Distributed Wind Energy Futures Study

McCabe, Kevin; Prasanna, Ashreeta; Lockshin, Jane; Bhaskar, Parangat; Bowen, Thomas; Baranowski, Ruth; Sigrin, Ben; Lantz, Eric

doi:10.2172/1868329

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…The technical potential for rooftop PV is 1,118 GW (Gagnon et al 2016), so there may be additional opportunities beyond the 190 GW deployed in this study (Prasanna et al 2021). Distributed wind has a similarly large technical and economic potential (McCabe et al 2022), as well as the potential to scale to 40 GW (Lantz et al 2016) from the 1.1 GW installed in 2020 (Orrell, Kazimierczuk, and Sheridan 2021).…”

Section: Text Box 4 the Role Of Distributed Resourcesmentioning

confidence: 95%

Examining Supply-Side Options to Achieve 100% Clean Electricity by 2035

Denholm

Brown

Cole

et al. 2022

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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 100% Clean Electricity by 2035 ScenariosWe evaluated four main 100% clean electricity scenarios, which were each compared to two reference scenarios: one with "current policy" electricity demand (Reference-AEO) 2 and a second with much higher load growth through accelerated demand electrification (Reference-ADE). The Reference-ADE case includes rapid replacement of fossil fuel use with low-carbon alternatives across all sectors, including electrified end uses and low-carbon fuels and feedstocks, Annual Energy Outlook (AEO) and the U.S. Long-Term Strategy (LTS) demand cases Supply-side sensitivities include renewable energy costs, storage costs, nuclear costs, electrolyzer costs, CCS cost and performance, transmission constraints, new natural gas restriction, natural gas fuel costs, expanded biomass supply, low-cost geothermal, and allowing DAC in the Infrastructure Renaissance and Constrained cases. xvThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.14 Given the reduction in motor gasoline and diesel fuels in the transportation sector under the ADE and LTS scenarios, this demand would likely decline over time. Hydrogen production to serve these needs is also expected to transition to lower-emissions pathways given the overall economywide decarbonization trajectory modeled under the scenarios. These dynamics are not modeled explicitly in our analysis.

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Section: Text Box 4 the Role Of Distributed Resourcesmentioning

confidence: 95%

Examining Supply-Side Options to Achieve 100% Clean Electricity by 2035

Denholm

Brown

Cole

et al. 2022

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“…Dado el potencial de mercado en desarrollo en los Estados Unidos (McCabe et al 2022) y a nivel mundial, incluso en climas extremos como el del norte de Alaska y el del Caribe, las tecnologías eólicas distribuidas probablemente sean aplicables en muchas áreas de Puerto Rico, como lo indica el potencial de recursos de energía eólica, como se muestra en la Figura 55 (Sección 4.2, pág. 101).…”

Section: 521unclassified

El Estudio de Resiliencia de la Red Electrica de Puerto Rico y Transiciones a Energia 100% Renovable (PR100): Informe Final

Baggu,

Burton,

Blair

et al. 2024

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del NREL por brindar revisiones técnicas del informe y a Juan Torres, Johney Green, Bill Tumas, Peter Green y Martin Keller por su guía y liderazgo. vi Este informe está siendo disponible sin costo por el Laboratorio Nacional de Energía Renovable, en www.nrel.gov/publications. xxii Este informe está siendo disponible sin costo por el Laboratorio Nacional de Energía Renovable, en www.nrel.gov/publications. 6 Inicialmente, definimos cuatro escenarios y, según los resultados preliminares del modelaje, redujimos el número a tres escenarios. Vea el análisis detallado en (Blair et al. 2023) [pág. 3]. xxiii Este informe está siendo disponible sin costo por el Laboratorio Nacional de Energía Renovable, en www.nrel.gov/publications. 7 A lo largo de este informe el término "robusto" se refiere al estado de conservación del sistema eléctrico.

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“…This involves fitting a multiple linear regression with the following form (Equation 1): Where 𝑤 ! "# is the observed wind speed at the meteorological tower, 𝑤 %&' is the WTK estimate for the wind speed, 𝑑 %&' is the direction of the model data in degrees, h is the hour of the day (0-23), and m is the month of the year (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Values for the coefficients x0, x1, x2, x3, and x4 are fitted with least squares regression.…”

Section: Bias Correctionmentioning

confidence: 99%

“…In the realm of wind energy, distributed wind (DW) involves 10to 60-meter turbines appropriate for deployment in a broad range of application areas including residences, farms, industrial sites, and campuses. Recent studies have found a significant opportunity to utilize distributed wind to meet US energy needs [1]. Despite this significant opportunity, adoption of such technology is hindered by the cost of projects, confidence in the underlying technology and resources, and availability of incentives.…”

Section: Introductionmentioning

confidence: 99%

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Phillips,

Duplyakin,

Sheridan

et al. 2024

J. Phys.: Conf. Ser.

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Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

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Distributed Wind Energy Futures Study

Abstract: This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 7 publications

References 15 publications

Examining Supply-Side Options to Achieve 100% Clean Electricity by 2035

Examining Supply-Side Options to Achieve 100% Clean Electricity by 2035

El Estudio de Resiliencia de la Red Electrica de Puerto Rico y Transiciones a Energia 100% Renovable (PR100): Informe Final

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

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