Photovoltaic Generation System Research Articles

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.

Photovoltaic energy generation systems monitoring and performance optimization using wireless sensors network and metaheuristics

The performance of photovoltaic energy generation systems is highly affected by exposure to different operating conditions. In order to optimize the power conversion efficiency in such conditions, the deviation from normal operation has to be detected. In this paper, an Adaptive Network Fuzzy Inference System (ANFIS) based model of the normal operation of the photovoltaic system is constructed and used as a reference for abnormal operation detection, where the training data of the ANFIS-based model has been acquired using a Wireless Sensor Network. The monitoring of the photovoltaic system uses the residual generated from the comparison between its actual state of operation and the output of the ANFIS model for the same operating conditions. As an optimal strategy that takes into account the different operating modes, a switching mechanism is designed to execute a conventional maximum power extraction technique in normal operating conditions and switches to a metaheuristic algorithm to search for global maximum power point when deviation from the normal operation is detected. Experimental results show the higher efficiency of the photovoltaic energy conversion system using the proposed monitoring and performance optimization approach in various environmental conditions.

Modelling and forecasting for solar irradiance from solarimetric station

This paper proposes two approaches for modeling solar irradiance time series. The first is an exploratory analysis and second is a periodic forecast model for solar irradiance data from solarimetric station in a country city of Bahia, Brazil. Two normality hypothesis tests were applied, Anderson-Darling and Shapiro-Wilk. The shape and symmetry of the data were also analyzed using boxplot and histograms to investigate the extreme points, as well as great asymmetry in the distribution of data at different times. Through adherence tests, the normal distribution of solar irradiance in most hours within the annual periodic is rejected. A study by season was carried out, which showed a different behavior in relation to the symmetry of the data, with autumn being the most uniform and spring being the most stochastic. Spring and summer presented better conditions for installation of photovoltaic plates, due to the high solar irradiance rates, and autumn and winter presented satisfactory solar irradiance to maintain this form of generation throughout the year.The second approach is a forecast to solar irradiance in photovoltaic generation systems. The importance of this forecast type is to favor the routine of planning, operation and maintenance of these types of systems, in addition to serving as a basis for feasibility studies and expansion of solar generation. Given a stochastic resource and the periodic behavior of solar irradiance data, a periodic autoregressive model was considered; the statistics were used by the maximum likelihood method, based on hourly measurements of irradiance over a period of one year.

High step‐up buck–boost DC–DC converter with coupled inductor and low component count for distributed PV generation systems

AbstractThis paper presents a non‐isolated high step‐up buck–boost with a coupled inductor DC–DC converter relevant for distributed photovoltaic (PV) generation systems. The proposed topology can reach high voltage gain through the combination of a buck–boost converter with a coupled inductor. The configuration of the proposed converter allows to achieve a natural voltage clamp circuit for the switch, recovering the energy stored in the leakage inductance of the coupled inductor. Moreover, the converter allows soft‐switching conditions, that is, Zero Current Switching (ZCS) for the diodes and nearly ZCS for the active switch. Also, the proposed converter presents a low component count and common ground connection of the input and output. The proposed converter is evaluated theoretically by the principle of operation in continuous‐conduction mode (CCM) and discontinuous‐conduction mode (DCM), voltage gain derivation, external characteristics, semiconductor voltage stress, current stress, and design guidelines. Finally, a 650‐W, 36/380‐V, 50‐kHz prototype was built in the laboratory to experimentally evaluate the proposed converter, which reached a maximum efficiency rate of 96.58%.

Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting

Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.

Compensation of Unbalanced Low-Voltage Grids Using a Photovoltaic Generation System with a Dual Four-Leg, Two-Level Inverter

In this paper, a grid-connected photovoltaic (PV) generation system is proposed with the purpose of providing support to low-voltage grids, namely through the elimination or attenuation of the grid imbalances. This compensation must consider the load types, which can be either linear or non-linear, and whether the reactive power and current harmonics generated by the non-linear loads need to be compensated in addition to the unbalanced active power. This must be well considered, since the compensation of all aspects requires oversized PV inverters. Thus, the different unbalanced compensation schemes are addressed. Several schemes for the generation of the inverter current references taking into consideration the compensation and load type are presented. For this PV generation system, a dual four-leg, two-level inverter is proposed. It provides full unbalanced compensation owing to the fourth leg of the inverter and also extends the AC voltage, which is important when this compensation is required. To control this inverter, a control scheme for the inverter that considers several compensation factors is proposed. A vector voltage modulator associated with the controller is another aspect that is addressed in the paper. This modulator considers the balance between the DC voltages of the inverters. Several compensation schemes are verified through computational tests. The results validate the effectiveness of the proposed PV generation system.

SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.

Energy homeostasis management strategy for building rooftop nanogrids, considering the thermal model and a HVAC unit installed

This paper presents a case study on power control and energy management for a 60 apartments’ residential building with solar generation and energy storage tied to the grid in Santiago, Chile. A new energy management algorithm based on energy homeostasis is designed for a small electro thermal generation system (nanogrid), with smart metering. The test bed employs supervisory control with energy management that regulates the temperature inside a large room by the action of an HVAC (Heating/Ventilating/Air Conditioning) unit. The main objective of supervisory control is to allow temperature comfort for residents while evaluating the decrease in energy cost. The study considers a room with rooftop grid-tie nanogrid with a photovoltaic and wind turbine generation plant, working in parallel. It also has an external weather station that allows predictive analysis and control of the temperature inside the abode. The electrical system can be disconnected from the local network, working independently (islanding) and with voltage regulation executed by the photovoltaic generation system. Additionally, the system has a battery bank that allows the energy management by means of the supervisory control system. Under this scenario, a set of coordination and supervisory control strategies, adapted for the needs defined in the energy management program and considering the infrastructure conditions of the network and the abode, are applied with the aim of efficiently managing the supply and consumption of energy, considering Electricity Distribution Net Billing Laws 20.571 and 21.118 in Chile (https://www.bcn.cl/historiadelaley/historia-de-la-ley/vista-expandida/7596/), the electricity tariffs established by the distribution company and the option of incorporating an energy storage system and temperature control inside the room. The results show the advantage of the proposed tariffs and the overall energy homeostasis management strategy for the integration of distributed power generation and distribution within the smart grid transformation agenda in Chile.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.