Performance Of Photovoltaic Plant Research Articles

Improvement of Solar Farm Performance based on Photovoltaic Modules Selection

The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis. Consequently, the PV module (PV7), characterized by its high efficiency, low temperature coefficients, and affordable price, result in a significant OR (73.81%), increased installed PV power capacity (1568kW), enhanced net energy output (2269029 kWh/year), improved yearly PR (83.4%), and lastly, the shortest payback period (around two years). Instead of optimizing shadow length in existing research, this paper aims to improve the performance of large-scale solar farm based on PV module selection which results in less computation and structure installation efforts.

A Comparative Study of Data Mining Methods for Solar Radiation and Temperature Forecasting Models

Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC). 

Experimental and Numerical Study of the Effect of Cloud Cover on the Electrical Performance of Photovoltaic Plants

<p>The production of electricity from the sun mainly consists of transforming the light emitted by the sun into electrical energy through photovoltaic cells. This production varies proportionally with the light that illuminates the solar photovoltaic module. According to the literature, we noted that during the period of cloudy passage, the production of the solar modules could see a fall going from 5 to 60% according to the size of the photovoltaic fields. The present work consisted of studying the impact of cloudy passages on the electrical production of PV fields at four sites (Loumbila, Sandogo, Ouaga 2000 and IRSAT). To achieve this, a theoretical and experimental study was conducted. The simulation results were compared to the experimental results of two daily sunshine profiles (sunny and cloudy). The results show significant differences between the experimental and simulated data for the two sunshine profiles considered. The losses of electrical production generated by these cloudy passages vary from 27 to 32% and from 35 to 52%, respectively, for the simulated and experimental results. These differences were explained by the probabilistic character of the cloudy passages and by the fact that other major parameters (aerosols, dust deposits and ageing of the solar modules) were not considered. As perspectives, we recommend a continuation of the work by studying, in addition to the cloudy passages, the influence of aerosols and dust deposits on the PV fields realized by us.</p>

Energy and exergy analysis of a 20-MW grid-connected PV plant operating under harsh climatic conditions

Abstract Large grid-connected photovoltaic (PV) plants are increasingly being installed around the world, including in harsh desert climates. Evaluating their performance can help improve the design and operation of PV systems. This study performed an energy and exergy analysis of a 20-MW grid-connected PV plant under desert climatic conditions in southern Algeria over a period of 1 year. The PV plant was divided into two 10-MW subsystems. Energy analysis was performed using actual irradiation, power output, wind speed and ambient temperature data. The annual average energy efficiency of the plant and subsystems was 10.82%, 10.95% and 10.69%, respectively. Solar radiation had the most significant impact (80% determination coefficient) on thermal exergy loss. The exergy efficiency of the plant was lower than the literature values, likely due to the harsh desert conditions. The comprehensive energy and exergy analysis provides insights into the performance of large-scale PV plants in desert climates. The results can help guide the system design and operation improvements for such conditions. Regular cleaning and cooling could improve performance.

A 5-MWp grid-connected photovoltaic plant's performance analysis and challenges under Algerian Sahara conditions

ABSTRACT The proposed study presented some challenges and experimentally evaluated the performance of a five MW solar plant placed in Reggane, Algeria, southwest of the desert from 1 May 2022 to 30 April 2023, the solar system was monitored using the standard IEC 61724. This paper's basic goal is to explore how these harsh environmental conditions affect the efficiency of the photovoltaic (PV) power plant, with a focus on the impact of high-level temperature, where the examined system operates at temperatures exceeding 45°C during summer days, ranking this region among the hottest in the world. The results show that the median ratio of performance, final yield and capacity factor was 72.3%, 4.9 h/d and 20.6%, respectively. Additionally, the study examined how the output energy and performance ratio of the PV system changed with temperature and sun irradiation. It found that the PV system only generated 56% of its expected output at very high or low temperatures. These findings can be utilised in improving the prediction of future large-scale solar plants in hot desert conditions, as well as in both the planning and operation of novel connected to the grid PV systems.

An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants

The key performance indicators are crucial for monitoring the performance of photovoltaic solar plants, thus significantly enhancing their overall efficiency. This critical role underscores the importance of making informed decisions regarding the evaluation criteria of these indicators, which would streamline the PV installations evaluation process. Despite the significant importance of this role, the weighting and prioritization of KPI selection criteria in the specific field of solar plants have not been addressed in the literature. The article aims to address this gap by developing a methodology that identifies the most relevant aspects for evaluating the performance of photovoltaic plants, while taking into account specific needs and evaluation contexts, integrating a hierarchical decomposition tree of criteria, which serves as a framework to guide criterion prioritization through collaborative weighting with the thematic team. This methodology incorporates the Analytic Hierarchy Process, a multicriteria analysis approach designed to assist decision-makers in addressing their specific needs, ensuring optimal alignment with the characteristics of the optimization problem, whether it involves single or multiple objectives.

Benchmarking photovoltaic plant performance: a machine learning model using multi-dimensional neighbouring plants

Benchmarking photovoltaic plant performance: a machine learning model using multi-dimensional neighbouring plants

Power generation enhancement analysis of a 400 kWp grid-connected rooftop photovoltaic power plant in a hilly terrain of India

Photovoltaic (PV) technology projects operate for 25–30 years under different climatic conditions. However, for consistent economic returns, the system performance has to be assessed regularly. As per international standards, indicators and tests are prescribed to ensure reliable PV plant performance. In this study, performance analysis of a 400 kWp grid-connected solar plant with 10 subsystems is carried out, in a western Himalayan location of India. The annual solar power generation is found to be 431,088.539 kWh which is significantly low due to non-optimized installation and other factors. The minimum and maximum performance ratio of PV subsystems, are found to be 37 % and 92 % respectively. The total performance ratio of the plant is found to be 68 % which is relatively low as compared to similar PV plants installed in India. The average maximum power generated by a subsystem installed at near optimum tilt angle is found to be 4kWh/day/kWp, signifying the relevance of optimized system installation. The results show that the optimized PV panel tilt and orientation correction will lead to enhance energy production by 7.22 % and all corrective measures to identified factors will enhance the solar power generation by 121,833 kWh/year and reduction of 113 tons CO2 emissions.

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.

Performance evaluation and comparative study of three 52-kW PV plants in India: a case study

Developing countries like India are rapidly transitioning from traditional energy sources to sustainable energy sources, due to the increase in demand and the depletion of fossil fuels. Grid-connected photovoltaic (PV) systems attract many investors, organizations, and institutions for deployment. This article studies and compares the performance evaluations of three 52-kW PV plants installed at an educational institution, SRMIST (SRM Institute of Science and Technology), in Tamil Nadu, India. This site receives an annual average temperature of 28.5°C and an average global horizontal irradiation of 160 kWh/m2/m. The prediction model for the 52-kW power plant is obtained using solar radiation, temperature, and wind speed. Linear regression model-based prediction equations are derived using the Minitab 16.2.1 software, and the results are compared with the real-time AC energy yield acquired from the three 52-kW plants for the year 2020. Furthermore, this 52-kW plant is designed using PVsyst V7.1.8 version software. The simulation results are compared with the energy yield from the plants in 2020 to identify the shortfall in the plant performance. The loss analysis for the plant is performed by obtaining the loss diagram from the PVsyst software. This study also proposes a methodology to study the commissioned PV plant's performance and determine the interaction between variables such as direct and diffused solar radiations, air temperature, and wind speed for forecasting hourly produced power. This article will motivate researchers to analyze installed power plants using modern technical tools.

Long-term outdoor performance and degradation evaluation of CIS PV plant under the semi-arid climate of Benguerir Morocco

The effectiveness of a solar photovoltaic module relies on location related factors, including latitude, seasonal variations, irradiance levels, clearness index, and similar elements. In compliance with International Electrotechnical Commission (IEC) standards, performance and degradation assessment studies for photovoltaic (PV) technologies have been conducted in a variety of geographical areas with diverse climatic conditions in recent years. A comprehensive study of PV systems employing several technologies will provide some operators and stakeholders with statistically significant findings for assessing system performance. As a result, long-term and short-term performance and feasibility sur may provide information about the characterization of next-generation PV panels. The objective of this study is to perform an outdoor performance assessment and analysis of a 10.44 kWp grid-connected photovoltaic PV system comprised of Copper Indium Selenium (CIS) modules. This work will investigate how a CIS PV plant performs and degrades in Morocco’s semi-arid climate. The study is performed in the period between 2018 and 2021. The measured and estimated performance indicators are evaluated with the aim of assessing the technology’s adaptability and degradation; the methodology applied comprises performance evaluations of the PV system in accordance with IEC-61724 standard guidelines. After 6 years of outdoor exposure, the CIS system’s output power declined with a degradation rate of 3.13%/year obtained using linear regression. I–V curve measurements performed in real test conditions (RTC) determined that power output degradation reached −21.6%, which was more observable in short-circuit current and varies between −8.1% and −18.1% for individual modules.

Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications

The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor.

UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency

The field of solar photovoltaic (PV) plants has seen significant growth in recent years, with an increasing number of installations being developed worldwide. However, despite advancements in technology and design, the impact of shading on the performance of PV plants remains an area of concern. Accurate 3D models produced using unmanned aerial vehicle (UAV) photogrammetry can provide aid to evaluate shading from nearby surroundings and to determine the potential of a site for electricity production via solar PV plants. The main objective of this paper is to address the problem of shadows significantly reducing energy yield in solar PV plants by proposing a methodology that aims at assessing the shading effects on PV systems and determining the optimal configuration for a PV module array using an accurate digital environment 3D model built using UAV photogrammetry. A high-level-of-detail 3D model allows us to evaluate possible obstacles for PV module array construction and accurately recreate the proximities that can cast shadows. The methodology was applied to grid-connected PV systems in Kaunas, Lithuania. The results of the case study show that electricity production in PV modules is highest at a 15° tilt angle when the distance between PV rows is 1.25 m. The proposed methodology gives an 11% difference in PV yield due to shading compared with other tools that do not include shading. This study also highlights that at least 30% financing support is necessary for solar PV plants to be economically attractive, resulting in a payback of 9 years and an internal rate of return of 8%. Additionally, this study can help optimize the design and layout of PV systems, making them more efficient and cost-effective.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Additional Compound Damping Control to Suppress Low-Frequency Oscillations in a Photovoltaic Plant with a Hybrid Energy Storage System

The use of the conventional dual closed-loop control strategy by photovoltaic (PV) plants with grid-connected inverters may weaken the damping of a power system, which may aggravate low-frequency oscillations (LFOs). This influence will become more severe as the penetration of PV plants increases. Therefore, it is necessary to incorporate damping controls into PV plants to suppress LFOs. This paper proposed an additional compound damping control (ACDC) system that combines additional damping control (ADC) for the inverter with ADC-based dynamic power compensation control (DPCC), allowing hybrid energy storage systems (HESSs) to suppress LFOs. First, the feasibility of suppressing low-frequency oscillations in PV plants is demonstrated by the torque method and a small signal model. Then, an additional damping controller is added to the active power control link of the PV inverter to enhance the damping abilities of the system. However, given that the damping performance of PV plants with only ADC is limited by the compensated power, PV plants require devices that can rapidly compensate for the damping power. Therefore, we added the HESS to the DC bus and proposed DPCC. Finally, a three-machine nine-node system for a PV plant was modeled and simulated in the PSCAD platform. The simulation results showed that the proposed control strategy could provide effective damping for interarea oscillation.

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Mercatorian and spatial studies of solar power potential (SPP) provide technical guides for mapping actual solar installation areas for the efficient performance of photovoltaic plants. Acquisition and processing of satellite and on-station data on clearness index, relative sunshine hours, latitude, longitude and SPP preceded their modeling and simulations. The mercatorian SPP model is a geometric function of latitude and longitude, whereas the spatial SPP model is a function of x and y coordinates developed from the Haversine formula. Multiple isodose lines and a single maximum isodose line characterized the distributed and concentrated SPP contours, respectively. The present geometric SPP model validated well with the measured SPP with insignificant error results for the study areas. The Concentrated SPPs: 757.5, 635.2, 557.5 and 405.9 W/m2 with their corresponding percentage concentrated areas (actual): 28.85(29084.6), 41.48(15368.6), 4.37(1179.6) and 0.75(635.7)%(m2) for Northern Region (NR), Eastern Region (ER), Central Region (CR) and Western Region (WR), respectively. These results support the efficient performance of solar facilities within the confine of the SPP concentrated areas. The effective mercatorian coordinates were useful in identifying districts within the SPP concentrated areas. Furthermore, the high magnitude of the SPP in ER and NR supports that they are favored for the installation of solar facilities.

Evaluation of extreme weather impacts on utility-scale photovoltaic plant performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. This analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems. These insights can inform planning activities, especially as renewable energy continues to expand into new geographic and climatic regions around the world.

Cost and performance of CSP and PV plants of capacity above 100 MW operating in the United States of America

• CSP and PV not competitive but complementary technologies. • PV is cheaper at about 3 c/kWh but only available daytime. • CSP with TES more expensive at about 6 c/kWh but fully dispatchable. • CSP with TES less expensive than PV with batteries. • Synergy is needed coupling CSP with TES, PV, and batteries. This work aims to compare the cost and performance of Photovoltaic (PV) and Concentrated Solar Power (CSP) solar plants utility-size >100 MW built in the United States (US) and model predictions to forecast their optimal synergetic use in a near-future solar (and wind) only grid. There are in the country very few CSP, and many more PV plants, that are representative of the latest technologies. The largest number of PV plants is the result of the cost advantage and better technology-readiness, compared to the mistakenly promoted CSP solar tower (ST) with thermal energy storage (TES) plants, while neglecting the CSP parabolic trough (PT), and not factoring dispatchability. Without TES, CSP PT could provide comparable performances, at an acceptable cost, reaching the mass production of current PV technology. The addition of TES, still suffering from maturity issues, adds dispatchability, is a huge added value but at increased cost and reduced reliability. It is concluded that further technological progress and mass production of CSP, both PT and ST, with further expanded TES, is needed, targeting dispatchability. These plants should be coupled to PV production during the daytime to progress towards a stable solar energy supply with limited external battery energy storage.

Identifiability Evaluation of Crucial Parameters for Grid Connected Photovoltaic Power Plants Design Optimization

This paper aims to assess the impact of different key factors on the optimized design and performance of grid connected photovoltaic (PV) power plants, as such key factors can lead to re-design the PV plant and affect its optimum performance. The impact on the optimized design and performance of the PV plant is achieved by considering each factor individually. A comprehensive analysis is conducted on nine factors such as; three objectives are predefined, five recent optimization approaches, three different locations around the world, changes in solar irradiance, ambient temperature, and wind speed levels, variation in the available area, PV module type and inverters size. The performance of the PV plant is evaluated for each factor based on five performance parameters such as; energy yield, sizing ratio, performance ratio, ground cover ratio, and energy losses. The results show that the geographic location, a change in meteorological conditions levels, and an increase or decrease in the available area require the re-design of the PV plant. A change in inverter size and PV module type has a significant impact on the configuration of the PV plant leading to an increase in the cost of energy. The predefined objectives and proposed optimization methods can affect the PV plant design by producing completely different structures. Furthermore, most PV plant performance parameters are significantly changed due to the variation of these factors. The results also show the environmental benefit of the PV plant and the great potential to avoid green-house gas emissions from the atmosphere.

Investigating Fourteen Countries to Maximum the Economy Benefit by Using Offline Reconfiguration for Medium Scale PV Array Arrangements

Over the past few years, electricity demand has been on the rise. This has resulted in renewable energy resources being used rapidly, considering the shortage as well as the environmental impacts of fossil fuel. A renewable energy source that has become increasingly popular is photovoltaic (PV) energy as it is environmentally friendly. Installing PV modules, however, has to ensure harsh environments including temperature, dust, birds drop, hotspot, and storm. Thus, the phenomena of the non-uniform aging of PV modules has become unavoidable, negatively affecting the performance of PV plants, particularly during the middle and latter duration of their service life. The idea here is to decrease the capital of maintenance and operation costs involved in medium- and large-scale PV power plants and improving the power efficiency. Hence, the present paper generated an offline PV module reconfiguration strategy considering the non-uniform aging PV array to ensure that this effect is mitigated and does not need extra sensors. To enhance the economic benefit, the offline reconfiguration takes into account labor cost and electricity price. This paper proposes a gene evolution algorithm (GEA) for determining the highest economic benefit. The proposed algorithm was verified using MATLAB software-based modeling and simulations to investigate fourteen countries to maximize the economic benefit that employed a representative 18-kW and 43-kW output and the power of 10 × 10 PV arrays in connection as a testing benchmark and considered the electricity price and workforce cost. According to the results, enhanced power output can be generated from a non-uniformly aged PV array of any size, and offers the minimum swapping/replacing times to maximize the output power and improve the electric revenue by reducing the maintenance costs.