Upload
sandia-national-laboratories-energy-climate-renewables
View
718
Download
8
Embed Size (px)
DESCRIPTION
2014 PV Performance Modeling Workshop: Optimization strategies with Pvsyst for large scale PV installations: Bruno Wittmer, Pvsyst
Citation preview
PVSYST SA - Route du Bois-de-Bay 107 - 1242 Satigny - Suisse www.pvsyst.com
Any reproduction or copy of the course support, even partial, is forbidden without a written authorization of the author.
Optimization strategies with Pvsyst for large scale PV installations
Bruno Wittmer [email protected]
Page 2 Page 2
• Introduction
• Batch simulations
• Optimization
– Basic results
– Economical evaluations
• Summary and Outlook
Page 3 Page 3
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
Page 4 Page 4
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
Page 5 Page 5
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
Page 6 Page 6
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
Page 7 Page 7
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
Page 8 Page 8
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
Page 9 Page 9
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
Page 10 Page 10
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
Page 11 Page 11
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
Page 12 Page 12
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
Page 13 Page 13
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
*
*
Page 14 Page 14
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
Page 15 Page 15
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
Page 16 Page 16
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
Page 17 Page 17
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
Page 18 Page 18
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