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Geophysical constraints on the reliability of solar and wind power in the United States
Abstract
We analyze 36 years of global, hourly weather data (1980–2015) to quantify the covariability of solar and wind resources as a function of time and location, over multi-decadal time scales and up to continental length scales. Assuming minimal excess generation, lossless transmission, and no other generation sources, the analysis indicates that wind-heavy or solar-heavy U.S.-scale power generation portfolios could in principle provide ∼80% of recent total annual U.S. electricity demand. However, to reliably meet 100% of total annual electricity demand, seasonal cycles and unpredictable weather events require several weeks’ worth of energy storage and/or the installation of much more capacity of solar and wind power than is routinely necessary to meet peak demand. To obtain ∼80% reliability, solar-heavy wind/solar generation mixes require sufficient energy storage to overcome the daily solar cycle, whereas wind-heavy wind/solar generation mixes require continental-scale transmission to exploit the geographic diversity of wind. Policy and planning aimed at providing a reliable electricity supply must therefore rigorously consider constraints associated with the geophysical variability of the solar and wind resource—even over continental scales.
... Realistic models with geophysical constraints influencing the availability of wind speed and solar radiation underscore the need for several weeks' worth of energy storage (Shaner et al., 2018;Houssainy and Livingood, 2021;Sadhukhan et al., 2022). Pumped hydro storage (PHS), compressed air energy storage (CAES), and hydrogen energy storage (HES) systems constitute the seasonal energy storage (SES) category. ...
... Therefore, robust energy storage evaluations are essential for local and regional authorities planning to deploy wind and solar power heavily for NZE to avoid unintended costs or GHG consequences. Technologydriven studies have considered state-of-the-art options that provide a few hours of battery energy storage (Yao et al., 2011;Al-Ghussain et al., 2018), while climate-driven studies have suggested several weeks of energy storage requirements as the direction for future developments (Lund and Vad Mathiesen, 2009;Shaner et al., 2018;Bakhtvar et al., 2021). When the stored electricity dispatch time exceeds a few hours to a few weeks, seasonal storage is required in the form of CAES, PHS, and HES. ...
... There are a few available optimization models for energy system planning targeted to achieve large-scale NZE. The principal methodologies include linear constrained optimization studies (Dorfner, 2016;Brown et al., 2018) and statistical analyses (Shaner et al., 2018;Tong et al., 2021) that evaluate the minimum-cost electricity mix from local (Weber and Shah, 2011;Gil et al., 2021) to national or regional (Brouwer et al., 2016;Zeyringer et al., 2018) to global (Tong et al., 2021) scales. These approaches span annual (Sameti and Haghighat, 2018) to multiyear (Zhang, 2014) and multidecade (Shaner et al., 2018) considerations, with resolutions of a few minutes (Zhang, 2014;Safaei and Keith, 2015) to hourly (Monforti et al., 2014). ...
Energy system optimization is needed for optimal sustainable net-zero electricity (NZE) mix even at regional/local scales because of the energy storage needs for addressing the intermittency of renewable energy supply. This study presents a novel regional/local energy planning model for optimum sustainable NZE mix under spatiotemporal climate/meteorological and electrical load demand constraints. A generic robust non-linear constrained mathematical programming (NLP) algorithm has been developed for energy system optimization; it minimizes the levelized cost and greenhouse gas emissions while maximizing reliability against stored energy discharge analysis (RADA). Reliability, defined as the ratio of excess stored renewable power discharge to unmet load demand, is a measure of the extent of unmet load demand met by the excess stored renewable power. Coupled with the NLP, the RADA and energy storage evaluations are used to determine the seasonal energy storage (SES) conditions and realistic renewable proportions for NZE. The significance of the proposed framework lies in determining the maximum hours of viable electrical energy storage beyond which the reliability enhancement is infinitesimal. The significant observations of this work include 96 h of maximum viable electrical energy storage beyond which the reliability enhancement is infinitesimal. While this observation is robust based on previous reports for the case of the United States, a realistic NZE mix for Southern United Kingdom is obtained as follows. Direct wind and solar sources can meet 63%, 62%, and 55% of the electricity demands in the southwest, Greater London, and southeast regions of the United Kingdom, respectively; further, battery energy storage systems can increase the renewable proportions by 21%, 22%, and 13% in these three regions. The unmet demands can be met by renewable electricity through SES. Compressed air energy storage (CAES) and pumped hydro storage offer viable SES. Following these, natural gas with carbon capture and storage (CCS), bioenergy, and hydrogen SES are the choices based on increasing cost per lifecycle climate impact potential to meet the electricity demands.
... To establish the role of LDES solutions in electrical power systems [5] SDOM To assess an optimal storage portfolio based on variable renewable power deployment [6] IMRES To explore cost optimisation and the potential value of energy storage in deep decarbonisation of the electricity sector [14] DIMENSION To analyse if there is a need for additional incentive mechanisms for flexibility in electricity markets with high shares of renewables [25] PLEXOS, ReEDS To explore system-level services and associated benefits of long-duration energy storage [24] GridPath, RESOLVE To improve on previous modelling approaches to better reflect the capabilities and value of long-duration energy storage resources To investigate if energy storage can cost-competitively shape intermittent resources into desired output profiles and compare diverse storage technologies [30] NA To analyse the influence of storage size and efficiency on the pathway towards a 100% renewable energy scenario [29] Simple Transparent Model To quantify the coverability of solar and wind resources as a function of time and location over multi-decadal time scales and up to continental length scales [28] DIETER To analyse the role of power storage in systems with high shares of variable renewable energy sources [20] FuturES and powerGAMA To present a new framework for developing future electricity scenarios with a high penetration of renewables [31] Linear Program (LP) Investment Model To compare the possible opportunities of power-to-gas as a long-term storage option, to the opportunities of short-term storage technologies [22] ReEDS, PLEXOS, RODeO To propose a model-based approach for comprehensive techno-economic assessments of grid-integrated seasonal storage ...
... The scoped region (its size and circumstances) naturally has a significant impact on the outcome of one's model, e.g., as in Shaner et al. [29], where the wind resource peaks in spring, while the electricity demand is at its minimum. This match or mismatch between electricity generation and demand establishes the opportunity for energy storage. ...
... Lund et al. [36] also found that wind speeds tend to be higher at night, necessitating longer energy storage durations for an optimal system. As the electricity generation profiles between solar and wind power differ, they may even complement each other [10,12,15,18,29,30], which also influences the potential demand for LDES, as an optimised VRE portfolio will increase grid stability. ...
The relationship between a region’s dependency on variable renewable energy (VRE) and the viability of long-duration energy storage (LDES) technologies is recognised through various electricity grid modelling efforts in the contemporary literature. Numerous studies state a specific VRE penetration level in total electricity generation as an indicator of the emergence of an LDES market. However, there is considerable variability across studies when comparing VRE penetration levels in conjunction with LDES technology utilisation, and significant diversity exists in electricity grid modelling approaches. This review aims to highlight these inconsistencies by offering an overview of disparate findings and dissecting the influencing variables. Sixteen parameters are identified from reviewed studies, complemented by an additional five recognised through in-depth analysis. This comprehensive examination not only sheds light on critical aspects overlooked in previous reviews, requiring further investigation, but also provides novel insights into the complexity of this correlation, elevating the understanding of LDES market creation by unravelling the factors that influence the technology adoption across various contexts. Furthermore, it provides clarity in LDES research terminology by rectifying ambiguous language in the existing literature. Altogether, seven databases were explored to produce a trustworthy foundation for the study.
... The "green grid" features a lower carbon emissions factor, but an unintended consequence is that the short-term electricity generation becomes more volatile (Shaner et al. 2018). From data provided by the Energy Information Administration (EIA), we create a generator panel dataset by month from 2013 to 2022. ...
... There is little solar power during hurricanes or storms, and solar panels' efficiency declines under extreme heat. The increasing penetration of intermittent renewables thus poses new challenges to resource adequacy (Shaner et al. 2018;Wolak 2022). Regional transmission organizations (RTO) require electric utilities to maintain excess capacity. ...
... As an increasing share of electricity is generated from wind and solar resources, environmental disasters may give rise to new challenges in generation management. These intermittent renewables lead to long-term resource adequacy problems as their productivity decline under extreme weather events (Shaner et al. 2018;Wolak 2022). Regional electricity planning entities such as the RTOs determine the generation mix to meet the reliability targets. ...
Access to electricity is a crucial determinant of quality of life and productivity. The United States has a highly reliable electricity grid, but it faces new resilience challenges due to more intense disasters and ambitious green power requirements. Over the past decade, utilities have faced tradeoffs between achieving carbon mitigation goals, offering reliable power access, and keeping retail prices low. Using a generator panel dataset from 2013 to 2022, we document that electricity generation from renewables declines during extreme weather events. Based on an electric utility panel dataset over the same period, we find that disasters also disrupt electricity distribution. Although utilities have made some adaptation progress, investments in green and reliable green power are associated with higher electricity prices.
... As New England moves towards increasing wind and solar electricity generation, as they are the preferred resources for low-carbon electricity systems [11], the inter-annual variability and uncertainty of these resources will pose challenges to power system planning and operations, as it impacts the amount of capacity required to meet demand and reserve requirements, raising concerns about system reliability. Therefore, understanding and identifying how to overcome seasonal and weather-driven variability of such resources will be essential to meeting the region's decarbonization goals. ...
... Then, solar and wind capacity factors were estimated with the same resolution as MERRA-2 for each grid cell in the region. The calculated factors reflect actual energy output in contrast with the rated energy output of the system, which is computed as power capacity times a 1-h duration [11,20,24]. To obtain the solar capacity factor, the following three variables from MERRA-2 data are utilized: SWGDN -Surface incoming shortwave flux [W⋅m − 2 ]; SWTDN -Top-of-atmosphere incoming shortwave flux [W⋅m − 2 ]; and the T2M − 2-m air temperature [K]. ...
... where the variable U represents the eastward wind component, and the variable V represents the northward wind component [22]. Subsequently, a piecewise function is applied as follows [11,31,32]. ...
In this study, the potential of wind and solar power to reliably meet the electricity demand of New England is evaluated, as well as the role of energy storage in improving the reliability of the region's renewable energy system. Using 44 years of hourly weather data from 1980 to 2023 obtained from the NASA MERRA-2 reanalysis product, the variability of these renewable resources and their impact on the region's electricity supply and demand is investigated. With varying mixes of wind and solar resources and sufficient resources capacity to generate electricity equal to annual demand, we find that a wind-dominant system can meet approximately 73% of the region's hourly electricity demand, whereas a solar-dominant system can only meet about 69%. However, incorporating 12 h of energy storage enhances the overall reliability of a wind-dominant system to 86%. In comparison, incorporating the same amount of energy storage in a solar-dominant system results in an overall reliability of approximately 87%. Ultimately, our analysis shows that achieving 100% reliability in meeting the annual electricity demand of New England requires addressing the mismatch between electricity demand and resource availability in terms of both location and time. This can be achieved through the integration of significant amounts of energy storage and/or wind and solar resources installations capable of generating electricity that exceeds peak demand by at least 3 times.
... WPP variability on seasonal to interannual time scales is critical to investment decisions, capital costs (Lee and Fields 2021) and electrical demand matching (Pryor et al. 2020a;Shaner et al. 2018). Peak electricity demand across much of North America occurs during the summer. ...
... Peak electricity demand across much of North America occurs during the summer. Geophysical wind resources, and WPP, analyzed using reanalysis products such as ERA5 (Pryor et al. 2020a), MERRA-2 (Shaner et al. 2018) and the Climate Forecast System (CFS) reanalysis (Becker et al. 2014;Yu et al. 2015), tend to be highest in winter and spring in many midlatitude regions (Part I and Part II). Most deep decarbonization scenarios for the U.S. electricity supply rely on wind and solar power becoming the largest generation sources for many regions (Bistline et al. 2022) coupled to dispatchable sources such as hydropower (Liu et al. 2020;Shaner et al. 2018) or battery storage (Amrouche et al. 2016;Arent et al. 2022;Gan et al. 2020;Liu et al. 2020). ...
... Geophysical wind resources, and WPP, analyzed using reanalysis products such as ERA5 (Pryor et al. 2020a), MERRA-2 (Shaner et al. 2018) and the Climate Forecast System (CFS) reanalysis (Becker et al. 2014;Yu et al. 2015), tend to be highest in winter and spring in many midlatitude regions (Part I and Part II). Most deep decarbonization scenarios for the U.S. electricity supply rely on wind and solar power becoming the largest generation sources for many regions (Bistline et al. 2022) coupled to dispatchable sources such as hydropower (Liu et al. 2020;Shaner et al. 2018) or battery storage (Amrouche et al. 2016;Arent et al. 2022;Gan et al. 2020;Liu et al. 2020). According to data from the U.S. Energy Information Administration (EIA), in 2021 the residential sector accounted for 15% of national natural gas consumption (Energy Information Administration 2023a) and about half of residential properties used natural gas for space and water heating (Energy Information Administration 2023b). ...
Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes.
Significance Statement
Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.
... The average state Renewable Portfolio Standard (RPS) has increased from 4.1% in 2013 to 11.4% in 2022. The "green grid" features a lower carbon emissions factor, but an unintended consequence is that the short-term electricity generation becomes more volatile (Shaner et al. 2018). From data provided by the Energy Information Administration (EIA), we create a generator panel dataset by month from 2013 to 2022. ...
... There is little solar power when hurricanes or storms hit, and solar panels' efficiency declines under extreme heat. The increasing penetration of intermittent renewables poses new challenges to resource adequacy (Shaner et al. 2018;Wolak 2022). Regional transmission organizations (RTO) require electric utilities to maintain excess capacity. ...
... As an increasing share of electricity is generated from wind and solar resources, natural disasters may give rise to new challenges in generation management. These intermittent renewables pose long-term resource adequacy problems as their productivity decline under extreme weather events (Shaner et al. 2018;Wolak 2022). Regional electricity planning entities such as the RTOs determine the generation mix to meet the reliability targets. ...
Access to electricity is a crucial determinant of quality of life and productivity. The United States has a highly reliable electricity grid, but it faces new resilience challenges posed by more intense natural disasters and ambitious green power requirements. Over the past decade, utilities have faced a tradeoff between achieving local carbon mitigation goals and offering reliable power access. Using a generator panel dataset from 2013 to 2022, we document that electricity generation from renewables declines during extreme weather events. Based on an electric utility panel dataset over the same period, we find that natural disasters disrupt electricity distribution, but transmission lines are resilient to such shocks. Utilities have made some adaptation progress over time. The higher costs of supplying reliable green power are associated with higher retail electricity prices.
... In 2021, 29% of Australia's energy supply came from renewable sources, up from 8% in 2001 [2]. In a scenario where supply is entirely renewable, meeting demand with these sources requires new planning approaches [3][4][5][6]. The current power grid manifests from population density and sites of large stores of on-demand non-renewable supply [2,7], and owing to Australia's climate and geography, non-dispatchable renewable sources (solar photovoltaics and wind) are more feasible over most of the nation than dispatchable stored renewable sources [2,[8][9][10][11] (e.g. ...
... To minimise energy storage costs while meeting demand, a blend of daytime solar and night-time wind power will be necessary [12,13]. Ideally minimum wind supply at night should meet baseload demand [1,[3][4][5]. ...
... At the sub-farm scale variability can occur due to local topography [32][33][34], or extreme winds posing a hazard to infrastructure [1,13,33,35], while at the intrafarm scale large weather or climate modes can create significant changes in wind supply which persist for days to many years [29,[36][37][38][39][40][41]. For all locations, wind speeds in the atmospheric boundary layer have approximately Weibull distributions (a peak with a long right tail) and often a diurnal cycle [3,31]. This last point is particularly relevant for night-time wind supply, since there is an increased advantage in higher turbine hub heights to access the nocturnal jet over the slow, stably stratified near-surface flow [42][43][44][45]. ...
To meet electricity demand using renewable energy supply, wind farm locations should be chosen to minimise variability in output, especially at night when solar photovoltaics cannot be relied upon. Wind farm location must balance grid-proximity, resource potential, and wind correlation between farms. A top-down planning approach for farm locations can mitigate demand unmet by wind supply, yet the present Australian wind energy market has bottom-up short-term planning. Here we show a computationally tractable method for optimising farm locations to maximise total supply. We find that Australia’s currently operational and planned wind farms produce less power with more variability than a hypothetical optimal set of farms with equivalent capacity within 100 km of the AEMO grid. Regardless of the superior output, this hypothetical set is still subject to variability due to large-scale weather correlated with climate modes (i.e., El Niño). We study multiple scenarios and highlight several internationally transferable planning implications.
... Although wind and solar resources are widely available with low operating costs, their intermittent nature seriously threatens the stable and reliable electricity supply 6,7 . To mitigate this risk, energy storage must be widely deployed 8 . ...
... Third, although CCUS currently remains expensive with a global CO 2 capture capacity of only 36. 6 Mt per year in 2021 24 , its growth has been evident in recent years, with the number of demonstration projects under development or operation worldwide growing from 43 in 2018 to 136 in 2021 24,25 . This is reflected in the considered IPCC scenarios, with almost all integrated assessment model (IAM) scenarios incorporating CCUS under limiting global warming to 1.5°C or 2°C relative to preindustrial levels 26,27 , as the CCUS option generally yields lower costs in reducing carbon emissions than nuclear and renewable options under these scenarios 13,22,28 and provides a viable solution for carbon lock-in of fossil fuel energy infrastructure 7,29,30 , stranded assets, and industry employment losses 31,32 , although previous research has considered IAM-specific modeling assumptions (e.g., the application of general equilibrium theory-based IAMs) 33 . ...
... First, according to the monthly electricity consumption and the hourly electricity load on typical workdays and nonworkdays in each province in 2019 (the representative provinces in the eight regions are shown in Supplementary Fig. 12), the corresponding hourly electricity load on workdays and nonworkdays in each month were calculated by Eqs. (5)- (6). Second, considering the number of workdays and nonworkdays in each month, the average hourly baseline electricity load at the same hour in each month was obtained by Eq. (7). ...
Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.
... Some modelers envision very low-cost batteries or thermal storage that could provide hours, days, or even weeks of storage to address the different time scales of variation in renewable energy. Scenarios of 100% renewables in the U.S. would require weeks of storage of U.S. electricity demand (Shaner et al. 2018). 19 This might become feasible, but storage technologies have multiple technical and economic hurdles. ...
... t of a 24/7 reliability electrical grid. The cost curve of that grid is likely to become sharply convex as renewables approach 100% of the generating mix. Under some specific optimistic assumptions, this might be avoided, but again the key question turns on a broad versus narrow portfolio of climate solutions, all of which carry some uncertainty.19Shaner et al. (2018) estimate the capital cost of 3 weeks of battery storage at $26 trillion. The Jenkins and Thernstrom (2017) literature review suggests 8 to 16 weeks would be needed. ...
The evidence that climate change is underway grows
stronger every year, along with the evidence that it is
largely attributable to human activities. To avoid the
worst effects of climate change, the United States
and the world as a whole must dramatically reduce
greenhouse gas emissions over the next 30 years. In
the latter half of this century, nations collectively must
aim for net negative emissions and begin removing
carbon dioxide from the air. In the energy sector, CO2
emissions must be virtually eliminated by mid-century.
This will require the “deep decarbonization” of the
world’s economies, and the transition to a “clean energy
economy.” An energy transition of this scope will be
challenging in many ways, but it is technologically
and economically feasible, as are reductions in other
greenhouses, including: methane, nitrous oxide and
fluorinated gases (IPCC, 2018).
... 1,2 The integration of electrical energy storage into the grid is increasingly recognized as a pivotal solution for enhancing grid reliability and resiliency, as well as for facilitating the incorporation of inherently intermittent renewable energy sources such as wind and solar. 3,4 In this context, stationary energy storage emerges as a critical and timely application for battery technologies. 5 Candidate energy storage systems must be as cheap as possible, so that when they are coupled with power sources like wind and solar the total operation costs rivals that of current fossil fuel-based power. ...
... Newman-type continuum modeling framework was used to simulate the discharge behavior of the Zn/MnO 2 system studied in this work. The fundamental governing equations that were used as a base for this model are conservation of mass for the three primary electrolytic species considered (Zn 2+ , Mn 2+ , and SO 4 2− denoted by subscript j) given in Eq. 1, an electroneutrality condition for the three primary electrolytic species is given in Eq. 2, and a governing equation for solid-state (i 1 ) current given by Eq. 3. For simplicity of notation, the These equations were discretized using a finite-volume approach, and solved using Newman's published code. ...
This study introduces a framework for modeling the aqueous Zn/MnO2 rechargeable battery. A reaction system and a physics-based continuum model are proposed based on two reaction types, one involving insertion and the second related to dissolution and deposition of a solid reaction product. The model, fitted to empirical data, predicts voltage behavior and capacity limitations during cycling, identifying electrolytic zinc depletion as a limiting mechanism, depending on the original cell construction. The research suggests the need for further material characterization and reaction analysis, which will advance our understanding and facilitate the development of grid-scale energy storage solutions.
... It is an important renewable energy source for decarbonizing the energy system. However, solar energy varies significantly in space and time [1][2][3]. Assessing the abundance and stability of solar energy resources on a national scale at a fine spatial resolution is essential for renewable energy development planning [3,4]. ...
... However, solar energy varies significantly in space and time [1][2][3]. Assessing the abundance and stability of solar energy resources on a national scale at a fine spatial resolution is essential for renewable energy development planning [3,4]. ...
The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under climatological weather conditions. However, there are very few TMY-based solar energy assessments on the scale of China. On the national scale, the difference between the TMY and MYA methods, the requirement of the data record length, and the impacts of the selection of meteorological variables on the TMY-based assessment are still unclear. This study aims to fill these gaps by assessing mainland China’s solar energy resources using the TMY method and China Meteorological Forcing Dataset. The results show that the data record length could significantly influence annual total solar radiation estimation when the record length is shorter than 30 years. Whereas, the estimation becomes stable when the length is greater or equal to 30 years, suggesting a thirty-year data record is preferred. The difference between the MYA and TMY methods is exhibited primarily in places with modest or low abundance of solar radiation. The difference is nearly independent of the examined data record lengths, hinting at the role of regional-specific weather characteristics. The TMY and MYA methods differ more pronounced when assessing the seasonal stability grade. A total of 7.4% of the area of China experiences a downgrade from the TMY relative to the MYA methods, while a 3.15% area experiences an upgrade. The selection of the meteorological variables has a notable impact on the TMY-based assessment. Among the three meteorological variables examined, wind speed has the most considerable impact on both the annual total and seasonal stability, dew point has the second most significant impact, and air temperature has the least. The results are useful for guiding future research on solar energy assessment in China and could be helpful for solar energy development planning.
... Indeed, the relevant literature generally presents a partial view of how such constraints should be accounted for. Some studies, for instance, focused on the effects of geophysical forces on specific clean energy technologies (e.g., wind [19], solar [20], or both [21,22]), while others focused on the response of electricity systems to changes in a single, specific, force (e.g., climate change impacts on renewable resources [12,23]). In this article, we synthesize the current state-of-the-art with the aim of first presenting a more holistic view of the geophysical forces constraining grid decarbonization efforts ("An Overview of Key Geophysical Constraints"), and then identifying the most pressing issues and opportunities for energy systems modelling ("Representation of Geophysical Constraints into Grid Planning Models" -"Opportunities to Alleviate Geophysical Constraints"). ...
... Wind and solar power are predominantly dependent on wind speed and solar radiation, respectively, whereas their availability in a region is generally constrained by topographical conditions and availability of land and water areas [6,21,99]. Most decarbonization studies suggest a large-scale build-out of wind and solar technologies because of their declining costs and large potential, often greater than future expected electricity demand [26,100,101]. ...
Purpose of Review. Future electricity grids will be characterized by the high penetration of renewables to support the decarbonization process. Yet, this transition will further expose grids to a broad spectrum of geophysical forces, such as weather and climate or the availability of land and minerals. Here, we synthesize the current body of knowledge on the relationship between geophysical constraints and electricity grid planning.
Recent Findings. We show that there have been promising advances in the data, methods, and modelling tools needed to incorporate the effect of geophysical constraints on demand, resource availability, and grid operations. However, current research efforts are typically focused on the effect of a single constraint, thereby lacking a broader view of the problem.
Summary. More system-specific and finer-scale analyses are necessary to better understand how spatio-temporal variability in geophysical forces affects grid planning. Moreover, we need a broader focus on the multi-sectoral implications of decarbonization efforts, including the societal consequences of grid management decisions. Importantly, all these efforts are challenged by the computational requirements of existing power system models, which often limit our ability to characterize uncertainty and scale analyses across larger domains.
... The need for inexpensive storage over periods with different lengths, from seconds to days and even seasonal storage, has accelerated in accordance with the increasing share of VRE technologies in electric power systems [6,7]. Pumped hydropower storage (PHS) is an established technique for large-scale energy storage but can be used only in certain geographical areas. ...
... The availability of how low-carbon technologies impact the optimal capacity mix and generation patterns were demonstrated. [6] 2018 ...
... Long-duration electricity storage (LDES) systems with multi-day or seasonal storage capabilities are particularly advantageous for enabling deeper penetration of low-cost wind and solar power. [1][2][3][4][5][6] While most commercial electricity storage deployments and research and development efforts have focused on systems with durations of around 10 h at rated power, there is a growing recognition of the need for longer-duration storage solutions. 7,8 Existing technologies like pumped-hydro storage (PHS) can provide storage for up to 10 h but are limited in their ability to leverage the full benefits of LDES. ...
... starting Fe 2 O 3 /ZrO 2 composite material was prepared by the co-precipitation method. Briefly, the stoichiometric solutions of Fe(NO) 3 .9H 2 O (⩾99%, Sigma-Aldrich) and ZrO 2 (NO) 2 .xH 2 O(⩾99%, Alfa Aesar, x = 6) were dissolved in deionized water separately in the mole ratios of 85:15. Then these two solutions were mixed with a cation concentration of 0.1 M in a beaker and polyvinylpyrrolidone (PVP, Sigma-Aldrich) as a surface active agent was added while stirring constantly for 30 min. ...
Long-the duration energy storage (LDES) is economically attractive to accelerate widespread renewable energy deployment. But none of the existing energy storage technologies can meet LDES cost requirements. The newly emerged solid oxide iron air battery (SOIAB) with energy-dense solid Fe as an energy storage material is a competitive LDES-suitable technology compared to conventional counterparts. However, the performance of SOIAB is critically limited by the kinetics of Fe3O4 reduction (equivalent to charging process) and the understanding of this kinetic bottleneck is significantly lacking in the literature. Here, we report a systematic kinetic study of Fe3O4-to-Fe reduction in H2/H2O environment, particularly the effect of catalyst (iridium) and supporting oxides (ZrO2 and BaZr0.4Ce0.4Y0.1Yb0.1O3). With in situ created Fe3O4, the degree of reduction is measured by the change of H2O and H2 concentrations in the effluent using a mass spectrometer, from which the kinetic rate constant is extracted as a function of inlet H2 concentration and temperature. We find that kinetics can be nicely described by Johson-Mehl-Avrami (JMA) model. We also discuss the stepwise reduction mechanisms and activation energy for the reduction process.
... Flexibility refers to an energy system's ability to cope with the variability and unpredictability that variable renewable energy introduces on different time scales, while reliably supplying all the demanded energy to end users (Bardow et al., 2023). The need for flexibility increases rapidly for a share of renewable energy above 80% (Shaner et al., 2018). Flexibility can be provided from the supply side through imports and exports, dispatchable production, and energy storage. ...
National energy systems require flexibility to accommodate increasing amounts of variable renewable energy. This flexibility can be provided by demand-side management (DSM) from industry. However, the flexibility potential depends on the characteristics of each industrial process. The enormous diversity of industrial processes makes it challenging to evaluate the total flexibility provision from industry to sector-coupled energy systems. In this work, we quantify the maximum cost reductions due to industrial DSM in the net-zero sector-coupled Swiss energy system, and the relationship between cost reductions and various industrial process characteristics. We analyze the flexibility of industrial processes using a generic, process-agnostic model. Our results show that industrial DSM can reduce total energy system costs by up to 4.4%, corresponding to 20% of industry-related energy costs. The value of flexibility from industrial DSM depends not only on the process characteristics but also on the system’s flexibility alternatives, particularly for flexibility over seasonal time horizons. As one specific option for industrial DSM, we find that thermal energy storage (TES) technologies available today could realize between 28% and 61% of the maximum cost reductions from industrial DSM, making TES a promising DSM solution and showing that industrial DSM is an accessible and cost-effective flexibility option.
... Instead, using, e.g., coal for energy production, over 60% of the energy is expelled as heat (Marcinichen et al. 2012). However, this advantage is also a major weakness, which stems from the fact that with the use of wind turbines, there is no specific correspondence between the availability of wind electricity and the electricity demand pattern of the society (Shaner et al. 2018). Therefore, a control mechanism of the electricity production system is required. ...
It is a fact that worldwide energy reserves are constantly decreasing. Simultaneously, climate change poses a strong threat to our planet’s future. Therefore, it is imperative to turn to sustainable and environmentally friendly solutions, such as renewable energy sources. One of the renewable energy sources is wind energy, which has important characteristics and advantages and presents itself as a prominent solution to the issue that has arisen. The production of energy via wind is done by using wind turbines. However, disposing of wind turbines in landfills or incineration can cause serious health and environmental problems. As a result, recycling of wind turbines is a realistic approach for the renewable energy sector to assure the long-term sustainability. Based on the above, this work investigated recycling methods and relevant operations. In particular, it includes a concise review on the topic and a data analysis of previously unpublished data regarding wind turbines installed in Greece (data obtained from Greek Center for Renewable Energy Sources (CRES)), which were meticulously analyzed, offering the main findings of this scientific venture. The factors that contribute to the sustainability of wind turbines (whether small or large power) were explored. According to the results, the main recyclable materials are concrete (79.86%), steel (19.03%), fiberglass (0.73%), copper (0.24%), and aluminum (0.14%) of the total weight of the wind turbine. In 2024, it is expected that 59,110 t of concrete; 13,445 t of steel; 370 t of fiberglass; 127 t of copper; and 74 t of aluminum will be recycled. Therefore, the economic advantage is enormous, which could lead to new investment opportunities and job growth in the recycling industry. It is critical to make timely decisions on the recycling process. The larger the scale of recycling, the greater the economic and environmental benefits for societies.
... The third approach using batteries of some sort, can not only be electrical battery energy storage systems (BESS) but also alternative production, such as production of hydrogen, as some envision. Today, this is mostly a problem of scale, as shown by [47], and basic material requirements. In fact, [38] shows that the material requirements when it comes to critical minerals and metals needed for the production of renewable to replace one single generation of all the fossil power technologies for humanity is impossible. ...
... From a resource perspective, coupling wind turbines with photovoltaic panels significantly reduces the impacts of intermittence and is generally cheaper than a singleresource system [6][7][8]. To achieve a highly reliable energy system and overcome the intermittent nature of solar and wind resources, the addition of energy storage systems (ESS) is essential and has attracted a great deal of attention over the last few years because it allows the electric energy produced to be shifted over different time scales [9]. In this context, batteries, supercapacitors, hydrogen, and flywheels are the main technologies used in microgrid systems [10][11]. ...
In a global context marked by climate variability and in the fight against global warming, Morocco has adopted an energy strategy focused on the development of renewable energies, specifically wind and solar. However, these renewable energies are known for their intermittent nature, which can be offset by the addition of a practical energy storage system. Hydrogen storage systems have gained popularity in recent years, particularly in microgrids, as they offer flexible and long-term storage capabilities compared to battery storage. In this study, the techno-economic feasibility of three energy storage system scenarios for an autonomous microgrid based on solar and wind energy is evaluated in three distinct climate regions of Morocco, namely Laayoune, Tanger, and Oued-Zem. The HOMER software is used to evaluate and examine the techno-economic and ecological impacts. The NASA platform was utilized to generate the meteorological data for these three regions. The aim of this investigation is to address the subsequent inquiries: 1) In these three climatic regions of Morocco, which renewable energy production system can meet the community's demand while also providing economic benefits? 2) In these three regions, which energy storage system offers the highest level of flexibility and cost-effectiveness? 3) Under specific climatic conditions, how is the hybrid storage system deployed to obtain the most cost-effective scenario? 4) What would be the impact of the uncertainties of the climate on the energy plans of the different climate regions in the future?
... In the extreme, at very high P-H2-P power conversion costs relative to generation costs, increasing the generation capacity of wind as needed to meet demand is less expensive than the addition of H2-P discharge capacity. 44 Consequently, in this regime, least-cost systems have large amounts of curtailed generation for most hours. Hence, electricity system costs in this regime are relatively insensitive to changes in the efficiency of P-H2-P charging or discharging, because the required (minimized) amount of stored hydrogen needed to meet demand can readily be generated during most hours by abundant, essentially free, otherwise curtailed electricity. ...
In contrast to battery storage systems, power-to-hydrogen-to-power (P-H2-P) storage systems provide opportunities to separately optimize the costs and efficiency of the system’s charging, storage, and discharging components. The value of capital cost reduction relative to round-trip efficiency improvements of P-H2-P systems is not well understood in electricity systems with abundant curtailed power. Here, we used a macro-energy model to evaluate the sensitivity of system costs to techno-economic characteristics of P-H2-P systems in stylized wind-solar-battery electricity systems with restricted natural gas generation. Assuming current costs and current round-trip P-H2-P efficiencies, least-cost wind and solar electricity systems had large amounts of excess variable renewable generation capacity. These systems included P-H2-P in the least-cost solution, despite its low round-trip efficiency and relatively high P-H2-P power discharge costs. These electricity system costs were not highly sensitive to the efficient use of otherwise-curtailed power, but were sensitive to the capital cost of the P-H2-P power discharge component. If the capital costs of the charging and discharging components were decreased relative to generation costs, curtailment would decrease, and electricity system costs would become increasingly sensitive to improvements in the P-H2-P round-trip efficiency. These results suggest that capital cost reductions, especially in the discharge component, provide a key opportunity for innovation in P-H2-P systems for applications in electricity systems dominated by wind and solar generation. Analysis of underground salt cavern storage constraints in U.S.-based wind and solar scenarios suggests that ample hydrogen storage capacity could be obtained by repurposing the depleted natural gas reservoirs that are currently used for seasonal natural gas storage.
... Clean renewable energies such as solar and wind power have been valued as solutions to global warming issues arising from the depletion of fossil fuels and huge emission of greenhouse gases [1]. Nevertheless, the intermittency and instability of renewable energy sources present new requirements and challenges for the power system [2]. ...
... They are crucial parts that allow the rotor's rotation to be changed from the comparatively slow rotation needed by the generator to generate power to the high-speed revolution needed. For the complete wind turbine system to function at its peak, be dependable, and last a long time, gearbox design and analysis are essential [2].The process of designing necessitates a thorough comprehension of several variables, such as the electrical capacity of the turbine, wind speed and direction, required torque and speed, and functional environment. The ideal gear ratio is one that optimizes torque enhancement and speed decrease, minimizes losses, and ensures the structural integrity of the gearbox [3]. ...
The effective and dependable functioning of wind turbines depends on the construction and assessment of gearboxes in wind power installations. In transferring rotational energy through the wind turbine rotors to the electric power source, gearboxes are essential, and their performance has a direct bearing on the total efficiency of energy conversion. Yet, gearbox malfunctions can result in a lot of lost productivity and expensive repairs. To achieve the best overall efficiency and dependability of wind power networks, early identification and prediction of gearbox defects is essential. In order to address this problem, we introduce in this paper a new enhanced harmony search optimization-based feed-forward neural network (EHSO-FNN) technique. First, 20800 cases total, with 2600 examples for each of the 8 health categories. These instances included typical and unusual fault circumstances with variable speeds and workloads. In this investigation, 2000 records from each sample were provided, recording important operational factors, including temperature, motion, and oil quality. By using min-max normalization to record the basic gearbox health details, this data is cleaned up and turned into useful features. By using MFCC to analyze the motion and Acoustic information collected by wind turbines, we are able to identify a group of specific characteristics that are highly effective in describing the state of the system. The most insightful and pertinent features from the retrieved MFCC feature set are then chosen using EHSO. At last, a FNN model based on the selected elements is created to carry out the fault prediction. The suggested method's performance is assessed using the metrics of accuracy (98.98%), precision (98.92%), recall (99%), f1-score (98.96%), RMSE (0.021), MAE (0.028), and MAPE (0.032). The experimental findings show that, when compared to other methods(1DCNN-PSO-SVM, LSTM,TSVR, WF-MMD-JDA,SVM, and SCADA-DBN), the suggested method obtains the best prediction performance.Early fault detection is made possible by the recommended way, which also enables preventive repairs and reduces downtime for wind turbine installations.
... Green ammonia production is uncertain due to the variable renewable energy [9], which introduces risks to the ammonia market. Existing research on green ammonia mainly focuses on techno-economic analysis [10]; however, studies considering the impact of uncertainty on the market are still limited. ...
... If land suitability and availability are considered, the global potential for renewable energy is reduced to 50-400 PWh per year 12 , which is still~2-17 times higher than the 23 PWh 13 of global electricity consumption in 2018. However, the temporal variability of renewable resources may limit their potential for reliably meeting electricity demand 14 . Without energy storage and over-generation (more electricity generation than demand), wind and solar may fulfill only 70-90% of the current electricity demand 15 . ...
The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.
... These studies have spanned Europe (Amorim et al., 2014;Heuberger et al., 2017;Keatley & Hewitt, 2008;Knorr et al., 2014;Pleβmann & Blechinger, 2017;Schlachtberger et al., 2017), Australia (Elliston et al., 2014;Lenzen et al., 2016;Riesz et al., 2015), South America (Costa et al., 2022;Odeh & Watts, 2019), and North America (MacDonald et al., 2016;Mai et al., 2014;McPherson, 2023;Mileva et al., 2016;Radpour et al., 2021;Safaei & Keith, 2015;Sepulveda et al., 2018;Shaner et al., 2018). They vary from full net-zero electricity emission modelling e.g., (Ruhnau & Qvist, 2022) to pathway modelling which includes intermediary carbon constraints e.g., (Duan et al., 2022), and from simpler copper plate models e.g., (Tong et al., 2020) to advanced capacity expansion and unit commitment models e.g., (de Sisternes et al., 2016). ...
In response to climate change, electricity grids are decreasing their carbon intensity with the addition of wind and solar variable generation (VREN). This leads to a mid-transition period, where renewable energy is unable satisfy electricity demand without contributions from other fossil sources such as natural gas , but also generates sufficiently to constrain conventional generation—changing their operating and market conditions. We use a simplified copper plate model, which scales up and down historical wind and solar generation, to examine how and when the patterns and generation costs for fossil fuel power could change by the increasing capacities of VREN on the relatively isolated Alberta electricity grid. We find that beginning at 20% VREN an increasingly diverse range and reduced hours of dispatched capacity is necessitated from the existing generation. However, even as capacity factors for fossil fuel generation decrease their costs remain reasonable and we found this to be a low-cost pathway for achieving moderate to deep emission reduction goals. A full 86% of demand could be met with VREN before generation costs exceeded 100$/MWh, allowing for an emissions reduction of 28.4 to 9 million tonnes/year of CO2eq, on a lifecycle basis. In order to capture and use the renewable generation, new and existing fossil fuel units require market incentives both for flexibility and to ensure they remain in place throughout the transitionary period as they are crucial to backstop variable renewable generation.
... 11 Because of their cost structure, their efficiency, or their typical operating conditions, the techno-economic performances of each storage technology may vary significantly depending on the storage characteristics (i.e., capacity, power), the dispatch strategy of the electrical energy stored (i.e., baseload or tracking load), or the targeted dispatchability of the plant. The ability of solar and wind energy to cover an increasing fraction of the electric demand has been already evaluated both at the level of the US, 12 and at the world level. 13 A number of works published over the last years aimed at finding the optimal designs of solar plants to maximize electricity production and/or to minimize electricity costs while achieving a given dispatchability for selected geographical locations, most often comparing the performances of the different solutions proposed. ...
The techno-economic performances of five different solar-electricity conversion technologies (photovoltaic, solar tower, parabolic trough as well as two hybrid PV/CSP systems) associated with three energy storage means (electrochemical, thermal, and thermophotovoltaic) are evaluated thanks to representative models applied to four representative sites around the world. The evaluation is based on the ability to dispatch the power production throughout the year, the ability to maximize energy injection in the electrical grid, and the levelized cost of electricity. It is found that increasing the dispatchability of solar power plants will necessarily lead to the emergence of additional energy losses and important LCOE increase, either because of low round-trip efficiency of the storage system, or because of its high cost of energy capacity. Despite lower energy production for a given collecting area, combination of PV power plants with electrochemical storage or thermal energy storage surprisingly seem to be the most promising paths.
Fe anodes are emerging as cost‐effective components in long duration storage applications, with a benefit being the high natural abundance of Fe. Additive and electrode design have advanced the performance of Fe electrodes, but a more precise understanding of the electrode formation process and failure mechanisms is important for continued optimization. These interfacial electrochemical processes, which involve short‐lived intermediate species, require analysis with high spatial and temporal resolution to provide a full picture. This study therefore explores the behavior of the Fe electrode in alkaline electrolyte using electrochemical liquid cell transmission electron microscopy, extending the technique toward high pH (10–13) conditions. By combining identical location imaging and diffraction, in situ imaging, and benchtop experiments, this study shows distinct microstructural changes on cycling as a function of pH, in particular the appearance of multiple electrodeposited Fe species and a passivation layer. The dependence on electrochemical parameters is discussed, showing that the observations can be related to stability predictions from the Pourbaix diagram. However, it is also necessary to consider kinetic effects, such as the solubility and diffusion of soluble species. Strategies to control these material transformations are discussed as a function of potential, along with opportunities for further optimizing the Fe electrode.
Green hydrogen has the potential to address two pressing problems in a zero-carbon energy system: balancing seasonal variability of solar and wind in the electricity sector, and replacing fossil fuels in hard-to-abate sectors. However, the previous research only separately modeled the electricity and hard-to-abate sectors, which is unable to capture how the interaction between the two sectors influences the energy system cost. In this study, focusing on China, we deploy an electricity system planning model to examine the cost implications of green hydrogen to fully decarbonize the electricity system and hard-to-abate sectors. Our results reveal that green hydrogen enables a 17% reduction in the levelized cost of a zero-carbon electricity system relative to that without hydrogen. However, cost savings hinge on the availability of underground hydrogen storage capacities and electric transmission expansion. More importantly, coupling hydrogen infrastructure in the electricity and hard-to-abate sectors not only reduces energy costs compared to a decoupled energy system but also makes green hydrogen cost-competitive compared to fossil fuel-based gray and blue hydrogen in China.
A stylized macro-scale energy model of least-cost electricity systems relying only on wind and solar generation was used to assess the value of different storage technologies, individually and combined, for the contiguous U.S. as well as for four geographically diverse U.S. load-balancing regions. For the contiguous U.S. system, at current costs, when only one storage technology was deployed, hydrogen energy storage produced the lowest system costs, due to its energy-capacity costs being the lowest of all storage technologies modeled. Additional hypothetical storage technologies were more cost-competitive than hydrogen (long-duration storage) only at very low energy-capacity costs, but they were more cost-competitive than Li-ion batteries (short-duration storage) at relatively high energy-and power-capacity costs. In all load-balancing regions investigated, the least-cost systems that included long-duration storage had sufficient energy and power capacity to also meet short-duration energy and power storage needs, so that the addition of short-duration storage as a second storage technology did not markedly reduce total system costs. Thus, in electricity systems that rely on wind and solar generation, contingent on social and geographic constraints, long-duration storage may cost-effectively provide the services that would otherwise be provided by shorter-duration storage technologies.
Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980–2000 and 2001–2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.
Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an interconnector between Australia’s eastern and western grids. Subsequently, through an extensive sensitivity analysis, we investigate the impact of specific storage technologies and cost variations. Our findings demonstrate that the proposed interconnector offers a cost-effective solution, reducing generation and storage power capacity needs by 6 and 14%, respectively, resulting in 4% savings on overall investment costs. Moreover, the study’s sensitivity analysis reveals that wind generation provides 50–70% of the energy demand for the least-cost solution. Despite storage inefficiencies, long-duration storage would need to be deployed to support power capacity for 2–4 days, representing 15–40% of peak demand, depending on future technology costs. Subsequently, achieving a fully renewable electricity sector in Australia requires a significant expansion of generation and storage infrastructure, with a 13-fold increase in storage power capacity and a 40-fold increase in storage energy capacity compared to existing levels.
Variable renewable energy sources (VREs) have experienced steady global growth. The discontinuation of feed-in tariffs (FIT) for new projects in Japan has necessitated the exploration of alternative revenue certainty mechanisms for VREs. Power purchase agreements (PPAs) for supplying carbon-free energy (CFE) have emerged as a promising business model. This study provides a practical framework for integrating diverse flexibilities, encompassing renewable energy curtailment, battery energy storage systems (BESS), and time-shifting demand response (TSDR) for PPAs. To accommodate the unique characteristics of each type of flexibility, the suggested framework consists of sequential optimization problems that incrementally incorporate flexibility to achieve feasible solutions with minimal flexibility. A simulation-based assessment was conducted using a case study involving the provision of the CFE from an onshore wind turbine to a nearby data center. The synergistic relationships between these flexibilities were examined through simulations. The findings highlight that setting a target for BESS's charge state and leveraging the TSDR are key factors in reducing the required capacity of the BESS. For wind developers, this reduction corresponds to a 7.3% decrease in the initial cost. This study offers strategic insights aimed at strengthening the cost advantages of PPAs and enhancing the viability of renewable energy developers and environmentally conscious energy consumers.
Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.
Given that thermal power plants are the dominant mode of power generation in China, decarbonization from thermal power plants is significant to protect the global climate from further warming. CO2 capture and storage (CCS) is one of the most promising approaches for mitigating CO2 emissions; therefore, studies evaluating the effect of CCS retrofit on thermal power plants are urgently required. This study selected coal-fired power plant (CFPP) and natural gas combined cycle power plant (NGCC) as the representative technologies for thermal power plants, and proposed system models for their integrations with CCS. A comprehensive evaluation system was established using different metrics such as specific CO2 emission (CO2 emission per kWh), net electric efficiency, levelized cost of electricity (LCOE), and cost of avoiding CO2 emission (COAE). The detailed effects of fuel price fluctuations, technological advances, and different technology combinations on key metrics were analyzed, in order to propose policy recommendations for the decarbonization of China's thermal power plants using CCS technology. The following results were obtained: (1) The use of CCS to reduce CO2 emissions from thermal power plants will lead to a significant increase in LCOE. Each 100 g/kWh reduction in specific CO2 emission will result in 11 %–14.5 % increase in LCOE for CFPP + CCS, and 9.5 %–13.5 % increase for NGCC + CCS. (2) Technological advances toward reducing specific heat consumption for CO2 capture should be strongly promoted to reduce LCOE. Each 0.4 GJ/t reduction in specific heat consumption will result in an approximately 0.066 CNY/kWh reduction in LCOE for CFPP. (3) Both CFPP + CCS and NGCC + CCS exhibit potential for decreasing COAE below 600 CNY/t, although the technologies to meet this threshold are not currently realistic at higher fuel prices.
Plans for decarbonized electricity systems rely on projections of highly uncertain future technology costs. We use a stylized model to investigate the influence of future cost uncertainty, as represented by different projections in the National Renewable Energy Laboratory 2021 Annual Technology Baseline dataset, on technology mixes comprising least-cost decarbonized electricity systems. Our analysis shows that given the level of future cost uncertainty as represented by these projections, it is not possible to predict with confidence which technologies will play a dominant role in future least-cost carbon emission–free energy systems. Successful efforts to reduce costs of individual technologies may or may not lead to system cost reductions and widespread deployments, depending on the success of cost-reduction efforts for competing and complementary technologies. These results suggest a portfolio approach to reducing technology costs. Reliance on uncertain cost breakthroughs risks costly outcomes. Iterative decision-making with learning can help mitigate these risks.
In this paper, we present a stochastic dynamic planning model called SMART-Invest, which is capable of optimizing investment decisions in different electricity generation technologies. SMART-Invest consists of two layers: an optimization outer layer and an operational core layer. The operational model captures hourly variations of wind and solar over an entire year, with detailed modeling of day-ahead commitments, forecast uncertainties and ramping constraints. The outer layer requires optimizing an unknown, non-convex, non-smooth, and expensive-to-evaluate function. We present a stochastic search algorithm with an adaptive stepsize rule that can find the optimal investment decisions quickly and reliably. By properly capturing the marginal cost of investments in wind, solar and storage, we feel that SMART-Invest produces a more realistic picture of an optimal mix of wind, solar and storage, resulting in a tool that can provide more accurate guidance for policy makers.
The Modern-Era Retrospective Analysis for Research and
Applications-2 (MERRA2) version of the GEOS-5 Atmospheric General
Circulation Model (AGCM) is currently in use in the NASA Global
Modeling and Assimilation Office (GMAO) at a wide range of
resolutions for a variety of applications. Details of the changes in
parameterizations subsequent to the version in the original MERRA
reanalysis are presented here. Results of a series of
atmosphere-only sensitivity studies are shown to demonstrate changes
in simulated climate associated with specific changes in physical
parameterizations, and the impact of the newly implemented
resolution-aware behavior on simulations at different resolutions is
demonstrated. The GEOS-5 AGCM presented here is the model used as
part of the GMAO's MERRA2 reanalysis, the global mesoscale "nature
run", the real-time numerical weather prediction system, and for
atmosphere-only, coupled ocean–atmosphere and coupled
atmosphere–chemistry simulations.
The seasonal mean climate of the MERRA2 version of the GEOS-5 AGCM
represents a substantial improvement over the simulated climate of
the MERRA version at all resolutions and for all applications.
Fundamental improvements in simulated climate are associated with
the increased re-evaporation of frozen precipitation and cloud
condensate, resulting in a wetter atmosphere. Improvements in
simulated climate are also shown to be attributable to changes in
the background gravity wave drag, and to upgrades in the
relationship between the ocean surface stress and the ocean
roughness. The series of "resolution aware" parameters related to
the moist physics were shown to result in improvements at higher
resolutions, and result in AGCM simulations that exhibit seamless
behavior across different resolutions and applications.
Reanalysis models are rapidly gaining popularity for simulating wind power output due to their convenience and global coverage. However, they should only be relied upon once thoroughly proven. This paper reports the first international validation of reanalysis for wind energy, testing NASA's MERRA and MERRA-2 in 23 European countries. Both reanalyses suffer significant spatial bias, overestimating wind output by 50% in northwest Europe and underestimating by 30% in the Mediterranean. We derive national correction factors, and show that after calibration national hourly output can be modelled with R2 above 0.95. Our underlying data are made freely available to aid future research.
Carbon dioxide emissions from electricity generation are a major cause of anthropogenic climate change. The deployment of wind and solar power reduces these emissions, but is subject to the variability of the weather. In the present study, we calculate the cost-optimized configuration of variable electrical power generators using weather data with high spatial (13-km) and temporal (60-min) resolution over the contiguous US. Our results show that when using future anticipated costs for wind and solar, carbon dioxide emissions from the US electricity sector can be reduced by up to 80% relative to 1990 levels, without an increase in the levelized cost of electricity. The reductions are possible with current technologies and without electrical storage. Wind and solar power increase their share of electricity production as the system grows to encompass large-scale weather patterns. This reduction in carbon emissions is achieved by moving away from a regionally divided electricity sector to a national system enabled by high-voltage direct-current transmission.
Significance
The large-scale conversion to 100% wind, water, and solar (WWS) power for all purposes (electricity, transportation, heating/cooling, and industry) is currently inhibited by a fear of grid instability and high cost due to the variability and uncertainty of wind and solar. This paper couples numerical simulation of time- and space-dependent weather with simulation of time-dependent power demand, storage, and demand response to provide low-cost solutions to the grid reliability problem with 100% penetration of WWS across all energy sectors in the continental United States between 2050 and 2055. Solutions are obtained without higher-cost stationary battery storage by prioritizing storage of heat in soil and water; cold in water and ice; and electricity in phase-change materials, pumped hydro, hydropower, and hydrogen.
We explore the operations, balancing requirements, and costs of the Western Electricity Coordinating Council power system under a stringent greenhouse gas emission reduction target. We include sensitivities for technology costs and availability, fuel prices and emissions, and demand profile. Meeting an emissions target of 85% below 1990 levels is feasible across a range of assumptions, but the cost of achieving the goal and the technology mix are uncertain. Deployment of solar photovoltaics is the main driver of storage deployment: the diurnal periodicity of solar energy availability results in opportunities for daily arbitrage that storage technologies with several hours of duration are well suited to provide. Wind output exhibits seasonal variations and requires storage with a large energy subcomponent to avoid curtailment. The combination of low-cost solar technology and advanced battery technology can provide substantial savings through 2050, greatly mitigating the cost of climate change mitigation. Policy goals for storage deployment should be based on the function storage will play on the grid and therefore incorporate both the power rating and duration of the storage system. These goals should be set as part of overall portfolio development, as system flexibility needs will vary with the grid mix.
High cost and technical immaturity of bulk (multi-hour) electricity storage (BES) systems are often cited as major hurdles to increasing the penetration of intermittent renewables. We use a simple model to assess the economics of BES under carbon emissions constraints. Size and dispatch of a green-field generation fleet is optimized to meet a variable load at a 15 minute time resolution. Electricity supply options are wind, gas turbine, BES, and a generic dispatchable-zero-carbon (DZC) source as a proxy for fossil fuel plants with carbon capture or nuclear plants. We review the cost of selected BES technologies and parameterize the performance of storage, focusing on the energy- and power-specific capital costs. We examine sensitivity of the electricity cost to storage performance under a range of emissions constraints. Availability of inexpensive BES systems in general and particularly electrochemical technologies has a small impact on the overall cost of decarbonization. Proportional reductions in capital costs of wind and DZC lower decarbonization costs far more. We find no economic justification for seasonal storage. Intermittent renewables can be used to decarbonize the electricity supply with a proportionally small requirement for BES because gas provides much of the intermittency management even when the carbon emissions intensity is cut to less than 30% of today's U.S. average. Substantial BES is required only when emissions are constrained to nearly zero and DZC is not allowed.
We use reanalysis data to investigate the daily co-variability of wind and solar irradiance in Britain, and its implications for renewable energy supply balancing. The joint distribution of daily-mean wind speeds and irradiances shows that irradiance has a much stronger seasonal cycle than wind, due to the rotational tilt of the Earth. Irradiance is weakly anticorrelated with wind speed throughout the year (−0.4≲ρ≲−0.2−0.4≲ρ≲−0.2): there is a weak tendency for windy days to be cloudier. This is particularly true in Atlantic-facing regions (western Scotland, south-west England). The east coast of Britain has the weakest anticorrelation, particularly in winter, primarily associated with a relative increase in the frequency of clear-but-windy days. We also consider the variability in total power output from onshore wind turbines and solar photovoltaic panels. In all months, daily variability in total power is always reduced by incorporating solar capacity. The scenario with the least seasonal variability is approximately 70%-solar to 30%-wind. This work emphasises the importance of considering the full distribution of daily behaviour rather than relying on long-term average relationships or correlations. In particular, the anticorrelation between wind and solar power in Britain cannot solely be relied upon to produce a well-balanced energy supply.
The Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) version of the GEOS-5 Atmospheric General Circulation Model (AGCM) is currently in use in the NASA Global Modeling and Assimilation Office (GMAO) at a wide range of resolutions for a variety of applications. Details of the changes in parameterizations subsequent to the version in the original MERRA reanalysis are presented here. Results of a series of atmosphere-only sensitivity studies are shown to demonstrate changes in simulated climate associated with specific changes in physical parameterizations, and the impact of the newly implemented resolution-aware behavior on simulations at different resolutions is demonstrated. The GEOS-5 AGCM presented here is the model used as part of the GMAO's MERRA2 reanalysis, the global mesoscale "nature run", the real-time numerical weather prediction system, and for atmosphere-only, coupled ocean–atmosphere and coupled atmosphere–chemistry simulations.
The seasonal mean climate of the MERRA2 version of the GEOS-5 AGCM represents a substantial improvement over the simulated climate of the MERRA version at all resolutions and for all applications. Fundamental improvements in simulated climate are associated with the increased re-evaporation of frozen precipitation and cloud condensate, resulting in a wetter atmosphere. Improvements in simulated climate are also shown to be attributable to changes in the background gravity wave drag, and to upgrades in the relationship between the ocean surface stress and the ocean roughness. The series of "resolution aware" parameters related to the moist physics were shown to result in improvements at higher resolutions, and result in AGCM simulations that exhibit seamless behavior across different resolutions and applications.
This study addresses the task of identifying optimum locations for solar and wind power plants so that the wind-plus- solar power generation meets certain conditions of efficiency and stability, thus allowing to overcome the downside that the natural variability of these renewable resources represents. The method was based on a simulated annealing algorithm and applied over the Iberian Peninsula, a region whose commitment to renewable energy is growing relatively fast. Obtained results are encouraging since a number of different sensitivity experiments support the spatio-temporal complementarity between solar and wind power, at least at the monthly time-scale, in this region.
The variability in wind-generated electricity complicates the integration of this electricity into the electrical grid. This challenge steepens as the percentage of renewably-generated electricity on the grid grows, but variability can be reduced by exploiting geographic diversity: correlations between wind farms decrease as the separation between wind farms increases. But how far is far enough to reduce variability? Grid management requires balancing production on various timescales, and so consideration of correlations reflective of those timescales can guide the appropriate spatial scales of geographic diversity grid integration. To answer ‘how far is far enough,’ we investigate the universal behavior of geographic diversity by exploring wind-speed correlations using three extensive datasets spanning continents, durations and time resolution. First, one year of five-minute wind power generation data from 29 wind farms span 1270 km across Southeastern Australia (Australian Energy Market Operator). Second, 45 years of hourly 10 m wind-speeds from 117 stations span 5000 km across Canada (National Climate Data Archive of Environment Canada). Finally, four years of five-minute wind-speeds from 14 meteorological towers span 350 km of the Northwestern US (Bonneville Power Administration). After removing diurnal cycles and seasonal trends from all datasets, we investigate dependence of correlation length on time scale by digitally high-pass filtering the data on 0.25–2000 h timescales and calculating correlations between sites for each high-pass filter cut-off. Correlations fall to zero with increasing station separation distance, but the characteristic correlation length varies with the high-pass filter applied: the higher the cut-off frequency, the smaller the station separation required to achieve de-correlation. Remarkable similarities between these three datasets reveal behavior that, if universal, could be particularly useful for grid management. For high-pass filter time constants shorter than about τ = 38 h, all datasets exhibit a correlation length &; that falls at least as fast as &; . Since the inter-site separation needed for statistical independence falls for shorter time scales, higher-rate fluctuations can be effectively smoothed by aggregating wind plants over areas smaller than otherwise estimated.
With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics.Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling.A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter.An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/∼energymet/data/Cannon2014/.
Power systems have traditionally been designed to provide flexibility in a context where demand is met by bulk generation. The integration of variable and uncertain renewable generation sources, such as wind, increases the flexibility needed to maintain the load-generation balance. This paper aims to provide a systematic approach to evaluate the flexibility level and investigate the role of flexibility in generation planning and market operation. An “offline” index is proposed to estimate the technical ability of both the individual generators and the generation mix to provide the required flexibility. A dedicated unit construction and commitment (UCC) algorithm, able to consider plant investment and operation costs, is developed to determine the optimal investments in flexible generating units. Market simulation models are then implemented to determine the profits obtained from providing flexibility in different market designs. An adapted RTS-96 system is used to test the proposed models and the flexibility metric. Results demonstrate the validity of the UCC algorithm and the coherence of the proposed flexibility index. The analysis of the profits of flexibility shows that the market design plays an important role in the efficient deployment and subsequent profitability of flexibility resources.
We model many combinations of renewable electricity sources (inland wind, offshore wind, and photovoltaics) with electrochemical storage (batteries and fuel cells), incorporated into a large grid system (72 GW). The purpose is twofold: 1) although a single renewable generator at one site produces intermittent power, we seek combinations of diverse renewables at diverse sites, with storage, that are not intermittent and satisfy need a given fraction of hours. And 2) we seek minimal cost, calculating true cost of electricity without subsidies and with inclusion of external costs. Our model evaluated over 28 billion combinations of renewables and storage, each tested over 35,040 h (four years) of load and weather data. We find that the least cost solutions yield seemingly-excessive generation capacity—at times, almost three times the electricity needed to meet electrical load. This is because diverse renewable generation and the excess capacity together meet electric load with less storage, lowering total system cost. At 2030 technology costs and with excess electricity displacing natural gas, we find that the electric system can be powered 90%–99.9% of hours entirely on renewable electricity, at costs comparable to today's—but only if we optimize the mix of generation and storage technologies.
We use time- and frequency-domain techniques to quantify the extent to which long-distance interconnection of wind plants in the United States would reduce the variability of wind power output. Previous work has shown that interconnection of just a few wind plants across moderate distances could greatly reduce the ratio of fast- to slow-ramping generators in the balancing portfolio. We find that interconnection of aggregate regional wind plants would not reduce this ratio further but would reduce variability at all frequencies examined. Further, interconnection of just a few wind plants reduces the average hourly change in power output, but interconnection across regions provides little further reduction. Interconnection also reduces the magnitude of low-probability step changes and doubles firm power output (capacity available at least 92% of the time) compared with a single region. First-order analysis indicates that balancing wind and providing firm power with local natural gas turbines would be more cost-effective than with transmission interconnection. For net load, increased wind capacity would require more balancing resources but in the same proportions by frequency as currently, justifying the practice of treating wind as negative load.
World wind power resources are abundant, but their utilization could be limited because wind fluctuates rather than providing steady power. We hypothesize that wind power output could be stabilized if wind generators were located in a meteorologically designed configuration and electrically connected. Based on 5 yr of wind data from 11 meteorological stations, distributed over a 2,500 km extent along the U.S. East Coast, power output for each hour at each site is calculated. Each individual wind power generation site exhibits the expected power ups and downs. But when we simulate a power line connecting them, called here the Atlantic Transmission Grid, the output from the entire set of generators rarely reaches either low or full power, and power changes slowly. Notably, during the 5-yr study period, the amount of power shifted up and down but never stopped. This finding is explained by examining in detail the high and low output periods, using reanalysis data to show the weather phenomena responsible for steady production and for the occasional periods of low power. We conclude with suggested institutions appropriate to create and manage the power system analyzed here.
Wind field statistics are evaluated from the ERA-40 data bank covering a period of 44 years with a temporal resolution of 6 h. Instantaneous wind speed values are provided in geographic cells of size 1°×1° (lat/long) for surface (10 m) and 1000 hPa pressure heights. Potential wind power generation is estimated in two steps. Firstly, the wind speed at hub height is approximated from surface data based on the statistical analysis of the wind and geopotential records for 1000 hPa pressure level. Secondly, the wind speed values are transformed by an idealised power curve fitted for measured data. The model time series are fed into various hypothetical electric networks. The main quantity of interest is the aggregated output from the networks. A reference power time series is determined for a static network connecting each continental site and an envelope of 1° around the shorelines (representing off-shore locations) over Europe. This time series exhibits a low average value and a marked annual periodicity. Wind power integration over limited areas results in higher average outputs at the expense of stronger fluctuations. The long-range spatial correlations of the wind field limit the level of fluctuations strongly which can not be eliminated either by an increase of the area of integration or by dynamic control. This study is fully conceptual, however it demonstrates the limitations of wind power integration over Europe.
A record of wind fields inferred from reanalysis of past meteorological data is used to assess the potential source of electricity that could be realized by tapping this global wind resource. It is concluded that the source would be more than sufficient to satisfy current and potentially future demands for global electricity, potentially for energy in all forms. Particular attention is directed to applications in China and in the United States, respectively, the world’s largest and second largest sources of greenhouse gases. Limitations are discussed with respect to the ultimate potential for extraction of power from wind.
The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. We find that the number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviations drops by a factor of 3. The systematic dependence of generation-level probability distribution "tails" on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array. arXiv:1607.06702
Reanalysis data are attractive for wind-power studies because they can offer wind speed data for large areas and long time periods and in locations where historical data are not available. However, reanalysis-predicted wind speeds can have significant uncertainties and biases relative to measured wind speeds. In this work we develop a model of the bias and uncertainty of CFS reanalysis wind speed than can be used to correct the data and identify sources of error. We find the CFS reanalysis data underestimate wind speeds at high elevations, at high measurement heights, and in unstable atmospheric conditions. For example, at a site with an elevation of 500 m and hub height of 80 m, a CFS reanalysis wind speed of 8 m/s is 0.2 m/s higher to 1.3 m/s lower than the measured wind speed. We also find a seasonal bias that correlates with surface roughness length used by the reanalysis model during the spring season. The corrections we propose reduce the average bias of reanalysis wind speed extrapolated to hub height to nearly zero, an improvement of 0.3–0.9 m/s. These corrections also reduce the RMS error by 0.1–0.4 m/s, a small improvement compared to the uncorrected RMS errors of 1.5–2.4 m/s.
Reanalysis data sets have become a popular data source for large-scale wind power analyses because they cover large areas and long time spans, but those data are uncertain representations of “true” wind speeds. In this work we develop a model that systematically quantifies the uncertainties across many sites and corrects for biases of the reanalysis data. We apply this model to 32 years of reanalysis data for 1002 plausible wind-plant sites in the U.S. Great Plains to estimate variability of wind energy generation and the smoothing effect of aggregating distant wind plants. We find the coefficient of variation (COV) of annual energy generation of individual wind plants in the Great Plains is 5–12%, but the COV of all those plants aggregated together is 3.0%. The year-to-year variability (of interest to system planners) shows a maximum step change of ∼10%, and the wind power varies by ±7.5% over a 32-year period. Similarly, the average variability of quarterly cash flow to equity investors in a single wind plant is 29%, but that can be reduced to 18–21% by creating small portfolios of two wind plants selected from regions with low correlations of wind speed.
Wind integration studies are an important tool for understanding the effects of increasing wind power deployment on grid reliability and system costs. This paper provides a detailed review of the statistical methods and results from 12 large-scale regional wind integration studies. In particular, we focus our review on the modeling methods and conclusions associated with estimating short-term balancing reserves (regulation and load-following). Several important observations proceed from this review. First, we found that many of the studies either explicitly or implicitly assume that wind power step-change data follow exponential probability distributions, such as the Gaussian distribution. To understand the importance of this issue we compared empirical wind power data to Gaussian data. The results illustrate that the Gaussian assumption significantly underestimates the frequency of very large changes in wind power, and thus may lead to an underestimation of undesirable reliability effects and of operating costs. Secondly, most of these studies make extensive use of wind speed data generated from mesoscale numerical weather prediction (NWP) models. We compared the wind speed data from NWP models with empirical data and found that the NWP data have substantially less power spectral energy, a measure of variability, at higher frequencies relative to the empirical wind data. To the extent that this difference results in reduced high-frequency variability in the simulated wind power plants, studies using this approach could underestimate the need for fast ramping balancing resources. On the other hand, the magnitude of this potential underestimation is uncertain, largely because the methods used for estimating balancing reserve requirements depend on a number of heuristics, several of which are discussed in this review. Finally, we compared the power systems modeling methods used in the studies and suggest potential areas where research and development can reduce uncertainty in future wind integration studies.
Wind and solar PV generation data for the entire contiguous US are
calculated, on the basis of 32 years of weather data with temporal resolution
of one hour and spatial resolution of 40x40km, assuming
site-suitability-based as well as stochastic wind and solar PV capacity
distributions throughout the country. These data are used to investigate a
fully renewable electricity system, resting primarily upon wind and solar PV
power. We find that the seasonal optimal mix of wind and solar PV comes at
around 80% solar PV share, owing to the US summer load peak. By picking this
mix, long-term storage requirements can be more than halved compared to a wind
only mix. The daily optimal mix lies at about 80% wind share due to the nightly
gap in solar PV production. Picking this mix instead of solar only reduces
backup energy needs by about 50%. Furthermore, we calculate shifts in FERC
(Federal Energy Regulatory Commission)-level LCOE (Levelized Costs Of
Electricity) for wind and solar PV due to their differing resource quality and
fluctuation patterns. LCOE vary by up to 35% due to regional conditions, and
LCOE-optimal mixes turn out to largely follow resource quality. A transmission
network enhancement among FERC regions is constructed to transfer high
penetrations of solar and wind across FERC boundaries, based on a novel
least-cost optimization approach.
This paper provides global and seasonal estimates of the “practical” wind power obtained with a 3-D numerical model (GATOR-GCMOM) that dynamically calculates the instantaneous wind power of a modern 5 MW wind turbine at 100-m hub height at each time step. “Practical” wind power is defined as that delivered from wind turbines in high-wind locations (year-average 100-m wind speed ≥ 7 m/s) over land and near-shore, excluding both polar regions, mountainous, and conflicting land use areas, and including transmission, distribution, and wind farm array losses. We found that seasonal variations in the global practical wind resources are significant. The highest net land plus near-shore capacity factors globally are found during December–January–February and the lowest during June–July–August. The capacity factors in the transitional seasons (March–April–May and September–October–November) are rather similar to one another in terms of geographical patterns and frequency distributions. The yearly-average distributions of capacity factors, whether in terms of geographic patterns or frequency distributions, differ from those in all four seasons, although they are closest to the transitional seasons. Regional practical wind resources are sensitive to seasons and to thresholds in year-average wind speed and bathymetry, but are more than enough to supply local electricity demand in all regions except Japan.
This report provides a full description of the Western Wind and Solar Integration Study (WWSIS) and its findings.
In Ontario (Canada), the integration of renewable power is a priority policy goal. Since 2004, the circumstances under which the integration of renewable power is evaluated have changed due to successive changes in price as well as concerns that its over-production may add to grid congestion. This research investigates the value of increasing complementarity (both proximate and geographically dispersed) of wind and solar resources as a means by which electricity planners and researchers might advance electricity sustainability in Ontario. More specifically, this paper asks the following questions: 1) Does the combination of solar and wind resources in selected locations in Ontario serve to ‘smooth out’ power production, i.e., decrease instances of both high and low values, as compared to either resource producing individually? 2) Can this ‘smoothness’ be further improved by dispersing these resources geographically amongst locations? and 3) Does increasing the number of locations with solar and wind resources further ‘smooth out’ power production? Three years (2003–2005) of synchronous, hourly measurements of solar irradiance and wind speeds from Environment Canada’s Canadian Weather Energy and Engineering Data Sets (CWEEDS) are used to derive dimensionless indices for four locations in Ontario (Toronto, Wiarton, Sault Ste. Marie and Ottawa). These indices are used to develop three transparent and accessible methods of analysis: (1) graphical representation; (2) percentile ranking; and (3) using a theoretical maximum as a proxy for capacity. The article concludes that the combination of solar and wind within locations and amongst two locations improves ‘smoothness’ in power production, as compared to when each resource is produced on its own; moreover, it is further improved once more than two resources and two locations are combined. However, there is neither further benefit, nor drawback, associated with the geographic dispersion of complementarity between solar in one location and wind in another, when compared to both resources in one location.
We examine the changes to the electric power system required to incorporate high penetration of variable wind and solar electricity generation in a transmission constrained grid. Simulations were performed in the Texas, US (ERCOT) grid where different mixes of wind, solar photovoltaic and concentrating solar power meet up to 80% of the electric demand. The primary constraints on incorporation of these sources at large scale are the limited time coincidence of the resource with normal electricity demand, combined with the limited flexibility of thermal generators to reduce output. An additional constraint in the ERCOT system is the current inability to exchange power with neighboring grids. By themselves, these constraints would result in unusable renewable generation and increased costs. But a highly flexible system - with must-run baseload generators virtually eliminated - allows for penetrations of up to about 50% variable generation with curtailment rates of less than 10%. For penetration levels up to 80% of the system's electricity demand, keeping curtailments to less than 10% requires a combination of load shifting and storage equal to about one day of average demand.
This paper describes a model for prediction of the power produced by wind farms connected to the electrical grid. The time frame is from 0 to 36 h ahead. The goal is to develop a model that can be integrated in the dispatching system at a utility. The physical basis of the model is the predictions generated from forecasts from the high-resolution limited area model (HIRLAM) of the Danish Meteorological Institute. These predictions are then made specific for individual sites (wind farms) by applying a matrix generated by the submodels of WASP (Wind Atlas Application and Analysis Program). To verify the model one year's worth of data from 17 wind farms have been used. The farms are located in Denmark on the Zealand (14) and Bornholm (3) islands and are all controlled by the Danish utility ELKRAFT/SK Power.
Electricity Storage Handbook in Collaboration with NRECA
- A A Akhil
- Akhil
A. A. Akhil, et al. DOE/EPRI 2013 Electricity Storage Handbook
in Collaboration with NRECA.
Lazard's Levelized Cost of Storage Analysis
- Lazard
Lazard, Lazard's Levelized Cost of Storage Analysis, Lazard,
2015, pp. 1-30.