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MERRA derived hourly time series of GB-aggregated wind power, solar power and demand

CREATORS: Daniel Drew, Hannah Bloomfield, David Brayshaw, Janet Barlow and Phil Coker University of Reading

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0. SECTIONS
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1. Project
2. Dataset
3. Terms of Use
4. Contents
5. Method and Processing

1. PROJECT
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Title: Solar PV Forecasting Phase 2
Dates: 1/8/2016-30/6/2018
Funding organisation: Network Innovation Allowance: National Grid Electricity Transmission
Grant no.: NIA_NGET0183

These data were produced at the University of Reading as part of a project funded by National Grid (NIA_NGET0183). The authors would particularly like to thank Andrew Richards (National Grid) for his support throughout the project.

2. DATASET
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Title: MERRA derived hourly time series of GB-aggregated wind power, solar power and demand 

Description: MERRA reanalysis data (>34 years available) have been used to estimate the hourly aggregated wind and solar power generation for a predefined (fixed) distribution of wind and solar farms which is considered to be representative of current (2017) capacity. 
Along with an estimate of the GB-aggregated electricity demand. 

The data have been produced using the models developed at the University of Reading. The wind model is free to download from here: http://centaur.reading.ac.uk/37430/.

WIND MODEL
For more information about the wind model see:

http://www.met.reading.ac.uk/~energymet/data/Cannon2015/Model.php

and
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.
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.

SOLAR MODEL
For more information about the solar model see:
http://www.smarternetworks.org/project/nia_nget0183

DEMAND MODEL
For more information about the demand model see: 
Bloomfield et al. (2016) Quantifying the increasing sensitivity of power systems to climate variability. Environmental Research Letters, 11 124025


Publication Year: 2019

Creator(s): Daniel Drew, Hannah Bloomfield, David Brayshaw, Janet Barlow and Phil Coker

Organisation(s): University of Reading

3. TERMS OF USE
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This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.

4. CONTENTS
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File listing

Wind_solar_demand_time_series.csv: This file contains the date and time (dd.mm.yyyy hh:mm:ss) with the corresponding solar and wind power output expressed in the form of capacity factor.

Capacity Factor = 100 % × [Total Power Generated (MW)] ÷ [Total Capacity (MW)].

The full demand is the GB_aggregated electricity demand (in GW).

The Sunday equivalent demand (in GW) is the GB_aggregated electricity demand if every day were a Sunday.

5. METHOD and PROCESSING
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WIND
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.

SOLAR
The MERRA reanalysis has also been used to derive an hourly time series of GB-aggregated solar PV generation based on the current distribution of solar panels (capacity 12.5 GW as of June 2017).
The model is based on multi-linear regression. Full details are available from:
http://www.smarternetworks.org/project/nia_nget0183

DEMAND
An hourly time series of electricity demand for Great Britain was derived from meteorological variables in MERRA. 
The model was developed on a daily resolution using a regression based technique which is then downscaled to an hourly resolution using a seasonally varying diurnal cycle.

Full details are available in:
Bloomfield et al. (2016) Quantifying the increasing sensitivity of power systems to climate variability. Environmental Research Letters, 11 124025