Grid-connected Photovoltaic System Research Articles

Dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage

Dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage

Research on photovoltaic grid-connected inverters with source filtering function

Abstract To suppress the current harmonics caused by the nonlinear load connected to the grid when the photovoltaic system is connected to the grid for power generation, this paper adopts a composite control strategy based on the PI-RC controller to realize the synthesis of the command current signal in different modes. According to the different command current signals, the system can work in three modes: photovoltaic grid-connected harmonic compensation control, photovoltaic grid-connected control, and harmonic compensation unified control. Under the composite control strategy, the system realizes the active output of the photovoltaic grid-connected system with the function of source filtering, compensates for the harmonics caused by nonlinear loads, successfully reduces the harmonic distortion rate of grid current, and then realizes the function expansion of photovoltaic grid-connected inverter. Finally, the feasibility and rationality of the unified control strategy designed in this study are verified by simulation experiments.

Utilizing a Five-Level Inverter for Grid-Connected PV Systems: Implementing an MPPT Algorithm with Super Twisting Sliding Mode Control

Utilizing a Five-Level Inverter for Grid-Connected PV Systems: Implementing an MPPT Algorithm with Super Twisting Sliding Mode Control

Passivity-based adaptive current control loop of cascaded H-bridge multilevel converter for grid-tied photovoltaic system

This paper proposes an algorithm to passivity-based control (PBC) for the current control loop of the cascaded H-bridge converter (CHB) connecting the grid-connected PV system with the space vector modulation (SVM) algorithm using the law of expanding any number of voltage levels. The algorithm SVM in this paper is suitable for CHB converters. It can create a number of high levels of alternating voltage that other methods cannot do. Passivity-based control (PBC) algorithm aims to create a flexible controller that can change its structure or parameters to adapt to changes in the system, ensuring stable system quality, reducing switching losses and gaining energy conversion efficiency. It enhances the capability of accurately delivering power on demand to the grid with the CHB inverter in situations in which the grid demands the mobilization of maximum power from the photovoltaics (PV) system. The simulation results of the 9-level CHB system performed by MATLAB/Simulink software have proved the feasibility of the proposed algorithm.

A new adaptive Perturb & Observe controller via robust approach for PV grid-connected

Given the importance of renewable energies, several countries are integrating these energies into their energy strategies, such as their connections to the grid, in particular photovoltaic energy. In this paper, a PV grid-connected system in a two-stage configuration is studied. In this configuration, an improvement to a control MPPT is proposed. To achieve this, we propose a novel concept for MPPT control that consists of generating an adaptive step size for P&O control that is based on sliding mode control in order to minimise ripple phenomena and chattering phenomena. The proposed control approach was then tested using MATLAB/Simulink simulations under varying irradiation conditions. We compared this approach to the conventional P&O method as well as the adaptive step size of P&O and hybrid P&O-sliding mode control. The results of the test indicate that the proposed control strategy significantly minimises the two phenomena and improves overall efficiency.

PERFORMANCE EVALUATION OF A 67.2 KWP SI-POLY PHOTOVOLTAIC SYSTEM CONNECTED TO THE GRID USING PVSYST TOOL

This paper presents the simulated performance evaluation of a 67.2 kWp grid-connected Si-poly photovoltaic system. This study was carried out to assess the feasibility of installing a photovoltaic system to supply the electrical loads at the Alioune DIOP University of Bambey, Senegal, a public institution. Indeed, the UADB is facing load shedding problems caused by its exorbitant energy consumption. The study system comprises 240 Si-poly modules. Each module has a rated output of 280 Wp. All photovoltaic modules are connected in 12 strings, each string consisting of 20 modules in series. Three solar inverters, each rated at 20 kW, are used for interconnection with the grid via a public meter. For the case study, PVsyst 7.4 is used for simulation. PVsyst 7.4 is a PC software package for the design, dimensioning and data analysis of complete photovoltaic systems. Meteonorm 8.1 meteorological data sets on solar radiation, ambient temperature, wind speed and humidity from the PVsyst database were used for this analysis. In this study, the parameters evaluated were the photovoltaic arrays effective energy production, the energy injected into the grid, the performance rate and the normalized energy production per kWp installed. The 67.2 kWp photovoltaic system generates 111.287 MWh/year of DC energy, of which 108.980 MWh/year of AC energy is fed into the grid. The systems annual performance rate is around 81.5%, and the loss diagram over the whole year is also computed.

An Optimized 3DOF-FOPID2-DF Controller with Improved Power Quality for Multi-Level Inverter in Grid-Connected PV Systems

Multi-level Inverters (MLIs) are increasingly employed in grid-connected Photovoltaic (PV) systems to optimize power quality. The use of MLIs offers several benefits over traditional inverters, including improved output waveform quality, reduced Total Harmonic Distortion (THD) and increased efficiency. However, the control of MLI is complex, and a precise control system is required to ensure that the system operates at optimal conditions. Therefore, an Adaptive Chaotic Mapping Philippine Eagle Optimization (ACM-PEO) algorithm-based Three degrees of freedom Fractional Order Proportional Integral Derivative with Dual Filter (3DOF-FOPID2-DF) controller is proposed. The dual derivative and dual filter component introduced in the 3DOF-FOPID controller improve the transient response of the system even under sudden load changes. The ACM-PEO algorithm further enhances the system performance of the 3DOF-FOPID2-DF controller by optimizing the parameter gains. The proposed ACM-PEO-based 3DOF-FOPID2-DF control technique is simulated in MATLAB and the simulation results demonstrate that it has improved transient and dynamic response, reduced THD, faster convergence and better tracking of the reference signal. These benefits enhance the efficiency and reliability of the systems.

A hybrid cyber–physical risk identification method for grid-connected photovoltaic systems

Identifying risks in modern electric power systems is essential, and one of the main difficulties concerns covering the wide range of technologies that permeate its cyber and physical domains. Different risk identification methods have been proposed, but applying them individually does not guarantee coverage of both domains. On the other hand, the simple non-articulated application of a set of existing risk identification methods can lead to an exhaustive and inefficient process. This paper proposes a new Cyber–Physical Risks Identification Method (CPRIM) to comprehensively and efficiently identify risks in electrical power systems. To systematically cover risks ranging from the cyber domain to the physical domain, CPRIM combines in a complimentary and articulated way the National Institute of Standards and Technology (NIST) Cybersecurity Framework, a Risk Factor model, and the HAZOP, establishing a novel hybrid risk identification approach. This work also proposes a method based on Jaccard and overlap indexes to quantitatively assess the complementarity and superposition that may exist when applying different risk identification methods to electrical power systems. The results obtained in a real computer-managed photovoltaic plant indicate that CPRIM can efficiently identify cyber–physical risks, showing a reasonable trade-off between system coverage and redundancy in identified risks.

Power Factor Analysis of Grid-Connected Solar Inverter under Different Irradiance Levels throughout the Day

The power factor (PF) plays a crucial role in determining the quality of energy produced by grid-connected photovoltaic (PV) systems. When irradiation levels are high, typically during peak sunlight hours, the PV panels generate more electricity. In this scenario, the PF tends to be higher because the real power output closely matches the apparent power drawn from the grid. Whereas, when irradiation levels are low, such as during cloudy weather or nighttime, the PV panels produce less electricity. In these conditions, the power factor may decrease because the real power output diminishes compared to the apparent power drawn from the grid. This could be due to reduced efficiency or increased reactive power flow. PF decreases linearly at solar irradiance values lower than 220 (W/m2). At the same time, it approaches unity at higher solar irradiance values than 220 (W/m2). In this study, the variation of the power coefficient of the grid-connected PV solar system depending on solar irradiation was modeled and analyzed using MATLAB/Simulink 41016490. The analytical expression of the power factor depending on the change in solar irradiation was found using the curve fitting method.

Maximizing energy transfer of solar-battery charge controller using voltage balancing strategy

Off-grid and grid-connected photovoltaic (PV) systems with battery storage rely heavily on efficient energy transfer to maximize PV power utilization and battery lifespan. However, existing literature often overlooks the crucial role of energy transfer efficiency in these systems. This paper proposes a novel, fundamental-based PV power flow strategy that addresses this gap by employing a concept of source-load voltage matching. The proposed strategy ensures optimal voltage matching between the PV array and the battery bank. This is achieved by configuring the battery bank voltage range (between nominal and fully charged states) to closely align with the variation in PV voltage at its Maximum Power Point (MPP) across different operating temperatures. This systematic approach requires specifying the DC load voltage, configuring the battery bank, and selecting PV modules with compatible Vmp (voltage at maximum power) parameters. The system utilizes a high-current blocking diode and current limiter for safe connection between the PV array and battery bank. Additionally, the strategy protects against battery overcharge by controlling parallel fans used for PV panel cooling. Experimental results with a 1.62 kW PV system employing lead-acid batteries demonstrate improved efficiency compared to traditional methods. This innovative approach has the potential to significantly enhance energy transfer efficiency, contributing to improved energy sustainability in PV systems.

Novel five-level inverter based on DC power-capacitor series charge-discharge switching strategy

Photovoltaic power generation systems generally include four modules: solar cells, batteries, inverters and controllers. Among them, the inverter converts the direct current generated by the photovoltaic array into a power conversion device that meets the grid-connected requirements for industrial frequency alternating current, which is the key equipment for grid-connected photovoltaic power generation system. Most multilevel inverters in the market today are based on the Neutral-Point-Clamped (NPC) structure to generate voltages at different levels, and then control the magnitude and direction of the voltages through H-bridge circuits. This topology has been maturely used in three-level inverters, but if we want to build five-level, seven-level and more level numbers of inverters based on NPC, the number of capacitors used is increasing, and the circuit becomes more and more complex. Therefore, this paper proposes a five-level inverter topology based on single power supply-single capacitor series charging and discharging. (Power-capacitor series charge-discharge switching, PCSCDS). This inverter has a simple structure, comprehensible control strategy and requires only one DC power supply and one large capacitor to generate a five-level voltage. The topology is subsequently simulated and analyzed by MATLAB/Simulink simulation, which proves the effectiveness of the novel inverter.

Analysis of AI Used in Power Quality Improvement of MPPT Grid Connected PV System

Analysis of AI Used in Power Quality Improvement of MPPT Grid Connected PV System

Techno-economic optimization of photovoltaic (PV)-inverter power sizing ratio for grid-connected PV systems

- The accurate sizing of the inverter, specifically the power sizing ratio (PSR) plays a vital role in maximizing energy production and economic benefits. Existing studies often overlook the complex interplay between maximizing energy capture and minimizing inverter-related economic costs when selecting the optimal PSR. This research presents a techno-economic approach to optimizing the PSR for grid-connected photovoltaic (PV) systems. A simulation model is developed, incorporating real weather data and inverter efficiency curves. The model is calibrated using a Pattern Search Algorithm (PSA) to ensure an accurate representation of real-world inverter behavior by achieving a minimum relative difference of 13.78 % (Norm-RMS) in AC power output. The analysis explores the trade-off between PSR, annual energy yield, and inverter clipping. An optimal PSR of 1.19 is identified, balancing energy capture (up to 2000W inverter capacity) and economic efficiency. This approach promotes cost-effective inverter selection and wider PV adoption.

Enhancing LVRT capability of single stage grid connected PV system in accordance with Indian grid code using proportional direct axis current control technique

Enhancing LVRT capability of single stage grid connected PV system in accordance with Indian grid code using proportional direct axis current control technique

Design and techno-economic analysis of solar energy based on-site hydrogen refueling station

This paper presents a detailed techno-economic review and assessment of a hydrogen refueling station (HRS) powered by a grid-connected photovoltaic (PV) system to address the issues of carbon emissions and energy sustainability in transportation. In the study, the HRS system with 1, 3 and 5 MW PV installed capacity for Ankara, the capital city of Türkiye, is considered for different system lifetimes. In the proposed HRS, on-site hydrogen production is achieved through anion exchange membrane water electrolysis (AEMWE) using a grid-connected PV system, and the produced hydrogen is stored in a cascaded storage system and is utilized at the HRS station. In order to evaluate the cost competitiveness and economic viability of the designed HRS system, the levelized cost of hydrogen (LCOH) is determined by considering the initial investment costs, operating expenses and potential revenue streams. The results show that the HRS capacity, PV installed capacity and system lifetime significantly impact the LCOH. The technoeconomic analysis results show that the best system configuration was determined as 8.54 €/kg H2 in the 20-year long term refueling scenario for a 5 MW installed PV capacity with a daily refueling capacity of 170 kg H2. This study contributes to the development of sustainable energy infrastructure by providing a comprehensive framework for the design, calculation and economic evaluation of PV-integrated hydrogen refueling stations. The results provide valuable information for policymakers, industry stakeholders, and researchers to help achieve a carbon-neutral transportation sector and promote energy sustainability.

Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

UPQC-Based Hybrid Technique to Reduce Power Quality Problems in the Photovoltaic Grid System

In addition to Power Quality (PQ) problems caused by solid state devices in the distribution side of grid connected Photo Voltaic (PV) system, quality of power also suffers in the source side by the usage of converters. Generally, PQ problems mitigation strategy has turned into a critical issue in grid-connected PV systems. This paper proposes a control topology for PQ issues mitigation scheme in grid connected PV systems. The primary goal of this study is to enhance the PQ in power distribution system through eradicating the voltage interruption, voltage swell and sag as well as voltage fluctuation. To accomplish these changes, Unified Power Quality Conditioners (UPQCs) with controllers were utilized. Then the faults are mitigated by the utilization of UPQC with Tree Seeds Algorithm (TSA) — Adaptive Neuro-Fuzzy Interference System (ANFIS). At first, the normal properties of the grid-connected PV systems are dissected. From that point forward, a few interruptions are made in the system and the abnormal characteristics are analyzed. Then the abnormal characteristics of PQ problems are diminished in the utilization of UPQC and TSA–ANFIS controllers. UPQC is one of the best filters topologies which can mitigate and control all PQ problems. The combined algorithms of TSA-ANFIS are used to keep the dc-link voltage in UPQC at consistent level. The proposed hybrid approach is executed in MATLAB/Simulink platform and the performance is studied. Further, the proposed method is compared with existing Elephant Herding Optimization (EHO) technique and also with hybrid EHO-ANFIS techniques. The comparison results showed that the proposed technique is superior to the EHO and EHO-ANFIS techniques.

Integrating Machine Learning and Feature Extraction for Islanding Detection in Grid-Connected Photovoltaic Systems: A Hybrid Intelligent Approacha

Introduction: In recent years, the integration of renewable energy sources, particularly Photovoltaic (PV) systems, into the grid has garnered considerable attention. However, the distributed nature of these grid-integrated PV systems has introduced challenges concerning grid faults and maintenance. Method: This paper aims to present a pioneering approach to augment the monitoring of gridintegrated PV systems by integrating intelligent methods, specifically machine learning and feature extraction techniques. The primary focus of this approach is on islanding detection, which involves promptly identifying grid faults or maintenance challenges and initiating the grid's transition to an isolated mode of operation. To accomplish this, an intelligent signaling method is employed, capitalizing on the capabilities of Distributed Generation (DG) networks. By recording critical signals such as voltage, current, and frequency at common coupling points, fault conditions can be accurately detected. Signals obtained from the grid are subjected to wavelet transformation to extract pertinent information that characterizes fault conditions. These extracted features are then utilized as inputs to machine learning methods, facilitating the proposal of intelligent islanding scenarios. To assess the efficacy of the proposed approach, simulations are conducted on a grid-connected PV system. The recorded signals are meticulously analyzed, and the extracted features are employed to train machine learning models. The performance of these models is evaluated based on their ability to detect fault conditions and initiate appropriate islanding scenarios accurately. Result: The results obtained demonstrate the immense potential of the proposed approach in bolstering the monitoring of grid-integrated PV systems. Conclusion: By synergizing machine learning techniques with feature extraction and intelligent signaling methods with the KNN-Confusion Matrix, all predicted labels match the true labels, resulting in a 100% accuracy. The detection of grid faults and maintenance challenges can be substantially improved, thereby fostering more efficient and dependable operation of these systems.

Peak Load Shaving of Air Conditioning Loads via Rooftop Grid-Connected Photovoltaic Systems: A Case Study

Over the past few decades, grid-connected photovoltaic systems (GCPVSs) have been consistently installed due to their techno-socio-economic-environmental advantages. As an effective solution, this technology can shave air conditioning-based peak loads on summer days at noon in hot areas. This paper assesses the effect of solely rooftop GCPVS installations on the peak load shaving of commercial buildings in arid regions, e.g., the Middle East and North Africa. To this end, the load profile of a large building with 470 kW of unshaved peak power in Mashhad, Iran (36.2972° N, 59.6067° E) is analyzed after commissioning an actual 51 kW GCPVS. The results of this experimental study, exploiting 15 min resolution data over a year, endorse an effective peak shaving of the GCPVS without employing a battery energy storage system, with 12.2–18.5% peak power shaving on a summer day at noon. The monthly GCPVS self-sufficiency is also 10.2%, on average. In accordance with the studied case’s results, this paper presents valuable insights and recommends actionable policies to regions with similar solar potential and electricity supply challenges, aiming to expedite GCPVS development.

Impact of uncertainty in building operation patterns and electric energy demand on the design and techno-economic performance of solar photovoltaic systems in different climates

The optimal design of renewable energy systems for buildings requires accurately estimating building electric energy loads. While this energy demand highly depends on how occupants and facility managers use building systems, operation-related uncertainty is rarely accounted for in the renewable energy design workflow. This paper presents a comprehensive framework to quantify the impact of uncertainty in building operation patterns and electric demand on the techno-economic performance of grid-connected photovoltaic (PV) systems (with and without battery storage). A hybrid methodology is proposed, combining and comparing two approaches to studying building performance under uncertainty: (i) physics-based building performance simulations coupled with parametric variations of occupant behavioral profiles (e.g., austere vs. wasteful), and (ii) data-driven modeling using a large national building dataset. The approach is illustrated and validated on small, medium, and large-sized office buildings in different climate zones (hot dry, mixed marine, and cold humid). Results show that different building operational patterns could lead up to 60% increase in building electric demand, which translated to similar increases in PV energy capacity and the corresponding capital costs of the systems. The findings emphasize the urgent need to better account for uncertainty in building operational patterns in the design workflows of buildings with PV generation and energy storage capabilities.