ali salman, hussain a.ali, & alaa jameel dr. shaker haji ... grid... · 20 x 200 w p = 4 kw p...
TRANSCRIPT
Ali Salman, Hussain A.Ali, & Alaa Jameel
Dr. Shaker Haji & Dr. Mohamed Bin Shams
Feb 2-4, 2014 – Muscat, Sultanate of Oman
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Feb 21, 2010
Visitors Center Hall:10 x 60 W light bulbs + a 2 ton A/C
Total ~ 3 kW
20 x 200 Wp = 4 kWp
1.7 kW @ 12 m/scut in: 3 m/s
4 x 260Ah/12V (48 V DC System - Series)
2 x 60 NL/h H2 Generator
(water electrolyzers)
6 x 500 NL @ 10 bar H2 M-H
Canisters1.2 kW Nexa FC Stack
2 Renewable Energy Sources
Public Grid
Bapco’s Green Energy Station Configuration.5
Simplified Power Management Strategy [1]6
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Tem
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oC
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r W
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m/s
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Po
we
r [W
/m2]
Date & Time
Environmental Conditions
Solar Irradiation
Ambient Temperature
Module Temperature
Wind Velocity
24/06/2010 25/06/2010
Courtesy of Bapco
Climate Related Data
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Courtesy of Bapco
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ssu
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ar]
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we
r [W
]
Date & Time
AC Laod P [W]
PV P [W]
WT P [W]
FC P [W]
Bat. SOC [%]
H2 Pressure
24/06/2010 25/06/2010
Energy Related Data
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What does the performance of a Hybrid
Renewable Energy System depend on?
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Performance
Climate Conditions
Energy Generators
Power Demand
Energy Storage
(1) To analyze, model, & forecast
meteorological data (driving forces)
(2) To analyze the energy data from the hybrid
RE system
Ultimate Goal: Optimize the performance of the RE
system (e.g. maximizing CO2 saving)
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Data collection (1st May 2010 – 30th April 2011)
Bapco’s Green Energy Station Data.
Objective 1: To analyze, model, & forecast meteorological data Time Series Analysis.
• Why do we use Time Series Analysis?
- Data are correlated in time
Time Series Analysis:
AR: Auto Regressive
MA: Moving Average
ARMA
𝒚𝒕 = 𝜹 + Ф𝟏𝒚𝒕−𝟏 + Ф𝟐𝒚𝒕−𝟐 + ⋯+ Ф𝒑𝒚𝒕−𝒑 + 𝜺𝒕 −𝜽𝟏𝜺𝒕−𝟏 − 𝜽𝟐𝜺𝒕−𝟐 − ⋯−𝜽𝒒𝜺𝒕−𝒒
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Time Series Analysis:
. Data should be stationary.
StationaryConst. Mean
Const. Variance
Time Series Analysis :
• What if I don’t have stationary data?!• Transformations: ARIMA (p,I,q)
– (const. mean)
– (const. variance)
Differencing Transformation
Box – Cox Transformation
Mean Stabilization
Differencing Transformation [3]
BOX-COX Transformation [3]
Variance Stabilization
7. Other Statistical Analysis
6. Validating Forecasting Accuracy
5. Forecasting
4. Validating Models (Residual Analysis)
3. Parameter Estimation (Least Square Error)
2. Stationarizing data (if required)
1. Estimating Missing Points
Time Series Analysis (meteorological data)
Energy Data Analysis
1. Efficiency Calculations: various systems
2. Energy Calculations: Load, RE, Storage & PG
3. Calculations of CO2 Savings & Emissions
4. Feasibility Calculations: PV & WT
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Spee
d, m
/s
Actual wind speed Modeled wind speed
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Spee
d, m
/s
Actual Data Forcasted Data UCL LCL
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Spee
d, m
/s
Actual wind speed
• Average daily wind speed: ARIMA (1,0,0):
𝒚𝒕 = 𝟎. 𝟏𝟕𝟗𝟔𝟑𝟕 + 𝟎.𝟔𝟒𝟑𝟏𝟔 𝒚𝒕−𝟏 + 𝜺𝒕
-2.0
-1.5
-1.0
-0.5
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1.0
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Trans. wind velocity
𝒚 = 𝒙 𝟎.𝟑𝟑𝟏𝟔𝟔𝟖−𝟏
𝟎.𝟑𝟑𝟏𝟔𝟔𝟖
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Rad
iati
on
W/m
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UCL LCL Forecasted Data Actual Data
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Rad
aiti
on
W/m
2
Actual Solar Radiation Modeled Solar Radiation
• Average daily solar radiation: ARIMA (1,0,0):
𝒚𝒕 = 𝟖𝟔𝟐𝟏𝟔𝟔𝟗 + 𝟎.𝟓𝟑𝟓𝟐𝟐 𝒚𝒕−𝟏 + 𝜺𝒕
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Tem
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re,
C
Actual Data Forecasted Data UCL LCL
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Tem
per
atu
re,
C
Actual Ambient Temperature Modeled Ambient Temperature
• Average daily ambient temperature: ARIMA (0,1,2):
𝒚𝒕 = −𝟎. 𝟎𝟖𝟕 + 𝒚𝒕−𝟏 + 𝟎.𝟐𝟏𝟑𝜺𝒕−𝟏 + 𝟎.𝟐𝟔𝟔𝜺𝒕−𝟐 + 𝜺𝒕
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Tem
per
atu
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Actual Module Temperature Modeled Module Temperature
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Tem
per
atu
re, °
C
Actual Data Forcasted Data UCL LCL
• Average daily solar module temperature: ARIMA (0,1,1):
𝒚𝒕 = −𝟎. 𝟎𝟎𝟒𝟒 + 𝒚𝒕−𝟏 + 𝟎.𝟒𝟏𝟒𝟐𝟏 𝜺𝒕−𝟏 + 𝜺𝒕
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Ener
gy, M
J
Monthly load of the station
Load of the station, MJ
5.14 GJ30%
00%
10.7 GJ90%
1.25 GJ10%
11.95 GJ70%
Annual and systems contributions to load demand
Grid
Renewable Energy
Solar system
Wind Energy
0%
20%
40%
60%
80%
100%
Mo
nth
ly C
on
trib
uti
on
, %
Monthly systems contributions to load demand
Monthly Contribution of Renewable Sources, % Monthly Contribution of public Grid, %
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Ener
gy, M
J
Monthly energy out from storage system
Storage Energy Out from Batteries, MJ Storage Energy Out from Fuel Cell, MJ
4268 MJ95%
236 MJ5%
Annual energy out from storage system
Annual StorageEnergy Out fromBatteries
Annual StorageEnergy Out fromFuel Cell
• CO2 Savings:
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CO
2, k
g
Estimated CO2 Savings, kg CO2 Emitted, kg
Annual Emitted CO2 , kgAnnual Saved CO2 , kg
6211,429
• Units efficiencies:
(a) Average monthly efficiency
(b) Average instantaneous efficiency for October and February
Equipment Efficiency, % Maximum MinimumStandarddeviation
Reference
Wind Turbine 57.64% (a) 74.34% 37.29% 9.74% < 59% (Bitz) [4]
Solar Panel 7.00% (a) 11.56% 3.15% 2.46% < 15% max [5]
Fuel Cell 37.7% (b) 37.75% 37.65% - < 40-60% [6]
• Effectiveness, Payback period, energy cost of Wind & Solar systems:
Equip.Cost, $/W
Life-time, years
Rated Power,
kW
Avg. Meas. Power,
kW
Effectiveness, %
PBP, years Energy Cost, $/kWh
(fils/kWh)Comm. Resid.
Wind Turbine
2.4 20 1.7 0.0482 2.83 276 1349 1.18 (442)
Solar Panel
4.2 20 4 1.1161 25.76 133.5 680 0.59 (223)
* Effectiveness = Avg. Measured Power / Rated Power
– Saving the Environment!
– Building Capacities & Gaining Experiences
– Opportunities for Carbon Credit & Personal Carbon Trading
– Government Savings:
• on building & operating new power plants
• on the natural gas (NG) sold to local power plants at subsidized price.
• on the electricity purchased from private power plants
– Government Opportunities:
• in selling the saved NG at market price
• in using the saved NG in petrochemical industries
– The following models were found satisfactory in modeling:• ARIMA(1,0,0): Wind speed• ARIMA(1,0,0): Solar radiation• ARIMA(0,1,2): Ambient temperature • ARIMA(0,1,1): Solar module’s temperature data
– The station load was met by renewable energy (70%) and public grid (30%).
– Solar Panels contributed with 90% while the wind turbine contributed with 10% to the RE mix.
– Due to the governmental subsidies of electricity, non of the PV or WT was found feasible .
– The solar panel is more feasible than the wind turbine.
– Modify the mechanism, so the grid cover only the energy shortage due insufficient supply from renewable and storage systems.
– Regular maintenance and upgrading the data acquisition system.
– Subsidies should be provided to RE technologies for them to be feasible .
– Optimization of the station operation/configuration, for further CO2 savings.
– Bapco, Awali Services
– Prof Waheeb Al-Naser & Mr Hussain Al-Ansari
– Heliocentris (system manufacturer)
– Our students: Ali Salman, Hussain A.Ali, Alaa Jameel.
[1] Dr.Cluas Fischer and Dr. Nroman Siehl, 2010, Heliocentris Energirsysteme GmbH, Power Point presentation, Heliocentis, Berlin-Germany
[2] Montgomery etal, (2008). Introduction to Time Series Analysis and Forecasting, Wiley. River Street Hoboken
[3] Description of Transformation, http://www.xlstat.com/en/learning-center/tutorials/using-
differencing-to-obtain-a-stationary-time-series.html, [Last visit on 13 January 2013]
[4] The Royal Academy of Engineering (Wind Turbine Power calculations)
http://www.raeng.org.uk/education/diploma/maths/pdf/exemplars_advanced/23_wind_turbine.
pdf, [Last visit on 6 January 2013]
[5] Alfasolar, (Solar Module Series alfasolar pyramid 54) , http://www.alfasolar.de, [Last visit on 6 January
2013]
[6] EERE Information Center-U.S (Fuel Cell Technologies Program)
http://www1.eere.energy.gov/hydrogenandfuelcells/pdfs/fct_h2_fuelcell_factsheet.p
df, [Last visit on 6 January 2013]