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PVSYST SA - Route du Bois-de-Bay 107 - 1242 Satigny - Suisse www.pvsyst.com
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Optimization strategies with Pvsyst for large scale PV installations
Bruno Wittmer [email protected]
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• Introduction
• Batch simulations
• Optimization
– Basic results
– Economical evaluations
• Summary and Outlook
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Motivation
• Optimization process is often long and tedious
− Multivariate optimization
− Variables can have non-intuitive effects
− Often variables have complex correlations
• Optimization can be driven by different figures of merit
− ‘Technical’ Measures (EGrid, PR, etc. )
− Economic Measures (Returns, Payback, LCOE, etc.)
• Some design variables of a PV installation can be varied continuously (‘Batch Simulations’)
− This allows a more comprehensive analysis
− Move from single simulation variants to batch simulations
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Reference Project
• Be as specific as possible without compromising variation of batch parameters
Reference Project
Layout 40 sheds, 3 rows per shed
Modules Generic 250 W module
Inverters Generic 500 kW inverter
Power 11520 modules, Pnom = 2.88 GWp
Shadings According to strings ( & linear)
Meteo Input Meteonorm 6.1 for Geneva
No additional shading objects !
Large system
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Batch simulations
• PVsyst needs a CVS file with the parameters for the simulations
• Parameter filling and analysis were performed with a framework written in the R language
Reference
Project
Parameter and
Results selection
Template
CSV File
Batch
Execution
Results
CSV File
Parameter
Filling
Analysis and
Plotting
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Batch parameters
• Several simulation parameters can be varied in the batch simulations
• For this presentation only Tilt and Pitch were used
• More parameters will be added in the coming versions
Site and Meteo
• Site • Meteo File
Orientation
• Tilt • Azimuth
3D Shading
• Pitch N-S • Shed width
System
• PV module • Rserie
• Rshunt • Rshunt(0)
• Nr. Mod. Series • Nr. strings
• Module Qlty loss • Inverter model
• Nr. Inverters or MPPT
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Ground Covering Ratio (GCR) and Pitch
• PVsyst will vary the pitch in the batch simulations
• The plots in this presentation use the GCR
• For homogeneous sheds the GCR is defined as Width/Pitch
• Assuming that the system scales with the size, one can renormalize to a given area
Reference Project
Width 3.04 m
Pitch 6.8 m
GCR 45%
Batch Simulation
GCR 10% – 100% in steps of 2%
Pitch 30.4 m – 3.04 m, variable steps
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Input and Output Variables
• Input Variables added to the CSV template file:
2300 Simulations take
around 3h computing time
Param. Range Step Nr. steps
Tilt 1° - 50° 1° 50
GCR 10% – 100% 2% 45
Pitch 30.4 m – 3.04 m variable 45
• Output as CSV file(s):
− All PVsyst simulation variables can be chosen for output Between 60 and 90 variables depending on simulation type
− Output is saved as yearly sums
− Optionally: create hourly values for each simulation (not used here)
• Output variables in this presentation:
− Mostly EGrid
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What are the best GCR and Tilt?
• Most simple measure is Egrid
• One could also use EArray and optimize the inverter in a second step
• Optimal Tilt lies on the grey line
• Performance Ratio is not a good measure
• Fails to recognize different incident Energy as function of Tilt
• Inherent to definition of PR
Optimal Tilt for given GCR
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Fixed Pnom or fixed area?
• EGrid: scenario with fixed Pnom
• EGrid/pitch: scenario with fixed area
• Optimal Tilt line is the same for both fixed Pnom
fixed area
Note the different scale ‼
• GCR = 0 is not possible The surface has a cost
• GCR = 1 might not be profitable, because Pnom has some cost and Egrid some different revenue
Also economical aspects decide where the optimal solution lies
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Basic Economic Analysis
• Simplified Financial analysis: Balance = Revenues - Costs
• The most profitable scenario is in between the extremes GCR = 0 or 1
Pnom Area
Investment 1500 $ / kWp 8 $ / m2
O&M 29 $ / kWp yr 0.03 $ / m2 yr
Return 0.13 $ / kWh
Timespan 16 years
fixed Pnom
fixed area
Timespan is not necessarily
the system lifetime
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Profitability as function of time
• The best system design can be a function of time horizon
• Optimizing short term returns neglects future benefits
• Very sensitive to financial input variables
• This kind of analysis helps to get a feeling for the sensitivity to different variables
12 years
14 years
16 years
18 years
Fixed area scenario
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More complex economical analysis
• Levelized Cost of Energy (LCOE)
• Discounted Payback Period (DPB)
𝐿𝐶𝑂𝐸 = 𝐶𝑛1 + 𝑑 𝑛
𝑁
𝑛=0
÷ 𝑄𝑛1 + 𝑑 𝑛
𝑁
𝑛=1
Cn : Costs in year n Qn : Energy output / saving in year n d : discount rate
∆𝐼𝑛1 + 𝑑 𝑛
𝐷𝑃𝐵
𝑛=0
≤ ∆𝑆𝑛1 + 𝑑 𝑛
𝐷𝑃𝐵
𝑛=1
DIn : Incremental investment costs DSn : Annual savings net of future annual costs d : discount rate
• IRR, NPV, etc… * W. Short, D.J. Packey, T. Holt, ‘A Manual for Economic Evaluation of Energy Efficiency and Renewable
Energy Technologies’, March 1995, NREL/TP-462-5173
*
*
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Boundary conditions
• Boundary conditions help to zero in on optimal solution
• For example:
− Clearance between sheds
− Maximum / Minimum EGrid
− Maximum payback period
− etc.
• It can also help to identify weaknesses (like losses due to clearance, sizing too close to limits, etc.)
fixed Pnom
fixed area
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Net Metering
Load peaking at noon,
Constant over the year
Constant self-consumption
favors winter layout
• Best solution depends on price ratio of saved and sold energy
summer layout
winter layout
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More Examples
• Any figure that can be expressed as function of the design space, Pnom, area and the output variables, is a potential candidate for an optimization plot
Life Cycle Emissions
Pnom Area
Construction 150 kgCO2 / kWp 80 kg CO2 / m2
O&M 100 g CO2 / kWp yr 3 gCO2 / m2 yr
Avoided 0.5 kgCO2 / kWh
Timespan 16 years
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fixed area
Summary
• Batch simulations allow systematic variation of design parameters
• For large installations we assume scalability of variables
• Optimal configuration can quickly be found
• Scenario can be adapted (fixed area vs. fixed Pnom)
• Figures of merit give a measure for optimization
• Boundary conditions constrain design space and help to identify the optimal solution
fixed Pnom
This optimization is a guide towards the best design, it does
not replace a detailed simulation of the final design choice
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Outlook Further analysis
− Additional economic measures
− Superimposing of plots
− Simulation with variable grid tariffs
− Study variable E-W orientation
Implementation in PVsyst • Add more batch parameters and output variables
− Number of sheds
− Consider also tracking devices
− Output variables of financial evaluation
• Simplify the use of batch simulations
− Automatic generation of batch parameter files
− Parallel processing
• Integrate visualization of batch results into PVsyst