Assessment of PV system performance with incomplete monitoring data

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1 Loughborough University Institutional Repository Assessent of PV syste perforance with incoplete onitoring data This ite was subitted to Loughborough University's Institutional Repository by the/an author. Citation: KOUBLI, E.... et al., 5. Assessent of PV syste perforance with incoplete onitoring data. IN: Proceedings of 5 3st European Photovoltaic Solar Energy Conference (EU-PVSEC), Haburg, 4-8 Septeber 5, pp Metadata Record: Version: Published Publisher: c WIP Wirtschaft und Infrastruktur Rights: This work is ade available according to the conditions of the Creative Coons Attribution-NonCoercial-NoDerivatives 4. International (CC BY-NC-ND 4.) licence. Full details of this licence are available at: Please cite the published version.

2 3st European Photovoltaic Solar Energy Conference and Exhibition ASSESSMENT OF PV SYSTEM PERFORMANCE WITH INCOMPLETE MONITORING DATA E. Koubli *, D. Paler, P. Rowley, T. R. Betts R. Gottschalg Centre for Renewable Energy Systes Technology (CREST), School of Electronic, Electrical and Systes Engineering, Loughborough University, LE 3TU, United Kingdo *Tel.: , Eail: ABSTRACT: An analysis of PV syste perforance requires both eteorological and electrical data for the assessent period. However, actual in-field data acquisition is rarely %, often resulting in a significant aount of incoplete data sets for perforance assessent. These gaps, if not taken into account, ay add noticeable bias in yield assessent and thus estiations of the lacking data need to be ade. An approach of back-filling the required data is given and validated here. Three different categories of data loss are identified and case-specific ethods of synthesising issing data are developed. The integrity of the perforance assessent process is assessed. The three cases of data loss are defined as: issing eteorological data only, issing electrical onitoring data only and issing both electrical and eteorological data. Case-specific ethods are proposed and their perforance against easured data is evaluated statistically by eans of: root ean square error (RMSE), ean absolute error (MAE) and ean bias error (MBE). The inferred onthly perforance ratio on two of the selected cases showed accurate agreeent against easured data presenting significantly low MBE values, equal or less than -.. Keywords: PV syste, perforance, onitoring, issing data INTRODUCTION PV installations in the UK have increased significantly over the last 5 years, reaching a total capacity of 5 GW to date and heading well towards the national target of 5% of total energy production by renewable energy sources. This eans special iportance needs to be put on quality assurance and onitoring, to assure high PV energy yield and to avoid syste downtie and therefore energy loss []. Coplete onitoring data are required in order to evaluate the energy yield of a syste for a given tie period as well as financial perforance. Energy yield and perforance ratio are essential perforance etrics against which contractual guarantees are often verified. Thus, there is need for appropriate onitoring and various studies ai to outline guidelines on how proper onitoring and data analysis should be carried out [] [5]. These studies give recoendations on onitoring practices and analysis of the results, but do not sufficiently address back-filling requireents. Most large PV systes operate independent eteorological and electrical onitoring systes, often purchased through different providers. These systes occasionally ay lose data due to coinication issues or syste alfunctions. These periods of tie range fro inutes to days, weeks or even onths. Syste assessent requires an understanding of the resource (irradiance) and yield (energy yield). This can be obtained with confidence only if there are no significant gaps in the datasets. Coon back-filling strategies are, e.g. to use data fro previous day or sae day in the last year, but there are obvious shortcoings with these strategies. Using energy estiates fro previous dates is not ideal as weather variability is not taken into account. Due to variable weather as well as potential PV coponent degradation, it is not ideal to use data fro the past year. Using data fro co-located systes is another strategy. However, even identical systes often differ in perforance due to differnces in icro-cliates. CASES OF DATA LOSS This paper presents a strategy to back-fill data with good accuracy for both short and long ter periods, while taking into account weather as well as syste perforance variations. Three cases of data loss are identified. The first case is that of issing eteorological datasets, while electrical readings are available. This case is et in ost sall systes, either doestic or coercial, where installers reduce the cost by oitting the eteorological sensors. The second case is that of the electrical onitoring syste being interrupted. The third case is a failure of both onitoring sub-systes, which could be due to counication or hardware failures. The last two cases are often et in the ajority of solar fars. The proposed ethods are validated against real easureents using two case studies shown in Table I and the results are assessed by eans of i) root ean square error (RMSE), ii) ean absolute error (MAE) and iii) ean bias error (MBE). The RMSE describes the rando error in a distribution and tends to increase with outliers, MAE describes the absolute error and MBE indicates whether the odel overestiates or underestiates the easureent value and hence it is probably the ost iportant etric for PR assessent. For the first and third cases, a c-si odule (syste A) fro CREST PV facilities is used whereas for the second case, a case study fro the UK doestic field trials prograe (syste B) [6] is used. Table I: Table of PV systes Nae Syste A Syste B Module Type Crystalline silicon (c- Si) Polycrystalline silicon (pc-si) Noinal power (W) N o of Modules Data Origin 45. CREST outdoor onitoring syste UK Doestic Field Trials 594

3 In plane irradiation (kwh) Perforance Ratio Total horizontal irradiation (kwh) 3st European Photovoltaic Solar Energy Conference and Exhibition ESTIMATING IRRADIANCE Where irradiance data are lost, naely in cases, 3, syste perforance can be assessed by utilising synthetic cliatic data. The ethod to acquire horizontal irradiance and abient teperature is based on eteorological data collected fro ore than 8 ground eteorological stations on a national scale through MIDAS database [7]. Horizontal irradiance is interpolated to the nearest point of the PV syste and then it is translated into tilt irradiance using separation [8] and translation algoriths [9] given that the location, orientation and tilt of the syste are known. Both horizontal irradiation and abient teperature data are interpolated using Kriging spatial interpolation ethod, which is described in []. 3 ESTIMATING ELECTRICAL PERFORMANCE Module teperature is calculated fro in-plane irradiance and abient teperature using a siple linear theral odel, such as the one presented by Ross []: T = T a + k G () Where T, Ta and G, are odule teperature ( o C), abient teperature ( o C), and in-plane irradiance (W/ ) respectively. k is known as Ross coefficient and it takes different values according to the ounting configuration of the odule. In this work k was obtained by linear fitting of (T-Ta) against G hourly data. The criteria for the electrical odel were i) the available input data and ii) its training capability. The chosen electrical odel is based on siplified King odel for the axiu power point [] and the forula is given in the following for [3]: P' (G', T') G' ( k ln(g' ) k ln(g' ) k T ' k T 3 4 ' ln(g' ) k T 5 ' ln(g' ) k T ' ) 6 Where P =P/PSTC, G =G/GSTC and T =T-TSTC (STC = Standard Testing Conditions with GSTC =W/, T=5 o C) and P is axiu power (W). The odel yields a 3D power surface as the one depicted in Figure. For the training process hourly data of P,G,T around the issing period (i.e. the validation set) are fed into Eq.() and the coefficients (k-k6) are deterined via a curve fitting algorith. () Data quality checks were applied prior to feeding the odel, as invalid input data could corrupt the training process. Here, an optiisation algorith was used to detect the best training set for a period of one issing onth, which was used as the validation set. It was found that the best training set is approxiately 5 and 5 days before and after the issing period respectively, which results in 4 days of hourly data, in total. Syste perforance is, however, installation specific. Thus the agreeent is iproved by ongoing training to keep perforance descriptors recent. Finally, perforance ratio is calculated using Eq. (3): PR = (E G STC )/(H P STC ) (3) Where E is the energy output (Wh) and H is the in-plane irradiation (Wh/ ). 4 VALIDATION RESULTS ON CASES,, 3 Validation results for the three cases of data loss are discussed in the following sections. 4. Missing irradiance and teperature Syste A was used for case and onthly PR values were calculated for one year (4). Total horizontal and in-plane irradiation and PR are copared in Figure (a) and (b). The statistical results are given in Table II Measured Modelled Month of the year (a) Measured Modelled actual PR odelled PR Month of the year (b).6.4. Figure : Fitting curve for the optiu training set for syste A, using input data of axiu power output, odule teperature and in-plane irradiance. Figure : a) Coparison of interpolated and easured total horizontal irradiation for a year (4) b) Coparison of plane of array irradiation and perforance ratio for easured and interpolated data throughout the year. 595

4 Energy (kwh) Maxiu energy output (Wh) Perforance Ratio 3st European Photovoltaic Solar Energy Conference and Exhibition Table II: Statistical results for onthly and annual analysis of easured and interpolated cliatic data. Total Horizontal In-plane irradiation (kwh) irradiation (kwh) Monthly Annual Monthly Annual RMSE MAE MBE Global horizontal irradiance is estiated with a very good agreeent to easured data with a MBE of approxiately. (kwh) for the whole year. PR shows an average overestiation of about % throughout the year as in-plane irradiation is slightly underestiated with MBE of about -8. (kwh), which is priarily due to the odels involved in the process of separation and translation of horizontal irradiance to plane of the array [4]. 4. Missing electrical data In case, Syste B was considered and it has been assued that a whole onth of energy readings is issing, which is a very realistic case. Eq.() is used to calculate the energy output while eteorological data are available for this period. Data fro dates around the issing period i.e. the past and later days around this gap have been used to extract the coefficients for the electrical odel, using the ethod described in Section 3. The results are shown in Figure for daily analysis including the onthly result. This ethod gives a very good agreeent with easureents with daily and onthly RMSE of about.6 and. kwh respectively. odelled energy output and perforance ratio are shown in Figure Measured energy output Modelled energy output Actual PR Modelled PR Day of the onth Figure 4: Coparison of odelled and easured energy output and PR for the issing onth The following Tables III-V describe the statistical results for irradiation, abient and odule teperature as well as energy output and perforance ratio. Table III: Statistical results for abient and odule teperature coparisons Abient Module teperature (K) teperature (K) Daily Monthly Daily Monthly RMSE MAE MBE Real energy output Modelled energy output Table IV: Statistical results for in-plane irradiation Irradiation (kwh) Daily Monthly RMSE MAE MBE Table V: Statistical results for energy output and PR Days Figure 3: Coparison of odelled and easured energy output and PR for the issing onth (March 3). The last colun represents the onthly energy output. Moreover, using the replenished period to acquire the onthly PR, it was found that this can be calculated with a very sall onthly RMSE of about. (and MBE of about -.). This points out the efficiency of the proposed ethod to acquire the issing perforance data when eteorological data are available fro the onitoring syste. 4.3 Missing eteorological and electrical data Case 3 is a cobination of the two cases described above. It has been described in previous work [5], here, perforance ratio is also included in the analysis. A issing onth of eteorological and electrical data (April 4) is inferred. The coparison of real and Energy output (kwh) PR Daily Monthly Daily Monthly RMSE MAE MBE The results for abient teperature show that it can be interpolated to the location of interest with a very sall bias and RMSE. This is expected as teperature is teporally and spatially ore hoogeneous than irradiance considering the sae distance and the UK cliate. This bias increases for odule teperature as it propagates fro both in-plane irradiation (inherent underestiation) and abient teperature, but the effect is generally very sall (a couple of degrees less than the easured value). The statistical results for energy output are affected by in-plane irradiation and odule 596

5 3st European Photovoltaic Solar Energy Conference and Exhibition teperature resulting in slight underestiation of the final result with an MBE of about. However, the result shows very good agreeent for the PR. Daily and onthly statistical etrics appear to be very sall (MBE is -. and -. respectively). This is due to the fact that the inherent underestiation of in- plane irradiation in energy output is diinished (see Eq.(3)) in the PR. 5 CONCLUSIONS This paper exained typical data loss and identified three distinct cases requiring different back-filling solutions. The results were validated against easured data fro two PV systes. An interpolation technique which exploits cliatic data fro ground based stations was used to acquire the issing eteorological data at a given syste location and an epirical odel was used to calculate the energy output using as input data in-plane irradiation and odule teperature. Global horizontal irradiance showed very good agreeent with easured data with a MBE of. kwh for the annual result. For the first case of data loss, naely issing irradiance data, PR was overestiated by about %. Future work will focus on reducing this bias ainly resulting fro the underestiation of in-plane irradiation. For the second case a issing onth of electrical data was considered. The analysis gave an average onthly MBE in energy output of about.4% and perforance ratio of approxiately. for a whole issing onth, which points out the efficiency of the applied back-filling ethod. For the third case, a issing onth of both eteorological and electrical data was considered. The results were satisfactory for the back-filled onthly energy output and for perforance ratio with the latter presenting a significantly sall MBE of about -., due to the eliination of the inherent underestiation which derives fro in-plane irradiation. This is an iportant outcoe given that PR is a very significant perforance etric and it was estiated for a period of coplete lack of onitored data. 6 REFERENCES [] M. Perdue and R. Gottschalg, Energy yields of sall grid connected photovoltaic syste: effects of coponent reliability and aintenance, IET Renew. Power Gener., vol. 9, no. 5, pp , Jul. 5. [] IEC standard 674, Photovoltaic syste perforance onitoring Guidelines for easureent, data exchange and analysis, 998. [3] G. Barbose, R. Wiser, and M. Bolinger, Designing PV incentive progras to proote perforance: A review of current practice in the US, Renew. Sustain. Energy Rev., vol., no. 4, pp , May 8. [4] A. Woyte, M. Richter, D. Moser, M. Green, S. Mau, and H. G. Beyer, Analytical Monitoring of Grid-connected Photovoltaic Systes, IEA- PVPS T3-3:4, 4. [5] G. Blaesser and D. Munro, Guidelines for the Assessent of Photovoltaic Plants Docuent B: Analysis and Presentation of Monitoring Data, EUR 6339 EN, 995. [6] PVDFT Final Technical Report. [Online]. Available: _Final_Techn_Report.pdf. [Accessed: -May- 5]. [7] U. M. Office, Met Office Integrated Data Archive Syste (Midas) Land and Marine Surface Stations Data (853-Current), NCAS British Atospheric Data Centre,. [Online]. Available: 5c9cc9e4785a334bd. [Accessed: 4-Apr- 5]. [8] B. Ridley, J. Boland, and P. Lauret, Modelling of diffuse solar fraction with ultiple predictors, Renew. Energy, vol. 35, no., pp , Feb.. [9] D. Reindl, W. Beckan, and J. Duffie, Evaluation of hourly tilted surface radiation odels, Sol. Energy, vol. 45, no., pp. 9 7, 99. [] N. Hofstra, M. Haylock, M. New, P. Jones, and C. Frei, Coparison of six ethods for the interpolation of daily, European cliate data, J. Geophys. Res., vol. 3, no. D, Nov. 8. [] P. M. Segado, J. Carretero, and M. Sidrach-de- Cardona, Models to predict the operating teperature of different photovoltaic odules in outdoor conditions, Prog. Photovoltaics Res. Appl., 4. [] J. A. Kratochvil, W. E. Boyson, and D. L. King, Photovoltaic array perforance odel, SAND4-3535, Sandia National Laboratories (SNL), Aug. 4. [3] T. Huld, G. Friesen, A. Skoczek, R. P. Kenny, T. Saple, M. Field, and E. D. Dunlop, A powerrating odel for crystalline silicon PV odules, Sol. Energy Mater. Sol. Cells, vol. 95, no., pp ,. [4] M. Lave, W. Hayes, A. Pohl, and C. W. Hansen, Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States, IEEE J. Photovoltaics, vol. 5, no., pp , 5. [5] E. Koubli, D. Paler, P. Rowley, and R. Gottschalg, Replenishing Deficient Datasets in PV Syste Monitoring, in th Photovoltaic Science Application and Technology (PVSAT- ), 5, pp