Subtask 1: Economic of PV System Performance and Reliability

Transcription

1 TASK 13: PERFORMANCE AND RELIABILITY OF PV SYSTEMS Subtask 1: Economic of PV System Performance and Reliability Mauricio Richter (3E), Jan Vedde (SiCon) Mike Green (M.G.Lightning Electrical Engineering)

2 Outline SubTask objective, background and context Activities of past 6 months Work plan for next 6 months Discussion on risk mitigation strategies

3 ST1 Economics of PV System Performance & Reliability Objective: to collect and analyse data on PV financial models that describe current practices and to develop guidelines and scientifically based recommendations for how to use technical assumptions and knowledge in PV investment financial models, including means of possible risk management from technical viewpoint Activity Description 1.1 Overview of current practices in technical assumptions in PV investment financial models 1.2 Guidelines in accounting and managing technical risks in PV financial models by means of inputs from scientific inputs

4 ST1.1 Activities Past 6 Months First draft of two chapters in planned external report (next slide) Presentation at EU PVSEC paper submitted to Progress in Photovoltaics (still under review) manuscript (short version) delivered for proceedings PowerPoint for oral presentation developed

5 ST1.1 Activities Past 6 Months First draft of two chapters in planned deliverable: 3. Overview of Current Practices 3.1 Financial Models for PV Investment 3.2 Technical Assumptions Used in PV Financial Models Capital Expenditures Operating Expenditures Energy Yield Estimates 4. Review and analysis of technical assumptions used in PV financial models 4.1 Technical assumptions and scientific data 4.2 Reliability and failures of PV system components 4.3 Experimental validation of expected yield

6 Progress in Photovoltaics paper: Technical Assumptions Used in PV Financial Models

7 EU PVSEC proceedings paper: Technical Assumptions Used in PV Financial Models: review and analysis: review and analysis

8 EU PVSEC presentation: Technical Assumptions Used in PV Financial Models: review and analysis: review and analysis

9 Outline Background & Motivation Questionnaire and data-analysis Technical parameters used in energy yield calculations Technical parameters in relation to OPEX & CAPEX Financial modelling & Input parameter distributions Output variables Conclusions

10 Background Task 13 is a PVPS working group with experts in the field of reliability, component failures and energy yield modelling Since 2014 also the impact of technical parameters on the financial modelling has been investigated Experts in PV systems Quality & reliability contribution to PV economics Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 10

11 Introduction/motivation Residential PV systems - bought by individuals Utility scale PV - financed investment opportunities Profitability is important and so is uncertainty Technical parameters are involved but how? energy yield P50% values are only part of the equation How can the technical knowledge be of value to investors? Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 11

12 Questionnaire: Technical parameters in financial models Screening of 84 project presentations, 7 countries Information extracted - focus on uncertainty Data analysis revel bias towards Topics: 1. The project ownership, development status etc. 2. The site location, irradiation assumptions, installation basics (roof/ground mounting), lease conditions etc. 3. System design components selection, electrical engineering, grid connection etc. of relevance for PR. 4. System cost with relevant breakdown into BoM 5. Business model IPP, FIT, own consumption, net-metering. 6. Operation and maintenance degradation, soiling, replacement cost, labour, etc. Technical parameters seems only to relate to Energy Yield calculation Technical parameters used in PV financial models Example questions and answers 3.2 Has sources of soiling been identified, registered/measured and assessed? ; ; ; ; ; ; ; %; -1.0 %; -1.0 %; -1,5%; -2,4%; -3.0 %; -1.0% ; -1.0% ; -0,014; How is uncertainty in these inputs dealt with? ; ; ; ; ; ; ; ±1,0 %; ±1,0 %; ±1,0 %; ±1,0 %; ±1,0 %; ±1,0 %; ±1,0 %; ±3,0 %; ±1,0 %; 3.6 Has module power reduction due to string mismatch been assessed? ; ; ; ; ; ; ; %; -0.9 %; -0.4 %; -0,021; -0,007; -0.8 %; -1.0 %; -0,01; -0,01; How is uncertainty in these inputs dealt with? ; ; ; ; ; ; ; ±0,5 %; ±0,5 %; ±0,5 %; ±0,5 %; ±1,0 %; ±0.5 %; ±0.5 %; ±0,5 %; ±0,5 %; 3.7 Has the dc and ac cabling loss been calculated? Compared to another project an extra cable loss of 5% has been estimated; ; ; ; ; ; ; - 0,01; -0.9 % -0.1%; -0.6 % -0.0 %; -0,2% -0,7%; -0,2% -0,7%; -3.4 % -7.4 %; -0.1 % -6.2 %; -1.5% -2.0%; -1.1 % -0,3%; How is uncertainty in these inputs dealt with? ; ; ; ; ; ; ; ±0.2 %; ±0.2 %; ±0.2 %; ±0.2 %; ±0.2 %; ±0.2 %; ±0.2 %; ±0,2 %; ±0,2 %; Jan.Vedde@mail.dk m Page 12

13 Energy yield calculation - observations Many sub-models in cascade are in use to calculate energy Technical parameters are often presented with high precision, references & uncertainty (1σ) Irradiation from many sources But also guestimates are used - soiling: 1 % ± 1% - DC loss (current mismatch) Model operator is important Convey the impression that Energy can be calculated with high precision Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 13

14 Operational expenditures technical assumptions in use Contract regulate fee based on Technical Availability, PR response time etc. Wear profile of components (inverter lifetime), MTBF and O&M replacement strategy O&M contract is prepared by legal team but do reflect technical assumptions Technical parameters used in PV financial models m Page 14

15 Capital expenditures Construction cost/sales price is not a single number Will reflect Quality of components, inspection/pass criteria, warranty conditions & guarantee extensions, guarantee form EPC etc. Total installed PV system cost and weighted averages for utility-scale systems, , IRENA 2016 CAPEX value is seldom a single value but also reflect technical assumptions Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 15

16 Financial models discounted cash flow Standard models with many input parameters (but only one target value of each parameter) Profitability determined by the value of output variables (IRR, ROI, NPV etc.) We want to asses the impact related to known uncertainty in the technical input parameters First focus only on uncertainty in Energy Yield calculation How can profitability be calculated with technical parameters uncertainty? Technical parameters used in PV financial models m Page 16

17 Financial models Input parameters 1. Project Unit Best Worst Best estimate case case Module power [W p ] Number of modules [pcs] Not applicable 2. Site selection Horizontal global irradiation [kwh/m 2 /yr] 1000 ±3% Irradiation increase due to tilt angle [%] 15% Not included in analysis 3. System design Irradiation loss due to soiling [%] 2,0% ±1% Irradiation loss due to shading [%] 1,0% ±0.5% Module energy loss due to incidence effect (IAM) [%] 3,0% ±0.5% Module energy loss due to temperature [%] 1,0% ±0.5% Array mismatch loss [%] 1,0% ±0.5% Ohmic wiring dc-loss [%] 1,0% ±0.2% DC/AC-sizing factor [W p /W] 120% Not included in analysis Inverter loss during operation (conversion efficiency) [%] 2,5% ±1% Transformer loss [%] 1,5% ±0.8% 4. Project and system components costs Turnkey unit price [ /Wp] 1,0 1,2 1,0 5. Power production & sales Start year [year] 2016 Not included in analysis Technical lifetime of installation [year] 25 Not included in analysis Technical availability [%] 98% 95% 99% PV system [module] degradation [%/a] 0,30% 0,50% 0,20% Feed-In-Tariff (to be used for 20 first years of operation) [ /kwh] 0,10 Not applicable Market price for electricity (to be used from year 21) [ /kwh] 0,05 0,03 0,07 6. Land lease, O&M and guarantees O&M - fixed yearly fee [ /kw p ] Inverter replacement (according to expected lifetime) [year] Financing Economical operational lifetime [year] 25 Not in scope of paper Real discount rate [%] 5% Not in scope of paper Debt ratio [%] 90% Not in scope of paper Equity [%] 10% Not in scope of paper Bank Interest loan [%] 3% Not in scope of paper Amortisation and depreciation [year] 15/25 Not in scope of paper Uncertainty described by Target & Best case/worst case estimate Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 17

18 Input distributions; may not be normal distributed Module power is typically binned Horizontal irradiation may be normal distributed Inverter lifetime may follow wear profile Non-normal may be triangular Monte Carlo calculations generate results (combinations) Scenarios can be implemented Module power Horizontal irradiation Inverter replacement year Soiling & DC loss No reason to simplify input assumptions if a complex description exists Technical parameters used in PV financial models Jan.Vedde@mail.dk m Page 18

19 Output variables: non-normal distributions Graph highlights the inherent uncertainty From cumulative probability the P50 & P90 values can be extracted May also be characterised by skewness etc. First year energy production Internal Rate of Return Sum of income over 25 year Output distributions highlights the uncertainty of the calculation Technical parameters used in PV financial models m Page 19

20 TASK 13: PERFORMANCE AND RELIABILITY OF PV SYSTEMS Conclusion 84 PV project presentations have been screened Technical parameters related to energy yield calculation are found to be presented with many details and uncertainty CAPEX & OPEX values however are typically presented as fixed values in the PV financial models Known variations of the input parameters can be treated as distributions when Monte Carlo calculations are implemented in PV financial models Output parameters presented with distributions & probability provide a better visualisation of the uncertainty Thank you for your attention Acknowledgement: The contribution from 3E s.a. to this work has received funding from the European Union's Horizon 2020 research and innovation programme under the grant agreement No (Solar Bankability). Jan Vedde wishes to thank the Danish Energy Agency for support according to the EUDP programme with project number

21 ST1.1 Activities Next 6 Months Finalise D1-2 report Chapters 3 & 4 Discuss, draft and finalise chapters: 5. Mitigating and hedging financial risks of a PV investment 5.1 Classification of technical assumptions with highest risks 5.2 Opportunities for mitigating and hedging financial risks 6. Guidelines and recommendations 6.1 Good practices for mitigating and hedging financial risks in a PV investment 6.2 Recommendations 7. Conclusions

22 Chapter 5. Mitigating and Hedging financial risks of a PV investment 5.1 Classification of technical assumptions with highest risks Three examples of Bottom up analysis: Solar Bankability: Cost Priority Number Anecdotal evidence (systematisation of real life observations) Technical systematics approach Ranking of critical risk factors based on risk modelling Two examples of Top down analysis: Strategies to deal with Risks Uncertainties vs. Risks

23 Chapter 5. Mitigating and Hedging financial risks of a PV investment Anecdotal evidence (1/2) Planning conditions & building permit Remember to adhere to conditions stated in Environmental Impact Assessment, Archaeological constraints etc. Site, roads, fence etc. Remember to adhere to conditions on site access, road load bearing, fence type/height required etc. Mounting structures Zink coating thickness must be selected in accordance with soil chemistry, statics to be calculated according to local requirements by person with adequate skills and certification etc. Modules Performance warranty and product guarantees, manufacturer solidity and bankability, site specific product certificates (salt mist, ammonia), factory inspection report, country of origin statements, tax- and tariff payment documentation, incoming quality control/sampling for power rating, hotspots, PID, microcracks, installation according to manual etc.

24 Chapter 5. Mitigating and Hedging financial risks of a PV investment Anecdotal evidence (2/2) Inverter Manufacturer references and bankability, dc/ac sizing ratio according to datasheet, product approval according to country specifics etc. Cables, connectors, combiner boxes Connector compatibility with module & inverter manufacturer s requirements Transformer, earthing & grid connection Grid connection according to local rules and regulations. Power factor and remote control. Construction Cleaning of site and removal of waste, construction insurance, Health and Safety. documentation, handling of modules during installation, humidity in cable connectors etc. Yield study and commercial model Actual availability Business interruption insurance

25 Chapter 5. Mitigating and Hedging financial risks of a PV investment Technical systematics approach technical assumptions inherent risk in % module power mismatch loss soiling losses GHI==> inplane heat coeficient shading IAM albedo module conversion efficiency array losses- Ohmic losses combiner box loss MPPT tracking LID inverter losses GHI we are all very bored of this already transformer losses AC cable losses PID free conforming to planning requirements and regulations inverter fulfills all market requirements PF at delivery point will be unity or what was dictated power will not be curtrailed No Vars will be required accesability for maintenance soil aggresion study is correct static calculations are correct mounting structure will last 25 years country of orgin correct assume good work practice for: transportation of modules module installation correct power rating transportation of inverters

26 Chapter 5. Mitigating and Hedging financial risks of a PV investment Ranking of critical risk factors based on risk modelling a ) b ) c ) d ) a ) b ) c ) d )

27 Chapter 5. Mitigating and Hedging financial risks of a PV investment Top-down approach: Strategy: A prerequisite for any successful risk mitigation strategy is to ensure that the overall process is recognised by the top-level decision makers and that this management level take responsibility for defining an appropriate strategy and assignment of the necessary resources to undertake this process. Identify: Set a team that include a wide variation of skills and experiences; brainstorm and use checklists to make sure all potential risks are identified; asses and classify the risk factors according to expected occurrence frequency, severity in terms of financial impact and overall risk ranking. Understand: Analyse the root cause(s) of the various risk factors including possible interrelations between different factors. Identify the specific most important influencer that may be addressed by the project (like when the Bill-of-material listing of encapsulant material manufacturers are identified as a potential root cause for development of Potential Induced Degradation in crystalline silicon modules). Manage: Introduce and follow-up on actions to mitigate the identified risk items. Among other tasks, this risk management start with the selection of partners (developer and EPC) as well as components and overall solutions. As for the process of selecting competent, reliable and cost-efficient suppliers, reference can be made to other dedicated standards and studies including ISO The same goes for the careful inspection and verification of the content, relevance and solidity of those warranties that are issued for all components, services and design-solutions included in the project Start with the PV Financial model then select the important input parameters (technical or not)

28 Thank you