Wind Farm Power Output Research Articles

Adaptive power smoothing control in grid-connected and islanding modes of hybrid micro-grid energy management

This paper presents an innovative technique for energy management of hybrid micro-grid system during grid connected and islanding modes by adaptive power smoothing control. In order to perform the adaptive power smoothing control, fuzzy logic control algorithm (FLCA) is incorporated with conventional control scheme of dc-dc buck boost converter of the battery energy storage system. A standard battery energy limiter is considered with the dc-dc buck boost controller as smoothing trip controller which can disable the smoothing operation if the state of charge of battery (SOC) exceeds its limits. A secondary frequency control and grid resynchronization technique for the converter between the AC and DC buses are considered for islanding and grid reconnected operations, respectively, to demonstrate the effectiveness of the adaptive technique in smoothing wind farm output power. The proposed FLCA adjusts its reference automatically during transition from grid mode to islanding mode and vice-versa based on the battery's SOC. Several cases have been tested where it has been verified that the FLCA can efficiently manage the SOC, while performing smoothing operation and maintain energy balance due to its adaptive nature instead of completely disabling the wind power smoothing at some extreme conditions.

Priority Control Strategy of VSC-MTDC System for Integrating Wind Power

For the obvious advantages in integrating wind power, multiterminal HVDC transmission system (VSC-MTDC) is widely used. The priority control strategy is proposed in this paper considering the penetration rate of wind power for the AC grid. The strategy aims to solve the problems of power allocation and DC voltage control of the DC system. The main advantage of this strategy is that the demands for wind power of different areas can be satisfied and a power reference for the wind power trade can also be provided when wind farms transmit active power to several AC grids through the DC network. The objective is that power is well distributed according to the output power of wind farm with the demand of AC system and satisfactory control performance of DC voltage is obtained.

The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain

Wind farm micro-siting is to determine the optimal positions of wind turbines within the wind farm, with the target of maximizing total power output or profit. This paper studies the performance of the lazy greedy algorithm on optimization of wind turbine positions above complex terrain. Instead of the traditional linear models, computational fluid dynamics and virtual particle wake flow model are employed in the present study for a more accurate evaluation of wind energy distribution and wind power output of wind farm on complex terrain. The validity of the submodular property used by the lazy greedy algorithm is discussed for the wind farm micro-siting optimization problem. By conducting the numerical tests, results demonstrate that the combination of the lazy greedy algorithm and the virtual particle wake model is effective in optimizing wind turbine positioning on complex terrain, for it produces better solution in less time comparing to the previous bionic method.

A combination method for modeling wind power plants in power systems reliability evaluation

The production of wind energy often involves uncertainties due to the stochastic nature of wind speeds and the variation of the power curve. In this study, a method for modeling wind power plant in power systems reliability evaluation is proposed. This method is a combination of analytical and simulation methods. Initially, the mechanical behavior of each wind turbine generator (WTG) is modeled through sequential Monte Carlo method. Then, considering the wind speed of the area, the Markov model is used for modeling the power output of wind farm. The combination method is compared with the Auto Regressive Moving Average (ARMA) time series method and some other techniques such as Weibull and normal distribution methods in wind speed modeling. Also in this study, the effects of different parameters, such as failure, repair rate and number of output power levels of WTG on the system reliability are analyzed.

Power output of offshore wind farms in relation to atmospheric stability

Atmospheric stability is known to influence wind farm power output, by affecting power losses due to wakes. This research tries to answer what atmospheric stability does to the power production and how conventional simulations using the Jensen wake model compare and can be improved. Data is used from two offshore wind farms, Egmond aan Zee (OWEZ) and North Hoyle. Stability distributions are determined using metmast data. By combining this data with the production data, the influence of stability on the power output is studied. It is found that very unstable conditions result in higher power output (i.e. smaller wake losses) than near-neutral conditions, and these again show higher power output than during very stable conditions. Differences in normalized power output of 10-20% exist between the very unstable and very stable conditions. Simulations can be improved by adapting the wake decay constant (WDC). Observed WDC values are k ≥ TI, as opposed to the conventional k ≈ 0.5TI. A hypothesis for further research is proposed regarding the influence of vertical turbulence.

Mechanism Research about Impact of Large-Scale Wind Power Integration on Grid Loss

This paper firstly analyzes the mechanism of transmission line and transformer loss and illustrates the equivalent model and calculating method. Then creates a simple three-node model and discusses the main factors which affect the grid loss with adequate formula. At last, we draw a concise conclusion that there are several factors affecting grid loss. The main factors are the location of wind power access, the active power flow of transmission lines, the active power output of wind farms and the voltage level of wind power access.

An Analytical Solution for Wind Farm Power Output

An ad-hoc probabilistic analytical solution for modeling wind farm power output is proposed in this letter. In accordance with the unit impulse function described in signals and systems engineering, and combined with the output power versus wind speed curve with wake effect consideration and the wind speed probability density function (PDF), the PDF of wind farm power output is deduced analytically. The comparisons with Monte Carlo method are given to demonstrate the validity of the proposed model.

A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on Clustering Algorithm

A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.

A Set-Membership Affine Projection Algorithm-Based Adaptive-Controlled SMES Units for Wind Farms Output Power Smoothing

This paper presents a novel adaptive control scheme of the superconducting magnetic energy storage (SMES) units with the purpose of smoothing the wind farms' output power. The adaptive control scheme is based on the set-membership affine projection algorithm (SMAPA), which provides a faster convergence and less computational complexity than the normalized least-mean-square algorithm. In this study, two grid-connected fixed-speed wind farms are considered. The control strategy of the SMES units is based on a pulse width modulation (PWM) voltage source converter (VSC) and a dc-dc converter. The VSC and dc-dc converter is used to control the reactive and active power exchange with the power system, respectively. The SMAPA-based adaptive proportional-integral (PI) controllers are utilized to control both converters. For realistic responses, real wind speed data extracted from Hokkaido Island, Japan, and two-mass drive train model of the wind turbine generator system are used in the analyses. Also, a real 10 MVA SMES unit that was installed at a power plant in Kameyama, Japan, is connected to the point of common coupling of the wind farms. The validity of the proposed control scheme is verified by the simulation results, which are performed using PSCAD/EMTDC environment. With the SMAPA-based adaptive-controlled SMES units, the wind farms' output power can be smoothed easily avoiding huge effort for fine tuning the controllers' parameters.

Study of Wind Farm Power Output Predicting Model Based on Nonlinear Time Series

To solve the problem of the variancy of the wind power when wind farm connect with the power grid, a wind power predicting model of wind farm based on double ANNs is proposed in the paper. Wind velocity and wind direction on wind farm are the key of wind power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure, are also great influence on it. The observed values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by the nonlinear time series ANNs model. The wind power predicting model consists of double artificial neural networks. The first is consisted of five artificial neural networks which is used to prediction the circumstance conditions time series, the second is employed to prediction the power of wind farm use predicting value of the five conditions. A series of simulation show that the results of the predicting model is acceptable in engineering application.

Clustering methods of wind turbines and its application in short-term wind power forecasts

Commonly used wind power forecasts methods choose only one representative wind turbine to forecast the output power of the entire wind farm; however, this approach may reduce the forecasting accuracy. If each wind turbine in a wind farm is forecasted individually, this considerably increases the computational cost, especially for a large wind farm. In this work, a compromise approach is developed where the turbines in the wind farm are clustered and a forecast made for each cluster. Three clustering methods are evaluated: K-means; a self-organizing map (SOM); and spectral clustering (SC). At first, wind turbines in a wind farm are clustered into several groups by identifying similar characteristics of wind speed and output power. Sihouette coefficient and Hopkins statistics indices are adopted to determine the optimal cluster number which is an important parameter in cluster analysis. Next, forecasting models of the selected representative wind turbines for each cluster based on correlation analysis are established separately. A comparative study of the forecast effect is carried to determine the most effective clustering method. Results show that the short-term wind power forecasting on the basis of SOM and SC clustering are effective to forecast the output power of the entire wind farm with better accuracy, respectively, 1.67% and 1.43% than the forecasts using a single wind speed or power to represent the wind farm. Both Hopkins statistics and Sihouette coefficient are effective in choosing the optimal number of clusters. In addition, SOM with its higher forecast accuracy and SC with more efficient calculation when applied into wind power forecasts can provide guidance for the operating and dispatching of wind power. The emphasis of the paper is on the clustering methods and its effect applied in wind power forecasts but not the forecasting algorithms.

Probabilistic load flow computation using Copula and Latin hypercube sampling

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.

Experimental verification of computational predictions in power generation variation with layout of offshore wind farms

AbstractThe optimization of wind farms with respect to spatial layout is addressed experimentally. Wake effects within wind turbine farms are well known to be deleterious in terms of power generation and structural loading, which is corroborated in this study. Computational models are the predominant tools in the prediction of turbine‐induced flow fields. However, for wind farms comprising hundreds of turbines, reliability of the obtained numerical data becomes a growing concern with potentially costly consequences. This study pursues a systematic complementary theoretical, experimental and numerical study of variations in generated power with turbine layout of an 80 turbine large wind farm. Wake effects within offshore wind turbine arrays are emulated using porous discs mounted on a flat plate in a wind tunnel. The adopted approach to reproduce experimentally individual turbine wake characteristics is presented, and drag measurements are argued to correctly capture the variation in power generation with turbine layout. Experimental data are juxtaposed with power predictions using ANSYS WindModeller simulation suite. Although comparison with available wind farm power output data has been limited, it is demonstrated nonetheless that this approach has potential for the validation of numerical models of power loss due to wake effects or even to make a direct physical prediction. The approach has even indicated useful data for the improvement of the physics within numerical models. Copyright © 2014 John Wiley & Sons, Ltd.

IGDT-based multi-stage transmission expansion planning model incorporating optimal wind farm integration

SUMMARY In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information-gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors' strategies. The TEP model is formulated for the risk-averse and risk-seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi-stage time horizon. A genetic algorithm approach is employed to solve the bi-level IGDT-based optimization problem. Finally, this IGDT-based model is applied to the simplified Iranian 400-kV system, and the results are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Output power smoothing of variable speed wind farms using rotor-inertia

Due to variations of wind speed, the output power of small-scaled or mid-scaled wind farms is fluctuating and this may negatively impact the power system. This paper proposes a new fuzzy PID controller scheme for a variable speed wind farm. In this scheme, by using the rotor-inertia as an energy storage system, two purposes are satisfied. Firstly, the optimal power point of each turbine is tracked in such a way that the total output power of the wind farm is also smoothed. Secondly, the proposed scheme enables the wind farm to produce a predetermined output power. To show the performance of the proposed scheme, a doubly fed induction generator (DFIG) based wind farm is simulated and the results are compared with conventional scheme. Also, the tracking capability of the pre-scheduled output power is verified.

Temporal structure of aggregate power fluctuations in large-eddy simulations of extended wind-farms

Fluctuations represent a major challenge for the incorporation of electric power from large wind-farms into power grids. Wind-farm power output fluctuates strongly in time, over various time scales. Understanding these fluctuations, especially their spatio-temporal characteristics, is particularly important for the design of backup power systems that must be readily available in conjunction with wind-farms. In this work we analyze the power fluctuations associated with the wind-input variability at scales between minutes to several hours, using large eddy simulations (LES) of extended wind-parks, interacting with the atmospheric boundary layer. LES studies enable careful control of parameters and availability of wind-velocities simultaneously across the entire wind-farm. The present study focuses on neutral atmospheric conditions and flat terrain, using actuator-disk representations of the individual wind-turbines. We consider power from various aggregates of wind-turbines such as the total average power signal, or signals from sub-averages within the wind-farm. Non-trivial correlations are observed due to the complex interactions between turbines placed downstream of each other, and they lead to noticeable spectral peaks at frequencies associated with the inter-turbine spacings when the wind-direction is completely fixed. In that case we observe that the frequency spectra of the total wind-farm output show a decay that follows approximately a −5/3 power-law scaling regime, qualitatively consistent with some observations made in field-scale operational wind-parks [J. Apt, “The power spectrum of power from wind-turbines,” J. Power Sources 169, 369 (2007)]. We find that these features are still observed when the wind-speed varies in magnitude. However, significant changes in the wind-direction over time tend to smooth out the observed spectral peak and reduce the extent of the observed −5/3 power-law.

Optimal output power of not properly designed wind farms, considering wake effects

The goal of this paper is to present an algorithm for increasing output power in wind farms with high wake losses. In some wind directions, wake effects cannot be neglected because the distance between turbines may be less than 5–8 times of rotor diameter. The purpose of presented algorithm is to determine coefficient of performance (CP), thrust coefficient (CT), pitch angle (β) and rotational speed (ω) of each turbine by using wake effects equations so that wake losses in the wind farm becomes minimum which result in increasing output power of wind farm. PSO algorithm is used for optimization. Finally, a sample wind farm consisting of 16 turbines is used as a case study and the results show that there is a noticeable increase in the amount of wind farm output power rather than the time which no control and optimization is used. The conclusion is that the presented algorithm is suitable for wind farms with high wake losses and little distance between their turbines and is very effective for increasing their output power.

Wind farm performance in conventionally neutral atmospheric boundary layers with varying inversion strengths

In this study we consider large wind farms in a conventionally neutral atmospheric boundary layer. In large wind farms the energy extracted by the turbines is dominated by downward vertical turbulent transport of kinetic energy from the airflow above the farm. However, atmospheric boundary layers are almost always capped by an inversion layer which slows down the entrainment rate and counteracts boundary layer growth. In a suite of large eddy simulations the effect of the strength of the capping inversion on the boundary layer and on the performance of a large wind farm is investigated. For simulations with and without wind turbines the results indicate that the boundary layer growth is effectively limited by the capping inversion and that the entrainment rate depends strongly on the inversion strength. The power output of wind farms is shown to decrease for increasing inversions.

The investigation on computational grids in wind turbine positioning optimization using greedy algorithm

In this paper, the effect of the computational grids on the wind turbine positioning optimization is studied. The linear wake flow model is used to calculate the turbine wake flow. The power law is used to model the power curve of wind turbine. Greedy algorithm with repeated adjustments is introduced to solve the wind turbine positioning problem, with the target of maximizing the total power output of wind farm. Square grid and triangle grid with various orientations are used to discretize the area of wind farm. Three numerical cases are introduced to study the effect of the computational grids on the optimized results. The results show that the optimized power output can be improved through choosing appropriate grid orientation. The suggested grid orientations for single direction wind case and multi-direction wind case are given.

Hydrogen production with sea water electrolysis using Norwegian offshore wind energy potentials

This study looks into possibilities of hydrogen production on an offshore platform in Norway, to capitalize Norway’s offshore wind potential matching political goals to reduce emissions and make Norway’s transportation sector cleaner. The potential power output of a hypothetical offshore wind farm has been assessed using real operating data of other wind farms. The usable electricity was between and segmented into three scenarios. Solid oxide electrolysis cell and proton exchange membrane electrolysis are compared. Their function and the necessary technologies to operate them are described in detail. Based on these scenarios the annual hydrogen production was calculated with values between 1530 and 8020 tons per year. The second part of the study estimates the investment to find the production cost per kilogram hydrogen, which was compared to recent fuel prices in Norway to see whether the production of hydrogen was profitable. Prices vary between 5.20 € and 106.10 € per kg hydrogen.