DLR) for 125th-146th days in 2019, the last row of the file is 'run_annual_UC_dlr(case_inst,125,146)'You can also run a single day's SCUC simulation (with hourly DLR) by using: 'run_annual_UC_dlr(case_inst,single_day,single_day)'The SCUC/SCUC with DLR simulation results are saved in the 'UC_results' folders under the corresponding folder.Under 'UC_results' folder:'UCcase_Opcost.txt': total operational cost ($)'UCcase_pf.txt': the power flow results (MW). Rows represent lines, columns represent hours.'UCcase_pfpct.txt': the percentage of the power flow to the line capacity (%). Rows represent lines, columns represent hours.'UCcase_pgt.txt': the generators output power (MW). Rows represent conventional generators, columns represent hours.'UCcase_lmp.txt': the locational marginal price ($/MWh). Rows represent buses, columns represent hours.***Geographic information system (GIS) data:'Texas_GIS_Data' folder: includes the geographic information systems (GIS) data of the TX-123BT system configurations and ERCOT weather zones.The GIS data can be viewed and edited using GIS software: ArcGIS.The subfolders are:'Bus' folder: the shapefile of bus data for the TX-123BT system.'Line' folder: the shapefile of line data for the TX-123BT system.'Weather Zone' folder: the shapefile of the weather zones in Electric Reliability Council of Texas (ERCOT).*** Maps(Pictures) of the TX-123BT & ERCOT Weather Zone'Maps_TX123BT_WeatherZone' folder:1) 'TX123BT_Noted.jpg': The maps (pictures) of the TX-123BT transmission network. Buses are in blue and lines are in green.2) 'Area_Houston_Noted.jpg', 'Area_Dallas_Noted.jpg', 'Area_Austin_SanAntonio_Noted.jpg':The maps for different areas including Houston, Dallas, and Austin-SanAntonio are also provided.3) 'Weather_Zone.jpg': The map of ERCOT weather zones. It's ploted by author, may be slightly different from the actual ERCOT weather zones.***FundingThis project is supported by Alfred P. Sloan Foundation.***License:This work is licensed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) license.***Disclaimer:The author doesn’t make any warranty for the accuracy, completeness, or usefulness of any information disclosed and the author assumes no liability or responsibility for any errors or omissions for the information (data/code/results etc) disclosed.***Contributions:Jin Lu created this dataset. Xingpeng Li supervised this work. Hongyi Li and Taher Chegini provided the raw historical climate data (extracted from an open-access dataset - NLDAS).

This dataset tracks annual science proficiency from 2021 to 2022 for Energy Institute High School vs. Texas and Houston Independent School District

Texas is by far the leading U.S. state in terms of net electricity generation. In 2022, Texas generated around *** terawatt-hours of electricity, almost double the figure of second-ranked Florida. Pennsylvania ranked third, with some *** terawatt-hours generated that year.

United States Natural Gas: Proved Reserves: Texas data was reported at 149,062.000 Cub ft bn in 2021. This records an increase from the previous number of 114,732.000 Cub ft bn for 2020. United States Natural Gas: Proved Reserves: Texas data is updated yearly, averaging 48,104.000 Cub ft bn from Dec 1981 (Median) to 2021, with 38 observations. The data reached an all-time high of 149,062.000 Cub ft bn in 2021 and a record low of 37,847.000 Cub ft bn in 1993. United States Natural Gas: Proved Reserves: Texas data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB036: Natural Gas Proved Reserves.

United States Natural Gas Exports: LNG: To Portugal: From Freeport, Texas data was reported at 3,380.000 Cub ft mn in May 2021. United States Natural Gas Exports: LNG: To Portugal: From Freeport, Texas data is updated monthly, averaging 3,380.000 Cub ft mn from May 2021 (Median) to May 2021, with 1 observations. The data reached an all-time high of 3,380.000 Cub ft mn in May 2021 and a record low of 3,380.000 Cub ft mn in May 2021. United States Natural Gas Exports: LNG: To Portugal: From Freeport, Texas data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB022: Natural Gas Exports: Liquefied Natural Gas: by Point of Exit.

United States Natural Gas: Proved Reserves: Texas: RRC District 6 data was reported at 27,104.000 Cub ft bn in 2021. This records an increase from the previous number of 20,740.000 Cub ft bn for 2020. United States Natural Gas: Proved Reserves: Texas: RRC District 6 data is updated yearly, averaging 6,365.000 Cub ft bn from Dec 1979 (Median) to 2021, with 43 observations. The data reached an all-time high of 27,104.000 Cub ft bn in 2021 and a record low of 3,578.000 Cub ft bn in 1979. United States Natural Gas: Proved Reserves: Texas: RRC District 6 data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB036: Natural Gas Proved Reserves.

The United States electric grid, a vast and complex infrastructure, has experienced numerous outages from 2002 to 2023, with causes ranging from extreme weather events to cyberattacks and aging infrastructure. The resilience of the grid has been tested repeatedly as demand for electricity continues to grow while climate change exacerbates the frequency and intensity of storms, wildfires, and other natural disasters.

Between 2002 and 2023, the U.S. Department of Energy recorded thousands of power outages, varying in scale from localized blackouts to large-scale regional failures affecting millions. The Northeast blackout of 2003 was one of the most significant, impacting 50 million people across the United States and Canada. A software bug in an alarm system prevented operators from recognizing and responding to transmission line failures, leading to a cascading effect that took hours to contain and days to restore completely.

Weather-related disruptions have been among the most common causes of outages, particularly hurricanes, ice storms, and heatwaves. In 2005, Hurricane Katrina devastated the Gulf Coast, knocking out power for over 1.7 million customers. Similarly, in 2012, Hurricane Sandy caused widespread destruction in the Northeast, leaving over 8 million customers in the dark. More recently, the Texas winter storm of February 2021 resulted in one of the most catastrophic power failures in state history. Unusually cold temperatures overwhelmed the state’s independent power grid, leading to equipment failures, frozen natural gas pipelines, and rolling blackouts that lasted days. The event highlighted vulnerabilities in grid preparedness for extreme weather, particularly in regions unaccustomed to such conditions.

Wildfires in California have also played a significant role in grid outages. The state's largest utility companies, such as Pacific Gas and Electric (PG&E), have implemented preemptive power shutoffs to reduce wildfire risks during high-wind events. These Public Safety Power Shutoffs (PSPS) have affected millions of residents, causing disruptions to businesses, emergency services, and daily life. The 2018 Camp Fire, the deadliest and most destructive wildfire in California history, was ignited by faulty PG&E transmission lines, leading to increased scrutiny over utility maintenance and fire mitigation efforts.

In addition to natural disasters, cyber threats have emerged as a growing concern for the U.S. electric grid. In 2015 and 2016, Russian-linked cyberattacks targeted Ukraine’s power grid, serving as a stark warning of the potential vulnerabilities in American infrastructure. In 2021, the Colonial Pipeline ransomware attack, while not directly targeting the electric grid, demonstrated how critical energy infrastructure could be compromised, leading to widespread fuel shortages and economic disruptions. Federal agencies and utility companies have since ramped up investments in cybersecurity measures to protect against potential attacks.

Aging infrastructure remains another pressing issue. Many parts of the U.S. grid were built decades ago and have not kept pace with modern energy demands or technological advancements. The shift towards renewable energy sources, such as solar and wind, presents new challenges for grid stability, requiring updated transmission systems and improved energy storage solutions. Federal and state governments have initiated grid modernization efforts, including investments in smart grids, microgrids, and battery storage to enhance resilience and reliability.

Looking forward, the future of the U.S. electric grid depends on continued investments in infrastructure, cybersecurity, and climate resilience. With the increasing electrification of transportation and industry, demand for reliable and clean energy will only grow. Policymakers, utility companies, and regulators must collaborate to address vulnerabilities, adapt to emerging threats, and ensure a more robust, efficient, and sustainable electric grid for the decades to come.

Snapshot img

The wind turbine foundation market share is expected to increase by USD 8.50 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 7.37%.

This wind turbine foundation market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers wind turbine foundation market segmentation by application (onshore and offshore) and geography (APAC, Europe, North America, MEA, and South America). The wind turbine foundation market report also offers information on several market vendors, including ArcelorMittal SA, Bladt Industries AS, Blue H Engineering BV, ENERCON GmbH, Equinor ASA, Offshore Wind Power Systems of Texas LLC, Orsted AS, Peikko Group Corp., Ramboll Group AS, and Suzlon Energy Ltd. among others.

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Wind Turbine Foundation Market: Key Drivers, Trends, and Challenges

The change in energy mix is notably driving the wind turbine foundation market growth, although factors such as competition from alternative renewable sources of energy may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the wind turbine foundation industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Wind Turbine Foundation Market Driver

The energy mix is defined as the use of different proportions of energy sources such as fossil fuels, nuclear energy, and renewable energy to meet energy needs. Change in the energy mix because of factors such as evolving policy measures and technological advances will foster the growth of the market. The demand for energy is driven by the growing global population and rising disposable income in developing countries. The growing contribution of renewable energy sources in the global energy mix has resulted in the increased installations of wind towers, which, in turn, has driven the growth of the wind turbine foundation market.

Key Wind Turbine Foundation Market Trend

The rapid installation of offshore wind farms is one of the key emerging trends in the wind turbine foundation market. Of late, the offshore wind market has gained prominence in the global quest for installing clean and sustainable energy sources. Offshore wind farms are witnessing rapid installation because they have better operational conditions when compared with onshore farms. With offshore wind farms, much larger fans can be installed, which leads to a higher input even with a smaller number of turbines. The advantages have led to the approval of a wide-scale installation of offshore wind farms across multiple locations, which, in turn, will augment the wind turbine foundation market.

Key Wind Turbine Foundation Market Challenge

In the renewables sector, wind energy faces stiff competition from solar energy and hydropower. Of all the renewable sources of energy that are available, solar power has emerged as one of the least expensive sources of clean energy. The declining cost of solar energy generation due to initiatives and subsidies by governments, as well as competitive bidding processes, have significantly increased the number of solar PV panel installations globally. Hydropower is also considered one of the most common and least expensive forms of renewable energy. Therefore, the above-mentioned factors have resulted in the growing dependence on alternative sources of energy, such as solar and hydro, which is a challenge for the growth of the wind industry, which, in turn, will adversely affect the growth of the wind turbine foundation market.

This wind turbine foundation market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Parent Market Analysis

The growth in the global renewable electricity market will be driven by factors such as supporting policies and targets for deployment of renewable power, declining costs of renewable energy technologies, and increasing demand for renewable power due to environmental concerns. Our research report has extensively covered external factors influencing the parent market growth potential in the coming years, which will determine the levels of growth of the wind turbine foundation market during the forecast period.

Who are the Major Wind Turbine Foundation Market Vendors?

The report analyzes the market’s competitive land

The global electrical bushing market is projected to reach a market size of US$ 322.83 billion by 2033, registering a CAGR of 3.50% during the forecast period (2023-2033). The market is driven by increasing demand for electricity and the need to upgrade existing electrical infrastructure. The growing trend of renewable energy sources is also expected to contribute to the market's growth. Key market players in the electrical bushing market include ABB Group, TRENCH Group (Siemens), General Electric, Eaton, Elliot Industries, Gipro GMBH, RHM International, Toshiba, Webster-Wilkinson, Siemens (Germany), and Nexans (France). These companies are focusing on developing innovative products and technologies to cater to the evolving needs of the market. The market is also witnessing a trend towards consolidation, with larger players acquiring smaller companies to expand their product offerings and geographical reach. Recent developments include: May 2022: The first of its type in India, Hitachi's new transformer component production facility in Vadodara, Gujarat, will create resin-impregnated paper brushings., December 2021:ABB DOGDE Mechanical Power Transmission, the largest sale that was made publicly known.ABB has acquired 40 companies, seven of which were acquired in the previous five years. Private equity firms made 12 purchases in total. Additionally, eight assets were sold. Baldor Electric was the largest acquisition made by ABB to date, and it cost $4.2 billion., December 2021:According to an announcement, Gujarat Energy Transmission Corporation Limited (GETCO) received orders for transformers from Transformers and Rectifiers India Limited. The contract value for the entire order was INR 72 crores., October 2021:East Green Power Pte Ltd, one of Singapore's EPC firms, owns the urban-type underground substation for which Toshiba Energy Systems & Solutions Corporation ordered transformers and related equipment. The first 230 kV class urban-type underground substation in Southeast Asia, which will begin operations around 2025, will get three 200 MVA transformers and four 75 MVA transformers from Toshiba ESS., Hitachi Energy presented new products during CWIEME Berlin 2023, bringing innovative solutions. During a product launch and interactive sessions with industry professionals, the company exhibited its recent developments in transformer insulation and components for its new dry-type paperless bushing called EasyDry®, among others., In December 2023, Bechtel Energy Inc. awarded the contract to ABB to provide integrated automation, electrical and digital solutions for the Rio Grande LNG facility (RGLNG Phase 1) in Brownsville, Texas, designed by NextDecade Corporation., For example, in July 2021, GE Renewable Energy’s Grid Solutions announced that it had received orders for the supply and installation of multiple transformers and reactors rated at 765 kV from Power Grid Corporation of India Limited (PGCIL). As a part of this order, the company will provide 13 units of 765 kV transformers and 32 units of 765 kV reactors.. Notable trends are: Increase in investment in renewable energy sources is driving the market growth.

United States Natural Gas Imports: Avg Price: Pipeline: From Mexico: To Rio Grande, Texas data was reported at 3.100 USD/1000 Cub ft in Feb 2021. United States Natural Gas Imports: Avg Price: Pipeline: From Mexico: To Rio Grande, Texas data is updated monthly, averaging 3.100 USD/1000 Cub ft from Feb 2021 (Median) to Feb 2021, with 1 observations. The data reached an all-time high of 3.100 USD/1000 Cub ft in Feb 2021 and a record low of 3.100 USD/1000 Cub ft in Feb 2021. United States Natural Gas Imports: Avg Price: Pipeline: From Mexico: To Rio Grande, Texas data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P019: Natural Gas Import Price.

The Energy as a Service market offers a range of products and services, including:Energy monitoring and control systemsEnergy audits and assessmentsEnergy efficiency upgradesRenewable energy integrationEnergy performance contracting Recent developments include: In March 2024, HEA Energy strengthened its Offshore Wind and Oil & Gas service capabilities further by signing new contracts for a number of Jack Barges and a mix of Multi-Purpose Support Vessels (MPSV) and Dive Support Vessels (DSV) with extensive accommodation capacities for subsea intervention and operations support., Axis Energy Services announced in March 2024 that it had successfully deployed on Occidental operated wells a full electric well service rig., TGS, an energy data and intelligence provider based in Norway, entered into a strategic partnership with Enertel in February 2024. This will help operators access TGS-licensed well data through Enertel’s QuantumCast software platform as part of the collaboration targeted at delivering an integrated system., April 2023: Capstone Green Energy Corporation’s southern U.S. distributor, Lone Star Power Solutions, contracted to deliver another C800S Signature Series microturbine following an earlier contract for Energy-as-a-Service (EaaS) of 3.6 MW this year to one of West Texas’ big energy firms., March 2023: Honeywell has made a strategic investment in Redaptive to accelerate their collaboration on bringing out Energy-as-a-service (EaaS) capability for commercial and industrial buildings. It is meant to expedite the implementation of technologies aimed at reducing carbon emissions across a wide range of buildings., Orange SA – a French telecommunications company, signed an EaaS agreement with Engie SA - a utility company, whereby 355 kW solar panels would be installed at Orange's Data center located in West Africa's Côte d'Ivoire. As part of this arrangement, rooftop and carport installations shall be done by Engie so that Orange's main African data center can produce about 527 MWh per year using these panels., Besides, Schneider Electric announced GREENext is launching in December 2021. These two companies have united their forces for the purpose of providing energy-as-a-service to commercial and industrial customers through solar and battery hybrid microgrid technology., Honeywell and Alturas jointly announced a partnership in June 2021 to install battery energy storage systems worldwide. In this regard, Alturus will provide dedicated capital and structuring for Honeywell’s renewables & distributed assets projects.. Key drivers for this market are: Increased adoption of Distributed Energy Resources (DER), Increasing focus on decarbonization of global; Emergence of energy cloud platform. Potential restraints include: Lack of Skilled Expertise, High Deployment Cost.

Based in the northwestern region of Oklahoma, the Traverse Wind Energy Center is the largest wind power project in the United States by installed capacity, at approximately 999 megawatts. The Vineyard Wind I, in the Commonwealth of Massachusetts, is the second-largest U.S. wind project, and the first offshore utility-scale wind farm to be constructed in the country.

Texas leads for wind power

Two of the largest U.S. wind power projects are located in Texas, with the Aviator Wind and Goodnight Wind Energy projects. Texas is the leading state in terms of cumulative wind power capacity. Land availability is a major factor in Texas’s dominance in the U.S. wind market and land is often leased by private owners to for-profit energy companies.

Wind market growth in the U.S.

Cumulative installed wind power capacity in the United States has drastically increased in the past decade. This is in part due to greater investment in clean energy , especially after the 2008 Recession when oil prices spiked and the development of other renewable sources became more lucrative. Among renewable resources, solar generation has seen a larger increase than wind power over the last decades.

Natural gas distributors have benefited mainly from the enormous outburst of natural gas availability in the United States since the early 2000s because of the growing prevalence of advanced drilling techniques employed by upstream producers in the Oil Drilling and Gas Extraction industry. Natural gas is used to generate electricity, produce useful thermal output and as an industrial feedstock. Many end users, mainly electric power plants, have been pressed to transition to using this energy source at the expense of others because of its increased affordability and comparatively lower environmental impact. Despite the rising popularity of renewable energy like wind and solar, natural gas already has years of historical infrastructure built, making the supply chain much easier to navigate, leading the country to rely on it for most of its energy needs. Revenue is set to swell at a CAGR of 4.6% through the end of 2025 to $199.3 billion, including a 9.5% dip in 2025, as gas prices will rebound. Despite revenue growing swiftly as the need for gas overwhelmingly expanded during the current period, distributors have also endured wild swings in revenue because of highly volatile market conditions. For example, the price of natural gas fell in 2020 amid shutdowns as excess supply was built. Prices then spiked in 2021 and 2022 before falling again in 2023 as the industry stabilized following economic turmoil. Despite all this, the residential sector has been a saving grace, as prices have continued to climb yearly despite outside factors. Even so, overall profit has been pushed down as distributors lowered their selling prices. Natural gas production will climb marginally, while infrastructure investments will boost pipeline and export capacity. Thanks to global tensions, total domestic consumption is set to strengthen. Even so, consumption may be constrained growth as some markets slowly switch to renewable energy, constraining growth. Prices are also set to remain stagnant, which may prevent significant revenue spikes. Overall, revenue is set to climb at a CAGR of 0.7% through the end of 2030 to $205.9 billion.

United States Natural Gas: Proved Reserves: Texas: State Offshore data was reported at 11.000 Cub ft bn in 2021. This records a decrease from the previous number of 22.000 Cub ft bn for 2020. United States Natural Gas: Proved Reserves: Texas: State Offshore data is updated yearly, averaging 299.000 Cub ft bn from Dec 1981 (Median) to 2021, with 38 observations. The data reached an all-time high of 1,112.000 Cub ft bn in 1981 and a record low of 11.000 Cub ft bn in 2021. United States Natural Gas: Proved Reserves: Texas: State Offshore data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under Global Database’s United States – Table US.RB036: Natural Gas Proved Reserves.

Uninterrupted access to electricity is critical to the safety and security of American households. More frequent and extreme emergency events increase outages across the country, disproportionately impacting vulnerable communities that experience the most frequent and longest outages, are most sensitive to the loss of electric power, and have the least capacity to adapt to these conditions. This study devises a metric, the Electric Vulnerability Index (EVI), and validates this metric against the 2021 Winter Storm Uri in Texas. Though not ubiquitous, similar trends were observed between adjacent areas with higher EVI and those with higher outage rates from this storm. EVI is offered as a viable approach to quantify a population’s vulnerability to electric outages and maps that index across the continental United States to aid policymakers, advocates, and energy system stakeholders in the targeted deployment of resilience solutions, such as energy storage, to communities most in need. This dataset includes the geopackage file containing all relevant attributes used to generate the maps used in the accompanying paper.