Investigating the Wind Power Smoothing Effect Using Set Pair Analysis

Hourly modeled wind turbine power output time series – modeled from outputs from the mesoscale numerical weather prediction system WRF – are used to examine the spatial smoothing of various wind farm portfolios located on a complex isolated island group with a surface area of 1400 km2. Power spectral densities (PSD), hourly step change functions, and duration curves are generated, and the 5th and 95th percentiles of the step change functions are calculated. The spatial smoothing is identified from smaller high frequency PSD values, less hourly fluctuations, and more flat duration curves per installed wind power capacity, compared to single wind turbine outputs. A discussion on the limitation of the spatial smoothing for the region is included, where a smoothing effect is clear for periods up to 1–2 days, although most evident at the higher frequencies. By maximizing the smoothing effect, optimal wind farm portfolios are presented with the intention to minimize the overall wind power fluctuations. The focus is mainly on the smoothing effect in highest resolvable frequencies. Optimizing wind farm capacities at fourteen pre-defined good wind farm site locations has a minimal improvement on the hourly fluctuations. However, choosing good combinations of the individual wind farm site locations decrease the 1–3 hourly fluctuations considerably; the optimized wind farm portfolios consist of distant wind farms, while poor portfolios consist of clustered wind farms. The 5th and 95th percentiles are 15 % less for an optimized portfolio with four wind farms compared to a poor combination of four wind farms.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Strategy for wind power plant contribution to frequency control under variable wind speed

With large wind energy integration into power systems, wind farms begin to influence power systems in a much more significant manner. As wind energy systems utilize different generator technologies from the one utilized in the conventional power plants, the steady-state, transient and small-signal dynamics, as well as, power system stability will thus be significantly affected. The impact of wind energy systems on the power system dynamics and stability is thus of practical importance. As there is a significant increase in installation of wind turbines equipped with doubly-fed induction generator (DFIG) in recent years, a dynamic model of the DFIG wind turbine is firstly developed in this thesis. The model is validated against field measurement data, and the validation gives confidence about the accuracy and applicability of the developed model. DFIG wind farms consist of tens to hundreds of identical DFIG wind turbines increasing the complexity of the wind farm model and simulation time. A novel aggregation technique is developed in this thesis that incorporates a multiplication factor, namely mechanical torque compensation factor (MTCF), to the mechanical torque of the full aggregated wind farm model. The MTCF is initially constructed to approximate a Gaussian function by using fuzzy logic method. By optimizing the MTCF on a trial and error basis, less than 10 percent discrepancy is then achieved between the proposed aggregated model and the complete model. The proposed aggregation technique is then applied to a 120 MVA offshore wind farm comprising of 72 DFIG wind turbines and shows higher accuracy in approximating the wind farm dynamics as it appears at the point of common coupling (PCC) as compared to the full aggregated model. The proposed aggregated model computes faster than the complete wind farm model by 90.3 percent during normal operations and 87 percent during grid disturbances. To overcome the adverse effects due to the fluctuating nature of wind, two wind power smoothing methods are proposed using a fuzzy logic pitch angle controller for a smooth performance with a minimum drop in output power. One method performs partial smoothing with only 4.74 percent drop in the output power while the other method offers complete smoothing with a 8.28 percent drop in output power. The impact of wind energy integration on power system transient stability (PSTS) is studied quantitatively with the transient energy margin (TEM), which is calculated through the evaluation of the transient energy function (TEF). This study is carried out in two ways in the thesis. One is to analyse the impact of transient fault on the DFIG wind turbine as compared to SGs for different factors, like the fault clearing time, the grid coupling, the inertia constant and the voltage sag. The study reveals that transient stability of the DFIG wind turbine is hardly affected by the grid coupling, the inertia constant and the generator terminal voltage sag variations indicating its consistent transient performance within a wide range of these factors. The fault clearing time should be almost 11 percent faster for the system with the DFIG than the synchronous generator (SG). The other is to investigate the impact of the DFIG wind farm on the PSTS with the variation of different factors, which are the voltage sag, the fault clearing time, the load and the wind power penetration level. The study reveals that power systems integrated with DFIG wind farms are sensitive to transient events with high voltage sag, high fault clearing time, low load operation and high wind power penetration level. Machines at different locations individually possess distinct fault response and suffer from a large-scale power imbalance. As a result, reliable operation of DFIG wind farm integrated power systems demands upgraded equipment, such as advanced switchgear, fast breakers/isolators, efficient power reserve systems and advanced reactive power compensating device, etc.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

Abstract. With the number of commissioned and planned wind farms increasing rapidly, analysing wind farm cluster wakes becomes essential for resource assessment and lifetime considerations. Cluster wakes influence wind turbine power in downstream wind farms in certain meteorological situations. Our objective is to ascertain whether far-reaching cluster wakes (15 to 21 km) impact individual turbine loading in a downstream wind farm, considering the influence of atmospheric stratification. We utilized supervisory control and data acquisition (SCADA) data from an offshore wind farm and accelerometer measurements as the load proxy in the absence of load measurements to check short-term fatigue loading effects. We compared the absolute values of relevant SCADA variables of turbines in and out of the cluster wake. We found that while cluster wakes increase fluctuations of rotor speed and power, the load effects were lower than from turbines in the free wind, primarily due to lower wind speeds. We developed a new methodology to quantify loads of turbines affected by the cluster wake while separating the dependency of loads on the inflow wind speed. The turbines within the cluster wake showed a small increase in the load effects (≈2.5 %) when compared to turbines in free wind, but lower than loads of turbines within the wind farm affected by inner-farm wakes (both at the same local inflow wind speeds). We also found atmospheric stratification and the inflow wind speed to have no impact on the magnitude of loads within the cluster wake. Additionally, we found no additional blade mode excitations due to the presence of the cluster wake from the analysis of load spectra. We conclude that wind turbines affected by cluster wakes have a marginal increase in loads when compared to reference conditions in undisturbed inflow. The absolute load effects in the cluster wake are lower due to the lower wind speeds. We propose the use of additional data from load sensors to further determine possible lifetime fatigue effects of cluster wakes on offshore wind turbines. These new insights can potentially add to the design standards of future wind farm clusters.

With the increasing integration of the wind power into the power system, wind farm are required to be controlled as a single unit and have all the same control tasks as conventional power plants. The wind farm central controller receives control orders from Transmission System Operator (TSO), then dispatch the wind power reference to each wind turbine. One of the most commonly used dispatch methods is to dispatch the wind power reference to each wind turbine proportional to each wind turbine's available wind power without the consideration of the wake effect. The wake which depends on the thrust efficient of upstream wind turbines in the wind farm influences the downstream wind speed which determines the available wind power of the downstream wind turbine. Optimize the wind power production of each wind turbine in the wind farm by the optimization of the pitch angle and tip-speed-ratio of each turbine can increase the total available wind power of the wind farm. This paper proposed a new dispatch method which dispatches the wind power reference to each wind turbine proportional to each wind turbine's optimal wind power to increase the available wind power of the whole wind farm. Particle Swarm Optimization (PSO) is used to obtain the optimal wind power for each wind turbine. A case study is carried out. The available wind power of the wind farm was compared between the traditional dispatch method and the proposed dispatch method with the consideration of the wake effect.

This study presents a bi‐level decentralised active power control (DAPC) for a large‐scale wind farm cluster (WFC), consisting of several wind farms for better active power dispatch. In the upper level, a distributed active power control scheme based on the distributed consensus is designed to achieve fair active power sharing among multiple wind farms, which generates the power reference for each wind farm. A distributed estimator is used to estimate the total available power of all wind farms. In the lower level, a centralised control scheme based on the model predictive control is proposed to regulate active power outputs of all wind turbines (WTs) within a wind farm, which reduces the fatigue loads of WTs while tracking the power reference obtained from the upper‐level control. A WFC with 8 wind farms and totally 160 WTs, was used to test the control performance of the proposed DAPC scheme.

The contribution of wind power in conventional generation is increasing rapidly now-a-days. Hence a better understanding of wind power plants is required. But the wind power plants generally containing induction generators exhibit a rather different behavior than the conventional power plants which use synchronous generators. The purpose of this paper is to analyze and study transient stability of a wind farm based on conventional fixed speed Induction generator when it has been integrated with a weak grid. A comparative analysis of the transient stability improvement of a FSIG wind plant has been done, when it is supported with FACTS like S.V.C and STATCOM of equal ratings. Single case of transient condition i.e. a three phase fault has been studied by simulation. With the increase in demand of power and decrease of fossil fuels, mankind has been forced to search alternative sources for the generation of electricity. Wind power in spite of being stochastic in nature has proved itself as a viable solution to this problem. As the wind turbine technology is developing at a good pace, more and more wind power plants are being integrated with the conventional form of generation. With the increase in the ratio of wind generation to conventional generation, several problems related with integration of wind farms have emerged. These problems are due to distinct properties of the generators used with the conventional form (Thermal & Hydro) of generation and wind based generation (1)-(4). In thermal and hydro power based generation synchronous generators are used while in wind based generation mostly induction generators are used. Induction and synchronous generators do not exhibit similar behavior during power system stresses (transients). Moreover, increase in wind power based generation requires a reliable transmission system for power evacuation to the load centers. As the wind farms are mainly concentrated in far flung and rural areas their interconnection with the grid becomes a problem. This problem is mainly due to the existence weak transmission grids in the rural areas (1)-(4). Earlier when the share of wind power generation as compared with the conventional generation was low, during transient conditions; whenever the power system reached on the verge of instability the wind farm was disconnected from the grid and was reconnected when transients passed away. But as the share of wind power in the conventional generation increased, the practice of disconnecting the wind farms from the rest of the grid led to the drop in frequency of the power system. So now-a- days even if a fault occurs in the power system somewhere near the wind farms, they still remain connected with the grid for a critical time period. This phenomenon is called fault ride through (FRT) in wind farms (5). This paper has studied a transient condition (fault) in a grid connected fixed speed induction generator based wind farm and its fault ride through performance. A comparison of the performance of fact devices like static var compensators (svc) and static synchronous compensators (statcom) in improving the transient stability of wind farms during fault is also done.

An effective energy management strategy is crucial to ensure highest system reliability, stability, operation efficiency and cost-effective operation of renewable energy sources based standalone microgrid. This paper presents an efficient energy management system for microgrid incorporated with Photovoltaic system, PMSG based wind turbine and energy storages including battery, fuel cell-Electrolyzer. Implemented hybrid modified invasive weed optimization with perturbed and observed method for PV systems to harvest maximum energy during partial shading condition. A sliding mode controller is implemented for boost converter to work as maximum power point tracker for wind turbine. Three solar plants and three wind farms are considered in this paper to establish 1 MW microgrid. Each wind farm is established with multiple wind turbines and similarly each solar plant having multiple PV modules. Each wind turbine and solar plant has their own inverter to synchronize at point of common coupling (PCC). Effective controllers are proposed to supply quality power at PCC under linear status and nonlinear status of single and three phase loads. Small size battery is considered to work under transient time and electrolyzer-fuel cell set will be working under steady state condition to reduce the cost of the system. TS-Fuzzy based controllers are designed for all the converters and implemented hardware-in-loop on a Real Time Simulator (RTS) by using OPAL RT technology/modules. The results unveiled that the RTS precisely emulate the dynamics of the microgrid with proposed controllers.

The control strategy of energy storage externality for reducing wind curtailment from wind farm cluster

Researching on interaction between wind farm and power system, it usually uses simple model to describe the wind farm. The wind turbines in a wind farm always suppose as a coherent generator group, every wind turbines have identical wind speed. As wind turbine locates in the different site in the wind farm, the wind speed at each wind turbines will not change simultaneously. Wake effect is another factor that should take into consideration. So, it needs to get wind speed equivalent model. The equivalent model of wind speed in the wind farm is based on output of power. Gust wind and ramp wind are discussed. Wind farm will change gust wind into step varying wind and make ramp wind into different type wind. The arrangement of wind turbines will also affect equivalent model of wind speed. The result show that the length of wind farm will smooth the power output. It explains why the power output fluctuation of wind farm is smaller than single wind turbine. The onshore wind farm located in Dongtai Jiangsu province is proposed as an example. The equivalent wind speed model and equivalent impedance connected to grid are calculated. The results illustrate the impact of wind speed model on wind farm output.

Analysis of wind power productions by means of an analog model

Large renewable power plants connected to the high-voltage network have the potential to make an important contribution to the grid's reactive power management. This is especially true when rather than using a predetermined Q characteristic, the system operator can control the power plant's reactive power output via a direct interface. However, the intermittent nature of the primary resources and today's reactive power requirements inhibit the permanent availability of reactive power from wind and photovoltaic (PV) power plants. This study shows that a synchronized, so-called symbiotic control of PV and wind power plants can vastly increase their capability of fulfilling reactive power demands. This is demonstrated by simulating the properties of a real PV and wind farm located in Germany. Furthermore, generating units coupled to the grid via power electronics have an even larger potential of providing reactive power when their P-Q characteristics are adapted. On the downside, this can lead to an undesired coupling of reactive and active power control in the grid. A symbiosis of wind farms and PV parks can once again improve this situation when smart power allocation methods are employed. An appropriate algorithm has been developed for this study and is tested on the above-mentioned simulation model.