This article presents a short-term forecast of electric energy output of a photovoltaic (PV) system towards Tomsk city, Russia climate variations (module temperature and solar irradiance). The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values

Dinh, V. T.

Yuhao Yan

This article presents a short-term forecast of electric energy output of a photovoltaic (PV) system towards Tomsk city, Russia climate variations (module temperature and solar irradiance). The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values.</jats:p

V.T. Dinh

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Short-Term Forecasting of Electric Energy Generation for a Photovoltaic System

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Dinh V.T.

Yan Yuhao

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SHORT-TERM FORECASTING OF ELECTRIC ENERGY GENERATION FOR A PHOTOVOLTAIC SYSTEMV.T. Dinh, Yuhao YanaTomsk Polytechnic University, 634028, Tomsk, Russia Abstract. This article presents a short-term forecast of electric en-ergy output of a photovoltaic (PV) system towards Tomsk city, Russia climate variations (module temperature and solar irradi-ance). The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values. 1 Introduction The renewable technologies provide a feasible alternative of generating electricity which inexhaustible, clean and can be used in a decentralized way. Considering the geographical position and climatic characteristic of Tomsk Oblast, Russia, there is a good potential of renewable energy development in PV systems [1]. The prediction and optimization of elec-trical performance of PV system provides essential references values on maintaining the actual load stability as well as the system design considerations of GCPV system. The analysis of difference models showed that: the time series data of PV power generation output have chaotic characteristics, so they can be predicted. PV power generation predic-tion generally includes solar irradiance forecast, temperature forecast and model output statistics [1], and the key is solar irradiance forecast. According to the range of forecast time, solar power generation prediction can be divided into short-term forecast (1 hour–10 days), medium-term forecast (11–30 days), long-term prediction (1–5 years), and the most useful prediction is short-term prediction. All the PV power generation prediction models include two stages: data collection; and data processing. The role of data collection is to provide input information for the predic-tion model. As for PV power prediction, the most important information are meteorological parameters and historical PV power output data. The meteorological parameters come from measuring device (such as weather sensors, etc.) and weather forecast information.  Recently, there are few methods that involve in PV system outputs prediction. For example by employing Artificial Neural Network (ANN) which utilizes the solar radiation, ambient temperature and module temperature as it inputs whereas voltage and current are predicted as outputs. Besides, the evolutionary programming (EP) algorithm schemes is implemented a Corresponding author: sxsmymm@gmail.com © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017for adjusting the optimal value of the fill factor in order to achieve more accurate outputs of PV systems.  In this paper we demonstrate the necessity of short-term time series prediction of gener-ated power using mathematical approach based on fundamental equations. 2 Theoretical aspects of forecasting of PV electric energy 2.1 About photovoltaic system The tested PV system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University, Tomsk, Russia. The station includes 3 kW solar battery and 2 kW wind-driven electric plant [2]. The station is shown in Figure 1. The station consists of:  solar generator ARPS-250 (10 elements); wind generator 1000 W (2 elements); voltage transducer =48/~220V, 50Hz.Specification:  generator capacity, no more than 5 kW; output voltage ~220 V, 50 Hz; surface area of the PV modules, no more than 230 m2; effective area of the station, no more than 300 m2; height of wind generator tower 6 m.Operating conditions:  temperature, from -50 to 50o C.Fig. 1. Wind - solar station at Tomsk, Russia.2.2 Statistical model of PV power output The PV array power generates the DC component output, so the PV inverter performs the conversion of the variable DC output of the PV modules into a utility frequency AC current that can be fed into the commercial electrical grid or used by a local, off-grid electrical network. Therefore, the conversion efficiency of the PV inverter will affect the AC compo-nent of output power [3]. The maximum power output of the PV system can be expressed by S IVN A IVN A A A IVN P S MP P FF V I M M P           2MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017for adjusting the optimal value of the fill factor in order to achieve more accurate outputs of PV systems.  In this paper we demonstrate the necessity of short-term time series prediction of gener-ated power using mathematical approach based on fundamental equations. 2 Theoretical aspects of forecasting of PV electric energy 2.1 About photovoltaic system The tested PV system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University, Tomsk, Russia. The station includes 3 kW solar battery and 2 kW wind-driven electric plant [2]. The station is shown in Figure 1. The station consists of:  solar generator ARPS-250 (10 elements); wind generator 1000 W (2 elements); voltage transducer =48/~220V, 50Hz.Specification:  generator capacity, no more than 5 kW; output voltage ~220 V, 50 Hz; surface area of the PV modules, no more than 230 m2; effective area of the station, no more than 300 m2; height of wind generator tower 6 m.Operating conditions:  temperature, from -50 to 50o C.Fig. 1. Wind - solar station at Tomsk, Russia.2.2 Statistical model of PV power output The PV array power generates the DC component output, so the PV inverter performs the conversion of the variable DC output of the PV modules into a utility frequency AC current that can be fed into the commercial electrical grid or used by a local, off-grid electrical network. Therefore, the conversion efficiency of the PV inverter will affect the AC compo-nent of output power [3]. The maximum power output of the PV system can be expressed by S IVN A IVN A A A IVN P S MP P FF V I M M P           where, IVN is PV inverter efficiency;AP and MP are the PV array and PV module power output, respectively;AI and AV are the PV array current and voltage, respectively;The structure of EP based on the output power of photovoltaic system is shown in the Figure 2. Fig. 2. Structure of EP based on the output power of photovoltaic system model. 2.3 The solar irradiation model To calculate the sun’s position in the sky at any location and time, two angles should be known: Azimuth angle and the Altitude angle. Also the sunrise and sunset times will help determining the start and end points of azimuth angle. In this paper we follow the conven-tion of azimuth angle starting from zero degree at North Pole. Accordingly east, south and west poles are at 90°, 180° and 270°, respectively.  Azimuth angle (Figure 3) depends on latitude and time of year. The following equation is used to find the angle at any location [4]. 11 sin .cos sin .cos .coscos2 cosH        where, H = 𝐻 = 15 × (𝐿𝑆𝑇 − 12) hour angle; 𝐿𝑆𝑇 – local solar time;  – declination angle; ɸ – latitude of the place; α – Elevation angle. Fig. 3. Azimuth Angle. 3MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017In order to point the dependence of extraterrestrial solar radiation by the time of the year, the following equation were used [5]: 3601 0.033 cos365on scnG G       where, Gon is the extraterrestrial radiation measured on the plane normal to the radiation on the n-th day of the year; Gsc is the solar constant, Gsc = 1367 W/m2; n is the day of the year.2.4  Temperature forecasting value The detailed values of temperature forecasts over a 10 day period are provided by varies websites, in this paper we get the values from website wunderground.com by using python and packages Beautiful Soup 4 and Requests (Figure 4). The received values are organized with an array.  Fig. 4. Example screenshot of 10-day temperature forecast. 3 Results and discussion Software solarEnergy was created on the basis of the MATLAB 2012b. The main window screenshot is shown in Figure 5. The size of PV panel and daily average consumption are set in the field “Real size of panel” and “Daily average consumption” of the software. Plots of prediction value of output and balance are shown in the plot area (Figure 6). 4MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017In order to point the dependence of extraterrestrial solar radiation by the time of the year, the following equation were used [5]: 3601 0.033 cos365on scnG G       where, Gon is the extraterrestrial radiation measured on the plane normal to the radiation on the n-th day of the year; Gsc is the solar constant, Gsc = 1367 W/m2; n is the day of the year.2.4  Temperature forecasting value The detailed values of temperature forecasts over a 10 day period are provided by varies websites, in this paper we get the values from website wunderground.com by using python and packages Beautiful Soup 4 and Requests (Figure 4). The received values are organized with an array.  Fig. 4. Example screenshot of 10-day temperature forecast. 3 Results and discussion Software solarEnergy was created on the basis of the MATLAB 2012b. The main window screenshot is shown in Figure 5. The size of PV panel and daily average consumption are set in the field “Real size of panel” and “Daily average consumption” of the software. Plots of prediction value of output and balance are shown in the plot area (Figure 6). Fig. 5. SolarEnergy main window. Fig. 6. Example of plot of power output and balance. 4 Conclusion The performance of grid connected PV system is primarily influenced by the solar irradi-ance and module temperature. This paper demonstrated the prediction on PV system out-puts based on mathematical approach for the PV system, which is located at Tomsk Oblast, Russia. The proposed approach provides an effective method for prediction and feasible in time under the same accuracy. Based on the results, it shows a good correlation between actual and predicted of electrical performance on grid connected PV system. In the future we plan to continue the research, including prediction using neutral network and machine learning methods to improve accuracy and range of forecasting, as well as design applica-tions to wind farms and other renewable power sources. References [1] Z.W. Zheng, Y.Y. Chen, M.M. Huo, B. Zhao, ICSGC 12, 601 (2011) doi: 10.1016/j.egypro.2011.10.081 [2] A.V. Yurchenko, A Ohorzina, IFOST 7, 1 (2012) doi: 10.1109/IFOST.2012.6357698 [3] M P Thekaekara, A J Drummond, Natl. Phys. Sci 229, 6 (1971) 5MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017doi: 10.1038/physci229006a0 [4] S.A. Khalil, A.M. Shaffie. RSER 27, 853 (2014) doi: 10.1016/j.rser.2013.06.038 [5] L.T. Wong, W.K. Chow, Applied Energy 69, 191 (2011) doi: 10.1016/S0306-2619(01)00012-5 6MATEC Web of Conferences 155, 01033 (2018) https://doi.org/10.1051/matecconf/201815501033IME&T 2017

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