Solar Power Plant Reliability Incorporating Insolation Availability

Transcription

1 Solar Power Plant Reliability Incorporating Insolation Availability Elezabeth Paul Govt.Model Engineering College Thrikkakara, Cochin, Kerala, India Shouri P V Govt.Model Engineering College Thrikkakara, Cochin, Kerala, India Abstract Solar power generation has been growing drastically over the recent years owing to increasing energy demandsas well as growing concerns of fossil fuel consumption. The reliability estimation of the solar power plants has been receiving increasing attention. This is largely because of ongoing changes in generation investments and environmental constraints. Many of the researches for reliability estimation of solar system considered the reliability and availability of hardware components such as solar panels, inverters etc. only. But input source or solar insolation is an important element on which the reliability of hardware depends. Hence this work aims at determining the reliability of solar power plant that captures effects of input variability and failures of system components. Modelling is based on preparing a control chart using measured values of power and solar insolation for the given locality at different points of time. A model is developed for evaluation of solar insolation reliability beyond the lower limit of insolation using stream flow model hence reliability variation over time can be determined. Keywords Reliability, input variability, insolation, stream flow model. hardware needs to be considered.[8] The main objective of this work is to find the reliability of a solar power plant incorporating both solar insolation availability and hardware reliability and hence find the periods during which power plant is unreliable where alternate energy sources may be required.[7] II. MODEL APPLIED TO PRACTICAL SITUATION First, Reliability modeling is assessed using the practical data collected from grid connected PV system located at Govt.Model Engineering College, Thrikkakara, Ernakulam (latitude : north, longitude : east). This 30kW system operates from July The power generated by the solar plant is utilized at the consumer point, excess power is fed into the grid and deficient power is fed from the grid. I. INTRODUCTION Solar power generation is one of the most efficient and popular means of utilizing renewable energy owing to increasing energy demand and rising cost of alternate energy sources. Recently there is an increasing attention to estimation of solar system reliability which is mainly due to ongoing changes in generation investments and environment constraints.[1],[2] A grid connected photovoltaic system consist of many components such as solar panels, solar inverters etc. Many of the researches done so far in reliability estimation have considered only the reliability and availability of hardware components while neglecting the availability of input source. Input source or solar insolation is an important element which affects the operations and functions of the hardware components. Hence while estimating the system reliability, individual reliabilities of both solar insolation and Sl. No TABLE.1 Main components of plant Item Specification Make 1 PV Module 300 Wp, 72 Cell Polycrystalline 2 String inverter 15KW with inbuilt logger 3 AC energy meter 3phase 4 wire, 4 Tri-Vector Meter ER300P 230V, 10-60A, direct connected Frequency +/-10% 50Hz Australian Premium Solar Fronius L&T L&T, ER300P 63

2 International Journal of Applied Engineering Research ISSN Volume 13, Number 3 (2018) Spl. 2 This 30 kw rated system has an output DC voltage of V and an output AC voltage of 220 V. It comprises three strings of which two strings consist of 32 modules and one string of 36 APSP6-300/72 (300 W) PV modules. One 15 kw solar PV inverter (Fronius Symo M) is connected with 300w modules, 3 strings. L&T Tri-Vector Meter ER300P data logger acquires the data through a connection to the local grid through an inverter, a safety control box and a solar energy meter. III. July August September October November December Calculate R value R = R/n, where n is the number of samples. 5. Calculate control limits for the chart, given by UCL (Upper Control Limit) = X + R LCL (Lower Control Limit) = X - R, where = (constant value when number of samples is above 10) MODELING OF PV SYSTEM USING CONTROL CHART As mentioned earlier, objective of work is to arrive at solar power plant reliability and involves calculation of power outputs from solar insolation. A control chart is prepared using the measured values of power and solar insolation for the given locality at different points in time. The upper limit and lower limit corresponds to 3σ (standard deviation) limits and solar insolation below 3σ value corresponds to zero reliability. Since the plant is operated from July 2017, the unknown values of power can be calculated from the following equations : Figure 1.UCL and LCL for power Pout = ηcell * Pin Pin= (average solar insolation) * (area of panel) (1) (2) From the control chart we get LCL=14.284kW. This value is taken as the minimum acceptable value of power output and solar insolation values less than this corresponds to zero reliability. From the equations mentioned earlier value of minimum solar insolation = 4.8kWh/. Including the calculated values with the measured values, the monthly power outputs over the entire year can be obtained. Following steps can be used for obtaining the control chart. 1. Calculate the monthly average value of power or X for the year (kw) as follows IV. TABLE 2. Monthly average value or X January February March April May June July August September October November December RELIABILITY ESTIMATION OF THE SYSTEM As mentioned earlier this work captures the effect of both solar insolation availability and hardware component reliability. In order to build such a generation model, two modeling steps are taken for PV system, (1) modeling the dependency of output on variability of solar insolation and (2) dependency of failure rate of components on reliability. 2. Calculate X for the above value X = X /n, where n is the number of samples. A. Reliability of The monthly average data for solar insolation at the location is analyzed by using stream flow model to obtain the reliability of solar insolation. From the resultant graph the relation between solar insolation, power and reliability can be modeled. Following table gives the monthly average solar insolation (kwh/ ) data for the location. 3. Calculate range or R values as follows January February March April May June Solar Insolation 64

3 3 Table.3 Monthly average solar insolation January February March April May June July August September October November December The above data is analyzed using stream flow modeling to obtain the reliability using pre-defined class intervals as the table below. Here the first column Table.4 Reliability modeling of solar insolation Class interval with respect to mean Insolation(kWh/ ),I Frequency n Cumulative frequency N Failure probability F(I) Reliability R(I) I I - S I - S/ I I + S/ I + S I + 3S/ I + 2S I + 5S/ I + 3S I + 7S/ In Table.4, I refers to average of insolation (kwh/ ) and S is the variance. Column 1 gives just the intervals to which the data is grouped. B. Relation between Solar Insolation and Reliability Figure 3.System reliability Versus Insolation Figure 2. Reliability as a function of Solar Insolation Figure.2 indicates the probability or chance of having a given solar insolation. But higher the solar insolation, high er will be the power. Therefore system reliability or probability that system will deliver the required power will be compliment of this, given in Figure.3. 65

4 4 International Journal of Applied Engineering Research ISSN Volume 13, Number 3 (2018) Spl. From the graph an equation relating reliability and solar insolation is modeled given by, R(I) = i , R(I) is reliability when insolation is I (kwh/ ). C. Relation between Power and Reliability Using (1) and (2) relation between power and reliability can be modeled. Figure 5.Reliability Versus Time V. RESULTS AND DISCUSSION Figure 4. Reliability Versus Power From the graph an equation relating reliability and solar insolation is modeled given by, R(P) = 1E-06-7E p , where R(P) is reliability when power is p (kw). D. Reliability of System Components Reliability of system components (Solar panels, solar inverter, energy meter, tri vector meter ) is determined to find the overall reliability of system. Probability that the component will function satisfactorily for at least t units of time is given by R(t) = The overall system reliability can be obtained the equation = * * * *, where is solar insolation reliability and to refers to system component reliability. The product of reliability of components and solar insolation gives the system reliability. The reliability of the system for different months for 20 years are shown in the following figure. Here reliability of system is highest during March when solar insolation is highest (Table.1) and is lowest for June, when insolation is lowest. Figure.5 gives system reliability versus time graph for 20 years. With the passage of time, the reliability of solar insolation also varies with season and month. It can be observed that reliability is highest for the month of March (having highest solar insolation) and lowest for June (having the lowest solar insolation). The figure conceals for a particular location (latitude : north, longitude : east). Hence the total system reliability is also a function of season. VI. CONCLUSION Solar-energy-based photovoltaic (PV) systems are increasingly gaining worldwide attention due to the high electricity consumption in combination with the desired environmental friendly solutions for power production development. Indeed, PV systems are continuously exposed to many factors that significantly degrade their performances and efficiency. This paper develops a model for PV system reliability which captures the effect of both input insolation variability and hardware component reliability hence estimating the overall reliability of system which is important for when considering especially initial investment and environmental constraints. 66

5 5 Acknowledgements We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality. The authors would like to thank Govt. Model Engineering college for the support during the study. References [1] Samer Sulaeman, Mohammed Benidris, Joydeep Mitra, Modeling and Assessment of PV Solar Plants for Composite System Reliability Considering Radiation Variability and Component Availability, Department of Electrical and Computer Engineering Michigan State University East Lansing, Michigan 48824, USA. [2] Hamed Sabouhi, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Payman Dehghanian, Reliability modeling and availability analysis of combined cycle power Plants, Electrical Power and Energy Systems, 79 (2016) [3] Vincent R. Lalli, Photovoltaic Power systems Reliability Considerations, U.S. DEPARTMENT OF ENERGY, Energy Technology, Distributed Solar Technology Division, [4] E. Setiena, M. Frasqueta, G. Salioua, M. Silvaa, G. Pinnaa, R. Blázqueza V. Ruiza, Reliability analysis of Solar-Gas Hybrid Receivers for central tower plants, International Conference on Concentrating Solar Power and Chemical Energy Systems, SolarPACES 2014, 69, pp [5] L. H. Koh, Wang Peng, K. J. Tseng, Gao Zhi Yong, Reliability Evaluation of Electric Power Systems with Solar Photovoltaic & Energy Storage, 2014 IEEE. [6] S. A. Farghal, M. A. Tantawy, and A. E. EI-AIfy, Impact of Solar Thermal Power Plants on Economy and Reliability of Utility System,.IEEE P;ower Engineering Review, June 1987, pp [7] Tokhir Gafurov, Julio Usaola, Milan Prodanovic, Modelling of concentrating solar power plant for power system reliability studies, IET Renewable Power Generation, 2015, Vol. 9, Iss. 2, pp [8] Ravindra M.Moharil, Prakash S.Kulkarni, Reliability analysis of solar photovoltaic system using hourly mean solar radiation data, Solar Energy, volume 84, April, Pages [9] Peng Zhang, Wenyuan Li, Sherwin Li, Yang Wang, Reliability assessment of photovoltaic power systems: Review of current status and future perspectives. Applied Energy, Volume 104, April, Pages [10] Sairaj V Dhople, Alejandro.D.Dominguez, Estimation of Photovoltaic System Reliability and Performance Metrics, IEEE Transactions on Power Systems (Volume:27, 2012). 67