Grid-connected Solar Photovoltaic System Research Articles

Power quality enhancement of grid-integrated solar photovoltaic system with unified power quality conditioner

Introduction. To enhance the quality of power and ensure a consistent electricity supply, this study proposes the utilization of a unified power quality conditioner (UPQC) system integrated with solar photovoltaic (PV) technology. The innovation involves single DC-link connecting back-to-back voltage-compensating components arranged in series and shunt, forming the PV-UPQC. The shunt compensator utilizes energy from a PV array to address harmonics in the load current. The objective is to mitigate voltage dips and spikes by injecting voltage that is either in phase with or out of phase with the common coupling point through a series compensator. The method combines the benefits of generating renewable energy to enhance electrical quality. The goal of the paper is the power quality enhancement of grid-integrated solar PV system. The novelty of the proposed work consists of enhancement of grid-integrated solar PV system with UPQC. The purpose of integrating a UPQC into a grid-connected solar PV system is to enhance power quality by mitigating issues such as voltage fluctuations, harmonics and reactive power imbalance. Methods. The proposed topology is implemented in MATLAB/Simulink with grid-integrated solar PV system with UPQC. Results. Integrating UPQC with a grid-connected solar PV system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for grid-integrated solar PV system with UPQC applications. References 15, table 2, figures 9.

Direct Normal Irradiance Prediction based Optimum Interval Tilt angles for Enhancement of Energy Output, Levelized Cost of Energy and CO2 Emission in a Grid-Connected PV System

Abstract The tilt angle of photovoltaic (PV) panels is a crucial determinant of their performance and can be adjusted using different tracking methods. Periodically changing the tilt angle strikes a practical balance between efficiency and cost. This work introduces a Bi-directional Long Short Term Memory (Bi-LSTM)-based Direct Normal Irradiance (DNI) prediction to estimate the time intervals for the tilt angle adjustments. DNI Prediction involves 22-year (2000–2022) historical time series data and the Bi-LSTM deep learning model to predict DNI at different time frames for the location Madurai, India. Using the Predicted DNI, Tilt angle-based DNI is mapped using the tilt angle correlation through a nearest neighborhood interpolation method. DNI potential over a specific period is utilized to find the optimum time intervals for the tilt angle adjustments. The simulation study of this work is implemented with the design of a 5 kW grid-connected solar PV system in PVSYST software. The effectiveness of the proposed methodology is evaluated based on improvements in power output, Levelized Cost of Energy (LCOE) and carbon emission reductions and compared with other existing methods. The results showed that using the proposed optimal tilt angle intervals led to a 10.31% increase in PV output power, the lowest LCOE at 3.61 c/kWh and 8.363 tCO2/year carbon emissions.

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals.

Bio-inspired MPPT and advanced control for grid-connected solar photovoltaic systems with flyback converter

ABSTRACT This paper presents an enhanced approach for grid-connected photovoltaic (PV) systems using a flyback converter and Sovereign Butterfly Optimization for advanced Maximum Power Point Tracking (MPPT). The focus is on improving energy extraction efficiency from PV panels by dynamically adjusting system parameters. A flyback converter ensures reliable grid integration, while the Butterfly Optimization Algorithm, inspired by butterfly foraging, provides real-time MPPT to rapidly and accurately track the maximum power point, even under varying environmental conditions. A novel voltage harmonic mitigation technique processes three-phase currents through dq0 frames for closed-loop control, reducing harmonics and optimising voltage precision. The DC/DC flyback converter stabilizes AC conversion, minimizing high-frequency harmonics. Additionally, a Subsystem Synchronization Control Strategy synchronizes active and non-active grid currents, improving system stability. Simulations show a 99.3% tracking accuracy, 2% power oscillation around the maximum power point, and a Total Harmonic Distortion (THD) of 1.78%, meeting IEC 61,727 standards.

Feasibility Analysis of Grid-Connected Solar Photovoltaic Systems in Dhahran and Bisha, Saudi Arabia

Feasibility Analysis of Grid-Connected Solar Photovoltaic Systems in Dhahran and Bisha, Saudi Arabia

Enhancing grid-connected solar photovoltaic systems with high gain interleaved sepic-luo converter and optimized Anfis-Based MPPT

Enhancing grid-connected solar photovoltaic systems with high gain interleaved sepic-luo converter and optimized Anfis-Based MPPT

Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

Improve The Conversion Efficiency and Electrical Output of The Solar PV System by Developing and Implementing a Cooling System

The performance of the grid-connected solar photovoltaic (PV) system in the present study is improved using cooling technology. This technology reduces the temperature of solar PV modules and thus increases their efficiency and lifespan. The current solar PV system has been installed in northern Baghdad in the city of Taji. This study included improving electrical power, conversion efficiency, and performance ratio. All of these improvements are achieved by cooling using water. The performance ratio (PR) and efficiency maximum values of the reference (unimproved) and improved solar PV modules are at 10:10 am at 88.30% & 96.30% and 13.4% & 14.6%, respectively. Improved and reference PV modules maximum solar radiation values occur at 12:00 pm at 1.91 W and 1.566 W, respectively. The maximum solar radiation values of the reference and improved PV modules are at 12:00 pm at 922.9 W/m2 and 1012.22 W/m2, respectively. The maximum daily gain of the power and solar irradiance are 22% and 10%, respectively. The significant innovation in the study is the successful performance optimization of the grid-connected solar PV system. Using different equations to study performance by following different behaviors. Along with improving and studying CIGS PV module performance.

A Comparative Analysis of Various Simulation Software for grid-connected Residential Building: A Case Study at Jeddah, Saudi Arabia

Saudi Arabia receives significant energy daily from solar radiation, making it one of the best places to use solar energy to meet its energy needs. Grid connected Photovoltaic (PV) systems provide the electrical energy available from the solar radiation into the grid. Different simulation tools are currently available used to assess the performance of solar PV systems. The present study was conducted to design and evaluate the performance of 15 kW grid-connected solar PV system under climatic conditions of in Jeddah, Saudi Arabia. Two simulation models, Helioscope and PVsyst were used in the analysis and the results were then compared. The annual energy production, specific production, energy injected into grid, and annual performance ratio (PR) are evaluated by the software’s around a whole year at the same climatic conditions. Results showed that the maximum annual solar irradiation in May was 2212 kWh/m2. The PR based on PVsyst simulation (78.04 %) was higher than that of the Helioscope simulation (74.8%) by about 4.15%. The maximum energy production in May was about 6.1 kWh. The energy production from the PVsyst and Helioscope is 28.98 and 25.9 MWh/year with 289.8 and 259.13 MWh/year energy injected into the grid, respectively.

A novel dynamic/adaptive K-nearest neighbor model for the prediction of solar photovoltaic systems’ performance

The inherent variability in solar Photovoltaic (PV) systems motivates developing dynamic and adaptive data-driven models capable of accurately predicting the systems’ performance. A dynamic/adaptive K-Nearest Neighbor (K-NN) model to estimate the conversion efficiency of a solar PV system is proposed in this work. The proposed model estimates the conversion efficiency of a test pattern based on three steps: (i) identifying the similar historical operational conditions experienced by the system, (ii) assigning a weight to each condition based on its similarity to the test pattern at hand, and (iii) computing the weighted average conversion efficiency of the test pattern. Thus, the proposed model is adaptive and dynamic as the nearest neighbors and the associated weights vary from one test pattern to another. The proposed model is applied to a 7.98 kWp rooftop grid-connected solar PV system at the Hashemite University in Jordan. Its effectiveness is evaluated by resorting to the Mean Square Error (MSE), Accuracy, Coefficient of Determination (R2), and Adjusted R2 (R2adjusted), and comparing the results with the traditional K-NN and other techniques adequately developed in the same context, such as Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Multiple Linear Regression (MLR). Results show that the proposed model exhibited superior performance across all metrics, with a performance gain reaching up to 45.2 % and 6.8 % for the MSE and R2adjusted, respectively, compared to the base MLR. Further, the simplicity and computational efficiency of the proposed model validate its practical deployment in predicting PV system performance.

Sustainable solar energy development in Zakho, Iraq: a techno-economic and environmental assessment

This study presents a solar energy roadmap aimed at attracting investors to capitalize on the abundant solar resources in Zakho, Iraq, for clean energy technology. The objective is to mitigate global warming effects caused by fossil fuel combustion and promote sustainable technological development. To end this, the study employs RETScreen Expert software to validate the techno-economic and environmental viability of a grid-connected solar photovoltaic system utilizing climatic data from the Astronaut Office Information System (NASA) Database. Additionally, the Photovoltaic Geographical Information System (PVGIS) modeling method determines optimal angles for installing residential rooftop Photovoltaic (PV) systems across selected locations. Results indicate that all selected locations are suitable for solar photovoltaic projects, with Rezgari identified as the optimal district due to its highest annual solar radiation of 1863.117 kWh/m2 and the highest annual electricity production at 7905.38 kWh. The capacity factor (CF) values fall within the range of 17.395-17.574%. The study reveals an annual greenhouse gas (GHG) emissions reduction ranging from 6.9876 to 7.1123 (tCO2) across four districts. Considering the financial and environmental indicators, Rooftop Photovoltaic Systems emerge as a sustainable and efficient solution to enhance the environment and prove economically viable in Zakho City.

Optimum ground-mounted on-grid connected photovoltaic system

This research endeavors to enhance grid-connected solar photovoltaic systems by refining the methodology used to select suitable geographical photovoltaic sites. The prevalent criterion in existing literature for choosing sites emphasizes proximity to power transmission lines. However, this criterion overlooks essential grid requirements, such as approved bus bars and permissible sizes for new photovoltaic systems. To overcome this limitation, the study introduces a novel criterion. Instead of relying solely on proximity to transmission lines, the proposed approach replaces this with the concept of optimal geographical bus bar locations. Additionally, the generated findings can be fine-tuned through consideration of the specified system size of the grid, thereby improving the identification of geographically suitable sites more effectively.The proposed methodology lies in the incorporation of five distinct approaches. The primary approach employs the Artificial Bee Colony (ABC) algorithm to ascertain the most suitable bus bars and sizes for three Photovoltaic Distributed Generation (PV-DG), with a focus on minimizing power loss within Aden's electric grid. The second approach involves utilizing the Analytic Hierarchy Process-Weighted Linear Combination (AHP-WLC) method in conjunction with Geographic information systems (GIS) to accurately select appropriate PV sites. The third approach is an initial design to eliminate small geolocations that do not align with the PV system's size. Fourthly, the Least Costly Path Model (LCPM) is harnessed to choose a single geolocation. The final approach is dedicated to performing techno-economic analysis.The key findings reveal that the optimal bus bars are located at 30, 46, and 64 in the grid, with a total injected active power of 26 MW. Simultaneously, the identified optimal geolocations cover areas of 0.88, 0.48, and 1.37 km^2 for each bus bar, respectively. In the comparative analysis, the utilization of the proposed criterion involving optimal bus bars results in a larger geographical coverage (97.99 km2) compared to the traditional criterion (76.28 km2). Furthermore, the techno-economic evaluation indicates the viability of implementing PV systems in Aden.The authors have a strong conviction that the framework and outcomes of this study will garner significant attention and prove valuable in assisting decision-makers to improve the planning of grid-connected solar photovoltaic systems.

Techno-economic and environmental analysis of a grid-connected rooftop solar photovoltaic system in three climate zones

Abstract This study aims to fill a gap in research on technical-economic and environmental assessments of grid-connected photovoltaic (PV) panels for residential electricity supply. To combat this, a study examines the feasibility of grid-connected rooftop solar PV systems in three cities. Using PVsyst software, technical, economic, and environmental factors were analyzed, including energy injected into the grid, net present value (NPV), internal rate of return (IRR), levelized cost of energy (LCOE), and life cycle emissions. It is concluded from this study that PV power plants are technically, economically, and environmentally feasible for all three climate zones, but cold zone with the highest annual production of 10.66 MWh, the highest NPV ($5449.47), the highest IRR (14.28%), and the lowest LCOE ($0.063/kWh) is the most appropriate place to set up a PV power plant (Scenario 1: No tracking system). Furthermore, using horizontal (Scenario 2), vertical (Scenario 3), and two-axis tracking (Scenario 4) systems instead of the no-tracking system (Scenario 1) increases the amount of electricity injected into the grid by 7.56%, 24.67%, and 36.35%, respectively. According to life cycle emission, the annual production of 10.66 MWh by the 5 kW power plant installed in cold zone will prevent 102.02 tons of carbon dioxide emissions into the atmosphere (Scenario 1: No tracking system). To remove financial obstacles and increase the viability of renewable energy, the government must provide financial incentives, lower the cost of equipment, and enact strong renewable energy legislation.

Honey Badger Algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

Analysis of the efficiency of 300 kw grid-connected solar photovoltaic systems at Bukhara state university

Uzbekistan has a great potential for solar radiation in its vast regions, averaging from 5.9 kWh/m2. In order to increase the contribution of solar energy to the national grid, it is extremely important to evaluate the performance of existing solar photovoltaic systems and further assistance in the design and installation of new power plants. This study presents an analysis of the technical characteristics of a 300 kW solar photovoltaic system at Bukhara State University, which was monitored during the year from February to October 2023. An economic analysis of the solar photovoltaic system is carried out. In the study period of 2023, this solar power plant produced 486.8 MWh. According to the economic analysis, the cost of energy (LCOE) is 0.099 kW∙h and the payback period is 9.7 years, respectively. The study shows that the performance of the Bukhara State University system is similar to the characteristics of other solar power plants around the world

Analysis of the efficiency of a 300kw solar photovoltaic system in the climate of uzbekistan

In this research work, the efficiency of a 300 kW grid-connected solar photovoltaic system was analyzed over a year under the climatic conditions of different regions of Uzbekistan. It has been established that the maximum power value that a 300 kW solar photovoltaic installation can produce per year is 0.477 MW, and the minimum is 0.452 MW. Taking into account regional climatic conditions, it was determined that the shortest payback period for photovoltaic systems from generated electricity is 8.6 years, and the shortest is 9 years. It is estimated that between 90.4 and 95.5 tons of CO2 gases could be prevented in the region by 300 kW solar photovoltaic systems.

Smart Grid Resilience for Grid-Connected PV and Protection Systems under Cyber Threats

In recent years, the integration of Distributed Energy Resources (DERs) and communication networks has presented significant challenges to power system control and protection, primarily as a result of the emergence of smart grids and cyber threats. As the use of grid-connected solar Photovoltaic (PV) systems continues to increase with the use of intelligent PV inverters, the susceptibility of these systems to cyber attacks and their potential impact on grid stability emerges as a critical concern based on the inverter control models. This study explores the cyber-threat consequences of selectively targeting the components of PV systems, with a special focus on the inverter and Overcurrent Protection Relay (OCR). This research also evaluates the interconnectedness between these two components under different cyber-attack scenarios. A three-phase radial Electromagnetic Transients Program (EMTP) is employed for grid modeling and transient analysis under different cyber attacks. The findings of our analysis highlight the complex relationship between vulnerabilities in inverters and relays, emphasizing the consequential consequences of affecting one of the components on the other. In addition, this work aims to evaluate the impact of cyber attacks on the overall performance and stability of grid-connected PV systems. For example, in the attack on the PV inverters, the OCR failed to identify and eliminate the fault during a pulse signal attack with a short duration of 0.1 s. This resulted in considerable harmonic distortion and substantial power losses as a result of the protection system’s failure to recognize and respond to the irregular attack signal. Our study provides significant contributions to the understanding of cybersecurity in grid-connected solar PV systems. It highlights the importance of implementing improved protective measures and resilience techniques in response to the changing energy environment towards smart grids.

Performance evaluation of three grid-connected monocrystalline silicon solar photovoltaic systems in Nepal

This study evaluates three grid-connected solar photovoltaic (PV) systems using four criteria: final yield, performance ratio, capacity utilization factor, and system efficiency. The PV systems were installed on the rooftop of the Nepal Telecom office at Sundhara, Pokhara, and Biratnagar. The generation data was collected through the web interface of the data logger installed at the sites. Long-term comparison (four years) of the plant at Sundhara and the same system comparison of plants at Pokhara and Biratnagar were performed. Long-term analysis of the plant at Sundhara found that the final yield decreased from 2.91 kWh/kWp in 2019 to 2.21 kWh/kWp in 2022. Similarly, the performance ratio decreased from 91.0% in 2019 to 67.3% in 2022, capacity utilization factor decreased from 12.1% in 2019 to 9.2% in 2022, and system efficiency decreased from 16.8% in 2019 to 12.5% in 2022. The same system installed at Pokhara and Biratnagar had slightly varied performance owing to the variation in meteorological parameters. The average air temperature at Biratnagar (26.7°C) was higher than at Pokhara (13.1°C) while the amount of solar radiation at Biratnagar was just higher by 4%. This led to the reduction of final yield by 9% at Biratnagar than at Pokhara as higher temperatures at Biratnagar caused more reduction in the power output from the solar panels. Finally, this study provides insights to researchers, PV installers, and panel manufacturers on the actual performance of grid-connected solar PV in Nepal.

Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system

Reducing peak demand on the utility grid benefits both grid operators and consumers. However, achieving this goal while maintaining human comfort presents a significant challenge. This study proposes the deployment of an energy-efficient grid-connected solar photovoltaic (PV) and battery energy storage (BES) system to perform peak shaving. We employ an optimal rule-based control strategy that considers day-ahead PV power and load demand predictions, along with a daily load profile limit, specifically designed for the C1 tariff in Malaysian Commercial Buildings. The peak shaving control strategy proactively determines optimal schedules for battery charging and discharging, aiming to effectively minimize peak demand. To regulate the daily demand accordingly, we employ a root-finding algorithm to determine the optimal demand limit in conjunction with the formulated rule-based peak-shaving control strategy. The required optimal inputs for the rule-based control strategy are determined using a genetic algorithm, which minimizes peak grid power. We verify the effectiveness of the rule-based peak-shaving method using MATLAB. The results demonstrate significant reductions in both energy consumption and peak demand for different load profiles. Specifically, we achieve a reduction of 19.02%, 20.9%, 4.72%, and 0% in energy consumption, and 42.46%, 49.82%, 40.44%, and 33.1% in peak demand for case studies (CS) 1, 2, 3, and 4, respectively. Furthermore, we observe that the application of the rule-based peak shaving method maintains a state-of-charge (SOC) of the battery at 50% by the end of the day, allowing for day-to-day management flexibility.

Techno-Economic Feasibility Study of a 1.5 MW Grid-Connected Solar Power Plant in Bangladesh

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.