KnowledgeMapping of Hybrid Solar PV andWind Energy Standalone Systems: A Bibliometric Analysis

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive,and their use would release pollutants.Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy.Hybrid renewable energy systems(HRES)are viable for remote areas operating in standalone mode.This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades.More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables.Productive countries and highly cited authors are identified,and hot topics with hotspot articles are shown in landscape and timeline views.Emerging trends and new developments related to techno-economic analysis and microgrids,as well as the application of HOMER software,are predicted based on the analysis of citation bursts.Furthermore,the opportunities of hybrid energy systems for sustainable development are discussed,and challenges and possible solutions are proposed.The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems.

Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Numerical Assessment of the Thermal Efficiency of a Concentrated Photovoltaic/Thermal (CPV/T) Hybrid System Using Air as Heat Transfer Fluid

In this paper, we propose a thermal model of a hybrid photovoltaic/thermal concentration system. Starting from the thermal balance of the model, the equation is solved and simulated with a MATLAB code, considering air as the cooling fluid. This enabled us to evaluate some of the parameters influencing the electrical and thermal performance of this device. The results showed that the temperature, thermal efficiency and electrical efficiency delivered depend on the air mass flow rate. The electrical and thermal efficiencies for different values of air mass flow are encouraging, and demonstrate the benefits of cooling photovoltaic cells. The results show that thermal efficiency decreases air flow rate greater than 0.7 kg/s, whatever the value of the light concentration used. The thermal efficiency of the solar cell increases as the light concentration increases, whatever the air flow rate used. For a concentration equal to 30 sun, the thermal efficiency is 0.16 with an air flow rate equal to 0.005 kg/s;the thermal efficiency increases to 0.19 with an air flow rate equal to 0.1 kg/s at the same concentration. An interesting and useful finding was that the proposed numerical model allows the determination of the electrical as well as thermal efficiency of the hybrid CPV/T with air flow as cooling fluid.

Dynamic and Power Generation Features of A Wind-Wave Hybrid System Consisting of A Spar-Type Wind Turbine and An Annular Wave Energy Converter in Irregular Waves

Combining wave energy converters(WECs)with floating offshore wind turbines proves a potential strategy to achieve better use of marine renewable energy.The full coupling investigation on the dynamic and power generation features of the hybrid systems under operational sea states is necessary but limited by numerical simulation tools.Here an aero-hydro-servo-elastic coupling numerical tool is developed and applied to investigate the motion,mooring tension,and energy conversion performance of a hybrid system consisting of a spar-type floating wind turbine and an annular wave energy converter.Results show that the addition of the WEC has no significant negative effect on the dynamic performance of the platform and even enhances the rotational stability of the platform.For surge and pitch motion,the peak of the spectra is originated from the dominating wave component,whereas for the heave motion,the peak of the spectrum is the superposed effect of the dominating wave component and the resonance of the system.The addition of the annular WEC can slightly improve the wind power by making the rotor to be in a better position to face the incoming wind and provide considerable wave energy production,which can compensate for the downtime of the offshore wind.

Wake Effects on A Hybrid Semi-Submersible Floating Wind Farm with Multiple Hub Heights

Wind farms generally consist of a single turbine installed with the same hub height. As the scale of turbines increases,wake interference between turbines becomes increasingly significant, especially for floating wind turbines(FWT).Some researchers find that wind farms with multiple hub heights could increase the annual energy production(AEP),while previous studies also indicate that wake meandering could increase fatigue loading. This study investigates the wake interaction within a hybrid floating wind farm with multiple hub heights. In this study, FAST.Farm is employed to simulate a hybrid wind farm which consists of four semi-submersible FWTs(5MW and 15MW) with two different hub heights. Three typical wind speeds(below-rated, rated, and over-rated) are considered in this paper to investigate the wake meandering effects on the dynamics of two FWTs. Damage equivalent loads(DEL) of the turbine critical components are computed and analyzed for several arrangements determined by the different spacing of the four turbines. The result shows that the dynamic wake meandering significantly affects downstream turbines’ global loadings and load effects. Differences in DEL show that blade-root flapwise bending moments and mooring fairlead tensions are sensitive to the spacing of the turbines.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Implementation of FPGA Based Hybrid Power Generator for PV and Wind Grid Applications

This paper deals with implementation of Sinusoidal Pulse-Width-Modulation (SPWM) for a single-phase hybrid power filter generator for Photovoltaic (PV) and wind grid applications. Using policy iteration algorithm, an improved variable step-size perturbation and observation algorithm is contrived and it is implemented proficiently using a hard-ware description language (VHDL) (Very High Speed Integrated Circuit Hardware Description Language). Subsequently, the new generated grid source supplements the existing grid power in rural houses during its cut off or restricted supply period. The software is used for generating SPWM modulation integrated with a solar-power & wind power grid system which is implemented on the Spartan 3 FPGA. The proposed algorithm performs as a conventional controller in terms of tracking speed and mitigating fluctuation output power in steady state operation which is shown in the experimental results with a commercial PV array and HPW (Height Weight Proportional) show. Simulation results demonstrate the validity with load of the proposed algorithm.

Numerical Modeling and Technico-Economic Analysis of a Hybrid Energy Production System for Self-Consumption: Case of Rural Area in the Comoros

This study aims to provide electricity to a remote village in the Union of Comoros that has been affected by energy problems for over 40 years. The study uses a 50 kW diesel generator, a 10 kW wind turbine, 1500 kW photovoltaic solar panels, a converter, and storage batteries as the proposed sources. The main objective of this study is to conduct a detailed analysis and optimization of a hybrid diesel and renewable energy system to meet the electricity demand of a remote area village of 800 to 1500 inhabitants located in the north of Ngazidja Island in Comoros. The study uses the Hybrid Optimization Model for Electric Renewable (HOMER) Pro to conduct simulations and optimize the analysis using meteorological data from Comoros. The results show that hybrid combination is more profitable in terms of margin on economic cost with a less expensive investment. With a diesel cost of $1/L, an average wind speed of 5.09 m/s and a solar irradiation value of 6.14 kWh/m2/day, the system works well with a proportion of renewable energy production of 99.44% with an emission quantity of 1311.407 kg/year. 99.2% of the production comes from renewable sources with an estimated energy surplus of 2,125,344 kWh/year with the cost of electricity (COE) estimated at $0.18/kWh, presenting a cost-effective alternative compared to current market rates. These results present better optimization of the used hybrid energy system, satisfying energy demand and reducing the environmental impact.

Hybrid PV/Wind/Diesel Based Distributed Generation for an Off-Grid Rural Village in Afghanistan

Afghanistan has a tremendous resource potential of renewable energy especially solar and the wind. Therefore, utilization of these resources has a special rule for the remote areas where access to the electrical grid or secure power supply is a dream for most of the people. This paper presents a feasibility and usefulness of hybrid power generation based on PV/wind/diesel generator for an off-grid rural village that feeds the load at a rate of average 7.9 kWh/day with 1.32 kW peak load. GsT （geospatial toolkit） is used to obtain the solar and wind data of the site. Windographer software is used to analyze the wind resource data of the site. HOMER Pro software package is used to select the suitable and reliable hybrid generation system and calculate the optimal capacities and costs of the components. Through the study, it is found that this state of the art adaptation could provide vast opportunities for off-grid rural communities such as in Afghanistan where enough high penetration of renewable energy is available.

基于能值分析和多目标决策法的CCHP-PV-Wind系统综合性能评估研究

为充分利用丰富的新能源,满足用户多样化能源的需求,可采用含新能源的系统实现冷热电联供。当新能源作为电源或辅助冷热源时,可减少环境压力,但多种能源形式会导致不确定性、能源利用率低且成本升高,需要新的方法对含新能源的冷热电联供(CCHP-PV-Wind)系统的优劣进行评估。提出采用基于模糊层次分析(Fuzzy-Analytic Hierarchy Process,Fuzzy-AHP)和反熵权法(Anti-Entropy Weights,AEW)的多目标决策法(Multi-objective decision-making,MODM),建立指标体系并计算权重。同时引入能值指标来描述系统的可持续发展水平,并利用Copula理论中的Kendall秩相关系数(Kendall’t)处理CCHP和CCHP-PV-Wind系统相关性问题。最后,选取我国西部某城市建筑为研究对象,结合能值理论对CCHP和CCHP-PV-Wind两种系统进行综合评估与分析,研究结果可以为我国新能源的综合利用提供一定参考。

Experimental Study on a Modified Wind-Solar Hybrid System

Wind-solar hybrid systems are employed extensively due to certain advantages. However, two problems exist in their application: the PV modules operate at high temperatures, particularly during summer, and low wind power cannot be utilized. To solve these two problems, a novel hybrid system is designed based on PV/thermal systems, in which PV modules are cooled with fans driven by a wind turbine. This paper studies the practicability of the novel hybrid system. First, the electrical performance of the wind turbine is compared using a fan and battery load,respectively. Second, different types and numbers of fans are tested to obtain the largest air volume. Third, the height of the air duct on the back of the PV module is optimized and the cooling effect is studied. Results show that a 24 V DC fan is more appropriate for the novel system than a 12 V DC fan, as it provides a greater air volume, and with a switch wind speed of 3.0 m/s the power of PV module shows a maximum increase of 8.0%.

A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model

The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.

Optimal Sizing of Solar/Wind Hybrid Off-Grid Microgrids Using an Enhanced Genetic Algorithm

This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.

Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions

Photovoltaic(PV)systems are adversely affected by partial shading and non-uniform conditions.Meanwhile,the addition of a bypass shunt diode to each PV module prevents hotspots.It also produces numerous peaks in the PV array’s power-voltage characteristics,thereby trapping conventional maximum power point tracking(MPPT)methods in local peaks.Swarm optimization approaches can be used to address this issue.However,these strategies have an unreasonably long convergence time.The Grey Wolf Optimizer(GWO)is a fast and more dependable optimization algorithm.This renders it a good option for MPPT of PV systems operating in varying partial shading.The conventional GWO method involves a long conversion time,large steady-state oscillations,and a high failure rate.This work attempts to address these issues by combining Cuckoo Search(CS)with the GWO algorithm to improve the MPPT performance.The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe(P&O).A comparative analysis reveals that under non-uniform operating conditions,the hybrid GWO CS(GWOCS)approach presented in this article outperforms the GWO and P&O approaches.

Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method

This paper presents a real-time battery management unit designed by applying the Coulomb counting method and intended for use in an integrated renewable energy system for PV-Hybrid power supply. Battery management is required to stabilize hybrid systems and extend battery lifetimes. The battery management unit is divided into three main stages. Firstly, analysis of the basic components of the battery type used in the system is considered. Secondly, the state of charge (SOC) estimation method and the deterioration factor of the battery are analyzed. Finally, the overall battery management system, including a computer-based measurement and control unit, is constructed. The control system displays real-time information through LabVIEW 8.5 by estimating the state of charge through various measurements. The system will issue alerts when malfunctions are detected, and the operator can analyze and react to the system in real time to stabilize the system and extend the battery lifetime.

Hybrid Power Generation by Using Solar and Wind Energy: Case Study

Energy is critical to the economic growth and social development of any country. Indigenous energy resources need to be developed to the optimum level to minimize dependence on imported fuels, subject to resolving economic, environmental and social constraints. This led to an increase in research and development as well as investments in the renewable energy industry in search of ways to meet the energy demand and to reduce the dependency on fossil fuels. Wind and solar energy are becoming popular owing to the abundance, availability and ease of harnessing the energy for electrical power generation. This paper focuses on an integrated hybrid renewable energy system consisting of wind and solar energies. Many parts of Libya have the potential for the development of economic power generation, so maps locations were used to identify where both wind and solar potentials are high. The focal point of this paper is to describe and evaluate a wind-solar hybrid power generation system for a selected location. Grid-tied power generation systems make use of solar PV or wind turbines to produce electricity and supply the load by connecting to the grid. In this study, the HOMER (Hybrid Optimization Model for Electric Renewable) computer modeling software was used to model the power system, its physical behavior and its life cycle cost. Computer modeling software was used to model the power system, its physical behavior and its life cycle cost. The hybrid power system was designed for a building at the University of Al-Marj (MARJU). Through the use of simulations, the installation of ten 100-kW wind turbines and 150-KW solar PV was evaluated.

Research on Hybrid Power System with Dual Stator-winding and Its Decoupled Control Strategy

For the bi-power system adopted widely in future armored vehicles,a hybrid power generator with dual stator-winding was proposed.Its structure and working principle were analyzed first,and its main parameters were determined and verified according to the power requirements.The system's mathematical model was established,and a decoupled control method was put forward on the basis of the instantaneous reactive power theory.For the voltage building-up,a voltage control strategy was designed on the basis of mixed reactive power compensation to implement stabilized 28V and 270V outputs simultaneously.The simulation results show that the stabilization accuracy and disturbance rejection ability of the system are improved much more than other ordinary generators.

Feasibility Study of a Hydro PV Hybrid System Operating at a Dam for Water Supply in Southern Brazil

Dams for water supply usually represent an untapped hydroelectric potential. It is a small energetic potential, in most situations, usually requiring a particular solution to be viable. The use of pumps as power turbines often represents an alternative that enables the power generation in hydraulic structures already in operation, as is the case of dams in water supply systems. This potential can be exploited in conjunction with the implementation of PV modules on the water surface, installed on floating structures, both operating in a hydro PV hybrid system. The floating structure can also contribute to reducing the evaporation of water and providing a small increase in hydroelectric power available. This paper presents a pre-feasibility study for implementation of a hydroelectric power plant and PV modules on floating structures in the reservoir formed by the dam of Val de Serra, in southern Brazil. The dam is operated to provide drinking water to about 60% of the population of the city of Santa Maria, in the state of Rio Grande do Sul, in southern Brazil. The pre-feasibility study conducted with Homer software, version Legacy, indicated that the hydroelectric plant with a capacity of 227 kW can operate together with 60 kW of PV modules. This combination will result (in one of the configurations considered) in an initial cost of USD$ 1715.83 per kW installed and a cost of energy of USD$ 0.059/kWh.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.