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2014 PV Performance Modeling Workshop: Optimizing PV Designs with HelioScope: Paul Gibbs, Folsom Labs
The document discusses optimizing photovoltaic (PV) designs using the Helioscope tool, highlighting its design-driven, cloud-based approach that facilitates detailed performance modeling and value engineering. It presents case studies on optimizing parameters such as the ground coverage ratio and shading impacts, emphasizing the economic benefits of optimizing the DC subsystem. Additionally, it outlines future enhancements to Helioscope through the DOE SunShot program, focusing on automated optimization and financial modeling.
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2014 PV Performance Modeling Workshop: Optimizing PV Designs with HelioScope: Paul Gibbs, Folsom Labs
- 1. Optimizing PV Designswith HelioScope Sandia Performance Modeling Workshop Paul Gibbs May 5, 2014 paul.gibbs@folsomlabs.com
- 2. Agenda • What isHelioScope and why is it good for optimization? • Case Studies in PV System Optimization – Ground Coverage Ratio – DC Plant Design – Designing into Shade • Looking forward: automating optimization
- 3. HelioScope is adesign-driven PV modeling tool Principles • Design-driven • Component-level • Cloud-based Values • Throughput Velocity • Value Engineering
- 4. HelioScope Tour: Addinga Field Segment
- 5. HelioScope Tour: Modifyingan Array
- 6.
- 7. Production reports includea full bill-of-materials Performance Modeling: • Full Loss Tree • Condition Set Details • Hourly Data CSV Design Specifications: • Bill-of-materials • System Layout • Wiring Details
- 8. Why is HelioScopeideal for optimization? • Rule Based: Trivial to evaluate design alternatives • Design Driven: Bill-of-materials generated automatically • Granular Modeling: Performance model always in sync with design 180º Azimuth (Due South) 205º Azimuth
- 9. We designed ourinterface specifically to encourage value-engineering Designs Conditions
- 10. GCR optimization isan ideal area for optimization Key Issues: • Nameplate capacity • Upfront costs • Cross-bank shading • Energy/revenue stream Economic Drivers: • Space constraints • Interconnect Agreement • Site weather • Project latitude
- 11. We optimized areference designs conductors against a variety of parameters Modules per string Combiner box size Source circuit conductor Combiner box layout Wiring direction Home run conductor
- 12. Optimizing the DCsubsystem can reduce costs by 27% Total electrical costs were calculated • Wire quantity and cost • Combiner box quantity and cost • Electricity value lost from wire resistance Performance Driver Minimum Maximum Modules per string 10 15 Source circuit conductors #12 AWG #8 AWG Wiring direction Along racking Up & down racking Combiner box size 12 strings 24 string Home run conductors 0/1 AWG 4/0 AWG Combiner box layout Scattered throughout array Grouped at inverter 1.7 2.4 0.4 1.0 0.5 0.8 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Modules per string Source circuit conductor Wire direction Combiner size Home run conductor Combiner layout Impact on System Costs (¢/Wp)
- 13. Designing into shadeoften increases system size with minimal performance impacts 800 900 1,000 1,100 1,200 1,300 1,400 1,500 800 850 900 950 1,000 EnergyYieldofEachSegmentofModules(kWh/kWp) System Size (kW) With MLPE Standard Mismatch Baseline: Zero shade tolerance Shade allowed in December Shade allowed in Nov-Dec Shade allowed in Oct-Nov-Dec Shade allowed year-round Shade allowed year-round
- 14. ($250) ($200) ($150) ($100) ($50) $0 $50 $100 $150 $200 Year0 Year1 Year2 Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11 Year12 Year13 Year14 Year15 Year16 Year17 Year18 Year19 Year20 Year21 Year22 Year23 Year24 Year25 Thousands What are thecatches? • Need Financial Model – LCOE, IRR needed to truly optimize – Component costs, Rate database – How complex is good enough? • ‘Manual’ optimization – Why can’t the computer do the work? – Limits scope – How holistic should the optimization be? ($650) ($600)
- 15. DOE Sunshot Awardto extend HelioScope with Design Optimization features • Started 1Q2014 • Augments HelioScope with optimization features – Automated optimization – Financial modelCustomer feedback: staged optimizations are ideal – At start of project, goal is maximize energy or revenue – As project progresses, several deep dives (e.g. wiring)
- 16. Optimizations will haveobjective functions that are optimized under key constraints • Module Tilt • Row Spacing • Positive & Negative Space • Interconnect Shading Requirements (10 – 2) • Maximum Grid Power • Target ILR Range • Project IRR • Total Revenue/Energy • LCOE Independent Variables Constraints Objective Functions Ground Coverage Ratio Optimization Tilt Annual KWh Tilt Sensitivity 15º (optimal) Annual KWh Spacing Sensitivity 2,3m (optimal) Row-to-Spacing
- 17. Under the DOESunshot program we will implement staged optimizations Module Layout DC Subsystem AC Subsystem • Tilt/GCR • Azimuth vs TOU • Fixed vs Trackers • Shade Setbacks • String Length • Inverter Load Ratio • Conductor Selection • Conductor Routing • Component Selection • Conductor Selection • Transformers
- 18. Thanks! Paul Gibbs Founder, FolsomLabs paul.gibbs@folsomlabs.com Folsom Labs www.folsomlabs.com San Francisco, CA