Photovoltaic Generation System Research Articles

An algorithm to extract the maximum power from the PV-based generation systems under non-uniform weather

This paper presents a fast and simple algorithm to extract the maximum power under non-uniform weather from the photovoltaic (PV) based generation systems. The proposed algorithm’s three stages are the scanning stage, the tracking stage, the detecting and avoiding the hidden points stage. The hidden points are caused by a transition between the global maximum power point (GMPP) and a local maximum power point (LMPP) when the partial shading conditions (PSCs) are changed. This transition cannot be observed by monitoring only the power difference of the PV generation system. Simulation results with comparisons to other algorithms developed for global maximum power point tracking (GMPPT) under PSCs are provided to clarify and show the effectiveness of the proposed GMPPT algorithm. The average tracking speed of the proposed algorithm is two times faster than the compared MPPT algorithms, with about 2% more power generated with no additional cost. Moreover, the proposed GMPPT algorithm is implemented in real-time using National Instruments (NI) CompactRIO in field-programmable gate array (FPGA) mode to confirm the applicability of the proposed work.

Mathematical modelling, performance evaluation and exergy analysis of a hybrid photovoltaic/thermal-solar thermoelectric system integrated with compound parabolic concentrator and parabolic trough concentrator

This article discusses the electrical and thermal performance of a hybrid concentrator photovoltaic thermal and solar thermoelectric generator (CPV/T-STEG) system using a compound parabolic concentrator (CPC) and a parabolic trough concentrator (PTC). For the first time, the idea of merging imaging and non-imaging concentrators for a CPV and TEG hybrid system is examined, providing an option to retrofit or remodel existing PTC-based CSP systems. The thermal resistance concept is applied to establish a steady-state mathematical model of the proposed hybrid CPV/T-STEG system. A Newton-Raphson iterative approach is employed to solve the mathematical model and compute the temperature in every layer of the hybrid system. After validation, the mathematical model is employed to evaluate the overall performance of the hybrid system. The modelling results revealed that the electrical and thermal output of the developed hybrid system were higher by 2 and 1.6 times, respectively, when compared with the prior parabolic trough-based hybrid CPV/T-STEG system described in the literature. The effects of ambient temperature, wind speed, flow rate, number of TEGs, and solar concentration ratio on the electrical and thermal performance were investigated. The optimal number of TEGs required for maximum electrical performance under different solar concentration ratios is also obtained. Finally, the hybrid system’s exergy efficiency is investigated for various solar concentration ratios. The simulation results revealed that the increase in the Reynolds number from 100 to 2000 improves the net electrical and thermal efficiency by 10.21% and 5.7%, respectively. At a fixed solar concentration ratio (CCPC=4suns and WPTC=2WCPC), the electrical efficiency of TEG drops by 81.4%, but the thermal efficiency increases by 16.81%, provided that the number of TEGs is increased from 1 to 17. The highest exergy of the hybrid system is 8.36% when CCPC=2suns and WPTC=2WCPC. Due to the poor efficiency of commercial TEGs, the overall exergy efficiency of the hybrid system decreases with an increasing solar concentration ratio. In the proposed hybrid system, a fluid channel separates both the PV and TEG modules; hence the electrical conversion efficiencies of both modules are not closely related.

Efficient Integration of PV Sources in Distribution Networks to Reduce Annual Investment and Operating Costs Using the Modified Arithmetic Optimization Algorithm

The optimal integration of photovoltaic generation systems is a challenge for distribution utilities since these devices have a direct impact on company finances due to the large amount of investment required at the beginning of the planning project. In this investigation, the problem regarding the optimal siting and sizing of photovoltaic resources in medium-voltage levels is addressed from an economical point of view, where the optimization model that represents said problem corresponds to a mixed-integer nonlinear programming model. The maximum allowed size for single photovoltaic units in the distribution network is set at 2400 kW. The investment costs, energy purchase costs and maintenance costs for photovoltaic units, are considered in the objective function. Typical constraints such as power balance, generation capacities, voltage regulation, among others, are considered in the mathematical formulation. The solution of the optimization model is addressed by implementing a modified version of the Arithmetic Optimization Algorithm, which includes a new exploration and exploitation characteristic based on the best current solution in iteration t, i.e., xbestt. This improvement is based on a Gaussian distribution operator that generates new candidate solutions with the center at xbestt, which are uniformly distributed. The main contribution of this research is the proposal of a new hybrid optimization algorithm to solve the exact optimization model, which is based on a combination of the Arithmetic Optimization algorithm with the Vortex Search algorithm and showed excellent numerical results in the IEEE 34-bus grid. The analysis of quantitative results allows us to conclude that the strategy proposed in this work has a greater effectiveness with respect to the General Algebraic Modeling System software solvers, as well as with metaheuristic optimizers such as Genetic Algorithms, the Newton–Metaheuristic Algorithm, and the original Arithmetic Optimization Algorithm. MATLAB was used as a simulation tool.

Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations

In conventional low-voltage grids, energy-storage devices are mainly driven by final consumers to correct peak consumption or to protect against sources of short-term breaks. With the advent of microgrids and the development of energy-storage systems, the use of this equipment has steadily increased. Distributed generations (DGs), including wind-power plants as a renewable energy source, produces vacillator power due to the nature of variable wind. Microgrids have output power fluctuations, which can cause devastating effects such as frequency fluctuations. Storage can be used to fix this problem. In this paper, a grid-connected wind turbine and a photovoltaic system are investigated considering the atmospheric conditions and wind-speed variations, and a control method is proposed. The main purpose of this paper is to optimize the capacity of energy-storage devices to eliminate power fluctuations in the microgrid. Finally, the conclusion shows that, in microgrids with supercapacitors, the optimal capacity of microgrid supercapacitors is determined. This method of control, utilizing the combined energy-storage system of the battery supercapacitor, in addition to reducing the active power volatility of the wind turbine and photovoltaic generation systems, also considers the level of battery protection and reduction in reactive-power fluctuations. In the proposed control system, the DC link in the energy-storage systems is separate from most of the work conducted, which can increase the reliability of the whole system. The simulations of the studied system are performed in a MATLAB software environment.

Optimization of On-Grid Hybrid Renewable Energy System: A Case Study on Azad Jammu and Kashmir

Expansion of modern power systems due to increasing energy demands face the challenges of grid reinforcement cost, size and complexity, transmission losses, and environmental factors. Placement of renewable energy sources (RES) based generation systems addresses these challenges. However, the size and placement location of RES-based system require optimization of installation and operational cost with better return on investment and reduction of greenhouse gas emissions. This paper presents an optimized solution for RES-based generation system to be installed with the existing power system of Azad Jammu and Kashmir (AJK) region that is facing power shortfall and load shedding. The weather and climate data from NASA and National Renewable Energy Laboratory (NREL) have been used and various models of on-grid hybrid renewable energy system (HRES) are compared to highlight their techno-economic benefits. An optimal hybrid photovoltaic, wind, and hydroelectric energy-based generation system is proposed with a significant reduction in cost of energy, net present cost, initial costs, and GHG emissions. Installation of the proposed hybrid RES-based generation system guarantees reduction in system power losses and line flows with an improved voltage profile of the system.

Demand-Side Management Through Load Control and Local Generation in a Medical Education Facility: a Case Study

Electric energy generation from renewable sources is widely accepted thanks to the cost reduction that enables its transformation in an increasingly optimal way. However, these systems depend on climate variability. Accordingly, they cannot provide fixed generation. In order to improve the reliability of these systems, demand-side management strategies have been developed to optimize the operation of renewable generation systems. This article presents a case study of a medical education building with distributed generation. A dynamic tariff scenario is simulated according to the national demand in order to perform demand-side management with three types of loads: refrigeration, air conditioning, and ventilation. The model is fed with data obtained from the telemetering of the PV system generation and demand of the building. The result shows that about 16.2% of the electrical energy consumed can be saved through the implemented energy management system and complies the operation ranges allowed by the system.

Current Sensorless MPPT Control for PV Systems Based on Robust Observer

Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.

New Control Approach of Multicell Stacked Cell Inverter for Solar Photovoltaic System

This paper presents the study, modelling, and simulation of the DCSVM (Duty Cycle Space Vector Modulation) control technique applied to a new inverter topology dedicated to isolated or grid-connected photovoltaic systems using the MATLAB/Simulink software. This inverter is based on the structures of a stacked multicell converter (SMC) and an H-bridge. This new topology allows the voltage stresses of the converter to be distributed among several switching cells. It also allows the input voltage to be divided into several fractions so that the number of switching power semiconductors is reduced. The DCSVM is a control technique that generates control signals to the two-level voltage converter as well as the intermediate times. The main advantage of this control technique is the reduction in the number of calculations, especially for the trigonometric functions and the generation of the reference voltage. In this contribution, the general topology of this microinverter is described and the DCSVM control technique is presented. Finally, simulation results, the efficiency of this topology, and the validity of the DCSVM control in a grid-connected PV generation system are discussed. The results obtained are very satisfactory.

Power Quality Improvement of Photovoltaic Distributed Generation System using Artificial Neural Network for Environmental Preservation

Most of the economies are emerging with the growth in the renewable energy system. A solar photovoltaic system is one of the good sources of energy among them which provides clean and green energy. As it adds less pollution to the environment and hence advancement in technology of renewable energy system adds great effect on the environmental preservation. This paper describes a 1-ф photovoltaic distributed generation system having enhanced power quality features. Initially, the system has been implemented by using pulse width modulation-based switching schemes for the smooth control of the power flow between photovoltaic system, grid, and non-linear load. The system involves nonlinear current compensation and capacitor voltage balancing along with maximum power point tracking. Using this model, sample data has been collected for the training and testing of artificial neural networks. The artificial neural network was trained using the scaled conjugate gradient approach. The response of the neural network provides an estimated reference current for the controller to enhance power quality features. The inverter used in this work also acts as a shunt active power filter during night time. The system’s result is simulated and validated through MATLAB/Simulink.

Pool trading model within a local energy community considering flexible loads, photovoltaic generation and energy storage systems

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.

Improved cubic boost converter based on voltage closed-loop control

Due to the influence of environment, photovoltaic cell materials and other factors, the output voltage of small power distributed photovoltaic generation system is difficult to meet the requirements of grid connected voltage. In this paper, an improved cubic Boost converter is proposed. The main purpose of the extension is to combine the cubic Boost converter with the switched capacitor circuit to improve the voltage gain and the voltage stress of the switch is reduced by half compared with the traditional Boost. The output voltage closed-loop control system is designed by Simulink automatic code generation technology, which makes the input voltage is 12 V, stabilize the output voltage to 170 V. Using 32-bit floating-point processor TMS320F28335, an experimental prototype with input power of 300 W is intended to verify the feasibility of the circuit.

Dynamic Performance Evaluation of Grid-Connected Hybrid Renewable Energy-Based Power Generation for Stability and Power Quality Enhancement in Smart Grid

Today’s stochastic grid system is experiencing huge voltage fluctuations, which is responsible for power quality issues in the smart microgrid network due to its intermittent nature as well as penetration of hybrid renewable resources. Thus, the dynamic performance evaluation and their control are essential to sustaining the stability of the grid network. A d-q controller mechanism is suggested to maintain the balance of the distributed generation network and grid side network. A dynamic control mechanism of voltage source converter (VSC) is presented in the MPPT-based wind power generating station, where an induction generator generates the power by the optimal control of the wind energy-based subsystem. The distributed hybrid generation (solar PV and wind) subsystem’s output terminal is linked to the DC bus bar’s common link via the VSC. A VSI is utilized to convert the desired DC power to alternating current power. To regulate and improve the performance of the proposed hybrid power generating systems (HPGS), a supercapacitor (SC) is used to smooth out the ripple on the distribution side in the power grid. Furthermore, the dynamic stability of grid-connected solar PV and wind power generation systems is investigated. This article also proposed an effective control scheme for the SC in HPGS under the influence of weak grid conditions. This article aimed to validate the efficiency of the VSI topology; a PI controller stability enhancement approach is used in a proposed grid system under various disturbance conditions. Finally, the simulation results and FFT-based power quality response analysis are validated through the effective utilization of an SC.

Merit Evaluation of Peer‐to‐Peer Electricity Trading Between Prosumers

The enforcement of a feed‐in tariff (FIT) program in Japan has increased the number of installations of photovoltaic generation systems in its residential sector. After the end of an FIT program, the surplus electricity of an individual prosumer is purchased by the utility company at a reduced price, which may have an economic impact on the prosumer. To avoid this adverse effect, peer‐to‐peer (P2P) electricity transactions among prosumers based on blockchain and smart contract technologies as well as introduction of residence‐use stationary storage batteries are generating significant interest. In this study, the potential economic merit of P2P transactions and residential storage battery introduction are evaluated using a total optimization approach. For realizing a more practical perspective, this study also investigates the economic merit based on a partial optimization approach, in which an individual prosumer pursues its own profit. Furthermore, in this study, the grid‐side merit of P2P electricity transactions among prosumers is examined. Comparison of the merits determined by the total and partial optimization approaches shows that the realization of P2P transactions could provide a notable economic merit to the participants and reduce both the required facility capacity and power flow variation. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.

Photovoltaic distributed generation integrated electrical distribution system for development of sustainable energy using reliability assessment indices and levelized cost of electricity

AbstractReliability assessment index is one of the main performance indicator for analyzing the impact of distributed energy generation system in the utility distribution system and minimize the costs associated with energy interruptions. In this study, sustainable energy development by using integrated photovoltaic distributed generation (DG) system considering reliability assessment indices and levelized cost of energy are presented. Electrical transient and analysis program simulation software are used for evaluation and assessment of the reliability indices based on the failure rate, average annual power interruption/outage time and repair time. The analytical method are used to evaluate the expected interruption cost (ECOST) of the system. HelioScope software is a photovoltaic simulation tool are used to design and calculate the energy generation (kWh) as well as global horizontal irradiation (kWh/m2) of the photovoltaic system. Experimental test system is a radial distribution network consisting of an 11.0 kV feeder emanating from 132.0 kV distribution grid station Satiana. According to the reliability evaluation and assessment of experimental test system before integration of photovoltaic distributed energy generation, the average service availability index (ASAI) is 0.9648 per unit. Then, the expected energy not supplied (EENS) is 772.0308 MWh per year and ECOST is 386,652,468.18 PKR per year. After integration of Photovoltaic DG, ASAI is 0.9741 per unit, then the EENS is 563.8584 MWh per year and integration ECOST is 227,087,143.39 PKR per year. The results showed that integrated PV DG system improve the reliability of conventional energy distribution systems.

Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico

Renewable energy generation (REG) is irrupting throughout the globe and it points to be the path for a sustainable energy future. Nevertheless, due to their volatile nature, they present a challenge for integrating these intermittent sources into the grid. In this work we present itineraries for charging and discharging two ideal Battery Energy Storage Systems (BESS), one powered with a solar PV generation system and the other one powered with wind energy. Using predictions for REG and electric demand (ED), based on a hybrid Convolutional Long-Short Time Memory (CNN-LSTM) neural network, we propose accurate itineraries to know when to charge and when to discharge variable REG, in the area of Baja California Sur (BCS) in Mexico, pursuing to reduce the ED in peak hours. The convolution net will extract local features and the LSTM the temporal ones. The proposed itineraries of charge and discharge based on predictions with the hybrid CNN-LSTM model, are compared with itineraries made with a well known benchmark and itineraries based on true observations points of REG. The results show that the integration of two BESS with charging and discharging itineraries based con a CNN-LSTM model, can effectively mitigate two important peaks of the electric demand profile in the studied location.

Estimation of Photovoltaic Soiling Using Environmental Parameters: A Comparative Analysis of Existing Models

AbstractSoiling is the process where dust, dirt, and organic/inorganic contaminants accumulate on the surface of a photovoltaic (PV) module. It impacts the energy generation of PV systems and causes significant economic losses. It can be monitored through soiling estimation models, which evaluate its impact depending on a number of environmental parameters. The main focus of this study is to compare four existing estimation models in Jaén, Spain, and to analyze their performance in an environment different than that where each of them is developed. The results indicate that all models can reproduce accurately the seasonality of soiling in Jaén, if a cleaning threshold of 0.3 mm day−1 is considered. The models’ performance is found to be influenced not only by the mass concentration but also by the optical properties of each component of the atmospheric particulate matter, with an overestimation up to 4% in high organic carbon concentration periods. The natural cleaning role of rainfalls and its influence on the modeling error are also discussed through the paper.

Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

Photovoltaic generators: use by rural customers in Mato Grosso state

In the current Brazilian energy scenario, the use of photovoltaic systems for generating energy is an excellent choice, as it combines electricity savings and energy production with less harmful impact on the environment. Mato Grosso is a state of great importance for agricultural production, in a country where agribusiness has a large share in the Gross Domestic Product (GDP). The aims of this study are to investigate the growing use of distributed photovoltaic system by rural customers in the state of Mato Grosso and compare it with the number of installations implemented in other consumer classes to eventually evaluate the participation in the state energy matrix and its applications. The analysis was carried out with data up to 2020 available from the registration system of the distributed generation of the National Electric Energy Agency (ANEEL) and the local electricity company Energisa Mato Grosso (EMT). The number of consumer units having photovoltaic generation systems in the state has grown on average 342% since 2015, when the first units were installed. When analyzing only rural facilities, this percentage rises to 347%. Although the participation of rural customers in the total number of installations is 7.04%, which are less significant than the other consumer classes, these systems are 6.44 times larger than residential systems, with a participation in the total state installed power of 21.74%.

Energy Efficiency and Distributed Generation: A Case Study Applied in Public Institutions of Higher Education

This study focused on developing a sustainability project carried out in 11 Federal Institute of Education, Science, and Technology of Goiás (IFG) campuses wherein energy efficiency and distributed generation actions were developed. Energy consumption was optimized by retrofitting the lighting system, installing a photovoltaic (PV) generation system, quantifying the building efficiency, energy monitoring, training, and qualification, and focusing on the efficient use of electric energy. We first present the Brazilian legislation that regulates the Research and Development Program in the electric energy sector. Then, we describe the case study that was applied to the educational institution. In the lighting system, 18,377 inefficient lamps were replaced by lamps with more efficient technology, with an energy saving of 867.9 MWh/year and a peak demand reduction of 309.6 kW. The proposed generation system aimed to install 3076 PV modules on the roofs of selected campus buildings, totaling 1 MWp of installed power with an average annual power generation of 1736.9 MWh/year. The total project investment was USD 1,348,768.50 and the overall cost–benefit ratio of the project was 0.68, which will result in annual savings of approximately USD 197,321.85. This corresponded to a 58% reduction in energy bills. The project proposed in this work was considered technically and economically viable within the scope of the Brazilian Energy Efficiency Program.