#---------------------------------------------------------------------------- An hourly time series of GB-aggregated wind power generation from 1980-2013, based on a future distribution of wind farms with a high level of offshore capacity. CREATORS: Daniel Drew, David Brayshaw, Janet Barlow and Phil Coker University of Reading #---------------------------------------------------------------------------- 0. SECTIONS ------------- 1. Project 2. Dataset 3. Terms of Use 4. Contents 5. Method and Processing 1. PROJECT ------------ Title: Clustering effects of major offshore wind developments Dates: 1/4/2014-31/3/2016 Funding organisation: Network Innovation Allowance: National Grid Electricity Transmission Grant no.:NIA_NGET0128 These data were produced at the University of Reading as part of a project funded by National Grid (NIA_NGET0128). The authors would particularly like to thank David Lenaghan (National Grid) for his support throughout the project. 2. DATASET ------------ Title: An hourly time series of GB-aggregated wind power generation from 1980-2013, based on a future distribution of wind farms with a high level of offshore capacity. Description: MERRA reanalysis data (>34 years available) have been used to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms which is considered to be representative of a future scenario with high penetrations of offshore capacity. The data have been produced using the model developed at the University of Reading, which is free to download from here: http://centaur.reading.ac.uk/37430/. For more information about the data see: Drew, D., Cannon, D., Brayshaw, D., Barlow, J. and Coker, P. (2015) The impact of future offshore wind farms on wind power generation in Great Britain. Resources Policy, 4 (1). pp. 155-171. ISSN 0301-4207 doi: 10.3390/resources4010155. For more information about the model see: http://www.met.reading.ac.uk/~energymet/data/Cannon2015/Model.php and D. J. Cannon, D. J. Brayshaw, J. Methven, P. J. Coker and D. Lenaghan, 2015. Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75, 767-778. doi:10.1016/j.renene.2014.10.024. Publication Year: 2016 Creator(s): Daniel Drew, Dirk Cannon, David Brayshaw, Janet Barlow and Phil Coker Organisation(s): University of Reading 3. TERMS OF USE ----------------- This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 4. CONTENTS ------------ File listing data.csv: This file contains the date and time (dd.mm.yyyy hh:mm:ss) with the corresponding wind power output expressed in the form of capacity factor. Capacity Factor = 100 % × [Total Power Generated (MW)] ÷ [Total Capacity (MW)]. wind2020.csv: This file provides the details of the wind farms in the distribution (latitude, longitude, turbine hub height (m) and rated power(MW)). Details as to how this distirbution was produced are provided in: Drew, D., Cannon, D., Brayshaw, D., Barlow, J. and Coker, P. (2015) The impact of future offshore wind farms on wind power generation in Great Britain. Resources Policy, 4 (1). pp. 155-171. ISSN 0301-4207 doi: 10.3390/resources4010155. 5. METHOD and PROCESSING -------------------------- A model developed at the University of Reading (available here: http://centaur.reading.ac.uk/37430/) has been used to convert the hourly wind field in the MERRA reanalysis dataset into GB-aggregated wind generation. Full details of the model are given here: http://www.met.reading.ac.uk/~energymet/data/Cannon2015/Model.php.