Grid-connected Photovoltaic Power Plants Research Articles

A precise and efficient K-means-ELM model to improve ultra-short-term solar irradiance forecasting

To address the problems of intermittent and uncontrollable solar irradiance faced by grid-connected photovoltaic power plants, this paper proposes an ultra-short-term solar irradiance prediction model that combines K-means clustering with the Extreme Learning Machine (ELM). The K-means algorithm is first applied to cluster solar irradiance data from different seasons in Shanghai based on spatial similarity. Subsequently, the ELM algorithm is employed to train the model, significantly improving both prediction accuracy and training speed. Compared with the prediction accuracy of the model before clustering, the root-mean-square error (RMSE) of the clustered model is significantly reduced by 28.75 % in spring, and 0.30 %, 6.70 % and 5.92 % in summer, Autumn and winter, respectively. In addition, the model demonstrates stable predictive performance at different time resolutions (5, 10 and 15 minutes) with R2 values close to 1, confirming its accuracy and stability under small sample conditions. In terms of training speed, ELM’s training time is more than 100 times faster than Support Vector Regression (SVR) and significantly shorter than traditional models such as PSO-BP and Random Forest (RF), showing its great advantage in application scenarios that require fast response. Overall, the K-means-ELM model is able to accurately capture the overall trend and sudden changes in solar irradiance, which is of great application value for enhancing the efficiency and stability of solar power generation systems.

Empowering the solar energy landscape: The techno-economic analysis of grid-connected PV power plants in Uganda

Solar PV power is still under-utilized despite the abundance of solar radiation in Uganda. There is need for empowering renewable energy landscape through unlocking the technical and economic feasibility of solar photovoltaic power. We analyzed data from 56 locations for the techno-economic and environmental assessment of photovoltaic power facilities in Uganda. This was based on weather data availability and accessibility to the national power grid. Analysis of the energy generation and different input factors was done using PVsyst 7.2. A three stage approach to losses was adopted: absorption of sunlight, conversion to DC and DC to AC conversion. Findings indicate that most of the countryside is suitable for construction of large scale grid-connected photovoltaic power facilities. Due to longer sunshine duration and stronger Global Horizontal Irradiance (GHI) which are associated with high energy yield, northern Uganda performed better than the rest of the country, making it a preferential siting for large scale grid-connected photovoltaic facilities. South western Uganda performed the poorest. After a thorough energy accounting and a list of all performance metrics, the viability of investing in grid-connected photovoltaic power facilities was assessed.

Projeto e implementação de um sensor de radiação solar com arduino uno e célula de referência

Our work is pointed toward planning a device for estimating all out solar radiation in light of a reference photovoltaic cell. The rule of this device is chiefly founded qualities of a photovoltaic cell where sun oriented radiation is corresponding to the short circuit current, wich is estimated by a resistor.The voltage difference at these connections is measured by acquisition using a functional amplifier and then recorded into an analogue-to-digital converter for further processing. The Arduino Uno platform was chosen to process and display in real-time the solar radiation received from the reference cell surface. The affordable device has a wide range of uses, including monitoring solar radiation in both stand-alone and grid-connected photovoltaic power plants. The decision to utilize the Proteus design software environment has proven to be a key factor in the successful testing and validation of the device's performance. This integrated platform has enabled the efficient implementation and simulation of the hardware and software components, ultimately leading to the development of a cost-effective and reliable solar radiation estimation solution.

Techno-Economic Analysis of Grid-Connected PV Power Plants in Different Zones of Egypt

Techno-Economic Analysis of Grid-Connected PV Power Plants in Different Zones of Egypt

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

Modeling and performance analysis of a 5MW grid-connected photovoltaic power plant in the hot region of in Salah, Algeria

Modeling and performance analysis of a 5MW grid-connected photovoltaic power plant in the hot region of in Salah, Algeria

Evaluation of a grid-connected PV power plant: performance and agrivoltaic aspects

AbstractThe performance ratio, a globally recognized metric that correlates with reported global solar radiation values, serves as a crucial indicator for evaluating the efficiency of grid-connected PV plants. Also, a large scale PV power plant alone can afford some agricultural irrigation energy requirement of a region. In this study, the actual generation data from a power plant located in Bursa province in northwestern Türkiye, during its initial six years of operation have been analyzed. The analysis reveals that the annual electricity production of the power station reaches approximately 10 GWh. Notably, the time period between April and September witnesses a monthly electricity generation exceeding 1 GWh, with September emerging as the most productive month, characterized by an average performance ratio of 94.5% during this six-year period. However, over the span of six years, the highest average electricity generation occurs in July, peaking at 1.34 GWh. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%. From an environmental impact and global warming perspective, it is noteworthy that during the 36-month period in the summer season, the performance ratio exceeded 100% only three times. However, within the 32-month period in the winter season, the performance ratio exceeded 100% 19 times. This situation indicates that while the reported radiation rates by the managements are consistent with the actual values for the summer months, they need to be revised, especially for the winter months.

Simulation and experimental study of grid-connected photovoltaic power plants in a hot desert climate with fixed and dual-axis tracking

ABSTRACT This paper presents a comprehensive investigation into the performance of grid-connected photovoltaic (PV) power plants situated in a hot desert climate. The study employs a combination of simulation models and experimental data to assess the impact of two tracking systems – fixed and dual-axis – on the overall efficiency and energy yield of the PV installations. Through detailed simulations, we analyze the dynamic behavior of the PV systems under varying solar irradiance and the atmospheric conditions typical of hot desert environments. The dual-axis tracking system is compared against the conventional fixed-mount system to evaluate its effectiveness in maximizing energy production and improving the overall efficiency of the PV power plants. Experimental data collected from an actual grid-connected PV facility in a hot desert region is incorporated to validate the simulation results and enhance the accuracy of the study. The observed performance metrics include energy output, system reliability, and the impact of environmental factors on long-term sustainability. In general, in all weather conditions, solar tracking provides better yields than a fixed solar module, generating about 32% and 12% more energy in sunny and overcast weather conditions, respectively. Under sunny weather, the two-axis sun tracking system and a fixed tilt angle produce 333.228 kWh and 247.56 kWh of energy, respectively. The energy yield generated on a partly cloudy day by a two-axis sun tracking system and a fixed tilt angle PV system is 281.496 kWh and 257.856 kWh, respectively. In addition, the highest electricity gain for two-axis solar trackers versus fixed PV plants was found to be 34.6%, and the lowest value was found to be 9.16%. The experimental analysis shows that the efficiency of the polycrystalline PV module is 12% and 14% on sunny days and cloudy days, respectively. The comparison between fixed and dual-axis tracking systems contributes to the optimization of PV installations, enabling more informed decisions for sustainable energy production in regions characterized by high temperatures and intense solar radiation. To support the expanding applications of grid-tied PV systems with a sun tracker in the same or comparable location and climate conditions (Saharan environment), these studies have the practical utility of advising and offering helpful feedback to engineers, system designers, contractors, and investors.

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply–demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454–1.54] for the three studied PV stations.

A hybrid machine-learning model for solar irradiance forecasting

Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

Optimizing the power quality of the MV distribution network in fault condition based on D-STATCOM in accordance with the Moroccan Grid Code requirements

In this work, we present a novel method concerning a D-STATCOM integration to the Moroccan electrical distribution network, which is not yet adopted by national DSO’s (Distribution system Operator), the device is implemented at the PCC (Point of common Coupling) of a grid connected PV power plant, which is a typical case of the increasing penetration of renewable generators in the national electrical network. The device is proposed to compensate for voltage fluctuation and the dynamic support of grid voltage in the event of disturbances or the failure of renewable generators especially in context of the increasing integration of these sources. The control strategy is tuned to achieve performance constraints imposed by the national Grid Code. The proposed system overcomes network uncertainties and grid disturbances and significantly improves the power quality at the point of common connection PCC while absorbing and providing reactive power to the network in an acceptable reversing time due to voltage sags and swells and mitigating voltage flicker. The considered D-STATCOM (Distribution Static Compensator) was able to ride through grid faults in most cases which is necessary to achieve LVRT (Low voltage Ride through) and dynamic voltage support requirements. The results of the simulation are compared to the technical requirements according to the Moroccan Grid code and show good results in simulation while respecting the technical limits imposed by local regulations.

Techno-economic feasibility analysis of a commercial grid-connected photovoltaic plant with battery energy storage-achieving a net zero energy system

Grid connected Photovoltaic (PV) plants with battery energy storage system, are being increasingly utilised worldwide for grid stability and sustainable electricity supplies. In this context, a comprehensive feasibility analysis of a grid connected photovoltaic plant with energy storage, is presented as a case study in India. A novel smart net-zero energy management system is developed to reduce grid and fossil fuel-based backup electricity consumption during power outages and peak load shaving by controlling peak load demand A life cycle cost-benefit and levelized cost of energy (LCoE) analysis, is presented for five optimised photovoltaic plants with battery energy storage systems, with and without net metering regimes. The Solar Labs and PVSyst softwares are used for system planning and energy generation estimation followed by HOMER grid software and Excel sheet-based financial models for system optimisation and cost-benefit analysis. The sensitivity analysis is carried out considering most sensitive parameters to identify the best option. The results found a 200 kWp photovoltaic plant with 250-kWh battery energy storage system with net metering, as the best-optimised option with energy generation cost of INR 4.21/kWh, with 6.15 years payback period. The study results can be followed for sustainable solar power generation for commercial grid connected PV power plants worldwide.

Life cycle assessment of a 33.7 MW solar photovoltaic power plant in the context of a developing country

This work aims to determine the Energy Payback Time (EPBT) of a 33.7 MWp grid-connected photovoltaic (PV) power plant in Zagtouli (Burkina Faso) and assess its environmental impacts using the life cycle assessment tool according to ISO 14040 and 14044 standards. A “cradle to grave” approach was used, considering 1 kWh of electricity produced and injected into the national grid over 25 years as a functional unit. In addition to the baseline scenario, the other simulated scenarios combining three variables, module technology (mono c-Si, poly c-Si, and CdTe), type of mounting structure (aluminum and steel), and end-of-life treatments (landfill and recycling) were considered. SimaPro 9.4 software and the ReCiPe 2016 Midpoint (H) evaluation method were used for the calculations considering four environmental indicators. A sensitivity analysis of the change in the electricity mix was also performed. Results showed that the EPBT of the scenarios varies between 1.47 and 1.95 years, with the shortest and longest corresponding to scenarios 4 (CdTe modules, steel mounting structure, and recycling as end-of-life treatment) and scenario 3 (mono c-Si modules, aluminum mounting structure, and recycling as end-of-life treatment), respectively. All the EPBT scenarios studied can be considered acceptable given the long lifetime of PV systems (25 years). The following environmental impact results were obtained: climate change 37–48 CO2-eq kWh-1, freshwater ecotoxicity 4–11 g 1,4-DCB kWh-1, mineral resource scarcity 0.4–0.7 g Cu-eq kWh-1 and 11–13 g oil-eq kWh-1 for fossil resource scarcity. Scenario 3 (mono c-Si modules, aluminum mounting structure, and recycling as end-of-life treatment) dominates all environmental indicators studied except freshwater ecotoxicity, which is dominated by scenario 4 (CdTe modules, steel mounting structure, and recycling as end-of-life treatment). The sensitivity analysis showed that the change in the electricity mix could reduce around 30% the EPBT, climate change, and fossil resource scarcity. Considering the environmental indicators studied, using CdTe modules manufactured in a country with a less carbon-intensive electricity mix, using galvanized steel as the mounting structure, and completely recycling components at the end of their lifetime is the most environmentally friendly scenario. However, particular attention needs to be paid to the land occupation that this plant could generate.

A comprehensive analysis of eight rooftop grid-connected solar photovoltaic power plants with battery energy storage for enhanced energy security and grid resiliency

This study presents the outcome of a utility-run rooftop photovoltaic (PV) power plant with battery energy storage systems (BESS) as a viable solution for enhanced energy storage and grid resiliency at the distribution network level. A comprehensive techno-commercial analysis of PV plants with BESS for commercial and industrial (C&I) consumers of a distribution company (DISCOM) is presented. The analysis is based on consumer data from eight different sectors in India. The impact of voltage rise, thermal loading and reverse flow are analysed for different PV + BESS grid network integration scenarios, which are found to be factored in the individual capacity and number of distributed systems connected to the grid, length and current carrying capacity of the feeder, running load and the location of PV + BESS integration. The cost-benefit analysis results show that the maximum economic benefit from PV + BESS can be attained by managing peak load, reducing diesel generator use, and increasing solar fraction in the energy system. The normalised net benefit is higher when PV + BESS is installed with load profiles, which coincides with the DISCOM load profiles. Results show BESS needs to be an essential element of a PV plant for reliability, flexibility, and grid stability.

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.

Measurement and Simulation of 2.25 kWp grid-connected amorphous photovoltaic station in a hot desert environment

This work investigates measurements and simulations of a 2.25 kWp grid-connected amorphous photovoltaic power plant mounted on a parked car in a hot desert environment. This power station is located at applied research unit field (URAER), in the Ghardaia region, southern region. The simulation is carried out using PVSYS software. This includes evaluation of meteorological and electrical parameters performance of studied PV system such as reference PV system, PV array yield (YA), Final yield (YF), PV array and system losses, array and system efficiency, performance ratio (PR). The array nominal energy estimated at STC is 5695 kWh/year. The energy estimated injected into the grid is 4648 kWh/year.

Optimal scheduling and management of pumped hydro storage integrated with grid-connected renewable power plants

Pumped hydro-energy storage will become a fundamental element of power systems in the coming years by adding value to each link in electricity production and the supply chain. The growth of these systems is essential for improving the integration of renewables and avoiding dependence on fossil fuel sources, such as gas or oil. This paper presents the modeling and application of an optimal hourly management model of grid-connected photovoltaic and wind power plants integrated with reversible pump-turbine units to maximize the monthly operating profits of the energy system and meet electricity demand. The techno-economic dispatch model is formulated as a mixed-integer optimization problem. To assess the proposed model, it is applied to a Spanish case study system, and the results are obtained for an entire year. The combination of renewable energy and pumped hydro energy storage reduces energy dependence by decreasing energy costs by 27 % compared with a system without storage to satisfy the required electricity demand. The findings confirm that storage plays a key role in energy transition to ensure the security and stability of power systems with a higher share of renewable generation.

Corrigendum: Assessing the viability of a grid-connected PV power plant in Mubi, Adamawa State, Nigeria

Corrigendum: Assessing the viability of a grid-connected PV power plant in Mubi, Adamawa State, Nigeria

Performance Evaluation of Burkina Faso’s 33 MW Largest Grid-Connected PV Power Plant

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant’s performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system’s performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant’s effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso’s national grid, serves as a valuable model for other power plants currently under construction or in the planning stages.