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PHOTURGEN 1.0
AN OPEN-SOURCE RENEWABLE ENERGY MANAGEMENT TOOL
Daren Watson
CR2 Tutorial Room, Faculty of Science and Technology, UWI, Mona
Python Meet-Up, October 28, 2015
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Rationale/Justification
High Resolution Simulation
The need for high resolution simulations for the optimization of various renewableenergy systems.
Regional Research
Lack of regional research on application of RE to small scaled load demands.
Void in regionally developed tools for HRES optimization.
Variability
Intermittent nature of wind and solar resources.
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Rationale/Justification
High Resolution Simulation
The need for high resolution simulations for the optimization of various renewableenergy systems.
Regional Research
Lack of regional research on application of RE to small scaled load demands.
Void in regionally developed tools for HRES optimization.
Variability
Intermittent nature of wind and solar resources.
©2015 Watson Python Meet-Up
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Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
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Rationale/Justification
High Resolution Simulation
The need for high resolution simulations for the optimization of various renewableenergy systems.
Regional Research
Lack of regional research on application of RE to small scaled load demands.
Void in regionally developed tools for HRES optimization.
Variability
Intermittent nature of wind and solar resources.
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Rationale/Justification
High Resolution Simulation
The need for high resolution simulations for the optimization of various renewableenergy systems.
Regional Research
Lack of regional research on application of RE to small scaled load demands.
Void in regionally developed tools for HRES optimization.
Variability
Intermittent nature of wind and solar resources.
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Rationale/Justification
High Resolution Simulation
The need for high resolution simulations for the optimization of various renewableenergy systems.
Regional Research
Lack of regional research on application of RE to small scaled load demands.
Void in regionally developed tools for HRES optimization.
Variability
Intermittent nature of wind and solar resources.
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
©2015 Watson Python Meet-Up
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Data CollectionSoftware
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CreditsBibliography
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Hybrid Renewable Energy Systems
So
lar
Pa
nel
So
lar
Pa
nel
WindTurbine
BiodieselGenerator
BatteryBank
Inverter
NationalElectric
Grid
Load Demand
AC 50 Hz
AC 50 Hz
DC
DC
DCAC 50 HZ
Figure : Diagram showing an example of a Hybrid Renewable Energy System (HRES)
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General Objectives of Research
Photurgen. Pronounced ’Pho-tur-gen’
Open-source simulation tool for hybrid renewable energy systems. Modelling of energysystem (i.e. battery, solar module, wind turbine etc.)
Correlation Analysis
Intrinsic look at variables involved in the performance of energy systems (temperature,wind, solar, air pressure etc.).
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Procedure
Data Collection
Collection of meteorological data for a period of one year (temperature, solarradiation, wind etc).
Collection of load consumption data for a period of not less than a week forresidential properties.
Figure : Onsite Installation: Weather Station Figure : Onsite Installation: Power Meter
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Procedure
Data Collection
Collection of meteorological data for a period of one year (temperature, solarradiation, wind etc).
Collection of load consumption data for a period of not less than a week forresidential properties.
Figure : Onsite Installation: Weather Station Figure : Onsite Installation: Power Meter
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Procedure
Analysis
Identify methods of optimization for effective HRES design.
Identify mathematical models applicable to the simulation of HRES.
Perform statistical and numerical analysis of the collected data.
Example
Solar
Ppv = (Iph − Io
(exp
(q(Voc + Ipv Rs )
AKT
)− 1
)−
Voc
Rsh)Vpv (1)
Example
Wind
Pw = Pr
[exp(−( vcin
A)k ) − exp(−( vr
A)k )
( vrA
)k − ( vcinA
)k− exp(−(
vcout
A)k )
](2)
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Procedure
Analysis
Identify methods of optimization for effective HRES design.
Identify mathematical models applicable to the simulation of HRES.
Perform statistical and numerical analysis of the collected data.
Example
Solar
Ppv = (Iph − Io
(exp
(q(Voc + Ipv Rs )
AKT
)− 1
)−
Voc
Rsh)Vpv (1)
Example
Wind
Pw = Pr
[exp(−( vcin
A)k ) − exp(−( vr
A)k )
( vrA
)k − ( vcinA
)k− exp(−(
vcout
A)k )
](2)
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
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General User InterfaceWhy Python?
CreditsBibliography
End
Procedure
Analysis
Identify methods of optimization for effective HRES design.
Identify mathematical models applicable to the simulation of HRES.
Perform statistical and numerical analysis of the collected data.
Example
Solar
Ppv = (Iph − Io
(exp
(q(Voc + Ipv Rs )
AKT
)− 1
)−
Voc
Rsh)Vpv (1)
Example
Wind
Pw = Pr
[exp(−( vcin
A)k ) − exp(−( vr
A)k )
( vrA
)k − ( vcinA
)k− exp(−(
vcout
A)k )
](2)
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Procedure
Analysis
Identify methods of optimization for effective HRES design.
Identify mathematical models applicable to the simulation of HRES.
Perform statistical and numerical analysis of the collected data.
Example
Solar
Ppv = (Iph − Io
(exp
(q(Voc + Ipv Rs )
AKT
)− 1
)−
Voc
Rsh)Vpv (1)
Example
Wind
Pw = Pr
[exp(−( vcin
A)k ) − exp(−( vr
A)k )
( vrA
)k − ( vcinA
)k− exp(−(
vcout
A)k )
](2)
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
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CreditsBibliography
End
Procedure
Software
Create computational tools for the analysis of the collected data.
Model the performance of different energy systems (i.e. battery, solar module,wind turbine etc.).
Development of Photurgen; website, general user interface, etc.
Evaluate the tool using other software as benchmark eg. Homer Energy
©2015 Watson Python Meet-Up
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End
Procedure
Software
Create computational tools for the analysis of the collected data.
Model the performance of different energy systems (i.e. battery, solar module,wind turbine etc.).
Development of Photurgen; website, general user interface, etc.
Evaluate the tool using other software as benchmark eg. Homer Energy
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Procedure
Software
Create computational tools for the analysis of the collected data.
Model the performance of different energy systems (i.e. battery, solar module,wind turbine etc.).
Development of Photurgen; website, general user interface, etc.
Evaluate the tool using other software as benchmark eg. Homer Energy
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Procedure
Software
Create computational tools for the analysis of the collected data.
Model the performance of different energy systems (i.e. battery, solar module,wind turbine etc.).
Development of Photurgen; website, general user interface, etc.
Evaluate the tool using other software as benchmark eg. Homer Energy
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Procedure
Software
Create computational tools for the analysis of the collected data.
Model the performance of different energy systems (i.e. battery, solar module,wind turbine etc.).
Development of Photurgen; website, general user interface, etc.
Evaluate the tool using other software as benchmark eg. Homer Energy
©2015 Watson Python Meet-Up
IntroductionMethodology
Data CollectionSoftware
Graphical ResultsWebsite
General User InterfaceWhy Python?
CreditsBibliography
End
Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Harbour View, Kingston
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Portmore, St. Catherine
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Windsor Castle, Portland
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Photurgen (PTG)
Photurgen
Help PTGA
PTGGIS PTGO
ResourceAnalysis
System
Design
Output
Figure : Structural design of Photurgen
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Photurgen Analysis (PTGA)
Weather Analysis Load Analysis
Solar ModuleAnalysis
Wind TurbineAnalysis
WeatherStatistics
Load Statistics
Module Library
Maths Models
PTGA
Figure : Structural design of PTGA
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Photurgen Optimization (PTGO)
Load Economics
Solar ModuleEconomics
Wind TurbineEconomics
PTGO
Hybrid OptimizerModule Library
Figure : Structural design of PTGO
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Photurgen GIS (PTGGIS)
Shapefile Reader NetCDF Reader
Asc Reader
Module Library
PTGGIS
Figure : Structural design of PTGGIS
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Digital Elevation Models
Source: PTGGIS
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Load Consumption
Source: PTGA
Figure : Load consumption patterns
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Load Consumption
Source: PTGA
Figure : Load consumption patterns
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Levelized Cost of Energy
Source: PTGO
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Optimal System Configuration
Source: PTGO
255 WModule
255 WModule
255 WModule
255 WModule
255 WModule
255 WModule
255 WModule
BatteryBank
Efficiency: 90%
BatteryBank
Efficiency: 90%
InverterEfficiency
: 90%
NationalElectric
GridOptional :
Net Billing,Standby
Load DemandDaily: kWh/d
Annual: kWh/yr
AC 50 Hz
DC
DC
AC 50 Hz
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PDF Report of Results
Source: All Modules
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Table of Contents
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2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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www.photurgen.com
Figure : Please visit www.photurgen.com for more information
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Table of Contents
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2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Version 1
Current Version
Alpha
Testing
To Be Decided
Release Date
To Be Decided Figure : General User Interface
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Table of Contents
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2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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>>>import this
The Zen of Python
Beautiful is better than ugly. Explicit is better than implicit. Simple is better thancomplex. Complex is better than complicated. Flat is better than nested. Sparse isbetter than dense. Readability counts. Special cases aren’t special enough to breakthe rules. Although practicality beats purity. Errors should never pass silently. Unlessexplicitly silenced. In the face of ambiguity, refuse the temptation to guess. Thereshould be one– and preferably only one –obvious way to do it. Although that way maynot be obvious at first unless you’re Dutch. Now is better than never. Although neveris often better than *right* now. If the implementation is hard to explain, it’s a badidea. If the implementation is easy to explain, it may be a good idea. Namespaces areone honking great idea – let’s do more of those!
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Table of Contents
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2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Special Mentions
Invitation
David Bain - Organizer
Advisors
Prof. M. Taylor - Deputy Dean
Dr. T. Stephenson - Acting H.O.D
Dr. J.F. Dorville - Supervisor
Dr. K.L. Duncan - Sponsor
Staff
Mrs. R. Simmonds - Equipment
Mr. J. Lothian - Site visits
Data Collection
Weather Resources
Mr. S. Simmonds
Load Consumption
Mrs. C. Scarlett
Mr. S. Simmonds
Biomass/Kitchen Waste
Mr. S. Simmonds
Mr. T. Lumsden
Mr. B. Dennis
Mr. D. Williams
Mr. S. Simms
First Class Tutoring JA
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Table of Contents
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2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Bibliography I
Abbes, D., Martinez, A., and Champenois, G. (2013)Life cycle sot, embodied energy and loss of power supply probability for theoptimal design of hybrid power systemsMathematics and Computers in Simulation
Agrahari, R. P. and Tawari, G. N. (2013)The production of biogas using kitchen wasteInternational Journal of Energy Science
Agrahari, R. P. and Tawari, G. N. (2014)Comparative study of biogas production: Utilization of organic wasteInternational Journal of Environment and Resource
Anderson, P. and Bose, A. (1983)Stability simulation of wind turbine systems.IEEE Transactions on Power Apparatus and Systems
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Bibliography II
Bilal, B. O., Sambou, V., Ndiaye, P. and Ndongo, M. (2010)Optimal design of a hybrid solar-wind battery system using the minimization ofthe annualized cost system and the minimization of the loss of power supplyprobabilityRenewable Energy
Celik, A. N. (2013)Techno-economic analysis of autonomous pv-wind hybrid energy systems usingdifferent sizing methodsEnergy Conversion and Management
Connolly, D., Lund, H., Mathiesen, B. and Leahy, M. (2010)A review of computer tools for analysing the integration of renewable energy intovarious energy systemsApplied Energy
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Bibliography III
Feroldi, D., Degliuomini, L. N. and Basualdo, M. (2013)Energy management of a hybrid system based on wind solar power sources andbioethanolChemical Engineering Research and Design
Howells, M., Rogner. H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S.,Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M. and Roehrl, A. (2011)Osemosys: The open source energy modelling system: An introduction to itsethos, structure and developmentEnergy Policy
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Table of Contents
1 Introduction
2 Methodology
3 Data Collection
4 Software
5 Graphical Results
6 Website
7 General User Interface
8 Why Python?
9 Credits
10 Bibliography
11 End
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Questions?
All questions are welcomed!
©2015 Watson Python Meet-Up