Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India

Energy Conversion and Management 100 (2015) 429–439

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Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India Sivasankari Sundaram 1, Jakka Sarat Chandra Babu ⇑ Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, NH67, Tanjore High Road, Thuvakudi, Tiruchirappalli, Tamil Nadu, India

a r t i c l e

i n f o

Article history: Received 8 January 2015 Accepted 25 April 2015 Available online 26 May 2015 Keywords: Grid connected photovoltaic system Performance ratio System efficiency Dependency plot RETscreen Exergy

a b s t r a c t The main objective of this paper is to present the validated annual performance analysis with the monitored results from a 5 MWp grid connected photovoltaic plant located in India at Sivagangai district in Tamilnadu. The total annual energy generated was 8495296.4 kW h which averages around 707941.4 kW h/month. In addition to the above, real time performance of the plant is validated through system software called RETscreen plus which employs regression analysis for validation. The measured annual average energy generated by the 5 MWp system is 24116.61 kW h/day which is appropriately close to the predicted annual average which was found to be 24055.25 kW h/day by RETscreen. The predicted responses are further justified by the value of statistical indicators such as mean bias error, root mean square error and mean percentage error. The annual average daily array yield, corrected reference yield, final yield, module efficiency, inverter efficiency and system efficiency were found to be 5.46 h/day, 5.128 h/day 4.810 h/day, 6.08%, 88.20% and 5.08% respectively. The overall absolute average daily capture loss and system loss of the particular system under study is 0.384 h/day and 0.65 h/day respectively. A comparison is also made between the performance indices of solar photovoltaic system situated at other locations from the literature’s published. Furthermore the effect of input factors over the output of the system is emphasized by regression coefficients obtained through regression analysis. In-depth analysis dealing with energy and exergy of the system are also included to strengthen the study. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The trend for India’s energy consumption out of conventional sources is found to increase with increased industrialization and civilization aspects of the society (see Fig. 1). The total consumption of energy from conventional sources in India increased from 46,958 petajoules during 2011–2012 to 50,741 petajoules during 2012–2013, showing an increase of 8.06%. The per capita energy consumption increased from 3497.59 kW h in 2005–2006 to 6748.61 kW h in 2012–2013 with a cumulative annual growth rate of 8.56% and an annual increase of 8.76%. The estimated electricity consumption for various sectors such as domestic, commercial, agriculture, industry, traction and railways increased from 411,887e6 kW h during 2005–2006 to 852,900e6 kW h during 2012–2013, showing a cumulative annual growth rate of 9.53%. The percentage increase in electricity consumption for an annual period of 2012–2013 is 8.62% [1]. Thus ⇑ Corresponding author. E-mail addresses: [email protected] (S. Sundaram), [email protected] (J.S.C. Babu). 1 Tel.: +91 9444970220. http://dx.doi.org/10.1016/j.enconman.2015.04.069 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.

the increasing demand and scarcity in conventional sources has triggered the scientist to pave way for the development of research in the field of renewable energy sources especially solar energy [2]. India is a tropical country located along the equatorial belt of the earth with latitude lying between 7° and 37° which makes it to receive enormous radiant power. There are about 300 clear sunny days in most parts of the country per year with an average global insolation of 4–7 kW h/m2/day [3]. Due to the vast solar potential, the nation had eight initiatives launched under National action plan on climate change addressing the remedies for balancing the energy generation and demand. One of such initiative was Jawaharlal Nehru National Solar mission (JNNSM) set from January 2010 to deploy 20,000 MW of grid connected Solar power by 2020 [4]. India has installed solar photovoltaic (PV) projects of capacity amounting to 2208 MW out of which Tamilnadu contributes 31 MW. The International Energy Agency (IEA), under photovoltaic power systems programme (PVPS) have framed a series of 13 tasks [5] for the outreach of operation, performance and monitoring solar photovoltaic plants under the platform of research and development. As India, not being a member of an International Energy agency, the studies and discussions on solar photovoltaic power

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energy and energy efficient technologies, RETscreen for performance validation. RETscreen developed by the Government of Canada is used to validate the ongoing energy performance of energy systems. As the concentration on solar power projects brings out considerable support by the Government, a 5 MWp solar plant was set up at Sivagangai by Moserbaer Corporation with the aid of Government of Tamilnadu in 2010 in order to export the power generated by the solar PV panels to the southern grid. 2. System, measurements and application

Fig. 1. Energy consumption from conventional sources in India.

plants as per IEC 61724 standard is not available [6] and hence it becomes imperative to document the performance of PV systems for knowing its efficient operation. Also successful integration of PV system includes knowledge on their operational performance under varying climatic condition [7]. Monitoring input parameters for performance study yields several advantages such as optimal sizing and survival of the plant. Monitoring global horizontal irradiance (GHI) and ambient temperature helps us to predict the energy output making the idea of installation over the locality stronger. Monitoring module temperature helps us to quantify the thermal losses of the PV array system which thereby affects the energy and the exergic efficiency deliberately affecting the generated output power. Moreover the study on performance of the plant helps in identifying the operational uncertainty (weather risk involving variation in weather and system risk which involves aspects of inverter conversion and control) which helps in improving the yield. Also annual average analysis extends itself in developing a theoretical model for predicting the daily average power generated from the system which forms the hot topic of current research. Additionally the storage of electricity is not required in a grid tied plat as the electrical energy generated is fed to the grid with ease of installation, operation and maintenance with less payback period. Hence grid connected photovoltaic (PV) plant is focussed in this analysis. Long term annual average analysis results in the incorporation of seasonal variation such as rainfall, frost, snow and intermittent problems occurring with balance of system which is absent in short term performance analysis occurring for less than a year. The studies regarding the performance analysis of grid interactive photovoltaic plants in literatures deal with the evaluation of performance indicators. Some concrete literatures involving annual analysis are as follows. Drif [8] studied the performance characteristics of a 200 kWp grid connected PV system during 2000–2003. In 2011 Ayompe [9], conducted a performance study on 1.7 kWp roof top grid connected plant during the year 2008– 2009. He has deduced the performance indicators and compared the same with the reported results. Furthermore, Padmavathi and Arul Daniel [6] analyzed the performance of 3 MWp grid connected PV plant in Karnataka. Vikrant and Chandel [10], carried out a performance analysis of 190 kWp plant and validated its performance using PVsyst. In 2014 Trillo-Montero [11] developed a software employing visual basic express for evaluating the system losses and the performance of two different plants of installed capacity 217.6 kWp and 17.8 kWp. This study hence aims to analyze the performance of 5 MWp grid connected photovoltaic system in Sivagangai, India. Besides performance evaluation it differs from other literatures as cited above, by employing a simulation software called renewable

The inputs for the performance analysis is derived from the operational condition of the 5 MWp solar photovoltaic plant situated at about 8 km from Sivagangai in Rettaipalyam village with latitudinal and longitudinal ranges of 9.47°–9.48°N and 78.26°E– 78.27°E with an altitude of 102 m above the sea level. Sivagangai is located in the southeastern coast and is bounded by Madurai district on western side, Pudukkottai district on northern side and Ramanathapuram district on southern side. The annual average in-plane solar insolation for the 5 MWp site at Sivagangai is around 5.4149 kW h/m2/day which is measured by employing CMP11 pyranometer which has the sensitivity ranging from 7 to 14 lV/W/m2. The balance of system and its single line schematic is shown in Fig. 2. 2.1. PV array The modules installed were thin film modules manufactured by Moserbaer from an indigenous technology. A total of 61,020 PV modules of varying peak power capacity ranging from 76 Wp to 86 Wp were arranged amounting to peak power rating of 5 MWp. The power generation is segmented into 5 sections each contributing 1 MWp. Each MWp was formed from 226 array junction boxes (AJB), 33 sub main junction box (SMJB) and 8 module junction box (MJB). This arrangement repeats consecutively to form 5 MWp. There were 5 control rooms for monitoring the power delivered by the set of module junction boxes. The modules are predominantly south oriented, tiltled at an angle of 10°. The ambient and module temperature are monitored by Vaisala weather transmitter. 2.2. Power conditioning unit There were 10 numbers of inverter each of 500 kW capacity ensuring the conversion of 5 MWp DC to AC. Each power conditioning unit is supplied by 4 module junction boxes capable of generating power of 500 kWp. Thus a set of 2 power conditioning units form a segment of 1 MWp power generation. The energy generated by the inverter is measured by the energy guard sensor manufactured from Skytron and the data is transmitted through a RS485 data bus. 2.3. Power evacuation The inverter output is fed to 1250 kVA, 270/11 KV, 50 Hz transformer for stepping up the voltage to 11 kV. There were 5 transformers of 1250 kVA which was capable of handling the generated power. The total power output from the five transformers are fed to a main transformer of 6.3 MVA, 11 kV/110 kV, 50 Hz which further steps up of voltage for the Tamilnadu Electricity Board (TNEB) grid export. The power exported to the grid is monitored by a metering cubicle supplied by the TNEB. 2.4. Data monitoring system The solar photovoltaic plant runs for all working days except for exceptional technical faults interrupted the system. The data

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acquisition system was designed as per IEC61724. The parameters such as global horizontal irradiance (GHI), Direct normal irradiance (DNI), ambient and module temperature (Ta, Tm), wind speed (Vs), DC and AC energy generated (Edc and Eac) are measured instantaneously for every 5 min duration in a day. The parameters of wind speed and relative humidity are measured employing skyCONNI universal weather sensor manufactured by Skytron and are transmitted through METEON dataloggers through Canbus communication to the server which updates the information graphically. The ambient and module temperature are transmitted by RS485 USB cable which is connected to the server equipped with the Vaisala configuration software for storing the received data. The average of the observed instantaneous readings for a day is calculated and continued for the rest of the working days yielding the monthly measured parameter values for all working days. The server thus works on the principle of supervisory control and data acquisition (SCADA) for assessment of the monitored data. 3. Monitored input results The monthly average daily variation of in-plane solar insolation and wind speed are shown in Fig. 3 below. The monthly average daily solar insolation varies from a minimum of

4.38 kW h/m2/day in December to 5.98 kW h/m2/day in September and the average wind speed varies from a minimum of 1.5 m/s to 4.2 m/s. The monthly average daily measured Ta and Tm for the monitored period vary as shown in Fig. 4. The module temperature is always found higher than the ambient due to the generation of thermal losses which occurs evidently on power generation. The ambient temperature found to vary from 25.7 °C to 35.4 °C per day. Better system performance is provided with higher wind speeds and lower ambient temperature. Higher the wind speed, lower is the module temperature which further reduces the heat losses providing betterment in the system performance. As the wind speed is found higher which is 4.1 m/s for the month of December the corresponding module temperature is lower for the same measuring to 31 °C. The ambient temperature and module temperature are seen to increase as level of radiation increases. The value of ambient and module temperature for the irradiance of 359.1 W/m2 correspond to 26.9 °C and 31.4 °C respectively. But the value of the same for the irradiance of 474 W/m2 correspond to 29.6 °C and 36.9 °C respectively. The maximum temperature difference between the PV module and the ambient is 9.3 °C which is less by 17 °C as in Ayompe, Duffy (2011) [9].

226 AJB

226 AJB

33 SMJB

4 MJB

33 SMJB

4 MJB

4 MJB

DC distribution board

DC distribution board (1000V)

1000V

DC

4 MJB

DC

DC AC

DC AC

AC

Transformer -5 /1250KVA (270V/11KV)

Transformer -1/ 1250KVA (270V/11KV)

Transformer -6 /6.3MVA ( 11KV/110KV)

TNEB Metering / Substation

TNEB Grid

Fig. 2. Single line diagram for the 5 MWp grid connected system.

AC

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Edc;d ¼

t¼T Xrp

V dc  Idc  T r ;

Edc;m ¼

t¼1

N X Edc;d

ð1Þ

d¼1

Tr is the recording time interval and Trp is the reporting period and N is the number of operating days of plant in a month. 4.3. Energy output or Energy fed to utility grid (Eac) The energy generated by the PV system is the measure of same across the inverter output terminals for every 5-min duration by the data logger. The total daily monitored value of AC power output and the monthly AC energy generated is given by

Eac;d ¼

t¼T Xrp

V ac  Iac  T r ;

Eac;m ¼

t¼1

N X Eac;d

ð2Þ

d¼1

where Pac = Vac * Iac is the AC power recorded as specified above. 4.4. Array yield (Ya) Fig. 3. Variation of monthly average daily solar insolation and wind speed for the monitored period.

Array yield is time taken by the PV to operate at nominal power generating Edc. Hence it is the ratio of the daily or monthly average DC energy generated by the PV system to the rated PV power and is given by Eq. (3). The daily array yield are calculated by employing Eq. (3) as stated below

Y aðdailyÞ ¼ Edc;d =PpvðratedÞ

½h=d

ð3Þ

The monthly average daily array yield (Ya,m) is given by

Y a;m ¼

  X N 1  Y aðdailyÞ N d¼1

ð4Þ

The monthly average array yield varies from a minimum of 4.194 h/day to 6.5647 h/day. Array yield represents the actual operation of the photovoltaic generator. 4.5. Final yield (Yf)

Fig. 4. Monthly average variation of module (Tm) and ambient temperature (Ta) for the annual period.

4. Performance analysis of grid connected PV systems

The term final yield represents the time taken by the PV to generate Eac with respect to its nominal power capacity. Hence it becomes the ratio of final output power generated (Eac) to the rated PV power as specified by the manufacturer at standard temperature conditions. As array yield, final yield can also be calculated daily and as a monthly average. It is dependent on the mounting structure and on location [11]. The daily final yield is given by

Y f ðdailyÞ ¼ Eac;d =PpvðratedÞ   X N 1 Y f ðdailyÞ  Y a;m ¼ N d¼1

ð5Þ ð6Þ

4.1. Performance indicators for grid connected PV system 4.6. Reference yield (Yr) Performance parameters for grid connected photovoltaic systems are established by International energy agency which are described in IEC standard 611724. As described in literatures [7–14] the most appropriate ones include energy output, array yield, final yield, reference yield, PV module efficiency, inverter efficiency, system efficiency, energy loss which comprises of array capture loss and system loss, performance ratio and capacity factor. These normalized indicators act as key comparators for comparing the performance of the existing grid connected PV systems. 4.2. Energy generated by the PV array system (Edc) The total daily monitored value of DC power output and the monthly DC energy generated is given by

Reference yield is the total in-plane solar insolation or global in plane horizontal insolation divided by the reference irradiance under standard temperature conditions which is 1 kW/m2.

P Y r;daily ¼ T r 

day Gi

GSTC

ð7Þ

The reference yield depends on the daily in-plane solar radiance. It is a measure of theoretical energy available at a given location [6]. Reference yield is termed as corrected reference yield when it is corrected by the effect of module and the ambient temperature. Corrected reference yield is given by Eq. (8)

Y cr ¼ Y r ð1  C t ðT m  T STC ÞÞ

ð8Þ

S. Sundaram, J.S.C. Babu / Energy Conversion and Management 100 (2015) 429–439

where Yr, Ct, Tm and TSTC represent the reference yield, temperature coefficient (% °C1), module and ambient temperature respectively.

433

where gdegr represents degradation efficiency; represents gtemp temperature efficiency; gsoil represents soiling efficiency and ginv represents inverter efficiency.

4.7. PV Module efficiency or Energy efficiency (gpv) 4.11. Capacity factor (CF) It represents the effective energy generated by the module with respect to the available radiation. The instantaneous PV array efficiency is given by

gpv ¼ P dc =Gi  Am

ð9Þ

where P(dc) is the DC power generated by the PV array system, Gi represents the global solar irradiation and Am represents the area of the PV module. The monthly average PV module efficiency is calculated as

gpv;m ¼ Edc;d =ðGi  Am Þ  100%

Capacity factor is a methodology for presenting the energy delivered by an electrical power distribution system. If the system delivers full rated power continuously its CF will be unity. It is defined as the ratio of actual annual energy output to the amount of energy the PV system can deliver at its rated capacity for 24 h per day for a year [7–14].

CF ¼

ð10Þ

Y FðannualÞ Eac;annual ¼ ð24  365Þ P ðpvÞrated8760

ð16Þ

The capacity factor of a system can also be calculated as

where Edc,d represents the total daily DC energy output. h day

of the peak sun

4.8. Inverter efficiency (ginv)

CF ¼

Inverter efficiency presumes to be the highest of module and system efficiency. The inverter efficiency appropriately called as conversion efficiency is given by the ratio of AC power generated by the inverter to the DC power generated by the PV array system. The instantaneous inverter efficiency is given by

The capacity factor varies in proportion to the variation in final yield.

ginv ¼ Pac =Pdc

The operation of photovoltaic cell to generate power involves heat transfer through convection and radiation modes resulting in losses which further reduces the system performance. The losses which are more pronounced are the array capture loss, system loss and cell temperature loss. The cell temperature loss is negligible in comparison with the other two and hence neglected in this study. As a thumb rule, the peak power of the PV module decreases by 0.3–0.4% for every 1 °C rise in temperature above standard temperature conditions [9]. Under actual operating condition the following losses add up to the system performance:

ð11Þ

The monthly inverter efficiency is calculated by equation as follows

ginv;m ¼

Eac;d  100% Edc;d

ð12Þ

where Eac,d represents the total daily AC energy output. 4.9. System efficiency (gsys) The photovoltaic system efficiency is associated with the balance of systems comprising the PV generator and the inverter module. The instantaneous system efficiency can be calculated by applying Eq. (13)

gsys ¼ gpv  ginv

ð13Þ

4.10. Performance ratio (PR) Performance ratio represents the effect of losses (which occur due to the effect of temperature, inverter, wiring loss, mismatch loss and loss across the bypass diodes) on the performance or the output delivered by the photovoltaic system and pictures the incomplete utilization of incoming solar radiation as PR is a normalization factor with respect to incident solar insolation [10]. It is the true efficiency of the system and measures the closeness to the ideal efficiency. It acts as a key comparator for comparing the grid connected PV system irrespective of the location, mounting structure and their power capacity [11]. It also describes the energy transformation in a grid connected PV system. It is thus defined as the ratio of final yield to the array yield as given by

Performance ratio; PR ¼ Y f =Y r :

ð17Þ

4.12. Energy loss

1. Thermal losses due to elevated cell temperatures. 2. Optical reflection loss due to non-perpendicular irradiance and losses due to low irradiance levels. 3. Effects of shadowing (partial shadow) due to the objects surrounding the mounted PV structure. 4. The effect of conversion efficiency due to the decrease in irradiance and temperature reducing the performance ratio. 5. Non-continuous inverter operation which includes tripping of inverter and its failure. 4.12.1. Array capture loss (Lc) Array capture loss is given by the difference between the array yield and final yield. The loss which occur due to the variation of actual irradiance from the reference or theoretical irradiance.

Lc ¼ Y r  Y a

ð18Þ

where Lc represents the array capture loss in (h/day). There also occur losses which are constituents of the capture loss termed as thermal capture loss and miscellaneous capture loss. Thermal capture losses (Lct) are associated with the thermal energy loss which are evident due to increase in the module temperature of above 25 °C. It is given by the difference between reference and the corrected reference yield.

ð14Þ

It can also be expressed as the product of efficiencies represented below

PR ¼ gdegr  gtemp  gsoil  ginv

24 h=day

ð15Þ

Lct ¼ Y r  Y cr

ð19Þ

Miscellaneous capture losses are embedded with multiple causes such as wiring, diode loss, shading effects, low irradiance, dust accumulation over the module, mismatch losses and losses

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due to maximum power point tracking [11]. This is given by the difference between corrected reference yield and array yield.

Lcm ¼ Y cr  Y a

ð20Þ

4.12.2. System loss (Ls) These loss in (h/day) occur due to discontinuous operation of inverter over the monitored period and is given by [11]

Ls ¼ Y a  Y f

ð21Þ

4.13. Energy and exergy analysis of 5 MWp grid connected PV system Exergy analysis acts as an efficient performance assessment tool for determining the true performance of the system close to its ideal working condition. It thus becomes essential to know the maximum amount of useful work for an operating system by applying the above rational analysis. Among existing energy conservatory techniques exergy analysis terms to be a simper, fruitful and enlightening technique for performance prediction. Exergy analysis plays a decisive role in analysis, improvement, design, assessment and optimization of the energy system [15]. The main key features are to provide a true measure of actual plant performance and to identify the types, causes and location of thermodynamic losses in the system. 4.13.1. Energy efficiency of solar photovoltaic systems Energy efficiency of solar photovoltaic systems is defined as the ratio of DC power output generated by the PV or the electrical power generated to the energy input [16], which is the product of the solar array area and the insolation incident on the PV surface.

Energy efficiency of a photovoltaic system ¼

P pv EðsolarÞ

Energy of the solar radiation is given by EðsolarÞ ¼ Apv  G

ð22Þ ð23Þ

where Apv and G represents the area of PV array and instantaneous solar radiation. 4.13.2. Exergy efficiency of solar photovoltaic systems Exergy efficiency depends on the second law of thermodynamics where it is the ratio of output exergy to input system exergy.

Exergy efficiency of photovoltaic system ¼

Exout Exin

5. Results and discussion Monitoring the monthly average daily energy generated is responsible for calculating the final yield which largely depends on the performance ratio of the plant. The monthly average daily generated energy varies from a minimum of 19413.1 kW h/ kWp/day (December) to 27482.8 kW h/kWp/day (September). The average energy generated collectively for summer(March–May), winter(January–February), monsoon(June–September) and post monsoon months (October–December) is 25483.9 kW h/day, 24927.95 kW h/day, 24765.5 kW h/day and 21097.8 kW h/day respectively. Thus the energy output vary in accordance to the seasonal weather change which further depends on the bright sun shine hours. The monthly average final yield varies from a minimum of 3.882 h/day in December to maximum of 5.496 h/day in September as seen in Fig. 5. This is due to the fact that the monthly average in-plane solar insolation is minimum for the month of December (4.261 kW h/m2/day) to maximum for September (5.53 kWh/m2/day). The reference yield depends on the daily in-plane solar radiance. The higher the in-plane solar insolation the higher is the reference yield. The corrected reference yield is slightly lower than the reference yield due to the effect of difference between the module and the ambient temperature with the difference varying from a minimum of 0.127 h/day to a maximum of 0.524 h/day. It can be seen from Fig. 5 that the reference yield and final yield show a similarity in the nature of variation among the annual monitored period. They are found directly proportional to the in-plane solar insolation. The monthly average PV module efficiency varies from a minimum of 5.45% to a maximum of 7.06%. The monthly average inverter and system efficiency varies from 79.2% in December to 97.8% in September and 4.688% to 5.282% respectively as described in Fig. 6. The seasonal average inverter efficiency for the season of summer, winter, monsoon and post-monsoon is found to be 92.10%, 90.78%, 86.36% and 84.90% corresponding to the average in-plane solar insolation which is 5.891 kW h/m2/day for summer, 5.68 kW h/m2/day for winter, 5.465 kW h/m2/day for monsoon and 4.688 kW h/m2/day for post-monsoon respectively. This is due to the dependency of inverter efficiency over in-plane insolation which is yet proved by RETscreen through regression analysis in Table 4. The performance

ð24Þ

ExðoutÞ ¼ Electrical exergyðExele Þ þ Thermal exergyðExth Þ þ destruction The input irradiation received by the solar cell are converted into electrical energy which constitutes the electrical exergy. During the process of power generation there occurs heat loss or thermal loss in the system which corresponds to the thermal destruction. Thus exergy analysis accounts for the thermal destruction by the system. Exergy input or the exergy of the solar radiation is given by [17,18]

    4 Ta GA 1  3 Ts    Ta ¼ P pv  1  ½ð5:7 þ 3:8  tÞ  A  ðT m  T a Þ Tm

ExðinÞ ¼

ð25Þ

ExðoutÞ

ð26Þ

where t represents wind speed (m/s).

Fig. 5. Monthly average final and reference yield over the monitored period.

S. Sundaram, J.S.C. Babu / Energy Conversion and Management 100 (2015) 429–439

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Fig. 8. Monthly average daily array loss over the monitored period.

Fig. 6. Monthly average daily efficiencies for the monitored period.

Fig. 9. Monthly average thermal exergy destruction and temperature difference evaluated for the 5 MWp system. Fig. 7. Monthly average performance ratio and capacity factor over the monitored period.

ratio is fairly maintained a constant measuring an annual average of 89.15% as seen in Fig. 7 varying with minimum of 85.46–92.34%. The performance ratio for Summer (March–May) and Winter (January–February) is 86.67% and 87.86% respectively with the performance of the plant slightly higher in winter than in summer. This performance variation is decisively due to module temperature. The capacity factor varies in accordance with the final yield which ultimately varies with the AC energy generated. The monthly average capacity factor is high for September amounting to 22.90% where the final yield is also high as 5.496 (h/day) and is less for December yielding 16.17% where the final yield is also consecutively low to 3.882 (h/day). Fig. 8 shows the distribution of array capture and system losses during the monitored operational performance of the plant. The monthly average absolute capture loss varies from a minimum of 0.032 (h/day) to 0.758 (h/day) and system loss varies from 0.123 (h/day) to 1.174 (h/day). The negative the capture loss the less is the time taken by the photovoltaic system to produce DC energy at its nominal power capacity. Thus summarizing the above performance values the plant generates good amount of energy with fairly good performance ratio of 89.15% and final yield of

4.8106 h/day. The annual average of PV module efficiency is around 6.08% which is low as maximum power point tracking function which aids in ensuring maximum power extraction is absent. The variation of thermal exergy of 5 MWp photovoltaic system over a monitored period depends on the difference between module and ambient temperature. It is represented in Fig. 9 as follows. As seen from Fig. 9 the thermal exergy loss or destruction is higher for the month of May 2011 and least for the month of November 2011 as the difference between Tm and Ta is higher for May which is 9.3°°C and least for November which is 4.5 °C. The thermal exergy loss and exergy efficiency are inversely proportional. There occurs higher exergy efficiency with least thermal loss. This infact is justified in this study as shown in Fig. 10 which shows the variation of monthly average energy and exergic efficiency over the monitored period. The exergy efficiency is higher for November amounting to 5.66% as thermal loss is found to be least to 1.15 MW and least for May amounting to 2.25% as the average thermal loss for May is found to be higher to 2.78 MW. The highest energy efficiency in Fig. 10 occurs for the month of November 2011 evaluating to 7.06% and the least for September 2011 evaluating to 5.45%. This is because the difference between the energy produced and the maximum available energy is least

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The above Tables 1 and 2 shows a comparison of kWp and MWp plants separately to have a clear picture on the performance of the present plant with that of others available. The comparative results show that the actual energy generated, final yield and the performance ratio is higher for the 5 MWp Sivagangai plant as represented. 7. Validation of the system performance employing RETscreen

Fig. 10. Monthly average exergy and energy efficiency for the monitored period.

for November which is 37.22 MW h and higher for September which amounts to 49.42 MW h. Also as inferred from the formula (23), the energy efficiency and solar insolation are inversely proportional. This also holds good for the justification that the solar insolation is higher for September and hence least energy efficiency. It is also inferred that the module performance or efficiency is good for winter season (October, November and December) though the insolation is less as the module temperature goes low in winter 33.3 °C and higher for Summer which is 43.76 °C. Increase in module temperature will further lead to increased thermal exergy destruction thereby reducing the performance of the module. The overall annual average energy and exergy efficiency counts to 3.54% and 6.08%. As thermal energy destruction is incorporated exergy efficiency is less than energy efficiency. Further, the energy efficiency is relatively equal to the PV module efficiency. As seen the reduced energy efficiency occurs due to the generation of high thermal loss which is dependent on the fabrication technology adopted for PV production and module temperature. The thermal loss can be reduced by reducing the band gap width between the p-layer and the n-layer during fabrication or by controlling the module temperature which is done by surface cooling.

RETscreen plus is employed for predicting the monthly average daily DC Energy generated by the PV array system, AC energy generated, final yield, array yield, PV module efficiency, inverter efficiency and PV module efficiency by regression analysis. It is a unique decision support tool for monitoring, analyzing and evaluating the operational performance of renewable energy systems [20]. The percentage difference between the average predicted and actual values of Edc and Eac are graphically represented in Fig. 11. The percentage difference between the DC energy values range from 3.15% to 2.59%. Similarly the lowest and highest percentage difference for AC energy values occur for the month of November 2011 and April 2012 levelling to 0.04% and 2.74% respectively. A graphical comparison of the evaluated %difference for the predicted and actual values of PV module efficiency, inverter efficiency and system efficiency are shown in Fig. 12 below. As predicted from the graph below there occur a very less difference between the actual and the predicted values of efficiencies justifying the actual performance of the 5 MWp solar photovoltaic plant considered. 7.1. Statistical comparative analysis for validation The accuracy or the closeness in the prediction of performance indicators with the actual values are evaluated by enumerating the statistical indicators such as mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE) and t-static value commonly available in literatures [21,22].

Table 2 Performance comparison of MW scale plants in India. Reference

Capacity (kWp)

Actual generation in MW h

PV type used

Final yield (h/day)

Monitoring period

[19]

5000

7473

4.24

352 days

[6] [19] Present study

3056 1000 5000

4204 1130 8495.29

Crystalline-Si, thin film and CPV Crystalline-Si Crystalline-Si Thin film

3.77 3.09 4.81

365 days 365 days Annual

6. Performance comparison A comparison of performance indicators for the present system with some of the existing grid connected PV plants reported in literatures are tabulated below.

Table 1 Performance comparison of the present system with certain reported grid connected PV system. Ref no.

Capacity (kWp)

Total annual Yf (h)

Total annual Yr (h)

g(pv) Annual daily (%)

ginv (%)

PV type

PR

Monitored period

[8] [8] [9] [10] [13] [14] Present study

67.84 Spain 67.84 Spain 1.72 Ireland 190 Khatar khatar 13 Northern Ireland Poland Sivagangai

1000.1 846.8 868.7 905.2 620.5 839.5 1752.3

1558.5 1463.6 1029.3 2.99 – – 1976.4

9.21 7.50 13.52 – 7.5–10 13.7 6.08

87.82 95.88 89.6 – 87 89.5 88.2

– – MC–A-Si PC MC-Si A-Si Thin Film amorphous Si

0.65 0.58 0.82 0.73 0.62 0.69 0.89

Three years Annually Annually Annual Three years Annually Annually

MC-Si: Monocrystalline silicon. A-Si: Amorphous silicon. PC-Si: Polycrystalline.

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  X k 1  ððpredÞ  ðmeasÞÞ N i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi   X k 1 ^ RMSE ¼  ððpredÞ  ðmeasÞÞ 2 i¼1 N  X   1 k ððpredÞ  ðmeasÞÞ  100% MPE ¼ N i¼1 ðmeasÞ !2 ðN  1ÞðMBEÞ2 t-static ¼ ðRMSEÞ2  ðMBEÞ2 MBE ¼

Fig. 11. Percentage difference between the actual and predicted Edc and Eac over the monitored period.

Fig. 12. Difference between the actual and predicted gpv, ginv and gsys over the monitored period.

Table 3 Statistical indicators for the predicted and actual values of performance indicators. Estimated indicator

MBE

RMSE

MPE (%)

Regression coefficient (R2)

Edc Eac

1.63 61.369 8.3e5 0.0175 7.4e17

4.571 3.552 0.0487 0.336 0.0456

0.0291 0.2425 0.00617 0.0747 0.0013

0.977 0.997 0.994 0.902 0.977

gpv ginv gsys

ð27Þ ð28Þ ð29Þ

ð30Þ

The mean bias error gives accurate information on the long term performance of the model. A low value of MBE is always desired for better accuracy of the proposed model. A positive value of MBE shows an over-estimate while a negative value an under-estimate by the model. The RMSE test gives the information on the short-term performance of the proposed model by allowing a term-by-term comparison of the actual deviation between the predicted and measured GHI values. Although MBE and RMSE provide a reasonable methodology to compare models, they do not objectively indicate the model’s statistical significance from the measured counterpart [21]. Thus this study also includes the evaluation of t-static which additionally indicates whether the model estimates are statistically significant at a particular confidence value. The smaller the static t-value the better is the performatric accuracy of the predicted model. In order to determine whether the model’s estimates are statistically significant, one has to estimate a critical t-value from standard statistical table that is ta/2 at a level of significance and (n  1) degrees of freedom which should be always greater than the calculated t-value [22]. A comparison of best regression fit values generated by RETsceen for validation the real time performance of 5 MWp plant is tabulated below in Table 3. The more the regression coefficient approaches unity the lesser is the difference between the predicted and actual estimates. RETscreen encompasses wide range of trendlines numbering 200 for choosing the best fit for a particular parameter. Furthermore from RETscreen plus, the dependence of an output performance indicator over the input factors are clearly inferred from the value of regression coefficient of best fit as seen in Table 4. For instance, Edc is considered to be dependent on the in-plane solar insolation, ambient temperature and the module temperature. The coefficient of best fit for the variation of DC energy generated with respect to ambient temperature is 0.9442 as seen in Fig. 13, where as the coefficient of best fit for the variation of the same with the solar irradiance and module temperature bears a coefficient of 0.7082 and 0.4791 which predicts that the change in Edc is mostly affected by the change in ambient temperature for the present system. Similarly the interactional effect of change in responses or output performance indicators Eac, g(pv), g(inv) and g(sys) with respect to inputs are performed and the best value of regression coefficient generated with the variation of the same reflects high level interaction. Thus the effect of change in Eac, g(pv), g(inv) and g(sys) with

Table 4 Dependency plot of input factors over the responses as generated by RETscreen. Estimated performance indicator

Plot of X1 Vs Y

Plot of X2 Vs Y

Possible plot of X3 Vs Y

R2 for X1 Vs Y

R2 for X2 Vs Y

R2 for X3 Vs Y

Influential factor

Edc Eac PV module efficiency Inverter efficiency System efficiency

Gi Gi Gi Gi Gi

Ta Ta Ta Ta Ta

Tm Tm Tm Tm Tm

0.7082 0.9827 0.9901 0.7841 0.9301

0.9442 0.8452 0.6617 0.7521 0.8649

0.4791 0.7840 0.7418 0.6641 0.7697

(Edc, Ta) (Eac, Gi) (gpv, Gi) (ginv, Gi) gsys is more dependent on Gi

Vs Vs Vs Vs Vs

Edc Eac

gpv ginv gsys

Vs Vs Vs Vs Vs

Edc Eac

gpv ginv gsys

Vs Vs Vs Vs Vs

Edc Eac

gpv ginv gsys

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Best eight order polynomial fit

Fig. 13. Plot of monthly average daily solar insolation Vs Edc as generated by RETscreen.

Table 5 Summary statistics of the above dependency plot stated in Table 4. Dependency plot

No. of observation

No. of iteration

Sum of residual

Average of residual

Standard error of estimate

R2

Durbin Watson static

(Edc, Ta) (Eac, Gi) (gpv, Gi) (ginv, Gi) (gsys, Gi)

12 12 12 12 12

251 9 17 12 13

0.4137 0.6211 3.529e6 1.894e6 2.2181e6

0.0345 0.0518 2.941e7 1.578e7 1.848e7

712.46 577.57 0.0011 0.0531 0.0007

0.9442 0.9827 0.9901 0.7841 0.9301

2.208 1.941 1.926 1.918 1.970

respect to in-plane solar insolation is more significant as the regression coefficients for the variation of the same with respect to in-plane solar insolation are high amounting to 0.9827, 0.9901, 0.7841 and 0.9301 respectively. Thus the significant interaction between the input factors and responses paves way for the justification of selected factors resulting in mathematical prediction model for responses. Table 5 presents the summary of the interaction plot statistics as computed by RETscreen. Durbin Watson test is used in identifying autocorrelation in regression models [23]. By default the value of the same varies between 0 and 4. A value 2 of Durbin Watson constant indicates the absence of autocorrelation in the samples. Value approaching toward 0 indicate positive autocorrelation and if the same approaches toward 4 a space for negative autocorrelation is left [19]. 8. Conclusion A 5 MWp grid connected PV system located at Sivagangai was monitored annually between May 2011 to April 2012 and its performance were evaluated on monthly average daily basis. Depthful energy and exergy analysis were also carried out for the same. The significant conclusions of the analysis are as listed below.  The annual average of in-plane solar insolation, ambient and module temperature and wind speed were 5.414 kW h/ m2/day, 30.76 °C, 37.90 °C and 3.191 m/s respectively. The importance and the effect of solar insolation over energy generation is emphasized. In addition, the importance of maximum power point tracking is clearly brought out as the module efficiency is found to be less at annual average of 6.08% though the performance ratio of the plant is found to be high at 0.8915 indicating its absence. In connection to the above, the inverter efficiency also reduces due to the fact of PV system’s inefficiency to track the instantaneous maximum power which aids maximum DC generation. Inference of highest annual energy output is made from 5 MWp plant as inferred from Table 2 as

the module titlt angle is approximately equal to the latitude of the site which will typically result in maximum energy extraction for grid tied installations which can be adopted [24].  Thus an improved performance than reported can be realized if the inverter employed is used for multi-MPPT function rather than master–slave operation as the occurrence of inverter failure is less than MPPT which is most essential for intermittent renewable energy sources such as solar. Improved inverter efficiency also predicts an increased power delivery. Also, the module efficiency is low than other evaluated system as seen in Table 1 because of the PV technology adopted. The present system employs thin film amorphous silicon PV cells for harnessing PV power which results in less PV module efficiency typically in the range of 4–9% only. Thin film CiGs and thin film CdTe posses typical module efficiencies in the range of 8–13.5% and 9–11% which indicated the usage of them. But the main disadvantage lying with the thin film CiGs and CdTe are fabrication complexity and raw material availability for production. Thus thin film silicon cells and monocrystalline cells (typical efficiency of 13–17%) together as a whole can be combined for power generation rather adopting same type of cells. The advantage behind thin film lies in its production or fabrication cost, weight and occupancy area. Furthermore, hybrid cell topology will also yield better system efficiency than the present, paving way for betterment. Thus to resolve the cited problem a combination of thin film with mono or polycrystalline would improve the PV harnessing capacity indigenously with a trade off met with the cost and efficiency. The energy analysis also suggest the technology improvement or module temperature control for improved system performance.  The significance of the real time PV system is yet validated by RETscreen plus where a overall coefficient of best fit for the selected key performance indicators varied from a minimum of 0.7841 to a maximum of 0.9901 which proves remarkably good agreement between the actual and the predicted values of the same. In addition, statistical validators such as MBE, RMSE, MPE were evaluated for the same and the overall annual average value of RMSE and MPE between 0.04 to 4.57 and

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0.0013 to 0.2425 respectively. The lesser the value of RMSE and MPE, the better is the operational accuracy of the plant.  The dependency of input factors over the output is essential for formulating theoretical models such as regression, time-series and neural network for the prediction of daily average of monthly based Eac where selection of input factors for output prediction forms the most important part. This would be the future scope of the present study.  Finally a comparison of the performance results obtained from this study to the other reported results as in Tables 1 and 2 reveal that the PV system’s annual average of the final yield and reference yield is higher than plants at Spain, Poland, Northern Ireland, Dublin and Khatar-khatar marking to 4.801 (h/day) and 5.4149 (h/day) thus indicating the higher annual average performance ratio of 89.15%. The PV module efficiency of 6.08% is least of all cited literature in Table 1 and inverter efficiency of the system tend to be the lowest of 88.2% compared to the Spain, Poland and Ireland sites. Collectively, Sivagangai shows vast solar energy potential which are utilized effectively and can still be made more efficient by inclusion of MPPT equipped inverter and employing a combination of thin film with mono or polycrystalline cells(hybrid technology) for PV power extraction.

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