{"id":2,"date":"2017-09-15T19:15:49","date_gmt":"2017-09-15T18:15:49","guid":{"rendered":"https:\/\/research.reading.ac.uk\/met-energy\/?page_id=2"},"modified":"2021-10-26T10:55:18","modified_gmt":"2021-10-26T09:55:18","slug":"cannon","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/met-energy\/data\/cannon\/","title":{"rendered":"Legacy datasets (Cannon et al, 2015 and Drew et al, 2015)"},"content":{"rendered":"<p>One of our first publicly available datasets &#8211; hourly time series of GB-aggregated wind power generation from 1980-2014 using wind speed records from\u00a0<a href=\"http:\/\/gmao.gsfc.nasa.gov\/merra\/\">NASA&#8217;s &#8220;MERRA&#8221; reanalysis<\/a>\u00a0&#8211;\u00a0was created in:<\/p>\n<ul>\n<li><a href=\"http:\/\/dx.doi.org\/10.1016\/j.renene.2014.10.024\">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.\u00a0<i>Renewable Energy<\/i>,\u00a0<b>75<\/b>, 767-778. doi:10.1016\/j.renene.2014.10.024<\/a><\/li>\n<\/ul>\n<p>and was subsequently extended to include possible Round-3 offshore in:<\/p>\n<ul>\n<li><a href=\"http:\/\/centaur.reading.ac.uk\/39743\/\"><span class=\"reading_name_6d367277be68c31789b6dbd44d683b0e\"><span class=\"person_name\">Drew, D.<\/span><\/span>, <span class=\"reading_name_23bad85524d65ad304443b297944bf03\"><span class=\"person_name\">Cannon, D.<\/span><\/span>, <span class=\"reading_name_98c9d84ddf12fab9cf567de94f32704e\"><span class=\"person_name\">Brayshaw, D.<\/span><\/span>, <span class=\"reading_name_b6b1f4fecbaaf61fb21f8e328d3c3435\"><span class=\"person_name\">Barlow, J.<\/span><\/span> and <span class=\"reading_name_b2466cb9ee86f7edeb8fe425233e4db8\"><span class=\"person_name\">Coker, P.<\/span><\/span> (2015) <em>The impact of future offshore wind farms on wind power generation in Great Britain.<\/em> Resources, 4 (1). pp. 155-171. ISSN 2079-9276 doi: https:\/\/doi.org\/10.3390\/resources4010155<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Both datasets have since been superseded by newer and more extensive versions available <a href=\"https:\/\/research.reading.ac.uk\/met-energy\/data\">here<\/a>. \u00a0The following datasets are therefore recommended as &#8220;updates&#8221; to the originals listed above:<\/p>\n<ul>\n<li><a href=\"http:\/\/dx.doi.org\/10.17864\/1947.272\">Bloomfield et al (2020) based on ERA5<\/a><\/li>\n<li><a href=\"http:\/\/dx.doi.org\/10.17864\/1947.239\">Bloomfield et al (2020) based on MERRA2<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>For legacy purposes, however, details for both the Cannon et al and Drew et al models (and access to code\/data) are provided below (Cannon et al 2015 freely on request\u00a0<a href=\"http:\/\/centaur.reading.ac.uk\/37430\/\">here<\/a>; Drew et al\u00a02015 is directly downloadable <a href=\"http:\/\/researchdata.reading.ac.uk\/75\/\">here<\/a>).<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>====<\/p>\n<h3><\/h3>\n<h2>Cannon et al (2015) Model Information<\/h2>\n<p>&nbsp;<\/p>\n<p>The model constructs an hourly time series of regional-total wind power over any specified time period since 1979, using MERRA reanalysis wind speed data. Written in Matlab, the model is run from the\u00a0<b>Master.m<\/b>\u00a0script, which builds a time series of regional total wind power generation from MERRA reanalysis data. It does so by calling other Matlab scripts to compute the required steps.<\/p>\n<p>The model is designed so that all user-defined settings can be edited in\u00a0<b>Master.m<\/b>, and the user need not edit the sub-scripts (<b>MERRA_interp.m<\/b>\u00a0and\u00a0<b>MERRA_clim.m<\/b>), which are called from\u00a0<b>Master.m<\/b>. However, all scripts are well commented to help users understand and, if required, modify the model to their own needs.<\/p>\n<p>&nbsp;<\/p>\n<h3>Model inputs<\/h3>\n<ol>\n<li>MERRA data containing wind speed data at 2m, 10m and 50m.<\/li>\n<li>Wind farm distribution and capacity data:\u00a0<b>&#8220;windfarms.dat&#8221;<\/b>\n<ul>\n<li>A data file containing a list of wind farm locations (longitude\/latitude), their capacity (in MW), and a farm-average turbine hub height above ground. See the example (<b>&#8220;windfarms.dat&#8221;<\/b>).<\/li>\n<li>Store this file in the same directory as\u00a0<b>Master.m<\/b><\/li>\n<\/ul>\n<\/li>\n<li>A wind farm power curve: A data file containing a list of wind speeds and corresponding power output (in fractional Capacity Factor: I.e., as a fraction of the total wind farm capacity). See the example (<b>&#8220;powercurve.dat&#8221;<\/b>).\n<ul>\n<li>Store this file in the same directory as\u00a0<b>Master.m<\/b><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3>Running the model<\/h3>\n<p>With these ingredients in place, the steps to build the time series are as follows:<\/p>\n<ol>\n<li>Set the user-defined settings in\u00a0<b>Master.m<\/b><\/li>\n<li>Run the\u00a0<b>Master.m<\/b>\u00a0script from an open Matlab session!<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3>Model outputs<\/h3>\n<ol>\n<li>If the interpolation step is computed, the model outputs MERRA wind speeds horizontally interpolated to each of the wind farm locations in\u00a0<b>windfarms.dat<\/b>\u00a0(stored in NETCDF format).<\/li>\n<li>The climatology step outputs:\n<ul>\n<li>A plot showing the power curve used.<\/li>\n<li>A time series of regionally-aggregated capacity factor (CF). \u00a0 \u00a0\u00a0[1]<\/li>\n<li>A time series of showing the date and time corresponding to each CF value.<\/li>\n<li>Both the date\/time and CF variables are saved to an ascii file, as well as to a &#8220;.mat&#8221; Matlab data file.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p id=\"footnote-1\">[1] \u00a0 \u00a0 Capacity Factor = 100 % \u00d7 [Total Power Generated (MW)] \u00f7 [Total Capacity (MW)].<\/p>\n<p>&nbsp;<\/p>\n<h3>Additional information<\/h3>\n<p>The model will loop through all days, months and years between the start and end dates specified in\u00a0<b>Master.m<\/b>, extract the MERRA wind speed data and interpolate it to the desired wind farm locations using the\u00a0<b>MERRA_interp.m<\/b>\u00a0script. The power curve, wind farm capacity and turbine hub height data is then used to calculate the wind power output of the entire fleet in\u00a0<b>MERRA_clim.m<\/b>.<\/p>\n<p>Note, the\u00a0<b>MERRA_interp.m<\/b>\u00a0script can be slow when computing a long time series and\/or a large distribution of wind farms. Therefore, the model stores the interpolated wind data so that any of the following inputs can be changed without the need to repeat the interpolation:<\/p>\n<ul>\n<li>Wind farm capacities<\/li>\n<li>Turbine hub heights<\/li>\n<li>Power curve<\/li>\n<\/ul>\n<p>However, any change in the distribution (longitudes or latitudes) of the wind farm distribution will require the recomputation of the interpolated data \u00a0 \u00a0\u00a0[2]. Please note the model has also been applied to a future wind farm scenario with a higher capacity (larger number of sites). This data is also available to download\u00a0<a href=\"http:\/\/researchdata.reading.ac.uk\/75\/\">here<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p id=\"footnote-2\">[2] \u00a0 \u00a0\u00a0<b>A useful tip<\/b>: If you want to use this model to study a number of different wind farm distributions, perform the interpolation step for all wind farm locations. Then, to test a particular distribution, just set the wind farm capacity to zero for any wind farms you do not wish to include. This will avoid having to recompute the interpolation step for each distribution.<\/p>\n<p>&nbsp;<\/p>\n<h3 id=\"MERRA\">Some notes about the MERRA data<\/h3>\n<p>To use this model, you must have the raw MERRA data downloaded\u00a0<i>in advance<\/i>. It can be downloaded from:<\/p>\n<ul>\n<li>\u00a0\u00a0\u00a0<a href=\"https:\/\/gmao.gsfc.nasa.gov\/reanalysis\/MERRA\/\">http:\/\/disc.sci.gsfc.nasa.gov\/daac-bin\/DataHoldings.pl<\/a>\u00a0\u00a0 \u00a0\u00a0[3]<\/li>\n<\/ul>\n<p>The model is set up to read in the MERRA data product named\u00a0<b>&#8220;IAU 2d atmospheric single-level diagnostics (tavg1_2d_slv_Nx)&#8221;<\/b>, using the\u00a0<b>&#8220;Daily Data Product&#8221;<\/b>. This contains hourly wind speeds which are stored in a separate file for each day. Once downloaded, the MERRA files should look like this:<\/p>\n<ul>\n<li>MERRA<b>NNN<\/b>.prod.assim.tavg1_2d_slv_Nx.<b>YYYYMMDD<\/b>.SUB.nc<\/li>\n<\/ul>\n<p>where\u00a0<b>NNN<\/b>\u00a0is an integer (this might be 100, 200, 300 or 301) and\u00a0<b>YYYYMMDD<\/b>\u00a0is the date of the file.<\/p>\n<p>The model uses U and V wind components from 2m, 10m, and 50m (<b>&#8220;U2M&#8221;<\/b>,\u00a0<b>&#8220;U10M&#8221;<\/b>,\u00a0<b>&#8220;U50M&#8221;<\/b>,\u00a0<b>&#8220;V2M&#8221;<\/b>,\u00a0<b>&#8220;V10M&#8221;<\/b>,\u00a0<b>&#8220;V50M&#8221;<\/b>), which must be available in the MERRA data files.<\/p>\n<p>The model assumes the data is stored in\u00a0<b>NETCDF<\/b>\u00a0format, and can be accessed at the location:\u00a0<b>pathname \/ year \/ filename<\/b>, where:<\/p>\n<ul>\n<li><b>&#8220;filename&#8221;:<\/b>\u00a0the name of the NETCDF file<\/li>\n<li><b>&#8220;year&#8221;:<\/b>\u00a0a folder containing all NETCDF files for the year<\/li>\n<li><b>&#8220;pathname&#8221;:<\/b>\u00a0the directory where the\u00a0<b>&#8220;year&#8221;<\/b>\u00a0folders are stored<\/li>\n<\/ul>\n<p>are all user-defined in the\u00a0<b>Master.m<\/b>\u00a0script.<\/p>\n<p id=\"footnote-3\">[3] \u00a0 \u00a0 Website available as of 18th February 2014. If the link no longer works, try\u00a0<a href=\"http:\/\/gmao.gsfc.nasa.gov\/merra\/\">http:\/\/gmao.gsfc.nasa.gov\/merra\/<\/a>\u00a0and navigate to the\u00a0<i>Modeling and Assimilation Data and Information Services Center (MDISC)<\/i>\u00a0page, and then find a link to\u00a0<i>MERRA Data Products<\/i>.<\/p>\n<p>&nbsp;<\/p>\n<h3>Drew et al (2015) offshore wind farm extension<\/h3>\n<p>In the few years after the original Cannon et al (2015) dataset was created, the geographical distribution of wind farms in Great Britain was expected to change significantly with the development of the \u201cround 3\u201d wind zones (circa 2025). At that time, however, the impact of this change in wind-farm distribution on the characteristics of national wind generation was largely unknown. The original Cannon et al (2015) wind power model was therefore used to study the <em>potential\u00a0<\/em>long term characteristics of GB-aggregated wind power for a &#8220;high wind power penetration&#8221; future scenario. \u00a0To represent the future wind farm distribution, a number of assumptions were made:<\/p>\n<ul>\n<li>All Round 3 zones are developed to full capacity<\/li>\n<li>All onshore wind farms under construction or with planning permission are fully commissioned.<\/li>\n<li>All existing farms remain generating at their current capacity.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>One of our first publicly available datasets &#8211; hourly time series of GB-aggregated wind power generation from 1980-2014 using wind speed records from\u00a0NASA&#8217;s &#8220;MERRA&#8221; reanalysis\u00a0&#8211;\u00a0was created in: D. J. Cannon,&#8230;<a class=\"read-more\" href=\"&#104;&#116;&#116;&#112;&#115;&#58;&#47;&#47;&#114;&#101;&#115;&#101;&#97;&#114;&#99;&#104;&#46;&#114;&#101;&#97;&#100;&#105;&#110;&#103;&#46;&#97;&#99;&#46;&#117;&#107;&#47;&#109;&#101;&#116;&#45;&#101;&#110;&#101;&#114;&#103;&#121;&#47;&#100;&#97;&#116;&#97;&#47;&#99;&#97;&#110;&#110;&#111;&#110;&#47;\">Read More ><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":150,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.8.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Legacy datasets (Cannon et al, 2015 and Drew et al, 2015) - Energy Meteorology Research Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/met-energy\/data\/cannon\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Legacy datasets (Cannon et al, 2015 and Drew et al, 2015) - Energy Meteorology Research Group\" \/>\n<meta property=\"og:description\" content=\"One of our first publicly available datasets &#8211; hourly time series of GB-aggregated wind power generation from 1980-2014 using wind speed records from\u00a0NASA&#8217;s &#8220;MERRA&#8221; reanalysis\u00a0&#8211;\u00a0was created in: D. 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