Grid-connected Photovoltaic Energy Research Articles

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Research on adaptive control strategies for grid-friendly photovoltaic storage network systems

Abstract To address the critical issue of frequency stability in grid-connected photovoltaic energy storage systems, a control strategy based on grid-friendly technologies is proposed. This strategy integrates the principles and topology of traditional Virtual Synchronous Generator (VSG) with adaptive VSG control and pre-synchronization techniques. Firstly, based on the traditional VSG control strategy, the frequency operation curve of the VSG is divided into three intervals corresponding to different control modes: islanding mode, PV-storage frequency stabilization mode, and adaptive regulation mode. Each mode is analysed to understand the impact of different inertia and damping coefficients on system output characteristics. A control strategy that allows for adaptive adjustment of inertia and damping coefficients is designed, referencing the power angle characteristic curve and frequency oscillation curve of a synchronous generator (SG). By introducing inertia elements, the robustness of the system against disturbances is enhanced. Utilizing the operational characteristics of energy storage, the combined PV and energy storage system can better interact with and support the grid. Finally, simulation experiments are conducted by using MATLAB/Simulink. The proposed strategy is compared with traditional adaptive control and VSG control strategies. The results demonstrate that the proposed grid-friendly VSG control strategy offers superior frequency stability, robustness, and response speed.

تحليل ومراقبة عمل العاكس احادي الطور المتصل بالشبكة

This paper summarized the behavior of the inverter as we know the Photovoltaic (PV) has several advantages such as less harmless to the environment. Furthermore, grid-connected PV energy generation represents a renewable energy growing alternative that is becoming more competitive due to the new favorable governmental laws and policies as recently introduced. In this regard, PV systems that supply power directly to the grid are becoming more popular because of the cost reduction due to the lack of a battery subsystem. The main components of a grid-connected PV (GPV) system include a series-parallel connection arrangement of the available PV panels and a power conditioning system in charge to extract and properly transfer the maximum available power present at the PV generator to the grid. A GPV system based on a single central inverter is one of the most prevailing configurations since, under uniform irradiance conditions of the PV panels; it represents a good trade-off between the extracted energy and the design complexity of the power inverter. In this configuration all of the available PV panels are connected to a single power stage. One of the most typical central inverter power converter topology is the single-phase single-stage full-bridge inverter. Keywords: Photovoltaic (PV), inverter, panels, grid-connected PV (GPV).

The static voltage stability analysis of photovoltaic energy storage systems based on NPU algorithm

Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type I classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.

Multi-Objective Cascaded Control Strategy for Grid-Connected Photovoltaic Energy Conversion System Using a Five-Level ANPC Inverter

Multi-Objective Cascaded Control Strategy for Grid-Connected Photovoltaic Energy Conversion System Using a Five-Level ANPC Inverter

Optimizing Grid-Connected Photovoltaic (PV) Battery Energy Storage through Multi-Objective Ant-Lion Optimization (MOALO)

Optimizing Grid-Connected Photovoltaic (PV) Battery Energy Storage through Multi-Objective Ant-Lion Optimization (MOALO)

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.

Solar-Powered Solutions for the Water and Energy Shortage Problem: The Case Study of Nahr El Bared, Lebanon

Solar power is an effective way to reduce the dependency on conventional fuels and mitigate the water and energy shortage issue. The main aim of the current paper is to assess and compare the techno-economic feasibility of rooftop grid-connected photovoltaic energy systems for generating electricity and generating drinkable water in Nahr El Bared, Lebanon. To this aim, the present paper first reviews previous scientific studies associated with the water resources and energy situation to summarize the current status in Lebanon. According to this review, Lebanon's water resources are highly polluted, domestic and industrial sewage is largely untreated, and intolerable agricultural practices further exacerbate the situation. Furthermore, population and economic growth and the continuous utilization of old power plants have led to an increase in the number of hours of power outages in the country. Accordingly, the proposed project aims to evaluate the viability of using solar energy as an alternative solution to the shortage of water and energy in the country. Secondly, the techno-economic performance of the proposed system in the selected region was evaluated based on the variations in financial parameters using RETScreen Experts software. The results demonstrate that 11770–13451kWh/yr could be generated from the solar system, which can help reduce the energy shortage and generate drinkable water. Furthermore, the investment was found to be economically viable and attractive for investors. This paper concludes that solar energy can be able to solve the energy shortage of electricity, reduce the country's electricity costs, and produce freshwater for drinking and domestic use in the country.

Investigation and analysis of interleaved dc-dc boost converter for grid-connected photovoltaic energy system

In this study, we focused on the synthesis of polymeric hydrogels that will support the sorption and controlled release of urea, which is a rich nitrogen source, from aqueous solutions and their usability in agricultural applications. N, N-Dimethylacrylamide (DMAAm) and Starch (St) were selected as monomers, and their superior properties, such as chemical stability, high sorption properties, biocompatibility, and the presence of modifiable groups, were utilized. A redox polymerization technique was used to create a poly(DMAAm-co-St)-based hydrogel that was then modified with acidic and basic agents to improve the properties of starch. The synthesized acid- and base-modified hydrogels were named DSt, DSt1, and DSt2, respectively. Swelling analyses were performed to examine the structural and morphological properties of DSt, DSt1, and DSt2 hydrogels, and Fourier-Transform Infrared Spectroscopy (FT-IR) and Thermogravimetric Analyzers (TGA) were used. Intense cross-linking, porosity, and the presence of hydrophilic groups were successfully detected by instrumental analysis and swelling results. The successful results of urea sorption by DSt, DSt1, and DSt2 hydrogels show that they can both minimize the harmful effects of urea in the environment and contain the nitrogen necessary for plants. At the same time, urea sorption behaviors were evaluated in terms of sorption isotherms and thermodynamic properties, and it was observed that urea sorption conformed to the Langmuir isotherm. The urea release results showed that DSt, DSt1, and DSt2 hydrogels exhibited different release properties in different pH solutions, and these results reached 94% at pH 6–8, 100% at pH 6, and 100% at pH 8–10, respectively. As a result of the gradual decrease in the water resources on the earth, the increase in the use of fertilizers in agricultural production, and the insufficient use of fertilizers, our study draws attention to the development and support of materials that absorb/store water, and forms of controlled release fertilizers and provides potential ease of application

Highly Efficient BBFIC for Grid-Connected Photovoltaic-Battery Energy Storage System Using Hybrid Optimization Assisted Framework

The conventional grid has been combined with a number of renewable energy solutions. As a result, the modern power system is constantly evolving with the help of various power electronics topologies. Due to the growing complexity and new system configuration, the modern power grid is subjected to many challenges and the risk factors are increasing exponentially. In order to evaluate the performance of grid-connected solar photovoltaic (PV) energy systems with battery energy storage system (BESS), highly efficient buck-boost-flyback integrated converter (BBFIC) experiments have been conducted. The whole cybernetics methodology utilized for real-time, closed-loop simulation, validation with other researchers, and numerous optimization techniques are applied in the research work. This paper is developing a new framework that focuses on increasing the voltage gain of the BBFIC for grid-tied PV-BESS. For enhancing the voltage gain of BBFIC, it is planned to optimize the duty cycle of the converter. For optimization purposes, this work deploys a hybrid algorithm termed as “alpha score based on salp-grey wolf optimization (AS-GWO)” that combines the concepts of both “grey wolf optimization (GWO) as well as salp swarm algorithm (SSA).” Finally, it has been demonstrated that the results are superior when compared to other approaches in terms of numerous aspects of measurements.

Research on grid-connected photovoltaic energy storage to stabilize power fluctuations

Due to the instability of photovoltaic power generation, device aging and other factors, the PI parameters of the original PI controller were no longer applicable. So a grid-connected control strategy of photovoltaic energy storage with PI controller parameters optimized by an algorithm was proposed to realize the smooth suppression of photovoltaic power fluctuations. Firstly, the PI controller parameters of the energy storage control part were optimized by using the algorithm, and the optimization process was designed. Then, the second-order low-pass filter was used to distribute the photovoltaic power, and the energy control mode of the energy storage part was designed. Finally, the PI parameter effects of the optimized energy storage control part were displayed by comparing the DC bus voltage in the isolated state, and the power suppression effects under grid-connected condition were displayed by the mixed energy storage power. The rationality of the control mode of the energy storage part was verified by the SOC state diagram of the mixed energy storage. The results showed that the control strategy could effectively suppress pv power fluctuation.

Optimal Design and Techno-Economic Analysis of a Grid-Connected Photovoltaic and Battery Hybrid Energy System

The application of green energy technologies (GETs) has been accepted universally due to the industrial revolution, increasing energy demand, high standard of living, population growth and fluctuation of crude oil prices. In view of this, GETs have been recognized on a global note as a promising and significant alternative to meet ever increasing power demand. This research work is aimed at optimal operation and design of hybrid renewable energy system (HRES) to enhance the performance of the power system while taking into consideration the energy produced, levelized cost of energy (LCOE), return on investment (ROI), solar fraction (SF), net present value (NPV), payback period and saved CO2 emissions based on the photovoltaic (PV) orientation. This is due to the fact that the solar panel generates more electrical output when its surface is perpendicular to the solar radiation. The PV orientation significantly affects the output of a solar farm, for this reason, fixed tilted plane, vertical axis tracking system and two axes tracking system are proposed in this research work to estimate their effects on the technical, economic and environmental performance of HRES. This paper presents a grid-connected HRES that comprises utility grid, PV, battery system (BS) and load. The modelling and simulation of HRES are implemented by using PVsyst.7 energy tools in conjunction with the meteorological data made available by the National Aeronautics and Space Administration (NASA). The research outputs show that the two axes tracking system is more techno-economic feasible when compared with the fixed tilted plane and vertical axis tracking system based on the following results: Energy obtained from the grid of 4.657 MW/yr, LCOE of 0.075 ZAR/kWh, ROI of 862.7%, SF of 0.6781, NPV of 828,881.74 ZAR, payback period of 3.5 years and carbon balance of 732.240 tons. The outcomes of the study can be used by the power system planners and designers as benchmarks to utilize the prospect of solar resources for power sector reform and the industrial revolution.

Optimizing a MINLP problem for the grid-connected PV renewable energy consumption under Spanish regulations

This paper concerns the problem of electricity consumption of a grid-connected photovoltaic renewable energy system under the net billing mechanism of the new Spanish regulation. The goal is determining the optimal photovoltaic solar kit grid-connected sizing with battery storage that minimizes the average energy cost throughout a finite planning horizon fulfilling the energy requirements in each period. To this end, a MINLP problem is formulated and solved to optimality by means of a linearithmic time algorithm based on the description of the extreme solutions of the problem and the binary search of the optimal sizing of the solar kit. It is proved that, under stationary weather and environment conditions, the battery storage is not necessary. This latter finding is aligned with previous results in the literature. Additionally, a computational experiment to analyze the profitability of the solar kit investment is performed. From the computational results, it can be drawn that, when the input data ranges within the intervals offered by the main Spanish retail companies and PV kits suppliers, the solar kit is only worth investing in when the number of periods in the planning horizon is on average above 1000 billing cycles. This experimental finding corroborates that the use of grid-connected PV energy systems is suitable for long-term investments, as claimed in foregoing research.

Techno-Economic Performance Analysis of a 40.1 kWp Grid-Connected Photovoltaic (GCPV) System after Eight Years of Energy Generation: A Case Study for Tochigi, Japan

In this paper, the grid-connected photovoltaic system in Tochigi prefecture, Japan, is presented, and its technical and economic performance after eight years is evaluated. The system has a peak power of 40.1 kWp and has been in operation since 2012. The entire electricity generated by the system was fed into the state grid. The system is suitably monitored for one year (2019) and analyzed using the parameters developed and defined in the standard IEC 61724-1 by the International Electrotechnical Commission. The system’s different parameters included array yield, final yield, capacity utilization factor, and performance ratio of the system. An analytical model with solar irradiation obtained from Power Data Access Viewer was developed to investigate and evaluate the efficiency of the system monthly and annual energy generation by comparing the simulated and measured energy acquired from the inverter. A positive linear relationship is observed between solar irradiation data obtained from Power Data Access Viewer and the grid-connected photovoltaic system energy injected into the utility grid. While an annual total of 48,521 kWh of energy was expected to be generated, 38,071 kWh was generated and injected into the utility in 2019. This study also introduces and explains the mechanism of the Feed-In-Tariff system in Japan. The performance of the grid-connected photovoltaic system under this study was compared with that of other systems installed across the globe.

Evaluation of the cost-effectiveness of grid-connected photovoltaic solar energy in wineries

The objective of this study was to analyze the cost-effectiveness of photovoltaic (PV) solar energy in wineries. The factors analysed were solar radiation, cost of PV installation, prices in the public electricity grid, size of the winery, power of installed panels, influence of the decreased consumption during weekends, and seasonality in the consumption. The study has been based on the European TESLA project, in which 39 energy audits were carried out in wineries in four European countries. A winery of 30,000 hL/year was characterized as a representative winery. The results showed that seasonality was key in the profitability of the system for self-consumption, related to the optimum power to be installed of PV. It was recommended to install, as an optimal PV power, the stable electrical power that is demanded in the daytime period. Optimizing the installed power of PV panels in the representative winery, the percentage of electrical energy consumption covered by photovoltaic energy varied between 16% and 22%. The percentage of the cost of electric energy covered varied between 18% and 24%, with payback values between 18 years (3.1 peak sun hours of solar radiation -PSH) and 10 years (5.6 peak sun hours of solar radiation - PSH). All the factors involved were analyzed.

A framework for the study of householders’ engagement with low-carbon energy demand practices in dwellings with grid-connected photovoltaic energy systems

Introduction: This paper introduces a framework to assess householders’ interaction with grid-connected photovoltaic (PV) systems in domestic buildings, in an attempt to research the question To what extent the engagement of occupants in energy demand management practices is relevant for the operational CO 2 emissions reductions in dwellings with on-grid PV systems? The focus is placed on identifying the engagement with low-carbon operation practices and its relevance for final CO2 emissions from electricity consumption. Specifically, PV electricity Self-consumption and energy conservation practices are studied. To this extent, the research builds on i) the energy services approach and ii) the social practices approach to domestic energy demand. The context of owner-occupied houses which have been retrofitted with PV installations in South Wales, UK is taken as an example. Methods: After a brief conceptual review on domestic energy demand, a mixed-methods framework for the study of householders’ engagement with low-carbon energy demand practices is presented, comprising three key aspects: a) the conditions for the practices to occur, b) frequency with which householders enact the practices, and c) the estimation of final operational CO2 emissions performance. Results: As part of ongoing research work, a framework to study households’ engagement with low-carbon energy demand practices in buildings with grid-connected PV technology is proposed; focusing on the obtention of a socio-technical lecture to complement other sources of in-use assessment data. Conclusions: Besides single case-studies, the framework has potential applications in recognising and grouping households’ engagement profiles. In this way, it is suggested that the framework might facilitate the analysis when extensive assessments are needed; such as in public policy evaluation or demand response studies. Grant Support: This work is funded by CONICYT PFCHA/DOCTORADO BECAS CHILE/2018 - 72180375.

Active Management of Photovoltaic System Variability With Power Electronics

Grid-connected photovoltaic (PV) energy systems present a variable power source. They are often treated as “negative load,” especially for small systems, and require grid reserve margins and management resources to address their stochastic operation. However, inverters linked to PV energy systems probably represent the fastest resource on the grid, able to adjust operation on a cycle-by-cycle basis. This work reviews and quantifies PV variability, with emphasis on fast time scales. Operating needs if batteries are used to manage variability are presented. A reference case establishes the rates of change and storage needs in megawatt-scale installations. Alternative power electronics operating strategies are explored to mitigate variability directly at the source. Variability measures based on a time-window standard deviation lead to fast adaptive strategies. Controls that trade off energy production against variability are presented. Measured data are used to determine and compare performance expectations among several different control methods. Results, validated with scaled experiments, show that fast variability can decrease by about an order of magnitude given the right control strategy, implying battery capacity reduction of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> compared to systems in which batteries manage variation. System-level economics strongly favor large variability reduction linked to modest energy reduction.

Intuitive control technique for grid connected partially shaded solar PV‐based distributed generating system

In this study, an intuitive control technique based on ‘fifth-order generalised-integrator (FOGI)’ is proposed for grid-connected solar photovoltaic (PV) energy conversions system (SECS). In the grid-tied SECS, a single-phase single-stage topology is considered. Moreover, on solar PV array, partially shaded condition is considered, where for global maximum power point tracking, the human psychology optimisation is utilised. The prime intention of the control technique is, feed all the generated solar power at the unity power factor into the grid, which is successfully achieved by the FOGI-based control method. During the evaluation of the performance of control based on FOGI, different adverse conditions related to the grid and dynamic change of solar insolation are considered, where the proposed control technique's performance illustrates the fulfilment of the motive of the work.