Charge and Discharge cycle pattern

Charge and Discharge cycle pattern

Citation Author(s):
Mina
Abedi Varnosfaderani
Loughborough University
Dani
Strickland
Loughborough University
Martin
Ruse
PowerVault
Enrique
Brana Castillo
Submitted by:
Mina Abedi Varn...
Last updated:
Mon, 09/09/2019 - 09:41
DOI:
10.21227/p59j-k833
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Abstract: 

 

This paper looks at six different applications for a domestically located battery system and determines how these could be translated into different electrical power application “drive” cycles. The applications considered are as follows: 1) a house with four people and a solar panel using the battery to absorb extra energy when the PV panel is producing more power than is absorbed in the house and then releasing this energy afterwards. 2) A house with four people and PV panels on a time of use tariff where the battery is used to absorb extra energy from the PV panel and release this when the tariff is highest. 3) A house with four people and no PV on a time of use Tariff –where the battery is charged at low tariff and discharged on high tariff. 4) The battery is operating as part of an aggregated frequency response system performing on the Firm Frequency Response (FFR) market. 5)The battery is operating as part of an aggregated frequency response system performing on the Enhanced Frequency Response (EFR) market. 6) The battery is operating as part of an aggregated system looking at competing in the day ahead market. It is important to be able to take the data generated in the 6 use cases over the course of the year and produce application-specific battery systems cycle testing to help get an indication of life span. The process around this is typical:1. Quantify the charge/discharge test profile associated with each application2. Develop accelerated ageing tests around the profile.The process is complicated by the processes around ageing and degradation. This includes the yearlong data that is turned into sweat test curves that represent the typical usage that the batteries could see over a year-long period of their life. This paper looks at the development of the charge and discharge profiles of these applications and defines a set of power application “drive” cycles which are published in excel alongside this paper for use by researchers longing at battery degradation.

 

 

 

Instructions: 

To generate sweat test curves that represent the typical usage that the batteries could see over a year-long period of their life two methods are purpose: 1) Haar Transform method and 2) Pragmatic method. 1) The MATLAB wavelet toolbox was used to generate square-shaped sequences of the modelled battery charge and discharge (Haar transform).The Haar transform approximation and details coefficient are plotted using charging and discharging battery power variations over time from January to December for each 6 use cases. Generated Data are in the Haar data file (zip file). In this file, the data of each scenario are represented as follow:PV Haar Data, PV_tariff Haar Data, Tariff Haar Data, FFR Haar Data, EFR Haar Data, Day-ahead price Haar Data.Each of these includes coefficients and figures folders and a comment file (text file). The figures folder includes generated Haar transform coefficient curves. The coefficients folder includes the details and approximations coefficients data. The more detailed data analysis of each 6 cases is available in the comment file and Journal paper " Sweat Testing Cycles of batteries for Different Electrical Power Applications".

2) In this method, the data are statistically analysed (using histogram chart) and an average daily cycle is composed.  This average cycle is then codded for the whole year. Generated Data are in the pragmatic method data file (zip file). In this file, the data of each scenario are represented as follow:Battery_Power_PV1-12, Battery_Power_PV_tariff1-12, Battery_Power_Tariff1-12, Battery_Power_FFR1-12_v2, Battery_Power_EFR1-12_v2, Battery_Power_Market_tariff1-12, Load Data and  Crest. These files are in Excel data format. The coded average cycle for the whole year is represented in January to December sheets for each case. The Load data and Crest excel files are row data that were used to calculate battery power over the whole year.The more detailed data analysis of each 6 cases is available in the comment sheet and Journal paper " Sweat Testing Cycles of batteries for Different Electrical Power Applications".

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[1] Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo, "Charge and Discharge cycle pattern", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/p59j-k833. Accessed: Sep. 20, 2019.
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doi = {10.21227/p59j-k833},
url = {http://dx.doi.org/10.21227/p59j-k833},
author = {Mina Abedi Varnosfaderani; Dani Strickland; Martin Ruse; Enrique Brana Castillo },
publisher = {IEEE Dataport},
title = {Charge and Discharge cycle pattern},
year = {2019} }
TY - DATA
T1 - Charge and Discharge cycle pattern
AU - Mina Abedi Varnosfaderani; Dani Strickland; Martin Ruse; Enrique Brana Castillo
PY - 2019
PB - IEEE Dataport
UR - 10.21227/p59j-k833
ER -
Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo. (2019). Charge and Discharge cycle pattern. IEEE Dataport. http://dx.doi.org/10.21227/p59j-k833
Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo, 2019. Charge and Discharge cycle pattern. Available at: http://dx.doi.org/10.21227/p59j-k833.
Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo. (2019). "Charge and Discharge cycle pattern." Web.
1. Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo. Charge and Discharge cycle pattern [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/p59j-k833
Mina Abedi Varnosfaderani, Dani Strickland, Martin Ruse, Enrique Brana Castillo. "Charge and Discharge cycle pattern." doi: 10.21227/p59j-k833