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The Wind Integration National Dataset (WIND) toolkit
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TLDR
The WIND Toolkit as mentioned in this paper provides a state-of-the-art national wind resource, power production and forecast dataset, as well as time synchronized with available load profiles.Abstract:
Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.read more
Citations
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Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
TL;DR: This work proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks that captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources.
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Wind farm power optimization through wake steering.
TL;DR: A wake steering control scheme that maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines was developed and tested in an operational wind farm in Alberta, Canada, resulting in statistically significant power increases.
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The role of energy storage in deep decarbonization of electricity production
TL;DR: An optimization model is applied to investigate the economic viability of nice selected energy storage technologies in California and found that renewable curtailment and GHG reductions highly depend on capital costs of energy storage.
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A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Sheraz Aslam,Sheraz Aslam,Herodotos Herodotou,Syed Muhammad Mohsin,Nadeem Javaid,Nouman Ashraf,Shahzad Aslam +6 more
TL;DR: A comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting, and discusses the datasets used to train and test the differentDL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work.
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Europe, China and the United States: Three different approaches to the development of offshore wind energy
Maite deCastro,Santiago Salvador,Moncho Gómez-Gesteira,X. Costoya,D. Carvalho,D. Carvalho,Francisco Javier Sanz-Larruga,Luis Gimeno +7 more
TL;DR: In this paper, a more streamlined licencing process, together with a long-term vision enshrined within stable economic incentives, could help to boost the offshore wind industry in the USA.
References
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