2014 pv performance modeling workshop: optimization strategies with pvsyst for large scale pv...

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 bruno.wittmer@pvsyst.com

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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