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Uncertainties in Yield Assessments and PV LCOE: Report IEA-PVPS T13-18:2020 November 2020
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-3168-1569
Institute for Renewable Energy, Eurac Research, Bolzano, Italy.
Institute for Renewable Energy, Eurac Research, Bolzano, Italy.
3E, Brussels, Belgium.
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2020 (English)Report (Other academic)
Abstract [en]

Yield assessments (YA) and Long-Term Yield Predictions (LTYP) are a prerequisite for business decisions on long term investments into photovoltaic (PV) power plants. Together with cost data (CAPEX, OPEX and discount rate), the output of a YA and LTYP (utilisation rate, performance loss rate and lifetime) provides to the financial investors the parameters needed for the calculation of the Levelised Cost of Electricity (LCOE) and to assess the cash flow model of an investment with relative Internal Rate of Return (IRR) and Net Present Value (NPV).

YA and LTYP outputs should be provided with a related exceedance probability. This gives the right tool to stakeholders involved in PV projects to take the best decision in terms of riskaversion. A reduction in the uncertainty of the energy yield can lead to higher values for a given exceedance probability and hence a stronger business case. Various efforts in the literature show the importance of having a common framework that can assess the impact of technical risks on the economic performance of a PV project.

The most important parameter influencing the energy yield assessment is the site-specific insolation. Several aspects need to be considered: reliability of the database, interannual variability, long term trends.

Site adaptation techniques combine short-term measured data and long-term satellite estimates. Short periods of measured data but with site-specific seasonal and diurnal characteristics are combined with satellite-derived data having a long period of record with not necessarily site-specific characteristics. Upon completion of the measurement campaign, which is typically around one-year, different methodologies can be applied between the measured data at the target site, spanning a relatively short period, and the satellite data, spanning a much longer period. The complete record of satellite data is then used in this relationship to predict the long-term solar resource at the target site. Assuming a strong correlation, the strengths of both data sets are captured and the uncertainty in the long-term estimate can be reduced.  

In Müller et al [1] an analysis on long-term trends for measured in-plane irradiance, Performance Ratio and energy yield for 44 rooftop installations in Germany was performed showing an average increase of in-plane irradiance of 1.1 %/year or about 11 %/decade over the period 2008 to 2018 for these systems. The increase in irradiance was especially higher than the observed Performance Loss Rate so that the energy yields of the systems analysed increased over the years with an average trend of 0.3 %/year.  

The typical output of Yield Assessments should report the contribution to each derating factor, starting from the Global Horizontal Irradiation to the energy injected in the grid. The starting point of PR = 100 is considered after applying the horizon shading as this become the annual insolation seen by the PV modules. The following table shows a best practice in providing an overview of gains/losses along each modelling step and the related uncertainty. The uncertainty related to each modelling step can be provided already referred to the irradiation/yield value or to the parameter that is modelled. The value in the table for the specific yield (including its uncertainty) is to be understood as an average value over the entire operating period. The possible deviations between the yields for individual recorded years and the specific yield calculated can be assessed by including interannual variability. 

For example, for temperature-dependent losses, the value of uncertainty could be referred to the temperature variability of the profile used in the assessment or to the temperature model used in the assessment. The ambient temperature variability and the various temperature models will lead to a different contribution in terms of yield loss and in terms of uncertainty.

An emerging challenge in YAs is also due to the deployment of novel technologies (e.g. bifacial PV modules) with a contribution in terms of uncertainty that needs to be properly assessed.

Building upon the knowledge available in the literature and the previous IEA PVPS Task 13 report [2], in this report we have moved forward from the uncertainty framework in yield assessment to two real implementations of it and the impact that uncertainties can have on lifetime yield predictions, on the LCOE and on the cash-flow.

One of the most relevant question that we have tried to answer is also the following: How reliable are YA’s?

This is an apparently simple question; however, the answer is not equally simple. Typically, investors require one YA. In some cases, more YAs might be requested if results are unclear. The various YAs can be averaged to assign a purchase value to a given project. In any case the question remains unanswered: why different assessors obtain different answers? Is one YA more reliable than others? 

Place, publisher, year, edition, pages
2020. , p. 72
Keywords [en]
Photovoltaics, yield, LCOE
Keywords [sv]
Soleller, utbyte, ekonomi
National Category
Physical Sciences
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-53310ISBN: 978-3-907281-06-2 (print)OAI: oai:DiVA.org:mdh-53310DiVA, id: diva2:1525064
Projects
IEA PVPS Task 13 Performance, Operation and Reliability of Photovoltaic Systems
Funder
Swedish Energy Agency, 2018-13839Available from: 2021-02-02 Created: 2021-02-02 Last updated: 2021-07-07Bibliographically approved

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