The system performance of autonomous photovoltaic–wind hybrid energy systems using synthetically generated weather data

The yearly system performance of autonomous photovoltaic–wind hybrid energy systems with battery storage is the subject of this article. The yearly system performance is simulated using synthetically generated solar radiation and wind speed data and compared to that simulated using measured hour-by-hour data. Two different synthetic weather data sets are generated: 3-day month and 4-day month, in which 3 and 4 days represent a month, resulting in a total of 36 and 48 days for a year. The hourly varying solar radiation data are synthesised from the clearness index value for each month. The daily constant wind speed data are synthesised using the Weibull wind speed distribution model, on a monthly basis. Using two different synthetic weather data sets, the effect of number of synthetic days on the system performance estimation is studied. Different sequences of synthetic solar and wind days lead to 36 and 576 combinations for 3- and 4-day months, respectively. Three predetermined combinations for both the 3- and 4-day months are chosen and the system performance of an autonomous photovoltaic–wind hybrid energy system with battery storage is simulated using these predetermined combinations. It is shown that the yearly system performance predicted from the 3- and 4-day synthetic data closely agrees with that obtained from the measured data, varying only slightly for different combinations.     

基于典型气象条件的风光互补系统容量优化

采用当地风速和辐射强度等气象因子日变化典型特征曲线为基础数据,以风光互补发电系统日输出功率曲线最接近当地负荷曲线为最优化目标,以风电和光伏装机容量为变量,建立一种并网风光互补容量优化配置模型,旨在提高清洁能源占比和利用效率。利用武汉某区域用电负荷数据和该区域测风塔风速数据及太阳辐射数据进行算例分析,结果验证了所提出优化配置模型的可行性。     

Simulation of a novel hybrid solar photovoltaic/wind system to maintain the cell surface temperature and to generate electricity

A hybrid solar photovoltaic/wind system is proposed and investigated theoretically. The hybrid system is based on attaching a converging inclined duct beneath the photovoltaic (PV) panels and directed upward after the end of the panels. A wind turbine is attached at the exit of the converging duct. The converging duct will capture wind currents that at its inlet and enhances these current by buoyancy effect created by the rejected heat from the panels. The mixed convection air flow is used in cooling the PV panels and in generating electricity by driving the wind turbine at the duct exit. A mathematical model is proposed to describe the system hydrodynamic and thermal behavior. In addition to the mixed convection case, the pure free convection case, when there is no wind speed, has been tested. The design of the wind duct capturing system is not included in this study, which should be carefully manufactured to eliminate the reversed flow. The simulation results show that the integration of both systems not only enhances the performance of PV cell due to the effective cooling but also generates more electric power from the inserted turbine. At low wind speeds, it is found that the ducting system helps more in cooling the panels rather than driving the wind turbine. At these low wind speeds, the buoyancy effect may have a significant effect. However, at high wind speeds, the ducting system acts in both cooling the panels and driving the turbine, and at these high speeds, the buoyancy effect is insignificant.     

Assessing the suitability of wind speed probabilty distribution functions based on wind power density

Three functions have so far predominantly been used for fitting the measured wind speed probability distribution in a given location over a certain period of time, typically monthly or yearly. In the literature, it is common to fit these functions to compare which one fits the measured distribution best in a particular location. During this comparison process, parameters on which the suitability of the fit is judged are required. The parameters that are mostly used are the mean wind speed or the total wind energy output (primary parameters). It is, however, shown in the present study that one cannot judge the suitability of the functions based on the primary parameters alone. Additional parameters (secondary parameters) that complete the primary parameters are required to have a complete assessment of the fit, such as the discrepancy between the measured and fitted distributions, both for the wind speed and wind energy (that is the standard deviation of wind speed and wind energy distributions). Therefore, the secondary statistical parameters have to be known as well as the primary ones to make a judgement about the suitability of the distribution functions analysed. The primary and secondary parameters are calculated from the 12-month of measured hourly wind speed data and detailed analyses of wind speed distributions are undertaken in the present article.     

Design and analysis of an integrated concentrated solar and wind energy system with storage

This study analyzes a renewable energy‐driven innovative multigeneration system, in which wind and solar energy sources are utilized in an efficient way to generate several useful commodities such as hydrogen, oxygen, desalted water, space cooling, and space heating along with electricity. A 1‐km² heliostat field is considered to concentrate the solar light onto a spectrum splitter, where the light spectrum is separated into two portions as reflected and transmitted to be used as the energy source in the concentrated solar power (CSP) and concentrated photovoltaics (CPV) receivers, respectively. As such, CSP and CPV systems are integrated. Wind energy is proposed for generating electricity (146 MW) or thermal energy (138 MW) to compensate the energy need of the multigeneration system when there is insufficient solar energy. In addition, multiple commodities, 46 MW of electricity, 12 m³/h of desalted water, and 69 MW of cooling, are generated using the Rankine cycle and the rejected heat from its condenser. Further, the heat generated on CPV cells is recovered for efficient photovoltaic conversion and utilized in the space heating (34 MW) and proton exchange membrane (PEM) electrolyzer (239 kg/h) for hydrogen production. The energy and exergy efficiencies of the overall system are calculated as 61.3% and 47.8%, respectively. The exergy destruction rates of the main components are presented to identify the potential improvements of the system. Finally, parametric studies are performed to analyze the effect of changing parameters on the exergy destruction rates, production rates, and efficiencies.     

Wind speed generation for dynamic analysis

A simple model to generate large band wind speed time sequences, especially easy to implement with a very reduced number of parameters, is presented. It is based on the calculation of a low‐frequency and a high‐frequency components. Low‐frequency component with 1 h sample time is obtained from a random process based on a conditional probability density function. Using real data from two different wind farms in two different months of the year, it has been found that Weibull distribution centered in the current hourly mean value seems to represent well the 1 h conditional PDF in all cases, and the standard deviation of this conditional Weibull is more or less in the range 1–1.3 m s^?1 independently of the season of the year or the location. Regarding to high‐frequency component, low‐frequency samples are used as initial and final values and, between them, the turbulence component values are inserted. For that, it has been used a stochastic process based on a Beta probability function and a simple rescaling procedure with two non‐linear parameters, calculated in a recursive way. Unlike the usual modelling procedures presented in the literature, spectral power density functions are not used. This simplifies the implementation significantly. Ten second sample‐time real speed wind data from two different wind farms have been used to validate the proposed high‐frequency model, obtaining excellent results. A thorough revision of the main models found in the literature to produce wind speed time sequences for dynamic analysis is performed in the paper. Copyright © 2017 John Wiley & Sons, Ltd.     

分布式风-光互补能源利用系统

提出充分利用风能和太阳能的互补性,设计了风能和太阳能联合供能系统在运行中能量产生、储备、利用等各个环节的工作方式,实现可靠、高效地运行,为用户提供了生活用电与供热。该系统很好地利用了风-光可再生资源在季节、天气、地域上的互补性,可拓展风能和太阳能经济利用的范围。该系统在风-光丰富的广大农村地区具有很好的应用和发展前景。     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.     

西沙地区建筑能耗模拟用典型气象年研究

典型气象年可为建筑节能设计提供必要的气象数据条件.在挑选典型气象年的过程中,通常要具备30 a的气象数据资料,包括如干球温度、太阳辐射、风速、露点温度等.中国南海岛礁地区地面气象观测数据缺失问题严重,特别是太阳辐射数据较难获得.针对这一问题,以西沙地区为例,基于中国气象局提供的近30 a(1985—2014)实测气象数...     

Application of energy capacitor system to wind power generation

In this paper, it is reported that energy capacitor system (ECS), which combines power electronic devices and electric double‐layer capacitor, can significantly decrease voltage and power fluctuations of grid‐connected fixed‐speed wind generator. The proper selection of wind farm output power reference is still a problem for smoothing the wind farm output power. This paper proposes exponential moving average to generate the reference output power of a grid‐connected wind farm. The objective of the control system is to follow the line power reference by absorbing or providing real power to or from the ECS. Moreover, the necessary reactive power can also be supplied to keep the wind farm terminal voltage at the desired reference level. Real wind speed data were used in the simulation analyses, which validate the effectiveness of the proposed control strategy. Simulation results clearly show that our proposed ECS can be suitable for wind power application. Copyright © 2007 John Wiley & Sons, Ltd.     

Short‐term wind speed prediction in wind farms based on banks of support vector machines

Wind speed prediction is a key point in the management of wind farms because it is directly related to the power produced by each of a farm's turbines. Wind speed prediction is usually one of the most important tasks in wind farming, and companies that manage these farms invest large amounts of money to improve their prediction systems. In this paper, we propose an improvement to an existing wind speed prediction system, using banks of regression Support Vector Machines (SVMr) for a final regression step in the system. Several novel SVMr structures are proposed in this paper to manage the diversity in input data arising from the use of different global forecasting models and several parameterizations of a mesoscale model, included in the basic version of the prediction system. We show that the system implementing SVMr banks outperforms the basic system without taking into account diversity in the input data. It also performs better than a similar system using banks of multi‐layer perceptrons. All the tests are carried out using real data from several wind turbines on a wind farm in southeast Spain. Copyright © 2010 John Wiley & Sons, Ltd.     

A multi‐predictor model to estimate solar and wind energy generations

Recent technological developments in renewable energy systems and significant growth of solar and wind energy have made these 2 renewable sources potential viable alternatives for conventional energy sources. However, due to intermittent nature, their reliability and availability are not similar to traditional sources. Hence, it is crucial to estimate the solar and wind availability and contribution more accurately. There are various factors affecting the generation capacity of renewable sources. There has been a vast research on the impact of factors related to climate condition such as wind speed, air temperature, and humidity on renewable energy generation. However, there are several other factors with indirect impact on renewable capacity and generation mostly overshadowed by the climate factors. In this study, a multi‐predictor regression model is developed and presented for solar and wind energy generation capacity across the USA. Our study of 50 states shows how the generation capacity can be affected by several indexes including human development index. Variables with the more significant impacts have been chosen using a regression analysis. A recommendation on the best transformation of the response variables and sensitivity analysis of the results has also been presented. The results provide a model to estimate the generation capacity using significant predictors. For instance, the impact of population growth on the wind turbine generation can be explored using these models.     

Influence of neural network training parameters on short-term wind forecasting

This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of neural networks (NNs) are used to forecast power generated via specific horizontal axis wind turbines. Meteorological data used are for a specific Western Australian location. Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the NN deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of these data, the length of training pattern within the overall span which is used to implement the predictions and whether the inclusion of solar irradiance data can appreciably affect wind speed prediction accuracy. There appears to be a relatively complex relationship between these factors and the accuracy of wind speed prediction via NNs. Predicting wind speed based on NNs trained using wind speed and solar irradiance data also increases the prediction accuracy of wind power generated, as can the type of network selected.     

Model predictive control for wind power gradients

We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We use the turbine inertia as an additional energy storage device, by varying its speed over time, and coordinate the flows of energy to achieve the goal. The control variables are turbine pitch, generator torque and charge/discharge rates for the storage device, each of which can be varied over given ranges. The system dynamics are quite non‐linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can transform the problem to one with linear dynamics and convex constraints. Thus, the problem can be globally solved, using robust, fast solvers tailored for embedded control applications. We implement the optimal control problem in a receding horizon manner and provide extensive closed‐loop tests with real wind data and modern wind forecasting methods. The simulation results using real wind data demonstrate the ability to reject the disturbances from fast changes in wind speed, ensuring certain power gradients, with an insignificant loss in energy production. Copyright © 2014 John Wiley & Sons, Ltd.     

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

Engineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.     

大型风力发电场选址与风力发电机优化匹配

从风能利用和风电成本两个角度出发,推导出风电场选址与风力机优化选型的目标函数,提出将风力机容量系数作为风电场选址与风力机选型的判据,同时给出了基于风速分布特性的风力机容量系数计算方法。通过我国云南省的13个实际风速观测点和国内外25种风力机代表机型的计算,给出了这些观测点的开发顺序及优化配置的风力机机型,并简要分析了影响风力机容量系数的主要因素。实践表明,这种方法物理意义明确,计算快捷方便,节省设计时间和设计工作量。     

Low power wind energy conversion system based on variable speed permanent magnet synchronous generators

This paper presents a low power wind energy conversion system (WECS) based on a permanent magnet synchronous generator and a high power factor (PF) rectifier. To achieve a high PF at the generator side, a power processing scheme based on a diode rectifier and a boost DC–DC converter working in discontinuous conduction mode is proposed. The proposed generator control structure is based on three cascaded control loops that regulate the generator current, the turbine speed and the amount of power that is extracted from the wind, respectively, following the turbine aerodynamics and the actual wind speed. The analysis and design of both the current and the speed loops have been carried out taking into consideration the electrical and mechanical characteristics of the WECS, as well as the turbine aerodynamics. The power loop is not a linear one, but a maximum power point tracking algorithm, based on the Perturb and Observe technique, from which is obtained the reference signal for the speed loop. Finally, to avoid the need of mechanical sensors, a linear Kalman Filter has been chosen to estimate the generator speed. Simulation and experimental results on a 2‐kW prototype are shown to validate the concept. Copyright © 2013 John Wiley & Sons, Ltd.