scholarly journals Nowcasting the Output Power of PV Systems

2018 ◽  
Vol 61 ◽  
pp. 00010 ◽  
Author(s):  
Marius Paulescu ◽  
Oana Mares ◽  
Ciprian Dughir ◽  
Eugenia Paulescu

This paper presents an innovative procedure for nowcasting the energy production of PV systems. The procedure is relayed on a new version of two-state model for forecasting solar irradiance at ground level and a simplified description of the PV system. The results of testing the proposed procedure against on field measured data are discussed. Generally, the proposed procedure demonstrates a better performance than the main competitor based on ARIMA forecasting of the clearness index.

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 50 ◽  
Author(s):  
G. Almonacid-Olleros ◽  
G. Almonacid ◽  
J. I. Fernandez-Carrasco ◽  
Javier Medina Quero

In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.


Author(s):  
Mahmoud Ismail

Performance ratio is one of the indicators used to describe the effectiveness of the PV systems. The sustainability of the PV system year after year as well as its reliability can be checked by measuring the performance ratio each year. This indicator will also enable us to carry out a comparison between the performances of different PV systems. In this paper, the performance ratios for five PV systems installed on the roof tops of some of PTUK university buildings have been calculated on monthly and yearly basis. The analysis has been carried out using the available data (energy production and solar irradiation) for the year 2019. It was found that the performance ratio has higher values for May and September in comparison with other months. On the other hand, its lowest values were obtained in winter months. This trend can be observed for all of the PV clusters on the five buildings.  When taking into account the overall system, the highest value for the performance ratio was 0.89, which was for September, whereas its lowest value of 0.70 was obtained in January. The performance ratio, which was calculated on yearly basis for the overall system, was found to be 0.80. When considering each building separately, the lowest value was 0.44 for the “Services” building whereas the highest value was 0.94 for the Science building.


2018 ◽  
Vol 60 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Bogdan-Gabriel Burduhos ◽  
Mircea Neagoe

AbstractA precise estimation of the electrical energy produced daily by photovoltaic (PV) systems is important both for PV owners and for electrical grid operators. It can be achieved if the received solar irradiance can be accurately estimated during any type of daily solar profile (clear, cloudy, mixed sky), not only average solar profile for larger periods of time, e.g. one month or season, as used in PV system design. The paper firstly describes an existing mathematical model, based on the Meliss approach, which uses mean monthly coefficients for estimating average direct and diffuse solar irradiance. This model is satisfactory for monthly/annual intervals but is not useful for daily estimations. Therefore in the second part of the paper an algorithm which allows to generate daily variations of the model’s coefficients for clear and cloudy sky conditions is proposed. The improved model with variable coefficients was tested during several representative days and can be used for estimating the effect which different meteorological conditions as fog/dew/frost have on the quantity and quality of the solar irradiance received by a PV convertor.


2016 ◽  
Vol 59 (1) ◽  
pp. 13-17 ◽  
Author(s):  
Sergiu Lucaciu ◽  
Robert Blaga ◽  
Nicoleta Stefu ◽  
Marius Paulescu

AbstractThe fluctuation of solar radiation at ground level represents a challenge in modeling the time series of solar irradiance. A procedure for the quantification of the instability of the solar radiative regime is reported. This procedure is based on the clearness index, as the ratio of the horizontal solar irradiance measured at ground level and the estimated one at extraterrestrial level. New quantities for classifying the days from the radiative stability point of view are being introduced. A procedure for classifying days according to their stability regime as stable, variable and unstable is presented.


2019 ◽  
Vol 25 ◽  
pp. 1-19
Author(s):  
Sindri Þrastarson ◽  
Björn Marteinsson ◽  
Hrund Ólöf Andradóttir

The efficiency and production costs of solar panels have improved dramatically in the past decades. The Nordic countries have taken steps in instigating photovoltaic (PV) systems into energy production despite limited incoming solar radiation in winter. IKEA installed the first major PV system in Iceland with 65 solar panels with 17.55 kW of production capacity in the summer of 2018. The purpose of this research was to assess the feasibility of PV systems in Reykjavík based on solar irradiation measurements, energy production of a PV array located at IKEA and theory. Results suggests that net irradiation in Reykjavík (64°N, 21° V) was on average about 780 kWh/m2 per year (based on years 2008-2018), highest 140 kWh/m2 in July and lowest 1,8 kWh/m2 in December. Maximum annual solar power is generated by solar panels installed at a 40° fixed angle. PV panels at a lower angle produce more energy during summer. Conversely, higher angles maximize production in the winter. The PV system produced over 12 MWh over a one-year period and annual specific yield was 712 kWh/kW and performance ratio 69% which is about 10% lower than in similar studies in cold climates. That difference can be explained by snow cover, shadow falling on the panels and panels not being fixed at optimal slope. Payback time for the IKEA PV system was calculated 24 years which considers low electricity prices in Reykjavik and unforeseen high installation costs. Solar energy could be a feasible option in the future if production- and installation costs were to decrease and if the solar PV output could be sold to the electric grid in Iceland.


2012 ◽  
Vol 135 (2) ◽  
Author(s):  
A. Charki ◽  
R. Laronde ◽  
D. Bigaud

This article presents a method developed for carrying out the energy production estimation considering the energy losses in different components of a photovoltaic (PV) system and its downtime effect. The studied system is a grid-connected photovoltaic system including PV modules, wires, and inverter. PV systems are sensitive to environmental conditions (UV radiation, temperature, humidity) and all components are subjected to electrical losses. The proposed method allows obtaining the production of photovoltaic system and its availability during a specified period using meteorological data. The calculation of the production takes into account the downtime periods when no energy is delivered in the grid during this period. The time-to-failure and the time-to-repair of photovoltaic system are considered following a Weibull distribution. This method permits to have a best estimation of the production throughout the lifetime of the photovoltaic system.


Author(s):  
David A. Torrey ◽  
James M. Kokernak

State-sponsored incentives have played a significant role in driving the demand for residential and small commercial photovoltaic (PV) systems. All state incentive programs are tied to the power rating of the system, though some states also offer energy production incentives. Unfortunately, there is a disconnect between the power rating of a PV system and the energy that system produces over its lifetime. It is extremely important to consider system productivity, which goes well beyond the efficiency of the components. System productivity is tied directly to the structure of the array, not just the efficiency of the components and the quality of the installation. This paper examines the issues associated with improving solar PV system productivity. The focus is on comparing a series-parallel array configuration to a series-string array configuration and the impact on energy production. Partial shade is used to highlight substantial differences between the operation of the two array configurations.


Author(s):  
Patrick Lilly ◽  
George Simons

More than two hundred sixty grid-tied photovoltaic (PV) systems sized 30 kW to 1.1 MW installed in California during 2002 through 2004 received partial funding through the Self-Generation Incentive Program (SGIP). The SGIP is administered statewide by PG&E, SCE, SoCalGas, and the San Diego Regional Energy Office. The incentive is structured as a one-time capacity based payment made at the time of system completion. The first PV system incentive was paid in Summer 2002. Through the end of 2004, a total of 269 PV systems had received financial support through the program. The cumulative generation capacity of these systems exceeded 30 MW and corresponded to $101 million of incentives paid. While originally slated to run through 2004, recently the program was modified and extended through the end of 2007. PV systems participating in the program are being monitored to support evaluation of the program. These data have been used to assess impacts of the Program on peak demand and energy consumption. These data have also been incorporated into the Program’s cost-effectiveness assessment. Well over one-half of the PV systems have already been subject to metering yielding 15-minute interval generator output data. The cumulative size of the directly monitored PV systems currently exceeds 33 MW as of late 2005. In 2004, the statewide California Independent System Operator (ISO) electrical system peak occurred on September 8 during the 16th hour (from 3 to 4 PM PDT). During this hour the electrical demand for the California ISO reached 45,562 MW. On this day, there were 235 PV systems funded under the SGIP installed and operating; interval-metered data are available for 107 of these projects. The resulting estimate of peak demand impact coincident with the ISO peak load totals 9,938 kW. The estimated peak demand impact corresponds to 0.39 kW per 1.0 kWRebated of PV system size and is based on rebated capacity. Those unfamiliar with PV system size ratings and PV system operating characteristics may be surprised that the overall weighted-average peak demand impact was not substantially higher at this hour and time of year. To help put this result in perspective, it can be compared to a simple engineering estimate of peak power output based on published information regarding PV system performance. First, we begin with 1 kW [basis: rebated size] of horizontal PV system capacity. For purposes of determining rebates, PV system sizes are calculated as the product of cumulative estimated module DC power output under PTC conditions and inverter maximum DC to AC conversion efficiency. Factors such as manufacturing tolerance, soiling, module mismatch, temperature effects, and wiring losses may result in actual full-sun power output levels of about 0.76 kW/kWRebated. When the 3 to 4 PM angle of incidence effects for the month of September are included the expected output value drops significantly further. The peak-day operating characteristics of the 107 PV projects for which peak-day interval-metered data were available are summarized in the box plot of Figure 4. System sizes were used to normalize power output values prior to plotting summary statistics of PV output profiles for individual projects. The normalized values represent PV power output per unit of system size. Treatment in this manner enables direct comparison of the power output characteristics of PV systems of varying sizes. The vertically oriented boxes represent ranges within which 75% of project-specific values lie. The vertical lines represent the total range (i.e., maximum and minimum) of project-specific values. The energy production of the group of metered PV systems varied according to season. In Figure 7, normalized energy production by month is illustrated (on the right axis). These values represent the monthly average capacity factor for the on-line PV system capacity. As expected, normalized energy production levels reach their maximum values in the summer season and decrease towards the winter season as the intensity and duration of incident solar radiation falls off, coupled with increased incidence of storms and other weather disturbances off the Pacific Ocean, which affect the availability of solar radiation upon the PV modules.


2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Li Fen ◽  
Yan Quanquan ◽  
Duan Shanxu ◽  
Zhao Jinbin ◽  
Ma Nianjun ◽  
...  

The rapidly growing markets for distributed and centralized grid-connected photovoltaic (PV) systems require the reliable and available information for reflecting and predicting the electricity generation of PV systems. In this work, the relationship between PV energy production and meteorological environmental factors is discussed by correlation analysis and partial correlation analysis. Meteorological data available, including the clearness index, diurnal temperature range, the global radiation on horizontal surface, and etc., are used as inputs. Then, according to factor analysis, these various interaction factors are extracted as two independent common factors. Finally, a new method based on factor analysis and multiple regression analysis has been developed for estimating the daily PV energy production. The meteorological data are collected from Wuhan Observatory, and power data from a roof grid-connected PV system located at Huazhong University of Science and Technology in Wuhan. The data of the whole year (from March in 2010 to February in 2011) has been used for model calibration and the following data of March in 2011 is used to test the predictions. The results show that there is significant positive correlation between the estimated values and the measured values; the rMBE per day is −0.14%, MAPE per day is 13.60% and rRMSE per day is 18.04%.


Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1087
Author(s):  
Emad Natsheh ◽  
Sufyan Samara

The photovoltaic (PV) panel’s output energy depends on many factors. As they are becoming the leading alternative energy source, it is essential to get the best out of them. Although the main factor for maximizing energy production is proportional to the amount of solar radiation reaching the photovoltaic panel surface, other factors, such as temperature and shading, influence them negatively. Moreover, being installed in a dynamic and frequently harsh environment causes a set of reasons for faults, defects, and irregular operations. Any irregular operation should be recognized and classified into faults that need attention and, therefore, maintenance or as being a regular operation due to changes in some surrounding factors, such as temperature or solar radiation. Besides, in case of faults, it would be helpful to identify the source and the cause of the problem. Hence, this study presented a novel methodology that modeled a PV system in a tree-like hierarchy, which allowed the use of a fuzzy nonlinear autoregressive network with exogenous inputs (NARX) to detect and classify faults in a PV system with customizable granularity. Moreover, the used methodology enabled the identification of the exact source of fault(s) in a fully automated way. The study was done on a string of eight PV panels; however, the paper discussed using the algorithm on a more extensive PV system. The used fuzzy NARX algorithm in this study was able to classify the faults that appeared in up to five out of the eight PV panels and to identify the faulty PV panels with high accuracy. The used hardware could be controlled and monitored through a Wi-Fi connection, which added support for Internet of Things applications.


Sign in / Sign up

Export Citation Format

Share Document