Hybrid PV Research Articles

Pre Feasibility Study of a Wind PV Hybrid System Supplying a Desalination Plant in Fernando de Noronha Island, in Northeastern Brazil

Pre Feasibility Study of a Wind PV Hybrid System Supplying a Desalination Plant in Fernando de Noronha Island, in Northeastern Brazil

Modelling and Simulation of a Hybrid Renewable/Battery System Powering a Cathodic Protection Unit

In this paper, the performance of the proposed off-grid wind-solar PV hybrid system powering the cathodic protection unit is simulated and analyzed using MATLAB/SIMULINK. Furthermore, the performance simulation for the battery energy storage system with PV-wind hybrid energy system under variable solar irradiance and wind speed respectively is also conducted. The hybrid system consists of a wind turbine which uses a permanent magnet synchronous generator driven directly from the turbine, a PV array and a battery bank. The simulated results reflect that the designed hybrid system of such capacity can adequately supply a cathodic protection unit with no power shortage at different weather conditions.

Evaluation of a multi-objective model in energy generation under the influence of different hydrological conditions based on Moth Search Algorithm

ABSTRACT One of the most notable features of solar energy is the alternation and instability due to environmental influences and wheatear change. Therefore, combining this renewable energy with other renewable energy sources such as hydropower can improve the instability feature of solar energy. One of the advantages of using hydropower is its easy adjustment and low cost. In this research, an optimisation algorithm for the Hydro – PV hybrid system is presented regarding the smoothness of the energy generation trend and the total yearly energy production of the system concurrently. The output of the PV voltage is calculated by temperature data and hourly solar radiation. For hydroelectric power due to excellent adjustability, monthly data are used. To reduce the variance of electricity generation annually, an optimal algorithm called Moth Search Algorithm (MSA) is used. The performance of this hybrid system is evaluated in three different hydrological periods, including very wet and very drought and normal. the results showed that PV and hydropower are complementary and can compensate for each other's shortcomings and energy reduce in different conditions.

PV/Wind Hybrid Energy System, Modeling and Simulation at variable weather conditions

This paper presents a modeling and simulation of a grid-connected wind / PV hybrid power system under variable weather conditions. This system includes a wind turbine system, a PV system that shares a DC bus, and no battery. The paper contains an overview of the hybrid system and some previous studies; it presents a brief overview of each component used for this system. Signal distortion remains the great obstacle when connecting to the grid, so the system architecture and its proposed control are also introduced to reduce the distortion of electrical signals to an acceptable value. A simulation of the system’s operation with specific weather conditions in three different modes was performed using the MATLAB Simulink to describe the effect of these weather conditions on the production of electrical energy. Simulation results show how these weather conditions affect the operation of this hybrid system. An acceptable distortion value of the produced current signals has also been reached. These results present an evaluation of the dynamic performance of this system under the proposed working conditions. It also shows the energy exchange with the grid.

Optimization with excess electricity management of a PV, energy storage and diesel generator hybrid system using HOMER Pro software

Optimization with excess electricity management of a PV, energy storage and diesel generator hybrid system using HOMER Pro software

Optimization with excess electricity management of a PV, energy storage and diesel generator hybrid system using HOMER Pro software

In this research, a standalone microgrid power system is proposed to electrify a small agricultural community in Palestinian territories. The load includes residential load and water pumping load. The community comprises about 30 households with some service buildings in addition to the water pumping system. The average load energy demand is 300kWh/day and the average power demand is 12.5kW, in the same context, the average energy demand for water pumping is 49kWh/day. The region has abundant solar radiation potential with a daily average of 5.4 kWh/m<sup>2</sup>. The optimum design was achieved using the HOMER Pro software. It took into consideration real incident solar radiation data, electrical demand profile for the community and water pumping system and market cost of all equipment. The optimization results showed that the best hybrid system among all feasible configurations is a PV system with an energy storage system combined with a diesel generator. The net present cost of the system is USD636,150 and the cost of energy (COE) produced is USD0.438/kWh. Sensitivity analysis is considered to study the impact of variations in PV cost, diesel fuel price, and maximum annual capacity shortages (MACS), the results showed that MACS has no effects. Energy management procedure is followed to reduce the excess electricity from 10.6% to 6.24% which in turn reduces the COE from 0.438 to USD 0.416/kWh.

Hybrid technique for an efficient PV system through intelligent MPPT and water cooling process

Solar panels have undergone several tests through research studies for the purpose of improving performance to increase the resulting electrical power. This study considers the fact of the necessity of Maximum Power Point Tracking working conditions of the PV panels to harvest maximum electrical power during the weather variations. It considers also the panel temperature reduction which affects positively the panel ability in terms of producing additional electrical power. By this consideration, this study proposes a new design, and simulation results with analysis of a hybrid PV system. The system is able to deliver 5 kW, through the day hours, with less number of PV panels based on a hybrid technique. The proposed technique combines two manipulating processes. The first one guarantees the Maximum Power Point Tracking (MPPT) condition during day hours through an artificial Neural Network (ANN) controller. Whereas the second one focuses on reducing the panel temperature by introducing a water cooler which is designed for this purpose. Simulink software of MATLAB is used to implement and evaluate the proposed system.

Hybrid Pumped Hydro Storage Energy Solutions towards Wind and PV Integration: Improvement on Flexibility, Reliability and Energy Costs

This study presents a technique based on a multi-criteria evaluation, for a sustainable technical solution based on renewable sources integration. It explores the combined production of hydro, solar and wind, for the best challenge of energy storage flexibility, reliability and sustainability. Mathematical simulations of hybrid solutions are developed together with different operating principles and restrictions. An electrical generating system composed primarily by wind and solar technologies, with pumped-storage hydropower schemes, is defined, predicting how much renewable power and storage capacity should be installed to satisfy renewables-only generation solutions. The three sources were combined considering different pump/turbine (P/T) capacities of 2, 4 and 6 MW, wind and PV solar powers of 4–5 MW and 0.54–1.60 MW, respectively and different reservoir volume capacities. The chosen hybrid hydro-wind and PV solar power solution, with installed capacities of 4, 5 and 0.54 MW, respectively, of integrated pumped storage and a reservoir volume of 378,000 m3, ensures 72% annual consumption satisfaction offering the best technical alternative at the lowest cost, with less return on the investment. The results demonstrate that technically the pumped hydro storage with wind and PV is an ideal solution to achieve energy autonomy and to increase its flexibility and reliability.

Optimal Control Strategy for a Marine Current Farm Integrated with a Hybrid PV System/Offshore Wind/Battery Energy Storage System

This paper suggests an automated control technique constructed on the Multi-Objective Particle Swarm Optimization to enhance the operation of a wind farm, a marine power plant and a photovoltaic array with a battery energy storing system. due to changes in PV / wind / tide, and to boost the efficiency of offshore wind farms and marine power stations connected to the battery-powered storage system, with a view to smoothing power production, the aim of projected automatic control strategy is to minimize power fluctuations and voltage variations. The battery energy storage network was used with an optimized demand response strategy based on the real-time pricing model to improve stability and power efficiency, reduce the power fluctuations and variations in bus voltage and address renewable energy generation instability. The multi-objective particle swarm optimization-based energy management programming model would be used to minimize running costs, pollutant emissions, increase the demand response benefits of micro grid operators and, at the same time, meet the load demand constraints from customers of all sorts, such as domestic, commercial and industrial users.

Optimal Electric Vehicle Charging Control Strategy Powered by Grid Linked Hybrid PV Wind Battery Renewable Energy System

An advanced model is proposed for grid connectivity of an interconnected network consisting of a charging station for electric automobiles. To automate the discharge procedure of charging/ the battery energy storing system, a wind network, the photovoltaic system, and the battery energy storing system is developed to efficiently increase the consumption degree of solar and wind energy sources and create renewable inner-city capacity. On the basis of DC bus architecture, the power design was planned such that buffered storage systems and renewable energy resources can be incorporated. The proposed optimal control algorithm uses the Swarm Optimization Algorithm consists of Multi-Objective Particle, developed for electric vehicles charging or discharge behaviors to minimize the overall actual energy loss and increase the integration of EVs with power networks due to the efficiency and economy of network activity, taking into account the economic issue and the satisfaction of consumers, the voltage limits and the parking availability pattern. To test the proposed EV charging strategy, simulation studies based on efficiency, and assessed major energy fluxes within the device. Energy management approaches have also been developed to optimize the power requirements and charging times of various electric vehicles. Results suggest that proposed model will substantially reduce the power grid’s operational costs while meeting the charging criteria of the customer. Improved performance on global search capabilities is also checked, as is the desired outcome of enhanced particle swarm optimization algorithm. The findings show that the new approach is in a position to prepare EV charging times optimally, taking into account electronic knowledge and uncertainty.

Performance Evaluation and Optimization of Wind-Solar PV Hybrid Electricity Generation and Application to Electric Vehicle

Electricity and transportation sectors are responsible for a significant percentage of the global energy-related greenhouse gases emission, and their emissions are increasing at a faster rate in comparison to other sources. Among all the sustainable solutions to energy-related emissions, Hybrid Renewable Energy Systems (HRES) are becoming a more effective option for rural electrification and other energy-related applications. This paper presents a novel approach for the performance analysis of HRES for electricity generation for typical household electricity generation and application to mini electric campus shuttle. Different benchmark models are employed in the modelling, simulation and validation of the designed HRES. The simulation results obtained on the designed HRES demonstrated the effectiveness of all the adopted methods in enhancing the modelling accuracy of renewable electricity systems in electricity generation. The technical analysis of the system based on the simulation and the validation results demonstrated that the system could deliver the full electricity demand of typical household as well as, the total electricity demand of BSC-P1 electric campus shuttle. Additionally, 100% Battery Energy Storage System (BESS) charging was achieved and the battery capacity was never reached at all-time indicating the viability of the system in supplying uninterrupted power to the electricity demand.

The New Control Scheme for the PV and Wind Hybrid System Connected to the Single Phase Grid

The proposed system comprises of the hybrid (PV and wind) system for 1 kw is integrated with the single phase grid. The new control scheme sine pulse width modulation (SPWM) switching with a deadbeat based PI controller is designed to a DC/AC single phase inverter. This controller is used to pure sinusoidal output voltage with low total harmonic distortion (THD), fast dynamic response control techniques and to keep the power output constant if any variation in the input from the PV and wind system occurs, and the output voltage at the grid remains constant. Even when the load varies, the sinusoidal voltage output maintains 230Vrms with 50 Hz frequency and also low THD is 2.54%. This controller proves the stiffness and robustness characteristics for maintaining the output voltage at desired value with low harmonics.

Optimal Designing and Synthesis of a Hybrid PV/Fuel cell/Wind System using Meta-heuristics

In this paper, a multi-objective optimization method has been established on a hybrid PV, wind, fuel cell, and battery system. The optimization is based on three models including energy supply reliability, electricity efficiency, and capital cost of the hybrid system. A new model of the Elephant Herding Optimization (BEHO) Algorithm is utilized to solve the multi-objective optimization problem and is validated based on different algorithms and benchmark functions. The main purpose is to determine the Pareto surface including a set of possible design solutions to help the decision-makers obtaining the global optimum solution. The final results indicated that the proposed method is an applicable approach for designing of the proposed hybrid system.

Dataset after Seven Years Simulating Hybrid Energy Systems with Homer Legacy

Homer Legacy software is a well-known software for simulation of small hybrid systems that can be used for both design and research. This dataset is a set of files generated by Homer Legacy bringing the simulation results of hybrid energy systems over the last seven years, as a consequence of the research work led by Dr. Alexandre Beluco, Federal University of Rio Grande do Sul, in southern Brazil. The data correspond to thirty papers published in the last seven years. Two of them describe hydro PV hybrid systems with photovoltaic panels operating on the water surface of reservoirs. One of these twelve papers suggests the model-ing of hydropower plants with reservoirs and the other the modeling of pumped hydro stor-age, and a third still uses these models in a place that could receive both the two types of hy-droelectric power plant. The other simulated hybrid systems include wind turbines, diesel generators, batteries, among other components. This data article describes the files that inte-grate this dataset and the papers that have been published presenting the hybrid systems under study and discussing the results. The files that make up this dataset are available on Mendeley Data repository at dx.doi.org/10.17632/ybxsttf2by.2.

Electrospun ZnO nanofiber interlayers for enhanced performance of organic photovoltaic devices

Organic photovoltaics (OPV) has improved significantly in recent years and power conversion efficiencies are getting close to those of hybrid and inorganic thin film PV technologies. In this work, we demonstrate high performance inverted organic photovoltaic cells from electrospun nanofiber based electron transport layers, which outperform their thin-film counterpart. FTO/ZnOfilm/ZnOnanofibers/PTB7:PC70BM/MoO3/Ag devices were designed with different ZnO nanofiber based electron transporting layers (ETL) varying the nanofiber width and thickness, using a conventional electrospinning technique for the nanofiber ETL fabrication. We demonstrate that 35 nm thick films of electrospun ZnO nanofibers results in an improvement of the short-circuit current density (Jsc) and fill factor (FF) in the cells, leading to power conversion efficiency of up to 7.6%. The improvement is assigned to an improved charge collection efficiency, due to realization of direct and continuous pathway for electron extraction inside the solar cell structure. The results highlight the application of one-dimensional nanofiber based charge extraction layers as a promising route for development of high performance OPV devices in the future.

A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids

Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid.

Hybrid PV and Wind Energy for Grid Connected System using SEPIC Converter

A hybrid generator is designed for both wind energy and photovoltaic system (PV) based on permanent magnet synchronous generator to maintain dc link voltage. In this paper, both the sources are connected with grid through a single ended primary inductor converter (SEPIC) and three phase inverter is followed by SEPIC converter. The fuzzy logic controller used to provide gate pulses for the converter and model predictive controller is providing pulses to three phase inverter which tracking the highest power from the wind and PV system. In the PV system the maximum power point tracking (MPPT) method is used to get higher power from the source. This new hybrid system operation is done by both the effective controllers and system steady is achieved. The integrated energy with high efficiency is directed to grid for distribution and the system is verified in Matlab/Simulink and simulation results are given with model.

Future hybrid of photovoltaic and fuel cell for Langkawi SkyCab

Langkawi SkyCab has the highest energy demand and it keeps increasing year by year. This study proposed alternatives energy of hybrid photovoltaic and fuel cell systems for the SkyCab’s operation. The best sizing and configurations were chosen based on Homer simulation software. A comparative study is done between a conventional system and other hybrid combinations. The results revealed that the proposed system had reduced the cost as well as CO2 emission almost by 39% and 79%, respectively. The hybrid PV and fuel cell are aligned with Malaysian government’s goals of reducing carbon emissions 40% by the year 2030.

Compensation Design Using RLS Algorithm for THD Reduction in a Solar PV Hybrid System

PV and wind hybrid are found to be the most lucrative solution for the diminishing traditional energy sources. Whereas these alternatives sources of the energy have many remarkable rewards like cost of energy and feasibility etc. The attributes of these sources of being cost effective and stable are possible due to their complementary nature as compared to independent energy systems. Therefore, these systems have admirable capability to meet energy crisis up to some extent. The proposed word has designed a hybrid energy system fit for driving residential loads using MATLAB/SIMULINK software. The work has proposed a compensator with RLS algorithm is place of traditional STATCOM device. The results has sown that the active power output available at the load terminal improved from 400 KW to 700KW. Also the work has analyzed THD level in voltage and current waveforms. The proposed RLS based compensator reduced the THD level in voltage to 0.81% and in current wave form to 1.26% . The model has also been integrated with the grid in order to make the system more reliable and efficient while driving loads

PV Prediction based on PSO-GS-SVM Hybrid Model

Photovoltaic power generation is affected by many factors, with volatility and intermittent characteristics. Large-scale photovoltaic access to the power grid poses great challenges to the safety and stability of power systems. Therefore, accurate prediction of photovoltaic power generation helps dispatchers adjust scheduling schedules in a timely manner, effectively reducing the adverse impact of photovoltaic power generation access on the power grid. This paper proposes a hybrid PV power prediction model based on PSO-GS-SVM. The particle swarm optimization (PSO) method is used to optimize the large step size of the support vector machine (SVM), and the parameter optimization range is obtained. GridSearch Method (GS) refined parameters optimization of PSO-SVM, and obtained PSO-GS-SVM hybrid model. The model is used to train and predict the normalized and dimensional sunny and non-clear working conditions data sets, and compared with BP neural network, SVM and PSO-SVM models. The results show that the PSO-GS-SVM hybrid model has better generalization ability and higher fitting effect.