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With increasing demand for carbon-free power to mitigate climate change, hybrid energy systems (HES) is expected to play an instrumental role in the coming decades. The study is focused on replacing all coal and natural gas electricity generation in the state of Texas with solar, wind, and nuclear power. As an integral part of the designed nuclear-renewable hybrid energy system (N-RHES), both thermal and hydrogen storages are used to provide daily short-term and seasonal energy storage. Each subsystem is dynamically modeled in Dymola/Modelica with hourly wind speed and solar irradiance data entering the wind turbine and solar PV modules. Using Year 2020 data, the N-RHES is collectively controlled to meet hourly energy demand of the state. Aiming to minimize the system's levelized cost of energy (LCOE) while meeting the demand at all times of the year, an optimization algorithm is developed to find an optimum design point of the system. The optimized LCOE of the system was found to be $73.04/ M W h , which results from 61 Natrium nuclear power plants, 5317 3.2- M W wind turbines, and 307.9 k m 2 of solar PV panels. While this optimized LCOE is greater than the current LCOE, it provides carbon-free electricity for the entire state of Texas. According to the design, most of the demand is met by nuclear power with the rest being a near even split between wind and solar power. Although the stored hydrogen in salt caverns contributed only to a small portion of the total electricity production, it provides a large amount of green hydrogen to future clean energy markets. • A unique nuclear-renewable hybrid energy system is developed for the state of Texas. • The design optimization is targeted for minimizing the levelized cost of energy (LCOE). • Both thermal energy and hydrogen storages are employed in meeting the variable loads. • Large amount of green hydrogen is made available while offering 100% carbon-free power. [ABSTRACT FROM AUTHOR]

The utilization of wind energy has become increasingly popular in the United States and many European countries due to its abundant nature and optimized design. While existing wind turbines are predominantly large-scale and not suitable for standalone or distributed power production, Lubbock County in West Texas offers a diverse range of renewable energy options to meet its energy needs. The region relies heavily on utility-scale wind energy sources to supply power to the Texas Grid, replacing conventional fossil fuel-based systems. Currently, standalone solar PV systems are the preferred choice for renewable energy generation. However, West Texas possesses an ample supply of wind energy that can be harnessed to establish a microgrid and provide standalone power to rural communities. Distributed wind energy offers localized power generation, reducing transmission losses and grid strain, while conventional wind farms require long-distance transmission, leading to efficiency gains. By employing the latest technology and optimizing efficiency, even in low-scale generation, a 6-kilowatt permanent magnet alternator-based distributed wind turbine has been designed. This paper focuses on analyzing the techno-economic aspects of implementing this wind turbine in a real-world scenario, taking into account wind attributes, such as velocity and available power, at the specific location. [ABSTRACT FROM AUTHOR]

The world is facing a formidable climate predicament due to elevated greenhouse gas (GHG) emissions from fossil fuels. The preceding decade has also witnessed a dramatic surge in blockchain-based applications, constituting yet another substantial energy consumer. Nonfungible tokens (NFTs) are one such application traded on Ethereum (ETH) marketplaces that have raised concerns about their climate impacts. The transition of ETH from proof of work (PoW) to proof of stake (PoS) is a step toward reducing the carbon footprint of the NFT sector. However, this alone will not address the climate impacts of the growing blockchain industry. Our analysis indicates that NFTs can cause yearly GHG emissions of up to 18% of the peak under the energy-intensive PoW algorithm. This results in a significant carbon debt of 4.56 Mt CO2-eq by the end of this decade, equivalent to CO2 emissions from a 600-MW coal-fired power plant in 1 y which would meet residential power demand in North Dakota. To mitigate the climate impact, we propose technological solutions to sustainably power the NFT sector using unutilized renewable energy sources in the United States. We find that 15% utilization of curtailed solar and wind power in Texas or 50 MW of potential hydropower from existing nonpowered dams can support the exponential growth of NFT transactions. In summary, the NFT sector has the potential to generate significant GHG emissions, and measures are necessary to mitigate its climate impact. The proposed technological solutions and policy support can help promote climate-friendly development in the blockchain industry [ABSTRACT FROM AUTHOR]

Producing green hydrogen from wind energy is one potential method to mitigate curtailment. This study develops a general approach to examine the economic benefit of adding hydrogen production capacity through water electrolysis along with the fuel cell and storage facilities in a wind farm in north Texas. The study also investigates different day ahead market bidding strategies in the existence of these technologies. The results show that adding hydrogen capacity to the wind farm is profitable when hydrogen price is greater than $3.58/kg, and that the optimal day ahead market bidding strategy changes as hydrogen price changes. The results also suggest that both the addition of a fuel cell to reconvert stored hydrogen to electricity and the addition of a battery to smooth the electricity input to the electrolyzer are suboptimal for the system in the case of this study. The profit of a particular bidding scenario is most sensitive to the selling price of hydrogen, and then the input parameters of the electrolyzer. This study also provides policy implications by investigating the impact of different policy schemes on the optimal hydrogen production level. [ABSTRACT FROM AUTHOR]

Intermittency of renewable power sources is a growing concern for grid reliability in the United States and it has not been properly addressed by public policy for very long. One simple way to mitigate this problem is to diversify the renewable energy portfolio. This work studies the case of Texas, a state that not only is the largest wind power producer in the US, but that also has faced considerable diversification in its renewable energy portfolio in the past ten years. A dispatch model is proposed to simulate how thermal generators respond to changes in the intermittency of renewable sources. Additionally, since higher renewable output usually means higher intermittency and vice-versa, a mean–variance analysis is used to study this trade-off. Results indicate that although intermittency decreases the reliability of the grid, reducing it can have adverse effects as increasing the dispatch of polluting power sources and decreasing the revenues of thermal generators, what can decrease important investment in dispatchable power sources in the long-term. Also, results show that the subsidy design adopted by the federal government is an obstacle to portfolio diversification. • The intermittency of aggregate wind and solar power generation can be reduced by portfolio diversification. • Reducing volatility of renewable power sources can unintended consequences as the increase of the dispatch of highly pollutant coal plants. • The current subsidies used in the US worsened the intermittency problem by distorting market signals and inducing concentration of renewable capacity. • This work lays a framework that can be used to analyze the intermittency versus generation tradeoff. [ABSTRACT FROM AUTHOR]

Wind energy in Texas has experienced exponential growth over the last two decades. This growth resulted from a combination of factors, including the availability of state and federal subsidies and Texas's mandate that electricity providers derive a minimum (and increasing) amount of their electricity from renewable sources. Today, wind energy accounts for nearly a fifth of electricity generation in Texas. While wind energy has many benefits, it also poses unique challenges to the reliability of the grid in the context of Texas's "deregulated" energy market. These challenges stem from three primary factors. First, government subsidies and policies influence wind generators to behave in a way that detrimentally affects wholesale electricity prices. Federal income tax credits incentivize wind generators to generate and sell electricity into the market at any price point-suppressing electricity prices when the winds blow. Additionally, wind speeds are seasonal. This means that the supply of wind-generated electricity often decreases when demand peaks, leaving traditional generators to fill the void in an electricity market with inelastic supply and demand. High wind speeds during most of the year only worsen the problem, sustaining wind generation at a magnitude that suppresses wholesale electricity prices in all but two or three months. This makes it economically difficult for traditional generators that are needed to ensure the grid's reliability when the winds calm. This is known as the "missing money problem." Finally, state policies concerning wind generation have resulted in much more prolific wind energy generation in the state than would otherwise exist, amplifying the aforementioned reliability issues. Industry analysts have proposed several solutions to this reliability problem-none of which are satisfactory. This Note, though, provides a novel solution that incorporates the benefits of the main proposed solutions. This proposal would require electricity providers-incentivized by market forces-to lock in long-term commitments from generators to produce electricity sufficient to meet expected demand, thereby providing the stability needed for long-term investment in reliable generation capacity. [ABSTRACT FROM AUTHOR]

This study analyzes 2050–2051 grid stability in the 50 U S. states and District of Columbia after their all-sector (electricity, transportation, buildings, industry) energy is transitioned to 100% clean, renewable Wind-Water-Solar (WWS) electricity and heat plus storage and demand response (thus to zero air pollution and zero carbon). Grid stability is analyzed in five regions; six isolated states (Texas, California, Florida, New York, Alaska, Hawaii); Texas interconnected with the Midwest, and the contiguous U.S. No blackouts occur, including during summer in California or winter in Texas. No batteries with over 4-h storage are needed. Concatenating 4-h batteries provides long-duration storage. Whereas transitioning more than doubles electricity use, it reduces total end-use energy demand by ∼57% versus business- as -usual (BAU), contributing to the 63 (43–79)% and 86 (77–90)% lower annual private and social (private + health + climate) energy costs, respectively, than BAU. Costs per unit energy in California, New York, and Texas are 11%, 21%, and 27% lower, respectively, and in Florida are 1.5% higher, when these states are interconnected regionally rather than islanded. Transitioning may create ∼4.7 million more permanent jobs than lost and requires only ∼0.29% and 0.55% of new U.S. land for footprint and spacing, respectively, less than the 1.3% occupied by the fossil industry today. [Display omitted] • Study examines grid stability with 100% WWS + storage in U.S. regions and states • Even with extreme weather, no blackouts occur in Texas, California, or any other state • Concatenating 4-h batteries provides long-duration storage plus high peak discharge • Costs in most states are lower when states interconnected versus isolated • Costs in all states are much lower than in a business- as -usual (BAU) case [ABSTRACT FROM AUTHOR]

• Siting model that directly incorporates a measure of uncertainty into decisions. • Proven that this model of the wind farm siting problem finds an optimal solution on the Pareto front. • Demonstration of accurate prediction of wind farm power output at a location without historical wind farm power data. Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertainty in wind power output. The overall framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Council of Texas (ERCOT). It is shown that the formulation of this model will find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overall social welfare in the electricity market. [ABSTRACT FROM AUTHOR]

As we move towards a decarbonized grid, reliance on weather-dependent energy increases as does exposure to prolonged natural resource shortages known as energy droughts. Compound energy droughts occur when two or more predominant renewable energy sources simultaneously are in drought conditions. In this study we present a methodology and dataset for examining compound wind and solar energy droughts as well as the first standardized benchmark of energy droughts across the Continental United States (CONUS) for a 2020 infrastructure. Using a recently developed dataset of simulated hourly plant level generation which includes thousands of wind and solar plants, we examine the frequency, duration, magnitude, and seasonality of energy droughts at a variety of temporal and spatial scales. Results are presented for 15 Balancing Authorities (BAs), regions of the U.S. power grid where wind and solar are must-take resources by the power grid and must be balanced. Compound wind and solar droughts are shown to have distinct spatial and temporal patterns across the CONUS. BA-level load is also included in the drought analysis to quantify events where high load is coincident with wind and solar droughts. We find that energy drought characteristics are regional and the longest droughts can last from 16 to 37 continuous hours, and up to 6 days. The longest hourly energy droughts occur in Texas while the longest daily droughts occur in California. Compound energy drought events that include load are more severe on average compared to events that involve only wind and solar. In addition, we find that compound high load events occur more often during compound wind and solar droughts that would be expected due to chance. The insights obtained from these findings and the summarized characteristics of energy drought provide valuable guidance on grid planning and storage sizing at the regional scale. [Display omitted] • Compound wind and solar energy droughts are analyzed at grid relevant scales • A metric for comparing the magnitude of compound energy droughts is developed • The longest compound droughts can last up to 37 continuous hours or 6 straight days • The longest hourly and daily droughts occur in Texas and California, respectively • Compound energy drought are partially connected with peak load events [ABSTRACT FROM AUTHOR]

Wind energy development causes bat fatalities. Despite emphasis on understanding and reducing these impacts, few data are available for the southwest region of the United States and northern Mexico. We monitored bat fatalities for a full year (March 2017–March 2018) at a wind energy facility in south Texas near the United States–Mexico border. We established search plots of 100-m radius at eight randomly selected turbines (of 255) and searched the roads and pads at an additional 92 turbines. We conducted weekly searches from spring through fall and bimonthly during winter. We used GenEst (Gen eralized Mortality Est imator) to estimate bat fatalities corrected for searcher efficiency, carcass removal, and density-weighted proportion of area searched. We found 205 bats during standardized searches, the majority of which were Brazilian free-tailed bats (Tadarida brasiliensis , 76%). The corrected fatality estimates were 16 bats/megawatt/year (95% confidence interval [ CI ]: 12 – 30 bats/megawatt/year) across all species. Species composition at our site is similar to that of northern Mexico, an area of expanding wind energy development with no published studies. [ABSTRACT FROM AUTHOR]

Wind and solar energy sources are climate and weather dependent, therefore susceptible to a changing climate. We quantify the impacts of climate change on wind and solar electricity generation under high concentrations of greenhouse gases in Texas. We employ mid-twenty-first century climate projections and a high-resolution numerical weather prediction model to generate weather variables in the future and produce wind and solar generation time series. We find that mid-twenty-first century projections based on five global climate models agree on the multiyear average increases across Texas in direct normal irradiance, global horizontal irradiance, surface air temperature, and 100-m wind speed of up to 5%, 4%, 10%, and 1%, respectively. These changes lead to multiyear average relative changes across Texas of − 0.6 to + 2.5% and of + 1.3 to + 3.5% in solar and wind capacity factors, respectively, with significant regional, seasonal, and diurnal differences. Areas with low solar resource show an increase in solar capacity factors but reductions in wind capacity factors. Areas with high solar resource show reductions in solar capacity factors. The spatial and temporal differences in our results highlight the importance of using high-resolution data sets to study the potential impacts of climate change on wind and solar power. [ABSTRACT FROM AUTHOR]

This paper develops models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations using Monte-Carlo simulation, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data are used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study is developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the NREL Futures Study 2050 Texas scenarios for 50% and 80% renewable energy. [ABSTRACT FROM AUTHOR]

Since the electric grid does not store electrical energy, the demand for electric power must be matched by the production almost instantaneously. The supply-demand balance imposes a severe constraint on the penetration of renewable energy sources – especially wind and solar – in the power production markets of all nations, because solar irradiance and wind energy are not available at all hours of the day and exhibit daily and seasonal variabilities. Using the hourly data of the electric power demand in the Electric Reliability Council of Texas, we calculate the implications of this constraint on the substitution of fossil fuel power plants with renewable energy sources. When the market penetration of renewables increases and the utilization of solar and wind energy sources expands to approximately 25%–30% of the annual electricity production, significant energy storage capacity is needed. Simulations of the entire electric grid are performed to determine the power ratings of the renewable sources and the necessary storage capacity to substitute part or all of the fossil fuel power capacity in Texas with renewable energy sources. • The production of electricity from renewables does not match the demand. • At renewable penetrations greater than 25–30% energy storage is necessary. • Energy storage becomes a limitation to the further development of renewables. • Less storage is needed for the substitution of coal with wind and solar energy. • Significant storage is required for the substitution of all fossil fuel plants. [ABSTRACT FROM AUTHOR]

• IBR fast frequency response can replace inertia without high prices. • Wind and solar can provide ancillary services without increasing energy prices. • Fast frequency response increases the use of wind and solar generation. • Reserve prices decrease with participation from renewable generation. Electric grids with high penetrations of utility-scale inverter-based resources (IBRs), such as wind, solar, and batteries, often have lower system inertia and need frequency support. One response is to keep traditional synchronous generators online to maintain adequate system inertia, which may reduce the number of IBRs on the system, increasing system emissions. However, IBRs can provide fast frequency response (FFR). If they can do so at low cost, the amount of conventional system inertia needed on the grid can be reduced, allowing higher penetration of IBRs. We examined an FFR requirement on utility-scale IBRs from a system price perspective. We used a unit commitment and economic dispatch (UCED) model to assess the competitiveness of wind and solar resources against batteries and thermal generators in the Electric Reliability Council of Texas (ERCOT) ancillary service market. We introduced an FFR requirement in the UCED model, focusing on energy and ancillary service price changes under actual 2020 and forecasted 2025 system conditions. Under 2020 conditions, which had low IBR penetration, adding an FFR requirement increases energy prices by 7% and introduces a high FFR cost, close to the price of energy. In 2025, which is expected to have high IBR penetration, an FFR requirement does not change energy or reserve prices, and the FFR price is approximately $0.3/MWh. FFR effectively replaces the need for inertia, which has no associated cost in today's markets. The low cost of FFR under high IBR conditions is an appropriate alternative to inertia, arguing for policies that incentivize IBRs to provide FFR. [ABSTRACT FROM AUTHOR]

• A two-stage method is proposed for electricity price forecasting. • The method diversifies the data resources improve the forecasting accuracy. • The method is tested using the real-world data of Texas electricity market. • Causal relationship combined with physical models guarantee better results. • Deep neural networks show a great performance in electricity price forecasting. Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved. [ABSTRACT FROM AUTHOR]

Abstract The primary objective of the project is to evaluate the benefits of wind and solar energy and determine economical investment sites for wind and solar energy in Texas with economic parameters including payback periods. A 50 kW wind turbine system and a 42 kW PV system were used to collect field data. Data analysis enabled yearly energy production and payback period of the two systems. The average payback period of a solar PV system was found to be within a range of 2–20 years because the large range of the payback period for PV systems were heavily influenced by incentives. This is in contrast to wind energy, where the most important factor was found to be wind resources of a region. Payback period for the installed wind system in Texas with federal tax credits was determined to be approximately 13 years. Highlights • Economic analysis was performed to evaluate the economic feasibility. • Two renewable energy (wind and solar) configurations were evaluated for an application in Texas. • The payback periods are provided for specific locations in Texas for wind and solar energy investment. • The overall feasibility study recommends the viable locations in Texas for solar and wind energy in the study. [ABSTRACT FROM AUTHOR]

Wind power forecasting tools are as crucial for independent system operators (ISO) as it is for individual wind farm owners. ISO integrate wind power forecasting tools into power system operations for a number of applications and processes such as balancing energy, regulation and energy reserve services. To deal with the challenges that have surfaced in implementing wind power forecasting tools from an ISO's perspective, a hybrid model is proposed which is based on the correlation between wind power and load demand. The proposed model has used a bi-sectional vector auto-regressive model in conjunction with a nonlinear auto-regressive with exogenous output model forming a hybrid model that gives better results on real-world data sets as well. The case study taken for validating the model is the Electric Reliability Council of Texas system that has seen tremendous increase in the installed capacity of wind power generation sources in the recent past. [ABSTRACT FROM AUTHOR]

A new approach for wind resource assessment in the Southern Plains of the US is proposed here. This new approach establishes the baseline frequency of occurrence of large-scale atmospheric circulations (weather regimes) by cluster analysis, using 38-yr NCEP-NCAR reanalysis daily data from 1979-2016. These baseline frequency values can help quantify the departure of wind resource from the long-term mean for a given month. In specific, two scenarios featuring favorable and unfavorable wind energy productions in the Callahan Divide Energy Center of Texas, US, are evaluated by the new approach of wind resource assessment in details. For the favorable scenario, it is found that the jet stream is configured to enhance the southwesterly flow over the Southern Plains, with a frequency of occurrence being nearly three times of the baseline frequency, whereas for the unfavorable scenario, the jet stream is found to suppress the low-level jet over the Southern Plains, with a frequency of occurrence being more than twice the baseline frequency. Hence, the new approach is proven to provide an objective and more efficient way in conducting wind resource assessment. [ABSTRACT FROM AUTHOR]

Texas electricity production and consumption profiles were reviewed to gain an understanding of the renewable energy role in a high-demand jurisdiction. According to NERC, peak demand on the ERCOT electricity grid is rising at exponential rates, yet the operating reserve margin is still set at 13.75%. The study shows that only recently has Texas been replacing generating units with adequate capacity, primarily wind and solar installations due to an increased offer cap in its competitive electricity market. [ABSTRACT FROM AUTHOR]

Competing power generation sources have experienced considerable shifts in both their revenue potential and their costs in recent years. Here we introduce the concept of Levelized Profit Margins (LPM) to capture the changing unit economics of both intermittent and dispatchable generation technologies. We apply this framework in the context of the California and Texas wholesale power markets. Our LPM estimates indicate that solar photovoltaic and wind power have both substantially improved their competitive position during the years 2012–2019, primarily due to falling life-cycle costs of production. In California, these gains far outweigh an emerging "cannibalization" effect that results from substantial additions of solar power having made energy less valuable in the middle of the day. As such, intermittent renewables in both states have been approaching or exceeding the break-even value of zero for the estimated LPMs. We also find the competitiveness of natural gas power plants to have either improved in Texas or held steady at negative LPMs in California. For these plants, declining capacity utilization rates have effectively been counterbalanced by a "dispatchability price premium" that reflects the growing market share of intermittent renewables. • Intermittent and dispatchable power sources experience changing costs and revenues • To capture these dynamics, we first introduce the concept of Levelized Profit Margins • We then calibrate the economic model for the California and Texas power markets • Cost declines of wind and solar power far outweigh a growing cannibalization effect • The competitiveness of natural gas power plants has either held steady or improved [ABSTRACT FROM AUTHOR]

Two dispatch protocols have been adopted by electricity markets to deal with the uncertainty of wind power but the effects of the selection between the dispatch protocols have not been comprehensively analyzed. We establish a framework to compare the impacts of adopting different dispatch protocols on the efficacy of using wind power and implementing a carbon tax to reduce emissions. We suggest that a market has high potential to achieve greater emission reduction by adopting the stochastic dispatch protocol instead of the static protocol when the wind energy in the market is highly uncertain or the market has enough adjustable generators, such as gas-fired combustion generators. Furthermore, the carbon-tax policy is more cost-efficient for reducing CO2 emission when the market operates according to the stochastic protocol rather than the static protocol. An empirical study, which is calibrated according to the data from the Electric Reliability Council of Texas market, confirms that using wind energy in the Texas market results in a 12% CO2 emission reduction when the market uses the stochastic dispatch protocol instead of the 8% emission reduction associated with the static protocol. In addition, if a 6$/ton carbon tax is implemented in the Texas market operated according to the stochastic protocol, the CO2 emission is similar to the emission level from the same market with a 16$/ton carbon tax operated according to the static protocol. Correspondingly, the 16$/ton carbon tax associated with the static protocol costs 42.6% more than the 6$/ton carbon tax associated with the stochastic protocol. [ABSTRACT FROM AUTHOR]

The article focuses on the transmission investment in the renewable energy industry in Texas in 2014. Topics include the creation of Competitive Renewable Energy Zones (CREZ) in Texas, efforts to develop the wind industry in Texas, and the growth of the renewable energy industry in Texas. Information is provided on electricity transmission capacity in Texas.

[Display omitted] • CO 2 -emission risk caused by wind power uncertainty should be considered. • Ten-year emissions may exceed expected levels by 100 megatons in the two markets. • CO 2 -emission risk comes from annual generation uncertainty and hourly volatility. • Installed capacity required should raise to hedge against the CO 2 -emission risk. • Energy storage and controllable zero-carbon energy help manage the risk. Wind power is a significant source of renewable energy, supporting reductions in emissions. However, the intermittency of wind power will cause uncertain carbon emissions and risks greenhouse-gas emission reduction. To address this issue, we analyzed the mechanism that converts wind-energy intermittency to carbon-emission uncertainty and developed metrics to assess the consequential risks of excessive carbon emissions. According to our empirical analyses on two American electricity markets, the Electric Reliability Council of Texas (ERCOT) and Pennsylvania-New Jersey-Maryland Interconnection (PJM), we found their carbon-emission risk is significant over the coming decade. The ten-year aggregated CO 2 emissions between 2020 and 2030 have a 10% chance of exceeding their expected levels by 100 megatons. Our further analysis also shows that the CO 2 -emission risk is induced by the uncertainty of annual generation uncertainties and volatilities in the hourly wind power supply. Particularly, the two driving forces contribute heterogeneously to the carbon-emission risk in the two studied markets. Although the annual-generation randomness causes similar degrees of carbon-emission uncertainty in the two markets, the volatility-driven risk is only significant in the PJM market. According to the risk analyses, we suggested two types of investigations that are foundations for risk management: assessing the necessary installed capacity of wind energy that limits CO 2 -emission risk at desired confidence levels, and exploring hours when carbon emission is sensitive to wind-power volatility. The former helps set renewable-energy targets, while the latter is critical for designing policies hiring energy storage and zero-carbon generation technologies. [ABSTRACT FROM AUTHOR]

Abstract: With the expansion of wind energy development, there is a need to update wind data periodically for the state of Texas for developers and landowners to see if their properties could support wind turbines. This study presents an updated wind power map and a Windtane contour map of the state of Texas. The Windtane map shows the height above ground level needed to reach a baseline wind power level of 350 W/m2. This level is where the middle of class 3 wind power occurs and is widely considered to be the lower limit of the annual power level fort wind turbines to be economically viable for installation. The Windtane map using ArcGIS can be reconfigured to represent different power levels if need to. Both maps use a ‘certainty zone’ concept to constrain the areas where wind power is projected and to give increased confidence in the data to those areas projected into. [Copyright &y& Elsevier]

U.S. wind generation capacity has grown by a factor of nearly 50 times in the past 20 years; now nearly a third of wind capacity is 10 years old or older. This ageing of this fleet affects the productivity of existing investments and raises the stakes for replacing these assets through repowering. Focusing on the Texas wind power fleet to examine declining productivity over time, and how those changes factor into replacement and retirement decisions, we corroborate earlier decline findings but observe a slower rate of repowering than in other settings. We then explore repowering decisions and find evidence that technological change has favored relatively unproductive sites, in contrast to the previous literature on this topic. We explore the policy implications of our findings, in particular the desirability and feasibility of using policy tools to promote more repowering. [ABSTRACT FROM AUTHOR]

Wind power has the potential to reduce emissions associated with conventional electricity generation. Using detailed, systemic hourly data of wind generation and emissions from plants in ERCOT (Texas), we empirically estimate the SO2, NOx and CO2 emissions offset by wind generation. Our estimation strategy implicitly captures both the marginal unit of generation displaced by wind on the electrical grid, and the marginal emissions reduction from that displaced unit. Our results also reveal substantial variation in emissions reductions, which appear to be strongly driven by differences in the generation mix. The environmental benefits from emissions reductions in ERCOT fail to cover government subsidies for wind generation. [ABSTRACT FROM AUTHOR]

CALIFORNIA HAS been the home of SPI for many years now – and for good reason. The Golden State represents the bulk of Photovoltaic (PV) installations, and has long been the engine for U.S. solar enthusiasm. But keynote speaker Rhone Resch, president of the Solar Energy Industries Association (SEIA), was quick to emphasise the renewable credentials of Texas. Texas for example leads the U.S. in wind power capacity. And while it may be late to the solar party, it now boasts the 16 MW Blue Wing Solar Energy Generation Facility in San Antonio, while utility CPS Energy has launched a 400 MW distributed generation program to showcase solar PV. On the morning of the show''s start, too, 15 Habitat for Humanity homes in Dallas were installed with PV and solar water heating systems, courtesy of SolarCity, Solar US, Trina Solar, Bosch Solar, Schletter Mounting, SES 21 and SolarTek. [ABSTRACT FROM AUTHOR]

Since 2000, U.S. wind-energy capacity has increased 24 percent per year, with Texas emerging as the leading state. Multidimensionality, economic decline, and ownership-participation hypotheses dominate recent geographical research on social perspectives toward wind energy. We examine these hypotheses regarding support of wind power from the perspective of a county that leads Texas in installed capacity. Using Q-method, we present empirically determined, statistically significant social perspectives regarding wind energy. Key actors surveyed included landowners with wind turbines, elected and civil service government officials, and prominent local business and community leaders. We identified five significant clusters of opinion varying in terms of degree of support for wind energy and concern for negative impacts. Stakeholders use economic decline discursively to support wind power, but views on tax policy and distribution of costs and benefits of wind power condition the overall favorable position of key actors to wind-energy development. Specific forms of ownership and participation and positions on tax abatements for attracting wind farms frame discourses of support for wind power. [ABSTRACT FROM AUTHOR]

Wind power can make an important contribution to the goal of reducing emissions of CO2. The major problem relates to the intrinsic variability of the source and the difficulty of reconciling the supply of electricity with demand particularly at high levels of wind penetration. This challenge is explored for the case of the ERCOT system in Texas. Demand for electricity in Texas is projected to increase by approximately 60% by 2030. Considering hourly load data reported for 2006, assuming that the pattern of demand in 2030 should be similar to 2006, and adopting as a business as usual (BAU) reference an assumption that the anticipated additional electricity should be supplied by a combination of coal and gas with prices, discounted to 2007 dollars of $2 and $6 per MMBTU respectively, we conclude that the bus-bar price for electricity would increase by about 1.1 ¢/kWh at a wind penetration level of 30%, by about 3-4 ¢/kWh at a penetration level of 80%. Corresponding costs for reductions in CO2 range from $20/ton to $60/ton. A number of possibilities are discussed that could contribute to a reduction in these costs including the impact of an expanded future fleet of electrically driven vehicles. [ABSTRACT FROM AUTHOR]

Abstract: 123km of high-resolution 2-D seismic lines and 6 sediment cores up to 16m in length were collected from Copano Bay, Texas. These data were used to examine the role of climate and sea-level changes in the evolution of the bay. Paleogeographic maps of the bay were constructed to show how the bay evolved through time. The overall transgression was punctuated by five environmental changes within Copano Bay over the past 9.6ky. The largest environmental change occurred at 8.2ka when the bay-head delta back-stepped approximately 7.5km as a result of a 0.4–1.2m rapid rise in sea level. With a well constrained model of how the bay changed through time, textural data from the most continuous record of middle-bay deposits was used to produce a proxy for wind strength over the last 8.2ky. Our wind strength proxy reveals that Copano Bay experienced relatively tranquil winds during the early Holocene followed by its windiest conditions from 5.2 to 4.1ka and a return to more tranquil winds during the late Holocene. The wind strength proxy was compared with other published paleo-climatic records for the region and a reasonable correlation was found between periods of higher wind energy and drier climate. This study illustrates that despite their complex depositional systems and many sediment and water sources, a system-wide approach to studying an estuary can still be used to extract paleoclimate archives from estuarine deposits. [Copyright &y& Elsevier]

In 2011, Texas is again at the forefront of an energy boom: the wind energy boom. In 2006, Texas surpassed California and became the U.S. state with the most installed capacity to produce wind energy, and Texas' level of installed capacity has continued to grow. But the law has not kept pace with this growth. Similar to the initial growth of the oil and gas industry in Texas, the wind energy industry was also born, and continues to grow, in the absence of clear legal and regulatory standards. Lack of regulation in the early development of the oil industry, contributed to oversupply and rampant waste of oil. Similarly, lack of regulation of the developing wind energy industry, could lead to wasteful practices regarding wind energy development. This Note argues that the Texas Legislature should pass laws clarifying that wind is a natural resource under the Texas Constitution, and that to promote "[t]he conservation and development" of wind as a natural resource, the Legislature should statutorily recognize wind rights as an interest severable from land ownership. [ABSTRACT FROM AUTHOR]

TEXAS IS MOVING CLOSER TO THE ESTABLISHMENT OF FIVE COMPETITIVE RENEWABLE ENERGY ZONES (CREZS), WHICH PROPONENTS SAY COULD DOUBLE WINDPOWER IN THE USA. AND AS LYN CORUM REPORTS, THE WIND INDUSTRY IN THE WESTERN USA COULD ALSO BE SET FOR A MAJOR BOOST, AS PLANS FOR MAJOR TRANSMISSION INITIATIVES MOVE FORWARD. [Copyright &y& Elsevier]

The Long-term Inflow and Structural Test (LIST) program, managed by Sandia National Laboratories, Albuquerque, NM, is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a site near Bushland, Texas. With the objective of establishing correlations between structural response and inflow, previous studies have employed regression and other dependency analyses to attempt to relate loads to various inflow parameters. With these inflow parameters that may be thought of as single-point-in-space statistics that ignore the spatial nature of the inflow, no significant correlation was identified between load levels and any single inflow parameter or even any set of such parameters, beyond the mean and standard deviation of the hub- height horizontal wind speed. Accordingly, here, we examine spatial statistics in the measured inflow of the LIST turbine by estimating the coherence for the three turbulence components (along-wind, across-wind, and vertical). We examine coherence spectra for both lateral and vertical separations and use the available ten-minute time series of the three components at several locations. The data obtained from spatial arrays on three main towers located upwind from the test turbine as well as on two additional towers on either side of the main towers consist of 291 ten-minute records. Details regarding estimation of the coherence functions from limited data are discussed. Comparisons with standard coherence models available in the literature and provided in the International Electrotechnical Commission (IEC) guidelines are also discussed. It is found that the Davenport exponential coherence model may not be appropriate especially for modeling the coherence of the vertical turbulence component since it fails to account for reductions in coherence at low frequencies and over large separations. Results also show that the Mann uniform shear turbulence model predicts coherence spectra for all turbulence components and for different lateral separations better than the isotropic von Kármán model. Finally, on studying the cross-coherence among pairs of turbulence components based on field data, it is found that the coherence observed between along-wind and vertical turbulence components is not predicted by the isotropic von Kármán model while the Mann model appears to overestimate this cross-coherence. [ABSTRACT FROM AUTHOR]

The article discusses how humans have always found ways to use wind power as a vital energy source. The wind helped humans explore the world; they had no other energy source. It continues to help us prepare foods and pump water. In some places, the wind is such a part of daily life that in its absence, silence blankets the landscape and puts us out of sorts. Putting the wind to work was our first conscious use of solar power. Perhaps the most widespread use of wind machines, at least in the United States, has been to pump water. Dotting the Great Plains by the hundreds of thousands, farm windmills--along with grain silos--were once as characteristic of the landscape as coal spoils were of Appalachia. Today, wind machines receive substantial notoriety and publicity, plus a controversial mix of support and resistance. Instead of a stream of water, modern wind machines are pumping a stream of electrons, a product proving to be a valuable asset to farmers who are trying to address present day economic realities of living off the land. The new appearance and mechanics of wind machines reflects their different role. Instead of producing mechanical power for the purposes of pumping and grinding, the new machines convert mechanical energy into electricity. Wind power's popularity is widespread and growing, a result of its increasing profitability and the perceived environmental benefits it engenders. It also results from the simple fact that, unlike fossil and nuclear fuels, wind is a widely available, familiar element of the environment. The first step is seemingly the easiest: finding it. Among other states attracting interest, Texas has been the pacesetter with more than $1 billion in new wind investment and 1,293 MW installed, mostly in the western part of the state near such communities as Big Springs and McCamey. Construction of a typical 100 MW wind farm produces more than 50,000 days (approximately 419,020 manhours) of employment. INSETS: WIND CLASSES;LARGER AND LARGER TURBINES;THE CAPE WIND PROJECT: A WIND POWER LIGHTNING ROD;WIND POWER AND BIRD MORTALITY

The Long-Term Inflow and Structural Test (LIST) program is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a test site near Bushland, Texas. Data from 491 ten-minute time data records are analyzed here to determine the dependency of fatigue and extreme loads on inflow parameters. Flap and edge bending moment ranges at a blade root are chosen as the structural response variable, z. Various parameters related to the inflow (including, for example, primary parameters such as the mean and standard deviation of the hub-height horizontal wind speed, and secondary parameters such as Reynolds stresses, vertical shear exponent, etc.) are each considered in an inflow parameter vector, x. Time series for the structural response, z, are processed in order to obtain a structural response parameter, y, where in separate statistical studies, y is taken to be either an equivalent fatigue load or an extreme load. This study describes a procedure by which the important "dependencies" of y on the various variables contained in the inflow parameter vector, x, may be determined considering all the available data. These dependencies of y on x are then recomputed using only the data with above-rated mean wind speeds (taken to be approximately 13 m/s). The procedure employed is similar to other studies, but we do not bin the data sets by wind speed since dependencies in one wind speed bin may, be different from those in other bins. Also, our procedure, in sharp contrast to previous studies, examines each inflow parameter in the vector, x, in a sequential analysis, rather than by using multivariate regression. Results from the present study suggest that the primary inflow parameters have a small amount of predictive power in establishing fatigue and extreme loads. In addition, large correlations that exist between several of the secondary parameters individually and each of the primary parameters make it difficult for the secondary parameters to provide any additional explanation of turbine response once the primary parameters have been accounted for. [ABSTRACT FROM AUTHOR]

21 st century energy production, conversion, and delivery systems need to go through a transition to be less carbon-intensive while meeting an increasing energy demand. In a more and more interconnected world, energy systems of various sectors (e.g. power, fuels, chemicals, etc.) go through this transition via shifting the primary energy sources from carbon-intensive fossil-fuels to renewable and sustainable resources. With this study, we present a multi-scale strategy for optimal design and operation of multi-product process systems that can produce power, synthetic fuels, chemicals, and energy carriers from renewable and fossil resources. This multi-scale approach combines process synthesis, scheduling, and supply chain concepts in a mixed-integer linear programming model to address the trade-offs between integrating various fossil and renewable technologies. Our strategy is applied to integration of low-emission (i) synthetic liquid transportation fuels, (ii) hydrogen, (iii) ammonia, (iv) methanol, and (v) renewable power production from natural gas, solar, and wind energy at a location in Amarillo, Texas. Case study results show that with our approach various energy systems can be modeled either separately and integrated with the same common representation. Sectors integration to produce low-emission products in the same facility can result in 17% reduction in total production costs. While solar and wind energy are favorable to produce renewable power, current state-of-the-art methane conversion technologies are more favorable to produce hydrogen and hydrogen-based products. • MILP model for multi-scale simultaneous design and operation optimization. • Integration of solar, wind, and natural gas conversion pathways. • Case studies for Texas on power, synthetic fuels, and chemicals production. • Systems integration results in cost savings in hydrogen and power subsystems. [ABSTRACT FROM AUTHOR]

Bats are killed at wind energy facilities worldwide and we must improve our understanding of why this is happening and implement effective strategies to minimize impacts. To this end, we need accurate assessments of which individuals from which bat species are being killed at individual wind projects and at regional and range-wide scales. Traditional fatality searches have relied on physical characteristics to ascertain species and sex of bat carcasses collected at wind turbines; however, the resulting data can be incomplete and inaccurate. In contrast, the use of readily available and low-cost molecular methods improves both the quality and quantity of available data. We applied such methods to a bat fatality dataset (n = 439 bats) from far-south Texas, USA. Using DNA barcoding, we increased accurate species identification from 83% to 97%, and discovered the presence of 2 bat species outside of their known geographic ranges. Using a PCR-based approach to determine sex, the number of carcasses with correct sex assignment increased from 35% to 94%, and we documented a female-biased sex ratio for all species combined and for Dasypterus ega. We recommend that molecular methods be used during future survey efforts to accurately assess the impacts of wind energy on bats. [ABSTRACT FROM AUTHOR]

Historically the state of Texas - which Texans proudly refer to as the Lone Star State - has been known for its enthusiastic embrace of the oil and gas industry. In recent years however it surprised many perhaps by installing over 1000MW of onshore wind. Now it looks as if Texas may be poised to reassert itself again in the renewable energy sector with an ambitious offshore wind development plan off the coast of Texas. U.S. Correspondent Don C. Smith reports. [Copyright &y& Elsevier]