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- 1. 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 bruno.wittmer@pvsyst.com
- 2. Page 2Page 2 • Introduction • Batch simulations • Optimization – Basic results – Economical evaluations • Summary and Outlook
- 3. Page 3Page 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
- 4. Page 4Page 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
- 5. Page 5Page 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
- 6. Page 6Page 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
- 7. Page 7Page 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
- 8. Page 8Page 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
- 9. Page 9Page 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
- 10. Page 10Page 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
- 11. Page 11Page 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
- 12. Page 12Page 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
- 13. Page 13Page 13 More complex economical analysis • Levelized Cost of Energy (LCOE) • Discounted Payback Period (DPB) 𝐿𝐶𝑂𝐸 = 𝐶 𝑛 1 + 𝑑 𝑛 𝑁 𝑛=0 ÷ 𝑄 𝑛 1 + 𝑑 𝑛 𝑁 𝑛=1 Cn : Costs in year n Qn : Energy output / saving in year n d : discount rate ∆𝐼 𝑛 1 + 𝑑 𝑛 𝐷𝑃𝐵 𝑛=0 ≤ ∆𝑆 𝑛 1 + 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 * *
- 14. Page 14Page 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
- 15. Page 15Page 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
- 16. Page 16Page 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
- 17. Page 17Page 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
- 18. Page 18Page 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

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