PR100 One-Year Progress Update: Preliminary Modeling Results and High-Resolution Solar and Wind Data Sets PDF Download
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Languages : en
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The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This presentation will be showcased in the form of a public webinar, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The presentation follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish: https://www.nrel.gov/docs/fy23osti/85201.pdf.
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Languages : en
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Book Description
The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This presentation will be showcased in the form of a public webinar, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The presentation follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish: https://www.nrel.gov/docs/fy23osti/85201.pdf.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This report, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The summary report follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish https://www.nrel.gov/docs/fy23osti/85144.pdf.
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Languages : en
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As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.
Author: Zhouyi Liao
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Solar PV installation is growing fast in recent decades across the world but high variability of solar power hinders its further penetration to the energy market. This variability mainly comes from cloud coverage, water vapor content and aerosol loadings, and has the greatest effect in short-term solar power prediction. This high volatility nature of solar insolation makes it difficult to integrate PV output to electricity grid. A more accurate short-term solar power prediction helps to develop bidding strategies for real-time markets or to determine the need for operating reserves. This work aims to tackle this problem by employing comprehensive spectral radiative models to calculate longwave and shortwave radiation through the atmosphere, estimating cloud properties from remote sensing data with the atmospheric model and building convolutional neural network model to model and forecast solar radiation. First, a Line-by-Line (LBL) spectral radiative model is built to capture details of the highly wavenumber-dependent nature of the irradiance fluxes. Then the broadband empirical model serves as a benchmark to validate the LBL model. For longwave spectrum that is emitted and absorbed by gases, aerosols, clouds and the ground, a high-resolution two-flux model with a recursive scattering method is developed. For the shortwave (solar) part of the spectrum, which includes scattering from atmospheric constituents and the ground, 3D comprehensive Monte-Carlo simulations are used. Beyond the basic model, some corrections or calibrations are made. Comprehensive Monte Carlo simulations are used for correcting deviations on the atmospheric downwelling longwave (DLW) flux caused by isotropic scattering assumptions in high aerosol loading regimes.The [delta]-M approximation input-based scaling rule is validated for a wide range of aerosol loading values except for very high aerosol loading conditions. This proposed scaling rules minimize substantially the computational effort of calculating anisotropic downwelling radiation from diverse types of aerosols under these extreme conditions. Earth curvature effect (air mass correction) is also tested. Although for solar zenith angles larger than 75°, the attenuation of the direct solar beam is overestimated in a plane-parallel atmosphere comparing to in a real spherical atmosphere, for most solar rays, a plane-parallel atmosphere approximation is accurate enough for modeling. A Spectral Cloud Optical Property Estimation (SCOPE) method that integrates the high-resolution imagery from GOES-R satellite and a two-stream, spectrally-resolved longwave radiative model was proposed, for the estimation of cloud optical depth and cloud bottom height. An improved model SCOPE 2.0 is also proposed which considers multi-layer clouds, clouds with ice crystals and aerosol corrections. A shortwave Monte Carlo simulation is developed and used to validate the derived cloud optical properties. With this comprehensive cloud cover estimate model, a convolutional neural networks (CNN) model is developed to correlate global horizontal irradiance (GHI) to the satellite-derived cloud cover (a "now-cast"). The performance of SCOPE method as well as CNN+SCOPE model is evaluated using one year (2018) of downwelling longwave (DLW) radiation and GHI measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. CNN+SCOPE model achieves test-set root-mean-square error (RMSE) of 30.5 - 62.6 W[subscript m]−2 with an average of 47.2 W[subscript m]−2, which is better then the National Solar Radiation Database (NSRDB) model (average RMSE is 66.9 W[subscript m]−2). A reference CNN model is also tested which directly use satellite ABI data that the SCOPE model uses with an average error equal to 69.4 W[subscript m]−2. This success at CNN+SCOPE "now-cast" model points to possible future uses for short-term forecast.
Author: Martin Junginger
Publisher: Edward Elgar Publishing
ISBN: 1849806845
Category : Business & Economics
Languages : en
Pages : 353
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Book Description
'This expert analysis provides an important contribution to understanding the technicalities of energy technology cost dynamics. Given the urgent need for delivery of low-cost renewable energy technologies in particular, it is vital to understand how to accelerate this process of technological learning.' - Miguel Mendonca, World Future Council, Germany
Author: Thomas Schmidt
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Very short-term solar forecasts based on sky images of ground-based cameras introduce a new forecasting methodology for solar energy applications which covers forecast horizons up to 30 minutes. In this thesis, a newly developed image-based forecasting model for the usage in different applications is presented. The core components of the model are cloud detection, cloud motion tracking, cloud shadow projection and irradiance modelling. The model takes raw camera images, local solar irradiance measurements and cloud base height estimations as input data and provides estimations of near-future surface solar irradiance distribution. Large data sets comprising sky images, pyranometer measurements and in some cases cloud base height and PV power measurements are the basis for an in-depth analysis of its forecast performance. Model forecasts are compared with persistence forecasts to evaluate its skill. engl.
Author: G.Y. Tsuji
Publisher: Springer Science & Business Media
ISBN: 9401736243
Category : Science
Languages : en
Pages : 405
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Book Description
The first premise of this book is that farmers need access to options for improving their situation. In agricultural terms, these options might be manage ment alternatives or different crops to grow, that can stabilize or increase household income, that reduce soil degradation and dependence on off-farm inputs, or that exploit local market opportunities. Farmers need a facilitating environment, in which affordable credit is available if needed, in which policies are conducive to judicious management of natural resources, and in which costs and prices of production are stable. Another key ingredient of this facilitating environment is information: an understanding of which options are viable, how these operate at the farm level, and what their impact may be on the things that farmers perceive as being important. The second premise is that systems analysis and simulation have an impor tant role to play in fostering this understanding of options, traditional field experimentation being time-consuming and costly. This book summarizes the activities of the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project, an international initiative funded by the United States Agency for International Development (USAID). IBSNAT was an attempt to demonstrate the effectiveness of understanding options through systems analysis and simulation for the ultimate benefit of farm households in the tropics and subtropics. The idea for the book was first suggested at one of the last IBSNAT group meetings held at the University of Hawaii in 1993.
Author: Utpal Biswas
Publisher: Springer
ISBN: 9811331227
Category : Technology & Engineering
Languages : en
Pages : 563
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Book Description
The book discusses the recent research trends in various sub-domains of computing, communication and control. It includes research papers presented at the First International Conference on Emerging Trends in Engineering and Science. Focusing on areas such as optimization techniques, game theory, supply chain, green computing, 5g networks, Internet of Things, social networks, power electronics and robotics, it is a useful resource for academics and researchers alike.
Author: F. M. Martin
Publisher:
ISBN:
Category : Nuclear energy
Languages : en
Pages : 212
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Book Description
Author: Christian Schittich
Publisher: Walter de Gruyter
ISBN: 303461554X
Category : Architecture
Languages : en
Pages : 352
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Book Description
Glass offers a wide variety of possible applications for the realization of even the most ambitious designs in architecture, and in the past two decades it has experienced an unparalleled burst of innovation. For planners, this means working constantly with this high-performance material. In compact and appealing form, the completely revised Glass Construction Manual presents the current state of the art on planning and building with glass, from the history through the technical foundations all the way to the most innovative applications. Astonishing perspectives on thermal insulation and solar protection and the addition of thoughtfully selected new practical examples round off this comprehensive reference work.
