PV Power Generation Research Articles

Potential of Using Medium Electric Vehicle Fleet in a Commercial Enterprise Transport in Germany on the Basis of Real-World GPS Data

The intensive electrification of the automotive sector means that the energy system must be able to adapt to the current market situation. The increase in energy demand is a major factor associated with electric vehicles. The study analyzed the operation of a grid-connected facility operating a vehicle fleet providing transport services in the region Halle/Saale, Germany. Measurement data were used in the analysis, including global positioning system data of the vehicles and technical data, including average fuel consumption on a given route section, daily load demand of the industrial facility, and energy generation from photovoltaics. This paper shows the impact of using a battery electric vehicles (BEVs) fleet in the load distribution for the industrial facility considered. The NEDC energy consumption profile for the Nissan e-NV200 were used in this study. Furthermore, the paper presented simulation results allowing one to determine the usage potential, energy demand, and consumption of EVs using real data, reliably representing the processes related to EV daily use. The measurement data were captured using available specialized equipment: Dako-Key (GPS data), PV power generation (Siemens 7KM PAC4200), and load (Janitza UMG 604-Pro) in September, 2018. On this basis, it is possible to identify the effects and variations in load on the power grid during the replacement of combustion vehicle fleets used currently by EVs for the provision of transport services. Three models were presented, making it possible to calculate changes in energy demand for each scenario. In the first model, EVs were charged exclusively from the distribution network. In the second, the energy generation from a renewable source was considered and the possibility of compensating the energy demand of the vehicles from this source was demonstrated. In the third model, the daily load profile and the period of maximum load in the electricity grid were considered. The results are presented in graphical and tabular form. Finally, the potential of using an EV fleet to increase the functionality of a modern industry object was determined and discussed. Based on data for the adopted scenarios, electrification of transport can increase demand for energy by 40.9% for individual enterprises. The electrification of the automotive sector will increase the instantaneous energy demand of businesses, forcing the integration of renewable energy sources during designing new invests.

Modeling, Design and Construction of a Zero-Energy PV Greenhouse for Applications in Mediterranean Climates

This paper concerns the design, modelling, and construction of a high-efficiency mini PV greenhouse performing as a Nearly Zero Energy Building (NZEB). The greenhouse is equipped with a semi-transparent roof-mounted photovoltaic system (3 kWhp) that feeds an air-source heat pump providing cooling and heating. The PV-generated power can be also stored in a battery. A dynamic simulation model (EPlus) of the real greenhouse is developed to predict its performance and investigate beneficial control strategies. The hourly profiles of different variables (energy, temperatures, illuminance) are deeply investigated. The energy-saving strategies, as reflective shading and controlled natural ventilation, prove to reduce the yearly energy needs by 30%. The energy storage model is developed by the Authors and coupled with hourly solar production. The PV electric model includes the temperature effect on module performance, the inverter efficiency curve, and the battery state of charge. The coupled dynamic analysis shows that the photovoltaic plant meets the air conditioning requirements for 94% of the hours of operation and that the energy surplus could feed the grid with approximately 1355 kWhel per year. The validation of the present model will be possible with future measurements and monitoring of the greenhouse once operating in place.

Economic Control for a Residential Photovoltaic-Battery System by Combining Stochastic Model Predictive Control and Improved Correction Strategy

Abstract Photovoltaic (PV) power generation can help reduce households’ electricity from the power grid and thus reduce electricity bills. However, due to the intermittence and time-varying nature of PV power generation, part of the clean energy will be wasted. Especially in some places where PV power is allowed to be sold to the power grid, the PV power that exceeds the feed-in limit will be curtailed to reduce the pressure on the infrastructure of the power grid. Battery energy storage systems (BESSs) as energy buffers have attracted increasing attention to help improve the penetration of PV power to households. This paper presents an adaptive energy management method to minimize the energy cost of residential PV-battery systems. First, the uncertainty of the predictive electricity demand and PV power supply is modeled. Then a stochastic model predictive control (SMPC) strategy is used to determine the optimal power flow of the system. Due to the deviation between the predictive input values and the actual ones, the power flow from SMPC is adjusted based on the improved correction strategy (ICS) proposed in this paper. By comparing with the other two methods (one considers the uncertainty and the other does not), the proposed method can increase the economic benefits of the system by 18% and 63%, respectively. The wasted PV power that exceeds the feed-in limit can also be reduced by 24% and 31%. This verifies the effectiveness of the proposed method to improve the system's economic benefits and self-consumption of clean energy.

Sliding Mode Control Enabled Hybrid Energy Storage System for Islanded DC Microgrids with Pulsing Loads

This paper aims to improve the control of Hybrid Energy Storage Systems (HESS) within an islanded DC microgrid with pulsing power loads. While the PV power generation unit operates as the main power source, a combination of battery and supercapacitor is incorporated to efficiently fulfill the excess power demand based on different loading conditions. In order to ensure the proper battery discharge rate, the conventional low pass filtering approach for decoupling the average and transient HESS current components is replaced with a single rate limiter. By the elimination of low pass filter (LPF), the challenges for non-systematic selection of cut-off frequency is rectified as more tangible factors, such as battery discharge rate, can be incorporated for decoupling purposes. In addition, the associated undesirable phase lag and enhanced nonlinear effect on the current responses caused by the low-order LPFs are avoided. To ensure proper tracking of the rate-limited current setpoints and avoid common windup issues associated with PI controllers in the presence of system nonlinearities and uncertainties, the Sliding Mode Control (SMC) is employed for the battery current regulation. In order to address the sluggish response of linear controller to the fast transients frequently observed on the supercapacitor current setpoints, the same sliding surface structure with modified tuning is utilized. For the supercapacitor cyclic charging instances, an on-the-fly charging approach is deployed, which unlike the previously reported schemes ensures uninterruptible charging in the presence of frequent load transients such as pulsing power loads. To verify the effectiveness of the proposed scheme, the real-time simulation studies using Typhoon HIL-402 is performed.

Grid balancing challenges illustrated by two European examples: Interactions of electric grids, photovoltaic power generation, energy storage and power generation forecasting

The past decade has not only witnessed a great shift in the energy mix, with electricity gaining a more and more prominent role, but has also seen electricity coming increasingly from renewable energy sources, whose proportion is forecasted to reach 50% by 2030. As the integration of photovoltaic energy cannot be deemed successful without the electricity supply being both sustainable and secure, such far-reaching developments prompt legislations and policy makers, including those of the European Union, to make changes to accommodate not only ever-changing technologies, including energy storage solutions, but also new players in the market. Currently, battery energy storage systems are not used for enhancing the precision of photovoltaic power generation schedules, so actors in the market find it difficult to make well-grounded decisions on the viability of utilizing batteries for such a purpose. The innovative novelty of the procedure presented in this paper is that it is suitable for the planning, analysis and evaluation of the schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion-based battery energy storage systems of various capacities. By the examples of two European Union countries, this article studied the deviations of day-ahead and intraday photovoltaic power generation forecasts from the actual electricity generation of 1000 MWp photovoltaic systems. The two main objectives of the project were to investigate the amount of the photovoltaic balancing needs, based on actual data compared to the day-ahead and intraday forecasts, and to establish to what extent sodium-sulfur and lithium-ion-based battery storage systems can decrease those requirements. Furthermore, by using a model for simulation, the potentials of a number of different energy storage capacities (nominal net storage capacities from 10 MWh to 10,000 MWh) for reducing grid balancing needs were assessed. The practical use of the model is that its application makes it possible to create a dynamic design, analysis and assessment system mechanism that can allow one to establish the multilevel schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion energy storage systems linked to solar power plant systems. Real photovoltaic data from Belgium and Hungary were used to find out how the accuracy of PV power generation forecasts influence the level of the annual utilization of energy storage systems.

What are the investment risk differences of solar PV power generation projects between constructional and operational periods? An empirical study in China

Purpose Under the pressure of environment degradation and energy consumption rises, solar photovoltaic power generation (SPPG) has been seen as a strategic emerging industry in China. However, the SPPG projects have many uncertain factors in the process of the life cycle. The purpose of this paper is to evaluate the investment risk comprehensively. Design/methodology/approach Specifically, this paper firstly identified the risk factors and consequences of SPPG project investment and constructed the investment risk factor evaluation index system. Secondly, the factor analysis method was used to reveal the key investment risk factors and risk consequences. Then, the research hypotheses and conceptual model were proposed, and the empirical analysis was conducted based on the structural equation model (SEM). Findings The results showed that the six key risk factors (political, economic, technological, management, market and natural) have a significant impact on the risks during the whole period. The influence degree of risk factors in the operational period is such that market factor > political factor > economic factor > management factor > natural factor > technical factor. In addition, the risk of constructional period has a significant impact during the risk of operational period. Except for natural factors, all risk factors are correlated among the mutual influence relationship of risk factors. Finally, this paper puts forward management implications based on the above conclusions: effectively identify investment risk factors and comprehensively improve risk management ability; (refine the risk factors of SPPG project investment and develop targeted risk prevention and control strategies; define risk management objectives and make relevant strategic plans. Research limitations/implications Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications It can also help stakeholders propose risk prevention strategies throughout the entire process, to promote the sustainable development of SPPG projects. Originality/value This study focuses on the investment risk of SPPG projects, by building an evaluation index system to identify key risk factors, and then establishes a SEM covering constructional period risks and operational period risks to better explore SPPG project risks, in an effort to reveal the mutual influence relationship and influence path of investment risk factors of the projects.

Design optimization and uncertainty analysis of multi-energy complementary system for residential building in isolated area

Due to extreme climatic and geographical factors, conventional power sources, which typically use large power-grids, have difficulties to supply energy to isolated areas. In addition, many isolated areas rely mainly on fossil fuel to ensure adequate energy-supply, which involves transport across long distances and high costs. Thus, finding a multi-energy complementary system (MECS) for residential buildings can help ensure the survival and improve the well-being of about 1.5 billion people that are currently living in isolated areas worldwide. In this study, a rough interval-Copula stochastic planning (RI-CSP) programming model is proposed to address this problem. Its purpose is to optimize the MECS in isolated areas, with the aim to reduce cost and provide high-quality energy. RI-CSP can deal with multiple uncertain parameters that are characterized by rough intervals and probability distributions in the MECS. It can also reveal the combined effect of different random variables. To prove the feasibility of RI-CSP, it was applied to a residential building on Yongxing island in the South China Sea. 24 different scenarios were selected that consider four renewable energy scarcity levels and 2 constraint-violation levels (p). Then, the optimal scenarios with minimum total system costs were selected. This study can help decision makers find better configurations for many alternative power-generation scenarios. The results show that the uncertainties that exist in the MECS have a significant effect on the power-generation configuration and total system cost. The results indicate that the total cost of MECS decreases with the increase in scarcity levels. Our results also revealed that, as the scarcity levels for renewable energy decrease, the fraction of PV power-generation gradually increases.

Evaluation of the viability potential of four grid-connected solar photovoltaic power stations in Jiangsu Province, China

Grid-connected solar photovoltaic (GCSPV) power generation is conducive to the large-scale promotion of PV power generation. The aim of this study was to analyze the feasibility of the construction of 1-MW GCSPV power stations at four locations in Jiangsu Province, China. The economic, environmental, sensitivity, and risk analyses of the proposed systems were performed using the RETScreen clean energy management software. The GCSPV systems in Xuzhou are more economically viable than those in the other regions under consideration, whereas the systems in Nanjing are relatively less economically feasible. The GCSPV power stations in Xuzhou have the largest reduction in annual greenhouse gases emissions, because of producing the most electricity from the proposed system. Sensitivity analysis results on the net present value (NPV) show significant variations owing to the variations of the electricity export rate (EER) and initial costs (ICs) parameters. The NPV of the proposed system increases from $707,589 to $2,046,766 with an increasing EER, while the NPV of the system decreases with increasing ICs at the same EER. The risk analysis shows the largest influence on the NPV of the project is the change of the EER with positive correlation, while the least impact on the NPV is the debt term with positive correlation.

Techno-economic and environmental assessment of utilizing campus building rooftops for solar PV power generation

ABSTRACT The academic institute, a huge consumer of energy, is still depending upon the grid and/or conventional fuel despite having potential areas of a solar PV installation. No such study has been reported in Nepal which has techno-economically discussed the potential use of academic institute rooftops for solar PV installation. In this study, we have investigated the techno-economic and environmental assessment of a 1 megawatt (MW) rooftop solar PV using PVsyst for Purwanchal Campus, Nepal. It has been estimated the plant will produce about 1,660 MWh of usable AC electricity annually and out of which 95% can be supplied to the grid. The remaining can fulfill the demand of the campus at sunshine hours. For this, a capital investment of about USD 961,404 is needed for the plant with an annual operating cost of USD 8,008. This project gives a 190% return of investment within its life of 25 years with a Levelized cost of energy (LCOE) of 0.069 USD/kWh. The performance ratio and payback period of the project are calculated at about 0.765 and 8.4 years, respectively. The system will produce about 97% less emission than that of diesel generator, which is currently being used for power backup. It is concluded that a 1 MW grid-connected solar PV system is feasible for the studied campus.

Optimum Configuration of a Renewable Energy System Using Multi-Year Parameters and Advanced Battery Storage Modules: A Case Study in Northern Saudi Arabia

Understanding the impact of global warming and the availability of renewable sources has motivated many countries to utilize solar and wind as an alternative to conventional energy sources. One county at the forefront in the development of these technologies is the Kingdom of Saudi Arabia (KSA). In KSA, investing in wind and solar energy is important because the country’s load demand is rapidly increasing, coupled with the over-reliance on fossil fuels. By fully utilizing the multi-year and the advanced battery storage modules in HOMER, in this paper, the techno-economic viability of utilizing a PV/wind/diesel/battery system for a remote location of Al-Jouf in the KSA has been investigated. The novelty of the work presented in this paper is that for the first time a PV/wind/diesel/battery system has been designed for the KSA, taking into account the impact of multi-year and advanced battery storage parameters such as the increase in fuel price, PV degradation, increase in the consumer load and battery degradation. Besides, due to the high temperatures experienced at Al-Jouf during the summer season, this paper investigates the sensitivity of ambient temperature on the system’s performance. The result shows that the multi-year input and battery degradation parameters have a significant impact on the system output over the 25 year lifetime of the project. PV production has dropped by 11.3%, while diesel production rose to 38% thereby increasing fuel consumption and CO2 emission. The system’s LCOE and NPC are 0.204 and USD206,919 respectively. According to the sensitivity analysis, ambient temperature has a significant impact on battery performance and PV power generation.

Residential Air Conditioning System Integrated with Packed Bed Cool Storage Unit for Promoting Rooftop Solar PV Power Generation

The increase in the share of renewable-based power in the gross power generation in most countries causes significant concerns over the addition of renewable power with the grid, results in stability issues in most developed nations. Energy storage is an emerging technology that is considered the ultimate solution in developing microgrids with distributed renewable power generation. The cool thermal storage plays a vital role in economically promoting renewable power among different storage units. The major objective of the research work is to demonstrate the integration of residential air-conditioning systems with packed bed cool storage units to promote rooftop solar power generation for residential space cooling applications. In order to achieve the said objective, an experimental investigation was made to study the charging/discharging characteristics of a packed bed cool-storage unit combined with a chiller and a cooling coil unit suitable for small capacity air-conditioning applications. The system consists of encapsulated spherical capsules filled with a phase change material blended with distilled water and pseudomonas (nucleating agent) and the heat transfer fluid as a combination of distilled water and Mono-ethylene glycol. A cooling coil unit was connected to the cool-storage tank to transfer cool energy from the storage tank to the space to be cooled when there is a demand. The important parameters, such as instantaneous and cumulative heat transfer during the charging/discharging processes, are presented. The average COP values of the chiller during the charging operation were estimated as 1, 0.93, and 0.89 when the HTF setpoint temperatures were -6°C, -9°C, and -12°C, which shows a decrease in performance as the setpoint temperature decreases. During the discharging process, a cooling load of 2.25 kW is obtained during the first cycle of operation and gradually reduces to 0.3 kW during the sixth cycle of operation. The increase in the HTF temperature during each cycle of operation indicates that the Phase Change Material (PCM) in the balls cannot release the heat as per the demand after a certain period of discharging. Hence, decreasing the internal thermal resistance by suitable measures is essential to achieve uniform heat flux and to operate the system successfully

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

Multi-step depth model predictive control for photovoltaic power systems based on maximum power point tracking techniques

To increase the PV power generation efficiency, this paper proposes a multi-step depth model predictive control algorithm based on maximum power point tracking techniques. The multi-step depth model predictive control algorithm combines deep neural networks and model predictive control to improve control performances. Deep neural networks can improve prediction accuracy and can reduce steady-state oscillations. The model predictive control technique can improve the tracking speed and dynamic performance of photovoltaic power systems. Compared with a perturbation and observation algorithm, an incremental conductance algorithm, and a model predictive control algorithm, the simulation results verify that the multi-step depth model predictive control algorithm can effectively obtain the fastest tracking speed (0.001 s), the smallest steady-state oscillation (6 W), and the highest power generation efficiency (99.06%) under the step, ramp, sine, and real stringent dynamic irradiance and temperature cases. A hardware-in-the-loop experimental test verifies the validity of the multi-step depth model predictive control algorithm.

Modeling and Control of a Multiport Converter based EV Charging Station with PV and Battery

As an environmental friendly vehicle, the increasing number of electrical vehicles (EVs) leads to a pressing need of widely distributed charging stations, especially due to the limited on-board battery capacity. However, fast charging stations, especially super-fast charging stations may stress power grid with potential overload at peaking time, sudden power gap and voltage sag. This project discusses the implementation and modeling of space vector modulation to multiport converter based EV charging station integrated with PV power generation, and battery energy storage system. In this paper, the control scheme and combination of PV power generation, EV charging station, and battery energy storage (BES) provides improved stabilization including power gap balancing, peak shaving and valley filling, and voltage sag compensation. As a result, the influence on power grid is reduced due to the matching between daily charging demand and adequate daytime PV generation. Simulation results are presented to confirm the benefits at different modes of this proposed multiport EV charging circuits with the PV-BES configuration with SVM technique. Furthermore, SiC devices are employed to the EV charging station to further improve the efficiency.

Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting

The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.

Agrophotovoltaic Designs : Irradiation Analysis on and under PV Modules

To secure agricultural benefits and PV power generation from an agrophotovoltaic system, it is vital that the PV modules and the crop are both irradiated by light. The configuration and design of PV modules have a practical effect on the irradiation uniformity in an agrophotovoltaic system. Irradiation non-uniformity can cause crop growth delays and quality deterioration during different cultivation periods. This paper proposes the design considerations and parameters of an agrophotovoltaic system to optimize crop yield and power energy production. The design parameters are the PV module installation height, ground coverage ratio, tilt angle, azimuth angle, and PV module installation type. The PV module installation type is classified into dual-axis tracking, single-axis tracking, south fixed, and horizontal fixed. We configured a 3D modeling simulation platform to visualize and analyze irradiation under the PV module. Finally, we suggest three evaluation parameters: The overall mean under PV modules, the overall standard irradiation deviation under PV modules, and the transposition factor of the agrophotovoltaic designs.

Study on matching characteristics of photovoltaic disturbance and refrigeration compressor in solar photovoltaic direct-drive air conditioning system

This paper presents a 3 HP solar direct-drive photovoltaic air conditioning system which operates without batteries, ice thermal storage is used to store solar energy. The refrigeration compressor will suffer from loss of power even cannot startup or shut down if the PV power generation suddenly fluctuates. In the case of the solar radiation fluctuations to keep the system running continuously and steadily, that requires a proper system design to match the power consumption of solar air conditioning system with a proper PV capacity, matching an adaptive controller and a suited compressor simultaneously. Solar air conditioners with different capacity of PV panel, with and without MPPT controller and different types compressors were built and tested outdoors to experimentally investigate the matching characteristics of photovoltaic disturbance and refrigeration compressor. The experimental results of the system with a variable speed compressor and a MPPT controller have shown good ice-making performance and reliable operation as well as a great improvement in the available solar energy. Finally, the highest system COP reached 0.289 when the cumulative daily total radiation was 18.2 MJ/m2 in Kunming, China. The research work shows that the direct drive ice storage by solar photovoltaic system has a certain application prospect in the regions where the electricity is tight but requiring cooling.

The resources, exergetic and environmental footprint of the silicon photovoltaic circular economy: Assessment and opportunities

The photovoltaic industry has shown vigorous growth over the last decade and will continue on its trajectory to reach terawatt-level deployment by 2022–2023 and an estimated 4.5 TW by 2050. Presently, its elaboration is driven primarily by cost reduction. Growth will, however, be fuelled by the consumption of various resources, bringing with it unavoidable losses and environmental, economic, and societal impacts. Additionally, strong deployment growth will be trailed by waste growth, which needs to be managed, to support Sustainable Development and Circular Economy (CE). A rigorous approach to quantifying the resource efficiency, circularity and sustainability of complex PV life cycles, and exploring opportunities for partially sustaining industry growth through the recovery of high-quality secondary resources is needed.We create a high-detail digital twin of a Silicon PV life cycle using process simulation. The scalable, predictive simulation model accounts for the system's non-linearities by incorporating the physical and thermochemical principles that govern processes down to the unit operation level. Neural network-based surrogate functions are subsequently used to analyse the system's response to variations in end-of-life and kerf recycling in terms of primary resource and power consumption, PV power generation capacity, and CO2 emission. Applying the second law of thermodynamics, opportunities for improving the sustainability of unit operations, the larger processes they are the building blocks of, and the system as a whole are pinpointed, and the technical limits of circularity highlighted. We show the significant effects changes in technology can have on the conclusions drawn from such analyses.

Modelling And Simulation Of Pv-Bes Based Microgrid System Operating In Standalone Mode

This work presents, a novel PV-Battery Energy Storage based microgrid system operating in standalone mode. The output of Solar PV changes with varying atmospheric condition. A boost converter is utilized as a DC-DC converter which extracts the maximum power coming out from the solar PV array, with the use of P&O algorithm. Battery Energy Storage System (BESS) is introduced to supply load uninterruptedly. In this project, lead acid battery is used. The energy management is done by the battery control mechanism. Battery and load are connected in parallel with the dc-dc boost converter. Based on the power generation in PV, battery gets charged and discharged. There are two cases considered in this project. In first case, both the load and battery is supplied with generated PV power. In second case, load is supplied with the help of both battery and the Solar PV. The proposed system is modeled and simulated in MATLAB. Parameters like battery current and voltage (Vbat,Ibat),load current and voltage (Vdc,Iout), SOC level of battery are observed and analyzed.

Analysis and control of a grid interfaced hybrid grid tied inverter based PV system with anti-islanding grid protection

Recent research on the design and simulation of grid-connected PV systems. The research evaluates the effect on electricity generation and power quality of the primary photovoltaic system parameters. It also explores the use of the Battery Energy Store (BESS) method to promote active power production by charging and unloading the surplus energy and reducing PV power generation. In comparison to the IGCI invert and capacitive CGCI (inverter interleave grid-connected), the proposed HGCI is distinguished by a large spectrum of operation and low operating voltage. The proposed HGCI has distinctive features. Cost along with maintenance costs of the system can be high based on these popular features. Furthermore, at the same time it can be exchanged active power and reactive energy, unbalancing power and harmonic influence. It addresses and proposes the analyzes of the HGCI framework, and architecture and control parameter system (HGCI).