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Returning monarch butterflies (Danaus plexippus) to sustainable levels of abundance will require an array of contributors to protect and restore habitat over broad areas. Due to the diversity and scale of land managed by electric power companies across the monarch range, plus an additional 32 million hectares needed for new solar arrays by 2050 to meet renewable energy goals, the industry may have potential to contribute to monarch conservation. However, it is challenging to clearly understand an individual company's potential for monarch conservation because of the scale and distribution of their specific land assets (ranging from 4,800 to 240,000 hectares in this study alone), the complexity of monarch science, and the lack of a science-based approach for evaluating large land assets for monarch habitat. With monarchs potentially being protected under the United States Endangered Species Act in the future and thereby limiting land management approaches, there is interest from electric power companies to understand how their lands relate to monarchs. In collaboration with companies, we developed a GIS-based model to identify company landholdings that contain high-quality monarch habitat and applied the model to specific landholdings of eight power companies in the United States. We then facilitated discussions with company teams to balance conservation goals, corporate risk, and social opinion. This paper describes non-confidential results for developing a national GIS-based monarch habitat model and applying it to electric power companies who are considering monarch conservation while simultaneously transitioning to a new clean energy future. The model and applied experience may be useful for other organizations working across large landscapes to manage monarchs. [ABSTRACT FROM AUTHOR]

This paper investigates the impact that economies of scale have had in the success of the wind industry in the United States since 1980 including the trend to larger wind turbines and wind farms and assembly learning curve effects. Analyses utilize average U.S. wind industry historical data to assess past performance and predict the future economic potential for both onshore and offshore wind turbine industries. The intent of this analysis is to serve as a case study to demonstrate the importance of selecting the right product design platform and how the ability to scale a technology can impact the success of products and, in this case, an entire multi-billion dollar industry. From the early stages of development, the wind industry recognized this opportunity and enabled the commercialization of larger and larger wind turbines and subsequently exploited the significant economies of scale possible in producing low-cost electricity via large, horizontal axis turbines and associated wind plants. The cost to produce electricity was reduced more than six-fold over this timeframe and is competitive today with conventional fossil-fuel power generation alternatives. The physics of horizontal axis wind turbines enables them to scale up disproportionately in electric power output and annual energy production with increased size/nameplate rating. [ABSTRACT FROM AUTHOR]

This paper describes GCAM-USA v5.3_water_dispatch, an open-source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (US) and represents the US economy and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land use regions, and 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of US water demands and supplies and sub-annual operations (day and night for each month) in the US electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for (and production of) energy, water, and crops at a subnational level in the United States while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land use outcomes. We conclude with information about how public users can access the model. [ABSTRACT FROM AUTHOR]

The debate about the threshold effect of sectoral renewable energy consumption on economic growth has attracted extreme attention, but only limited evidence supports this issue. Therefore, the objective of this paper is to study the non-linear relationship between economic growth, capital, labor and sectoral renewable energy consumption for the case of the United States during the period 1981Q1 and 2021Q1. Using the threshold detection method proposed by Hansen (2002), the effect of sectoral renewable energy consumption on economic growth is positive and significant only if the electric power, industrial, residential, commercial, and transport sectors exceed a renewable energy consumption threshold lnREC >7.095; lnREC >6.138; lnREC >4.897; lnREC >3.212; and lnREC >2.849, respectively. Indeed, for the U.S. to achieve positive economic growth from its renewable energy investments, it must exceed a certain threshold of renewable energy consumption for each sector. However, if the United States use renewable energy below a certain threshold, the effect of sectoral renewable energy consumption on economic growth is negative. These results have important implications for both policymakers and investors. [Display omitted] [ABSTRACT FROM AUTHOR]

While the US electric power sector transitioning towards a low-carbon system, the escalated adoption of renewable energy has paradoxically increased water consumption. Consequently, it is imperative to engineer a power generation matrix that mitigates CO 2 emissions and minimizes water utilization. The primary endeavor should be to meticulously identify the variables affecting water consumption changes, especially highlighting and addressing the inherent tension between water conservation and carbon reduction. Employing temporal and spatial Logarithmic Mean Divisia Index methodologies, this study meticulously investigated the factors driving water consumption changes across state-level electricity production sectors in the US from 2010 to 2018. The analysis revealed that the generation mix structure was a predominant factor in escalating water consumption, exerting a more substantial influence than total power generation. Moreover, while the water consumption coefficient initially mitigates the impact of other factors, its effectiveness diminishes over time. Utilizing spatial Logarithmic Mean Divisia Index techniques, this paper further delineated state-level disparities, illuminating the heterogeneous nature of water consumption across regions. Projections up to 2050 suggest that water consumption reductions could be realized even amidst a highly intense scenario. The study's robustness and reliability were further augmented by employing an adaptive Logarithmic Mean Divisia Index. • Temporal-spatial LMDI reveals significant shifts in the US power sector water consumption. • Water consumption coefficient, fuel structure, and generation identified as key drivers. • Scenario analysis forecasts significant water savings potential by 2050. • Findings highlight the necessity for integrated water-energy transition policies. • Study proposes targeted strategies for a sustainable energy-water nexus. [ABSTRACT FROM AUTHOR]

We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

Space weather, a natural hazard, can adversely impact human technological assets. High‐voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to space weather driven geomagnetically induced currents (GICs). One of the major challenges pertaining to the study of GICs over the continental United States has been the availability of GIC measurements, which are critical for validation of geoelectric field and power flow models, for example. In this study, we analyze GIC measurements collected at 17 Electrical Power Research Institute (EPRI) SUNBURST transformer locations across the United States for which a GIC value of 10 A or greater was recorded. This data set includes 52 individual geomagnetic storms with Kp index 6 and above during the period from 2010 to 2021. The analysis confirms that there is a good correlation between the number of geomagnetic storms per year and the number of recorded GIC events. Our results also show that about 76% of the top 17 GIC events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown, for the first time, that mid‐latitude positive bays can cause large GICs over the continental United States. Finally, this study shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity. Plain Language Summary: Space weather, a natural hazard, can adversely impact human technological assets. High‐voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to induced currents produced by enhanced space weather conditions. One of the major challenges pertaining to the study of these induced currents over the continental United States has been the lack of measurements. In this study, we analyze induced current measurements collected at 17 high‐voltage power transformer locations across the United States for which a value of 10 A or greater was recorded during the period from 2010 to 2021. The analysis confirms a good correlation between the number of geomagnetic storms per year and the number of recorded induced current events. The results also show that about 76% of the top 17 induced current events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown for the first time that mid‐latitude positive bays can cause large induced currents over the continental United States. Finally, this study also shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity. Key Points: We analyze geomagnetically induced current (GIC) measurements collected under the EPRI SUNBURST project from across the United States and CanadaAbout 76% of the top 17 GIC events occur during main phase (MP) of geomagnetic storms, while 24% during sudden storm commencementFor the first time it is directly shown that mid‐latitude positive bays can cause large GICs at US locations [ABSTRACT FROM AUTHOR]

Recent severe ice storms across the United States severely damaged trees resulting in extensive electrical power outages. Furthermore, trees and branches can fall on nearby roads, blocking traffic flow and reducing the safety of drivers. In this study, trees subjected to ice loads were analyzed using the finite element method and Monte Carlo simulation to develop analytical fragility curves. Two-dimensional, fractal trees were constructed with randomly generated geometric and mechanical parameters for four deciduous tree species: Acer saccharum, Tilia americana, Fagus grandifolia, and Quercus alba. Two load case scenarios were considered – with and without the effects of leaves – which were then subjected to varying ice accumulation thicknesses. The resulting fragility curves suggest that leaves have a substantial impact on tree branch damage under ice loads, which is significant because of the increase in unseasonably early ice storms due to climate change. [ABSTRACT FROM AUTHOR]

State-level renewable portfolio standards (RPSs) aim to encourage renewable energy and discourage greenhouse gas (GHG) emissions from the electric power sector in the United States. Do they work? Some prominent government agencies and advocacy groups assert that U.S. renewables growth has been largely due to RPSs. That seems unlikely, given that in most regions, renewables exceed RPS requirements. But it is not an easy question to answer, thanks to interstate trading and the possibility that states with abundant renewable resources might set the most ambitious RPS goals. We combine the best features of four recent academic studies, using ordinary least-squares and instrumental variables approaches. In some specifications, RPSs do appear to reduce the use of natural gas to generate electricity and decrease GHG emissions, while boosting the use of wind and solar power. But the effects are small--consistent with the academic findings and in contrast to the public claims and policy goals. [ABSTRACT FROM AUTHOR]

• Renewable hydrogen can compete with lithium-ion batteries for seasonal storage. • Hydrogen's competitiveness depends on heat rate of plant it is replacing. • Net present value of hydrogen-fired gas turbine varies based on operation of plant. • Lack of infrastructure will impede demand growth for hydrogen in the United States. Non-emitting variable renewable energy (VRE) resources are needed on the power grid if the United States is to "deeply decarbonize" the power sector. The intermittent nature of these resources makes them difficult to integrate into the power system. Existing energy storage technologies, such as lithium-ion (LI) batteries, could be used to aid the integration of these resources, but these technologies are sized to produce power for hours at a time before needing to be charged again. While these energy storage technologies could address daily imbalances between supply and demand for electric power, they cannot address the seasonal nature of power production of VREs. This paper details a methodology to estimate the levelized cost of energy (LCOE) of meeting this seasonal imbalance with either a hydrogen-fired gas turbine (HFGT) or lithium-ion battery system (LI) as a measure of economic efficiency of the technologies. Applying our model, we find the average LCOE associated with meeting this seasonal imbalance is $2400/MWh using a HFGT fueled with green hydrogen and $3000/MWh using a LI. If we allow the model to operate the HFGT with blue hydrogen the average LCOE decreases to $1560/MWh. However, we find the power prices required to justify investment in an HFGT to replace a natural gas-fired gas turbine are considerably higher than those seen in the market today. [ABSTRACT FROM AUTHOR]