Photovoltaic Grid Research Articles

Effect of Temperature on Condensed State Structure and Conductivity Characteristics of Micron-Level Biaxially Oriented Polypropylene Films.

Polymer-based dielectric films are increasingly demanded for devices under high electric fields used in new energy vehicles, photovoltaic grid connections, oil and gas exploration, and aerospace. However, leakage current is one of the significant factors limiting the improvement of the insulation performance. This paper tested the leakage current and condensed state structure characteristics of biaxially oriented polypropylene (BOPP) films and obtained the nonlinear characteristics of leakage current of BOPP films in the range of 40-440 V/μm and 40-110 °C. The conduction mechanisms of BOPP films are hopping conductivity within 40-120 V/μm and the Poole-Frenkel effect within 120-440 V/μm, and the threshold electric field of the two kinds conduction mechanisms decreases from 120 to 80 V/μm above 70 °C. The in situ FTIR results indicate that the structure of the BOPP films including band structure, amorphous phase, and isotacticity indices changes above 70 °C, corresponding to the threshold electric field of both conductivity mechanisms gradually decreasing from 120 to 80 V/μm at 70 °C. This study can provide guidance for the optimization design to suppress the leakage current of BOPP and improve its insulation performance.

Integrated inventory and capacity management for hydrogen production under consideration of fluctuating electricity prices

Integrated planning of production and inventory capacities for a risk-hedging supply chain is addressed with the presented paper. Of particular interest is a hydrogen production system, which faces uncertainties in energy supply and costs. In assessing hydrogen production through electrolysis, the model takes into account two sources of energy: stochastic photovoltaic (PV) supply and grid electricity with a stochastic price. As hydrogen produced using PV energy can be categorised as ‘green’, whereas the same is not guaranteed for hydrogen produced through grid energy, the use of PV energy is prioritised in the model. Models with two approaches for determining a threshold price for grid energy usage were developed. One method links the threshold price to Electrolyser utilisation using PV energy, while the other links it to inventory levels at a given time. The effectiveness of the models in a real-world hydrogen production environment was assessed through various performance metrics, yielding the following findings: (1) Decisions based solely on cost may lack adequacy due to the trade-offs in performance measures. (2) Inventory capacity has a major impact on renewable energy usage. (3) With appropriate inventory capacity, a threshold price based on inventory levels can ensure complete utilisation of renewable energy.

Study on the influence of distributed photovoltaic access grid on voltage quality based on virtual synchronous control strategy

Study on the influence of distributed photovoltaic access grid on voltage quality based on virtual synchronous control strategy

Design and performance analysis of a novel office building integrated photovoltaic system

ABSTRACT This paper describes a novel office building attached photovoltaic (OBAPV) system consisting of the photovoltaic (PV) array, office building, electric vehicle and power grid. Impact evaluation of three factors is launched, including the photovoltaic module layout, the tilt angle of PV module and the number of energy storage batteries (ESBs). The evaluation results show that the OBAPV system with twenty ESBs, two rows arranged horizontally and 10° PV module tilt angle is more superior. The operation characteristics, energy flow and economic performance of the OBAPV system are analyzed. The analysis results reveal that the OBAPV system has an annual active electricity production of 219 MWh, of which approximately 54.6 MWh is used by the office electrical equipment, and 164.4 MWh is transmitted to the power grid. The OBAPV system has an annual performance ratio of 73.8%. The operation time with average PV module temperature of 0 ~ 45°C occupies 93% of the annual total operation time of the PV array. The active electricity output and energy loss account for 78.1% and 21.9% of the annual total electricity production. The levelized electricity cost and payback period of the OBAPV system are 0.03575 $/kWh and 7.6 yrs, revealing reasonable economic feasibility.

Nature-Inspired Optimization with Deep Learning-Enabled Sustainable Predictive Model for Intelligent Systems

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

A twenty-year dataset of hourly energy generation and consumption from district campus building energy systems

Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power generators and beyond. To address this challenge, this study has collected and described a twenty-year dataset (from 2002 to 2021) consisting of hourly energy generation and consumption profiles from a campus distributed energy system, covering the entire lifespan of on-site generators. The dataset includes supply-side data such as gas consumption from combined heating and power (CHP) units (fuel cell, gas engine), absorption chiller, gas boiler, solar photovoltaic generation, CHPs output, and grid electricity import. Additionally, real-life electricity, hot water, space heating, and cooling energy load profiles from individual buildings within the campus were also collected. This long-term dataset can be utilized in various scenarios, providing researchers and policymakers with comprehensive insights into the energy efficiency of distributed energy systems.

Working Performance of Synchronous Generators and Grid Photovoltaic in Microgrid

Working Performance of Synchronous Generators and Grid Photovoltaic in Microgrid

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

Probabilistic load flow analysis for evaluation of photovoltaic grid connection applications

Uncertainty in power system analysis is increasing, mainly due to decentralized generation and the related spatial distribution of power plants, as well as the inherent volatility of renewable energy sources. In Austria, this development is reflected in a high number of photovoltaic grid connection applications in recent years. On the consumption side, there are changes in grid loads due to the increasing electrification of society, which is reflected, for example, in a rising number of electric vehicles. These aspects of uncertainty in generation and consumption must be considered in the planning process of distribution grids. A proper way to include uncertainty in the hosting capacity analysis of power grids are probabilistic load flow calculation methods. This paper provides an introductory overview of probabilistic load flow analysis, available calculation methods, required input parameters and the interpretation of results. This paper focuses on photovoltaics, so a correlation analysis of historical power generation measurements of spatially distributed photovoltaic power plants in Austria is presented. Furthermore, an insight into the development of Austrian photovoltaic grid connection applications from recent years is given.

An up-to-date perspective of levelized cost of hydrogen for PV-based grid-connected power-to-hydrogen plants across all Italy

Green hydrogen holds potential for decarbonizing the energy sector, but high production costs are a major barrier. This study provides a comprehensive techno-economic-financial-environmental analysis of PV-based grid-connected hydrogen production plants, targeting hard-to-abate industries having constant hydrogen demand across all Italy. Using real hourly data, the Multi Energy System Simulator (MESS), an in-house developed rule-based tool, was employed and integrated with Genetic Algorithm for optimal plant sizing. The aim is to minimize the Levelized Cost of Hydrogen (LCOH) while complying with regulatory frameworks for green hydrogen incentives access. Key findings show that hydrogen storage is more advantageous than battery storage for supply-side flexibility, and the optimal PV-to-electrolyzer size ratio ranges from 1.8 in Southern Italy to 2.1 in Northern Italy, with hydrogen tank designed for daily storage. Considering photovoltaic, electrolyzer and battery aging models grid dependence increases by 60 % when comparing the first and worst year of operation and leads to a 7 % increase in LCOH. Transitioning from the strictest (hourly) to the least stringent (annual) temporal correlation increases certified green hydrogen by 22 %, while LCOH decreases by only 3 %, suggesting that the environmental benefits of stringent temporal requirements outweigh their moderate economic drawbacks. These findings underscore the need for additional national-level incentives to allow the deployment of this technology and achieving cost parity with grey hydrogen.

Adaptive Generalized Extended State Observer for a Single Phase PV Grid-connected System Operating Under the Sudanese-Sahelian Climate of Cameroon

Knowing the health of a system allows to guarante its efficiency and sustainability. The state observer is one of several techniques used by authors to estimate system state. This paper focuses on the problem of simultaneous states estimation of DC (Direct Current) and AC (Alternating Current) sides of a single-phase Photovoltaic (PV) grid-connected operating under the Sudanese-Sahelian climate of Cameroon. A generalized extended state observer (GESO) has been designed to simultaneously estimate the three states and the three disturbances of the system. A good estimation of the state and disturbances is achieved by the appropriate choice of the observer gain and the disturbance compensation gain resulting from the correct pole placement. The GESO robustness has been tested by varying the PV voltage and grid voltage. When there are no input fluctuations, the estimation errors of nominal states and disturbances converge to zero. The fluctuation in PV voltage resulting from partial shading has a significant impact on the boost converter current. The boost converter current varies proportionally with the drop in voltage due to partial shading from 55% to 59%. Under the grid voltage fluctuation, the boost converter current remains stable while the DC bus voltage and inverter current are significantly affected. The proposed GESO prove its robustness to perturbations from the PV array and grid side into the Single-Phase PV Grid-connected System. This paper contributes to the study of observers applied to the PV system and points the way to future work on diagnosing faults in PV systems operating in Cameroon's Sudanese-Sahelian climate.

Application of radio frequency capacitively coupled Ar+H2 plasma on rapid annealing of Cu-based photovoltaic grid line

With the continuous thinning of photovoltaic silicon wafers for cost reduction, copper based photovoltaic grid lines (Cu-PGL) require annealing and softening treatment. However, for the commonly used short circuit annealing method in industry, some issues exist such as air surface oxidation and environmental pollution, which need to be addressed for large-scale development of high-performance Cu-PGL. In this study, we propose a medium-pressure capacitively coupled plasma driven by radio frequency (RF) for plasma rapid annealing of Cu-PGL to meet solar cell performance requirements. The experimental results show that the yield strength of Cu-PGL decreases from 336.5 MPa to 59 MPa after plasma rapid annealing while electrical conductivity increases from 87 %IACS (International Annealed Copper Standard) to 116 %IACS at the optimal condition of discharge pressure of 1.0 kPa, and input power of 150 W with wire speed of 50 m/min. The plasma annealing mechanism of Cu wire was disclosed by combining spectral diagnosis of the plasma and Cu wire performance characterization.

Control of transient power compensation for virtual impedance VSG in Quasi-Z-source photovoltaic grid integration system

The virtual impedance solution effectively addresses the power coupling issues and improves small-signal stability in grid-connected Virtual Synchronous Generator (VSG) systems but can deteriorate the system's transient synchronous stability, particularly in high-voltage lines with significant line inductance exacerbating this effect. To address these contradictions, the paper first analyzes the power coupling mechanisms of the system, highlighting how the line reactance ratio (R/X) and power angle (δ) affect system stability, and describes the transient stability of VSGs using the equal area criterion. Leveraging the advantages of quasi-Z-source inverters, they are applied to VSG grid-connected systems, proposing an improved VSG control strategy tailored for quasi-Z-source photovoltaic grid-connected systems. This strategy introduces virtual active power at the point of common coupling (PCC) and uses it as power feedback in the control loop, instead of using real active power. The control model is validated on an experimental platform, demonstrating that the VSG control strategy with transient power compensation significantly enhances system transient stability while preserving small-signal stability. This comprehensive approach bridges the gap between maintaining essential stability functions and enhancing dynamic performance under various operational conditions.

Exploration of Factors Affecting Resonance in Photovoltaic Cluster Grid Integration System

Abstract A significant number of distributed photovoltaics being connected to the grid leads to a more complex harmonic content in the photovoltaic cluster grid integration system. When a specific harmonic frequency approaches the system resonance frequency of the system, a resonance phenomenon occurs. This paper conducts an analysis of the complex resonance coupling relationship. Subsequently, it separately examines the impact of grid impedance, filter capacitor parameters, and the number of parallel inverters on the resonant frequency. It is verified through simulation that the aforementioned factors will bring about a change in the resonant frequency.

Analyzing variability and coordinated demand management for various flexible integrations of residential distributed energy resources

Deploying distributed energy resources (DERs) is crucial for decarbonizing the building sector, but challenges arise due to the energy mismatch between on-site generation and consumption. Impacts of large-scale development of distributed energy technology are seldom evaluated from a system-wide perspective. This study primarily utilizes high-resolution meter data to analyze residential energy consumption behaviors and variability of solar photovoltaics (PV), heat pump and fuel cell throughout the year. Then, rolling horizon optimization is applied to harness demand flexibility through scheduling developed integrated energy systems, considering synergies between on-site energy resources and demand side management. The techno-economic performances of the integrated systems are investigated by adopting wholesale spot prices for PV grid feed-in. Results show that flexible schedule of integrated energy systems can deliver multiple benefits, including energy saving, cost reduction and grid-support interaction, improvement in PV self-consumption through flexible coupling can exceed 40 % compared to the measured results. The effectiveness of demand-side solutions strongly depends on the density and variability of household energy consumption, micro cogeneration presents a greater potential of cost-saving and carbon reduction in winter months. Findings emphasize the importance of coordinated demand management strategy in enhancing effectiveness of energy usage and grid-friendly interactions under widespread DERs implementation.

Capacitor State Monitoring of MMC Photovoltaic Grid Connected Inverter Based on Capacitance Voltage Difference

Abstract Due to the high degree of modularity and high energy transmission efficiency, MMC is widely used in the field of photovoltaic grid connection. This paper focuses on the abnormal fluctuations of capacitor voltage caused by capacitor aging in MMC submodule, and comes up with a capacitance monitoring method based on the voltage fluctuation difference of submodule capacitors. The validity of the raised method is validated through simulation and experiments.

Spider Wasp Optimizer-Optimized Cascaded Fractional-Order Controller for Load Frequency Control in a Photovoltaic-Integrated Two-Area System

The integration of photovoltaic (PV) systems into traditional power grids introduces significant challenges in maintaining system stability, particularly in multi-area power systems. This study proposes a novel approach to load frequency control (LFC) in a two-area power system, where one area is powered by a PV grid and the other by a thermal generator. To enhance system performance, a cascaded control strategy combining a fractional-order proportional–integral (FOPI) controller and a proportional–derivative with filter (PDN) controller, FOPI(1+PDN), is introduced. The controller parameters are optimized using the spider wasp optimizer (SWO). Extensive simulations are conducted to validate the effectiveness of the SWO-tuned FOPI(1+PDN) controller. The proposed method demonstrates superior performance in reducing frequency deviations and tie-line power fluctuations under various disturbances. The results are compared against other advanced optimization algorithms, each applied to the FOPI(1+PDN) controller. Additionally, this study benchmarks the SWO-tuned controller against recently reported control strategies that were optimized using different algorithms. The SWO-tuned FOPI(1+PDN) controller demonstrates superior performance in terms of faster response, reduced overshoot and undershoot, and better error minimization.

Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances.

Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.