【PVPMC】DNVGL-Pramod-公用事业规模光伏电站运行性能模型的COD后评估-solarbe文库

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DNV GL © 09 December 2019 SAFER, SMARTER, GREENER©09 December 2019Pramod Krishnani, Senior Solar Consultant ENERGYPost-COD evaluation of utility scale operational PV plant performance model 113th PV Performance Modelling Monitoring Workshop DNV GL © 09 December 20192 AgendaAbout DNV GL01 Risk/Return profiles throughout a solar PV project Illustration experienced example02 Continual PV system improvement cycle, operations review scope and goal03 Case studies04 Key takeaways05 DNV GL © 09 December 2019 A global quality assurance and risk management company 3 DNV GL © 09 December 2019 Global reach – local competence 150years 100countries 100,000customers12,000employees 5 RDof annual revenueMARITIME DIGITAL SOLUTIONSBUSINESS ASSURANCEENERGYOIL GAS Technology ResearchGlobal Shared Services4 DNV GL © 09 December 2019 Broad and deep expertise in solar projects 5 FEASIBILITY ENGINEERING DEVELOPMENT CONSTRUCTION COMMISSIONING OPERATION❯ Feasibility studies❯ Utility grid integration❯ Environmental permitting❯ Component technology reviews❯ Component qualification testing❯ Type and component certification of PV inverters ❯ Due diligence / Independent engineering ❯ Owner’s engineering❯ Energy assessment❯ Pre-construction engineering❯ Interconnection support❯ Project certification ❯ Due diligence/ Independent engineering❯ Owner s engineering❯ Construction oversight❯ System testing and inspection❯ Project certification and grid code compliance ❯ Declaration of conformity❯ Module batch testing ❯ Project certification ❯ Performance validation❯ Resource and energy forecasting❯ Existing asset consulting, inspections and decommissioning ❯ Refinancing and mergers and acquisitions advisory services❯ Forensic investigations❯ Monitoring, control and asset management❯ Project certification*Our testing, certification and advisory services are independent from each other DNV GL © 09 December 20196 AgendaAbout DNV GL01 Risk/Return profiles throughout a solar PV project Illustration experienced example02 Continual PV system improvement cycle, operations review scope and goal03 Case studies04 Key takeaways05 DNV GL © 09 December 2019 Illustration Risk/return profiles throughout a solar PV project 7CASH FLOW MEDIUM RISK LOW INVESTMENT HIGH RISK HIGH INVESTMENT MEDIUM RISK HIGH RETURN LOW RISK LOW RETURNBREAK EVEN ILLUSTRATIVE EXAMPLEIdentify need objectives/ risk Demand/load forecasts Funding/ economic modeling Engg. DesignProcurement Const. Comm. acceptance testingOps service delivery Deterioration and maintenance Condition performance monitoring Restore ReplacementAsset origination EPC-C Stage Asset CODFinancial closeSITE DISCOVERY MEDIUM RISK MEDIUM RETURN RISK EXPOSUREAsset operational stage Asset restore and/or replacement Pre- financial close DNV GL © 09 December 2019 8642 0 2 4 6 百万 Experienced example Risk/return profiles throughout a solar PV project 8 Asset COD Projects Starts earning revenuePre- Financial CloseFinancial Close Asset Origination EPC-C Asset Operational StageProject financial Break even baseline Pred. Break even Pt. Actual Break even Pt.Shift of Breakeven Point Longer time on ROI0204060801001201401600 10 20 30 40 50 60 70 80 MayJun Jul AugSepOctNovDecJanFebMarAprMayQtr2 Qtr3 Qtr4 Qtr1 Qtr22017 2018 千 Actuals - Rev/Exp Budget - Rev/Exp Actuals/Forecast Target Debt repayment Note The data for these graphs are pro-rated to maintain confidentiality of the source. Identify need objectives/ risk Demand/load forecasts Funding/ economic modeling Engg. DesignProcurement Const. Comm. acceptance testingOps service delivery Deterioration and maintenance Condition performance monitoring Restore ReplacementAsset origination EPC-C Stage Asset CODFinancial closePre- financial closeSITE DISCOVERY Asset operational stage Asset restore and/or replacement DNV GL © 09 December 20199 AgendaAbout DNV GL01 Risk/return profiles throughout a solar PV project Illustration experienced example02 Continual PV system improvement cycle, operations review scope and goal03 Case studies04 Key takeaways05 DNV GL © 09 December 2019 Continual PV system improvement cycle§ In order to keep the trajectory of the actuals in line with the forecasted for operating and future plants, the following the necessary elements needs to be considered – PV reliability RAMS, repowering, data, OM, storage site issue identification and restoration plans– Data sharing agreement processes and procedures – PV systems engineering process, procedure structures agreements– Site specific specification prior to EPC bidding– Appropriately trained and educated EPC team– Third party commissioning based on defined requisite standards– Third party benchmarking to determine modelling accuracy– Clearly defined systems process to feed back information improving systems 10 Necessary elements forContinual PV system improvement cyclePV Reliability RAMS, Repowering, Data, OM, Storage Site issue identification and RestorationPlans Data Sharing Agreement Processes and ProceduresPV Systems Engineering Process, Procedure Structures AgreementsSite Specific Specification Prior to EPC BiddingAppropriately Trained and Educated EPC TeamThird Party Commissioning Based on Defined Requisite Standards Third Party BenchmarkingTo Determine Modeling AccuracyClearly Defined Systems Process to Feed Back Information Improving Systems Reference PAM System Health, Condition, Cost, Performance LCOE Alignment ModelBy John R. Balfour, MEP, PhD from PAM PV “System Deliver as Energy Infrastructure” December 2019 General Site Design Engr. Stage Prior to EPC StageEPC StageConstr. and Comm. StageOps Stage Ops Stage Ops Stage DNV GL © 09 December 2019 Continual PV system improvement cycle 11 Necessary elements forContinual PV system improvement cyclePV Reliability RAMS, Repowering, Data, OM, Storage Site issue identification and RestorationPlans Data Sharing Agreement Processes and ProceduresPV Systems Engineering Process, Procedure Structures AgreementsSite Specific Specification Prior to EPC BiddingAppropriately Trained and Educated EPC TeamThird Party Commissioning Based on Defined Requisite Standards Third Party BenchmarkingTo Determine Modeling AccuracyClearly Defined Systems Process to Feed Back Information Improving Systems Reference PAM System Health, Condition, Cost, Performance LCOE Alignment ModelBy John R. Balfour, MEP, PhD from PAM PV “System Deliver as Energy Infrastructure” December 2019 § In order to keep the trajectory of the actuals in line with the forecasted for operating and future plants, the following the necessary elements needs to be considered – PV reliability RAMS, repowering, data, OM, storage site issue identification and restoration plans– Data sharing agreement processes and procedures– PV systems engineering process, procedure structures agreements – Site specific specification prior to EPC bidding– Appropriately trained and educated EPC team– Third party commissioning based on defined requisite standards– Third party benchmarking to determine modelling accuracy– Clearly defined systems process to feed back information improving systems DNV GL © 09 December 2019 Levels of Ops review scope Asset Operational finance reviewDetailed asset operational review scopeHigh level operational review scope Post COD - Operational review scope to support continual PV plant improvement 12 Evaluate the data quality of the power plant technical and financial dataEvaluate the monitoring system equipment qualityEvaluate asset investment technical performanceEvaluate asset operational technical performance Evaluate Quantify OM activities1 Preventative maintenance 2 Corrective maintenance 3 Environment impact 4 Grid outage impact 5 Equipment performance impactReview operational related documentations1 OM contracts and agreements 2 Equipment Warranties 3 Production guaranteesEvaluate asset financials1 Preventative and Corrective maintenance cost 2Asset taxes 3 Asset insurance 4 Asset Land Lease Cost 5 PPA Revenue 6 Renewable energy credit revenues 1 2 3456 7 DNV GL © 09 December 2019 Levels of Ops review scope Asset Operational finance reviewDetailed asset operational review scopeHigh level operational review scope Goals of Post COD - Operational review 13 Evaluate the data quality of the power plant technical and financial dataEvaluate the monitoring system equipment qualityEvaluate asset investment technical performanceEvaluate asset operational technical performance Evaluate Quantify OM activities1 Preventative maintenance 2 Corrective maintenance 3 Environment impact 4 Grid outage impact 5 Equipment Performance impactReview operational related documentations1 OM contracts and agreements 2 Equipment Warranties 3 Production guaranteesEvaluate asset financials1 Preventative and Corrective maintenance cost 2Asset Taxes 3 Asset insurance 4 Asset Land Lease Cost 5 PPA Revenue 6 Renewable energy credit revenues 1 2 3456 7 § Goals of operational technical and financial review– Knowing your asset Evaluate and report if the asset is tracking to the projected ROI. – Model evaluation To check if the asset financial and technical models are representative of the asset’s true characteristics– Course Correction Quantify and classify factors driving variance to make necessary corrective actions to restore ROI. DNV GL © 09 December 201914 AgendaAbout DNV GL01 Risk/Return profiles throughout a solar PV project Illustration experienced example02 Continual PV system improvement cycle, operations review scope and goal03 Case studies04 Key takeaways05 DNV GL © 09 December 2019 Weather Avail. 0.6ControllableAvail.0.6Uncontrollable Avail.0.1Grid Avail. 0.2Plant Derate1.3Model Uncertainty/Unknown Issues5.3 Actual Meter Prod.92Ops assessment-sample power plantWeather Avail. Controllable Avail.Uncontrollable Avail.Grid Avail. Plant DerateModel Uncertainty/Unknown Issues Case study Sample of 1 asset – Less than 3 MWdc 15 86420 2 4 6 百万 Asset COD Projects Starts earning revenuePre- Financial CloseFinancial CloseAsset Origination Project Financial Break Even Baseline Pred. Break even Pt. Actual Break even Pt.Shift of Breakeven Point Longer time on ROI 020406080100120 140160 0 10 20 30 40 50 60 70 80 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr MayQtr2 Qtr3 Qtr4 Qtr1 Qtr22017 2018 千 Actuals - Rev/Exp Budget - Rev/Exp Actuals/Forecast Target Debt repayment Financial Upside§ Evaluation results– After adding all Avail. and de-rate losses to the actual prod., an amalgamation of model uncertainty and unidentified losses were identified. – Further evaluation pointed towards site design issue related to incorrect inverter model selection. EPC-C Asset Operational Stage Note The data for these graphs are pro-rated to maintain confidentiality of the source. DNV GL © 09 December 2019 Case study DNV GL modelling comparison w/ Competitor 16 97.3 93.71.1 3.41.7 1.90.1 0.993949596979899 100 DNV GL CompetitorLoss compared to Predicted Energy Predicted Energy Weather Loss Avail. Loss Model Accuracy unknown issues Model accuracy unknown issues Actual Energy DNV GL © 09 December 201917 AgendaAbout DNV GL01 Risk/Return profiles throughout a solar PV project Illustration experienced example02 Continual PV system improvement cycle, operations review scope and goal03 Case studies04 Key takeaways05 DNV GL © 09 December 2019 Key takeaways§ DNV GL advises to practice accurate modelling assumptions i.e. not too aggressive or not too conservative.§ Constant monitoring of operational assets to support close feedback loop for improvements of models on existing sites as well as pre-COD assets§ Benchmarking of operating assets by asset region and technology§ Advisable to take advantage of third party expertise to support the benchmarking 18 DNV GL © 09 December 2019SAFER, SMARTER, GREENERwww.dnvgl.com The trademarks DNV GL® , DNV® , the Horizon Graphic and Det Norske Veritas® are the properties of companies in the Det Norske Veritas group. All rights reserved. Thank You 19 Pramod Krishnani, Senior Solar ConsultantPramod.Krishnanidnvgl.com DNV GL © 09 December 2019SAFER, SMARTER, GREENERwww.dnvgl.com The trademarks DNV GL® , DNV® , the Horizon Graphic and Det Norske Veritas® are the properties of companies in the Det Norske Veritas group. All rights reserved. Supporting Slides 20 DNV GL © 09 December 2019 Case study DNV GL modelling comparison w/ Competitor§ DNV GL sample portfolio details – Size DC 36 MWdc – of assets 26 Assets§ Competitor sample portfolio size for evaluation– Size DC 38 MWdc – of assets 29 Assets§ Results – DNV GL model uncertainty is within /- 0.5 compared to competitor’s uncertainty of /- 1 21 InputAsset predictions energy models, energy, insolation, module tempInputAsset raw meteorological, plant energy and equipment data DNV GL processing toolAsset analysed in detailed OutputPredicted energy incl. Avail. Model assumptionOutputExpected weather corrected energy and site weather and system avail. Losses Comparison toolPrediction energy compared vs weather corrected expected energy corrected for equipment avail. LossMETHODOLOGY OF EVALUATION 97.3 93.71.1 3.41.7 1.90.1 0.993949596979899100 DNV GL CompetitorLoss compared to Predicted Energy DNV GL vs CompetitorPredicted Energy Weather Loss Avail. Loss Model Accuracy unknown issuesModel Accuracy unknown issuesActual Energy