David Moser Institute for Renewable Energy Bolzano - Reliable and bankable solar PV: the assessment of the economic impact of failures in the field
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Reliable and bankable solar PV: the assessment of the economic impact of failures in the field David Moser Institute for Renewable Energy Bolzano Italy
The Quest for Quality: towards reliable and bankable solar PV 2
Literature on quality 3
Quantifying «quality» Derisking Impact of reliability on LCOE LCOE Euro/kWh Drivers for cost-effective increase of performance and reliability: - Digitalisation - Common nomenclature - Risk framework and guidelines - A value-chain approach Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity, Eero Vartiainen, Gaëtan Masson, Christian Breyer, David Moser, Eduardo Román Medina, PIP 2019 https://doi.org/10.1002/pip.3189 4
Risk ownership Dealing with quality is complex Transfer of information / breaking SILOS 5
Stakeholders’ needs short defect warranty periods, minimum of additional guarantees and warranties, high sale price with low OPEX (short time horizon) manage all the conflicting requirements for a long period of time. The best condition for O&M operators is in fact in the presence of long defect warranty period and low sale price to allow for higher OPEX. ? limit their liability to failures PV plants, which meet technical market standards and are maintained on a regular basis long defect warranty periods, performance guarantees, reasonable low CAPEX and OPEX, high long-term plant performance and lifetime (ideally above the initial prediction). projects with a 10-15 year financing period and PV plant performance which can also be slightly below prediction. 6
Bankability in PV projects Great definition!! And in practice? 7
Bankability must be data-driven Data availability Large datasets are available: - Procurement / Testing - Monitoring - Field inspection - Ticketing O&M - Insurance claims - Third party inspections HOWEVER These datasets are rarely: - Organised - Interoperable and digitalised - Rely on interlinked digital platforms D. Moser et al, Identification of technical risks in the photovoltaic value chain and quantification of the economic impact, Progress in Photovoltaics, 7, 2017 8
Technical risks framework: towards a standardised approach to quality 9
Classification of technical risks Impact - on uncertainty (exceedance Probability) in Yield Assessments - on CAPEX (mitigation measures) - on Operational cost (CPN and other metrics, mitigation measures) 10
Uncertainties in Yield Typical uncertainties in YA Assessments and PV LCOE https://iea-pvps.org/key- topics/uncertainties- yield-assessments/ Uncertainty Range Solar resource Climate variability ±4% - ±7% Irradiation quantification ±2% - ±5% Conversion to POA ±2% - ±5% PV modeling Temperature model 1°C - 2°C PV array model ±1% - ±3% Best practice PV inverter model ±0.2% - ±0.5% Other Soiling ±5% - ±6% Mismatch Degradation Cabling Availability… Overall uncertainty on estimated yield ±5% - ±10% Typical uncertainty values (irradiance, temperature, soiling, shading, etc): ±5-10% [1] [1] D. Moser et al., “Technical Risks in PV Projects.” Solar Bankability Deliverable www.solarbankability.com 11
Yield and Exceedance Probability - Utilisation rate @P90 positively affected by reduction in uncertainty - P50 values will highly depend on the P50 choice of the insolation database P90 - Wrong assumptions can lead to under/overestimation of yield by >20% - Are YA reliable? Link with business models and LCOE calculation Typical uncertainty values on YA (irradiance, temperature, soiling, shading, etc): ±5-10% N. Reich, J. Zenke, B. Muller, K. Kiefer, and B. Farnung, “On-site performance verification to reduce yield prediction uncertainties,” in Photovoltaic Specialist Conference (PVSC), 2015 IEEE 42nd, 2015, pp. 1–6. M. Richter, T. Schmidt, J. Kalisch, A. Woyte, K. de Brabandere, and Lorenz, E, “Uncertainties in PV Modelling and Monitoring,” 31st European Photovoltaic Solar Energy Conference and Exhibition, pp. 1683–1691, Nov. 2015. D. Moser et al., “Technical Risks in PV Projects.” Solar Bankability Deliverable www.solarbankability.com D Moser, M Del Buono, U Jahn, M Herz, M Richter, K De Brabandere, Identification of technical risks in the photovoltaic value chain and quantification of the economic impact, Progress in Photovoltaics: Research and Applications 25 (7), 592-604, 2017 12
Site Selection Parameter Assumption Location Given Latitude/Longitude, tilt angle and azimuth Irradiance and transposition Each independent YA used their favourite database Temperature Each independent YA used their favourite database Location: Alice Springs, Australia Technology and mismatch PV module technology given (module datasheet). Mismatch and power tolerance, each YA applied their own consideration. Flash list with measure power was Data available since 2009 provided Technology: 3 crystalline technologies Inverter Given (datasheet) Shading Photos provided of near objects Soiling Each independent YA applied their own considerations Wind speed Each independent YA used their favourite database Long term insolation effects Each independent YA used their own considerations Degradation Each independent YA applied their own considerations Snow loss / snow fall Each independent YA applied their own considerations Availability Each independent YA applied their own considerations Uncertainties Please provide uncertainties for each parameter (when possible) and for the yield (mandatory). 13
Comparison of initial YAs 4% Problems along the modelling steps Large spread of values Real values within the P10-P90 range only for some YAs Averaging YAs might not be a good strategy! 14
Comparison of LTYPs It appears that the annual performance loss rate in Arid desert hot (BWh Köppen- Geiger climate zone) is much higher than expected, with all three systems discussed seeing a PLR of -1.1 %/year or worse, instead of the (historical) industry-standard assumption of -0.5 %/year. The significant over-performance by System C compared to predicted values suggests that thermal losses were over-estimated (for example by using not validated temperature coefficients and/or Nominal Module Operating Temperature, NMOT), and likely also suggests better light capture by these modules. 15
Risk matrix: taxonomy The importance of using common dictionaries Product Development Assessment of PV Plants Product Transportation Planning / installation O&M Decommissioning testing Modules …. …. …. …. …. • Insulation Inverter test • Soiling …. • …. …. Module mishandling • Hotspot …. • …. Undefined product • Incorrect cell • Shadow diagram (glass breakage) • Delamination recycling procedure Mounting structure• Modules…. soldering mismatch • …. …. Module mishandling • …. Glass breakage …. • Undersized bypass • Modules not certified (cell breakage) • Soiling diodeConnection & • Flash report …. not • …. …. Module mishandling • Shading …. …. • Junction box distribution boxes available or incorrect (defective backsheet) • Snail tracks adhesion • Special climatic • Incorrect connection • Cell cracks • Delamination Cabling at the ….not conditions …. of modules …. • PID …. …. edges considered (salt • Bad wiring without • Failure bypass diode • Arcing ….ammonia, …. …. …. box …. Potential module spots on equalization the grounding, LPS • Visually detectable ...) & corrosion, • Incorrect assumptions List of failures fasteners • and junction Corrosion in the junction box Weather station, hot spots of module…. …. …. • …. Theft of modules …. • Incorrect communication, power rating degradation, light • Module degradation (flash test issue) monitoring induced degradation • Slow reaction time for • Uncertified unclear warranty claims, vague Infrastructure components or …. & • Module quality unclear …. …. …. or inappropriate …. environmental production line influence (lamination, soldering) definition of procedure • Simulation parameters for warranty claims Storage system …. (low irradiance, …. …. • …. Spare modules no …. temperature….) longer available, costly Miscellaneous unclear, …. missing PAN …. …. …. string reconfiguration …. files www.solarbankability.org 16
Risk matrix: taxonomy The importance of using common dictionaries • An O&M ticket is characterised by 9 fields • Each failure in the field must have information for ”Component”, ”Subcomponent” and the name of the issue itself → failure_id • The optional field can give information of interest, for e.g. the root cause, the accountability, etc. • The used nomenclature must be standardised via ”dictionaries” MANDATORY OPTIONAL failure_id Component Subcomponent failure Description Cause Origin Accountability Detection grid.02 Grid Entire grid Limitation of deliverable power Operation Warning O&M platform inv.11 Inverter Entire inverter Overheated inverter Ventilation issues Operation Warning inverter mod.01 Module Backsheet Chalking Operation Insurance Visual inspection mount.12 Mounting Structure Tracking system Tracking failure Storm Operation 306 failures listed www.trust-pv.eu 17
Economic impact of failures CPN: metric that allows for New metrics Cost-based Failure Modes - Comparison between asset within the same and Effects Analysis PV plant portfolio (AM, O&M) (FMEA) for PV - Evaluate best strategies in EPC, O&M - Act as a link between the various phases of the value chain a) Economic impact due to downtime and/or power loss (kWh to Euros) - Failures might cause downtime or % in power loss - Time is from failure to repair/substitution and should include: Income / savings reduction time to detection, response time, repair/substitution time - Failures at component level might affect other components (e.g. module failure might bring down the whole string) b) Economic impact due to repair/substitution costs (Euros) - Cost of detection (field inspection, indoor measurements, etc) O&M cost increase - Cost of transportation of component Reserves decrease - Cost of labour (linked to downtime) - Cost of repair/substitution 18
CPN Results - Components and Market Segments • PV modules - Utility scale • Highest risk consists of a group of installation failures (mishandling, connection failures, missing fixation, etc. ) • Variety of failures detected by different techniques (VI, IR, EL, IV-Curves) 19 19
CPN Results - Components and Market Segments • Inverters 20 20
Economic impact of failures Treaceability Digitalisation becomes a necessity OPTIMIZATION OF THE COST PRIORITY NUMBER (CPN) METHODOLOGY TO THE NEEDS OF A LARGE O&M OPERATOR, G. Oviedo Hernandez et al, EUPVSEC 2019, 21
Process digitalisation Full integration of monitoring platforms and ticketing systems - Creation of standardised metadata (PV passport) - Development of an automated and therefore time-efficient solution for extracting key parameters from maintenance tickets to gain statistical insights from a large number of PV plants. - Development of a software tool for field technicians that would allow the precise and error-free recording of standardised parameters for the calculation of the O&M contractors KPIs necessary for an efficient implementation of the methodology - The O&M field practices must definitely move away from a manual input of tickets in text format and adopt a more standardised approach when human intervention is limited OPTIMIZATION OF THE COST PRIORITY NUMBER (CPN) METHODOLOGY TO THE NEEDS OF A LARGE O&M OPERATOR, G. Oviedo Hernandez et al, EUPVSEC 2019, 22
A value-chain approach Breaking silos Virtual construction of a facility prior to its actual physical construction (reduce uncertainty, improve safety, Conceptual Detailed design design work out problems, and simulate and analyze potential impacts) Bridge the information loss Decision associated with handling a project from design team, to Support construction team and to asset Modification Construction owner/operator System BIModel Dynamic information about the asset Operation (Configuration changes, sensor & measurements, control signals) Maintenance 23
When we have data…. 24
Benchmarking in IEA-PVPS Task 13 14 PV systems: high quality data 130 PV systems: low quality data 25
Benchmarking in IEA-PVPS Task 13 Performance Loss Rate Technology Climate Temperate Continental Performance Loss Rates of PV systems of Task 13 database, Sascha Lindig, David Moser, Alan Curran and Roger French, IEEE PVSC Chicago 2019 26
Benchmarking in PEARL-PV https://www.pearlpv-cost.eu/ Performance analysis and degradation of a large fleet of PV systems, S. Lindig et al, IEEE Journal of Photovoltaics, accepted, 2021 27
Energy Yield = . per year ෫ = . per year ഥ = . ° ഥ = . ° ഥ = . ° Performance analysis and degradation of a large fleet of PV systems, S. Lindig et al, IEEE Journal of Photovoltaics, accepted, 2021 28
Performance Ratio = . % ෪ = . % Performance analysis and degradation of a large fleet of PV systems, S. Lindig et al, IEEE Journal of Photovoltaics, accepted, 2021 29
Performance Loss Rates Jordan1 Kiefer2 -0.8 to -0.9 %/a -0.7 %/a ෫ -0.5 to -0.6 %/a [1] D. C. Jordan, et al, "Compendium of photovoltaic degradation rates," Progress in Photovoltaics Research and Application, vol. 24, no. 7, pp. 978-980, 2016. 30 [2] K. Kiefer, et al, "Degradation in PV Power Plants: Theory and Practice," in 36th EU PVSEC, Marseille, 2019.
PV Component-Benchmarking Big data techniques applied on one of the largest PV portfolio (+16GW) including metadata, operational data and ticketing data to evaluate the performance and reliability of PV components • Identify main performance and degradation losses factors • Modelling Climate Stressors and Reliability Indicators (time-to-fail, lifetime,…) Extending the datasets to Until now, data mainly on • Improve manufacturing processes, system designs, and O&M activities Desert Climates would Temperate Climates enable… • Establish the needs of new labels Based on operational data KG Climate Zone Steppe • A (Tropical) • B (Arid) • C (Temperate) • D (Continental) PV systems in desert • E (Polar) areas present the Desert highest climate stresses reflected in lower PR (and higher PLRs) www.trust-pv.eu 31
Conclusions - The PV sector must establish approaches to ensure and measure quality of components, systems and projects - Each PV project must ensure the presence of a reliability plan which is constantly updated and passed along the value chain - New metrics must be introduced to quantify the impact of decisions taken over the lifetime of a PV project - Silos culture between stakeholders must change. Decision taken during a phase have an impact on the next phases - Information must be carried along the value chain - Standardisation of data format and collected data (metadata / PV plant passport, product data, monitoring data, ticketing, etc) - Digital platforms must be interoperable Bankability must be based on hard facts / data Solar Bankability is an approach that heavily relies on data / quantification of quality Projects in underrepresented climates are particularly affected by lack of data Digitalisation is the driver that can finally ensure cost-effective increase of quality and reliability 32
Thank you for your attention www.eurac.edu/ David.moser@eurac.edu 33
Risk Year-0 Phase/field Procurement/ product selection Identified critical technical gaps 1. Insufficient EPC technical specifications to ensure that selected components are suitable for use in the specific PV plant environment of application. Gap analysis and testing 2. Inadequate component testing to check for product manufacturing Impact on deviations. 3. Absence of adequate independent product delivery acceptance test and quality of criteria. Planning/ 4. The effect of long-term trends in the solar resource is not fully accounted installation lifetime energy for. yield estimation 5. Exceedance probabilities (e.g. P90) are often calculated for risk assessment assuming a normal distribution for all elements contributing to the overall uncertainty. Impact on cash 6. Incorrect degradation rate and behavior over time assumed in the yield estimation. flow model 7. Incorrect availability assumption to calculate the initial yield for project investment financial model (vs O&M plant availability guarantee). Transportation 8. Absence of standardized transportation and handling protocol. Installation/ 9. Inadequate quality procedures in component un-packaging and handling construction during construction by workers. Impact on quality 10. Missing intermediate construction monitoring. Installation/ 11. Inadequate protocol or equipment for plant acceptance visual inspection. of installation provisional and 12. Missing short-term performance (e.g. PR) check at provisional acceptance final acceptance test, including proper correction for temperature and other losses. 13. Missing final performance check and guaranteed performance. 14. Incorrect or missing specification for collecting data for PR or availability Impact on risk/cost evaluations: incorrect measurement sensor specification, incorrect irradiance threshold to define time window of PV operation for ownership PR/availability calculation. Risks Operation 15. Selected monitoring system is not capable of advanced fault detection and during identification. operation 16. Inadequate or absence of devices for visual inspection to catch invisible Impact on risk/cost defects/faults. 17. Missing guaranteed key performance indicators (PR, availability or energy ownership and on yield). 18. Incorrect or missing specification for collecting data for PR or availability O&M stratgy evaluations: incorrect measurement sensor specification, incorrect irradiance threshold to define time window of PV operation for PR/availability calculation. Maintenance 19. Missing or inadequate maintenance of the monitoring system. 20. Module cleaning missing or frequency too low. 34
Process digitalisation Workflow App Measured PV 3D Vis Measured Digital plant electrical meteo-data Twin parameters Maintenance Monitoring System Ticket Priority + Fault O&M KPIs Ticketing System Decision Support Detection Cost of repair/sub Energy Loss CPN Cost of downtime Cost parameters PV system asset (FIT, PPA, labor, data and components) metadata 35
Process digitalisation Component geolocalised History / logging at component level Integration in digital platforms Common nomenclature: statistics Suggestions on actions H&S / skills management 36
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