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]
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2014 PV Performance Modeling Workshop: Optimization strategies with Pvsyst for large scale PV installations: Bruno Wittmer, Pvsyst
2014 PV Performance Modeling Workshop: Optimization strategies with Pvsyst for large scale PV installations: Bruno Wittmer, Pvsyst
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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
• 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