Multi-Scenario Based Generation Scheduling of Power System with Wind Farms

2013 ◽  
Vol 385-386 ◽  
pp. 1040-1044
Author(s):  
Lei Dong ◽  
Jian Kang Yang ◽  
Tian Jiao Pu ◽  
Hai Ming Zhou
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Prediction Error ◽  
Wind Farm ◽  
Wind Farms ◽  
Adjustment Costs ◽  
Environmental Benefit ◽  
Power Prediction ◽  
Power Fluctuation

Wind power penetration to power systems is increasing rapidly in the recent years due to its environmental benefit, while wind power fluctuation also brings some problems to power system operation which impacts the generation of conventional power units. For this reason, probabilistic optimal dispatching model based on multi-scenasio is developed in this paper. With the discretization of wind power fluctuations range, the scenario probability can be get by discretizing wind power prediction error distribution curves, at the same time considering the relevance of the prediction error of the adjacent periods in the time scale. By means of leading probability adjustment costs into objective function, the optimization result can consider the cost due to the fluctuation of wind power. The rationality and effectiveness of the proposed method is verified by testing and demonstrating IEEE-39 bus system with a wind farm.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

Analysis of Grid Code Technical Requirements for Wind Farms Connected to the Grid in Penghu Island

2015 ◽  
Vol 737 ◽  
pp. 199-203
Author(s):  
Shao Hong Tsai ◽  
Yuan Kang Wu ◽  
Ching Yin Lee ◽  
Wen Ta Tsai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Turbine ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Turbine Control ◽  
System A ◽  
Technical Requirements

Modern wind turbine technology has been a great improvement over the past couple decades, leading to large scale wind power penetration. The increasing penetration of wind power resulted in emphasizing the importance of reliable and secure operation of power systems, especially in a weak power system. In this paper, the main wind turbine control schemes, the wind penetration levels and wind farm dynamic behavior for grid code compliance were investigated in the Penghu wind power system, a weak isolated power system.

scholarly journals A Voltage Stability-Based Approach to Determining the Maximum Size of Wind Farms in Power Systems

2021 ◽  
Vol 16 (3) ◽  
pp. 245-250
Author(s):  
Giulio Lorenzini ◽  
Mehrdad Ahmadi Kamarposhti ◽  
Ahmed Amin Ahmed Solyman
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Voltage Stability ◽  
Wind Farm ◽  
Wind Farms ◽  
Maximum Size ◽  
Wind Generation ◽  
Stability Margins ◽  
Power Injection

Current methods to determine the wind farms maximum size do not consider the effect of new wind generation on the Voltage Stability Margins (VSMs). Installing wind power in one area may affect VSMs in other areas of the power system. Buses with high VSMs before wind power injection may be converted into weak buses after wind power injections in other parts of power systems, which may lead to limited future wind farms expansion in other areas. In this paper, two methods are proposed to determine two new wind farms maximum size in order to maximize wind power penetration level. In both methods, the size of any new wind farm is determined using an iterative process which is increased by a constant value. Proposed methods were used in the IEEE 14-bus power system. The results of applying these new methods indicate that the second method results in higher maximum sizes than the first method.

scholarly journals Wind Power Integration and Its Impact on Power Quality - A Didactic Approach

2020 ◽  
Vol 39 (2) ◽  
pp. 246-260
Author(s):  
Santos Kihwele
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Power Quality ◽  
Wind Farm ◽  
Wind Farms ◽  
Transient State ◽  
Inrush Current ◽  
Induction Generators ◽  
Grid Codes

This paper presents a model of a wind power system integrated with realistic power system, with intention of using it in addressing power quality issues in relations to grid codes during steady-state operation transient-state operation i.e., presence of grid fault events. For that case, a didactic approach of the normal performance of power systems due to the connection of fixed-speed wind turbine with induction generators is used. A study of integrating wind farms will be presented, including the incidence of high inrush current due to switching capacitor banks, out-rush current and voltage sags due to nearby three phase faults. As mandatory for the grid codes regulation, the incidence on the power quality at the point of common coupling is analyzed too. As a result of the contingency study, it will be shown that capacitors bank and fault current limiter can help the wind farm to ride-through a fault.

Preliminary Study on the Influence of Wind Power on Low-Frequency Oscillation under Outward Transmitting Thermal Generated Power Bundled with Wind Power Typical Scene

2013 ◽  
Vol 391 ◽  
pp. 271-276
Author(s):  
Peng Li ◽  
Ning Bo Wang ◽  
De Zhi Chen ◽  
Xiao Rong Zhu ◽  
Yun Ting Song
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Reactive Power ◽  
Simulation Analysis ◽  
Low Frequency ◽  
Simulation Software ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation

Increasing penetration level of wind power integration has a significant impact on low-frequency oscillations of power systems. Based on PSD-BPA simulation software, time domain simulation analysis and eigenvalue analysis are employed to investigate its effect on power system low-frequency oscillation characteristic in an outward transmitting thermal generated power bundled with wind power illustrative power system. System damping enhances markedly and the risk of low-frequency oscillation reduce when the generation of wind farm increase. In addition, dynamic reactive power compensations apply to wind farm, and the simulation result indicates that it can improve dynamic stability and enhance the system damping.

Operation and Simulation of Hybrid Wind and Gas Turbine Power System Employing Wind Power Forecasting

2012 ◽  
Author(s):  
Junrong Xia ◽  
Pan Zhao ◽  
Yiping Dai
Keyword(s):  
Power System ◽  
Gas Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Large Wind ◽  
Operation Scheduling

Due to the intermittence and fluctuation of wind resource, the integration of large wind farms in a power grid introduces an additional stochastic component to power system scheduling. This always brings challenges to maintain the stability of power system. Integrating gas turbine units with wind farms can compensate their output fluctuation. In this paper, a methodology for the operation scheduling of a hybrid power system that consists of a large wind farm and gas turbine units is presented. A statistical model based on numerical weather prediction is used to forecast power output of the wind farm for the next 24 hours at quarter-hour intervals. Forecasts of wind power are used for optimizing the operation scheduling. In order to study the dynamic performance of the proposed hybrid power system, dynamic modeling of this hybrid power system is addressed. Wind farm and gas turbine units are integrated through an AC bus, and then connected to a power grid. An aggregated model of the wind farm and detailed models of gas turbine units are developed, and are implemented using MATLAB/Simulink. Simulation studies are carried out to evaluate the system performance using real weather data. The simulation results show that the proposed hybrid power system can compensate fluctuating wind power effectively and make wind power more reliable.

scholarly journals M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction

2020 ◽  
Vol 10 (21) ◽  
pp. 7915
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Kunjin Chen ◽  
Xinyang Chen
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Task Graph ◽  
Probabilistic Prediction ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

PSS CONTROLLER FOR WIND POWER GENERATION SYSTEMS

2012 ◽  
Vol 26 (25) ◽  
pp. 1246012 ◽  
Author(s):  
J. L. DOMÍNGUEZ-GARCÍA ◽  
O. GOMIS-BELLMUNT ◽  
F. BIANCHI ◽  
A. SUMPER
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Small Signal ◽  
Small Signal Stability ◽  
Signal Stability ◽  
System A ◽  
Oscillation Modes ◽  
Power Generation Systems

Small signal stability analysis for power systems with wind farm interaction is presented. Power systems oscillation modes can be excited by disturbance or fault in the grid. Variable speed wind turbines can be regulated to reduce these oscillations, stabilising the power system. A power system stabiliser (PSS) control loop for wind power is designed in order to increase the damping of the oscillation modes. The proposed power system stabiliser controller is evaluated by small signal analysis.

Research on Wind Power Fluctuation and its Impacts on Power System Frequency

2013 ◽  
Vol 291-294 ◽  
pp. 407-414 ◽  
Author(s):  
Guo Peng Zhou ◽  
Fu Feng Miao ◽  
Xi Sheng Tang ◽  
Tao Wu ◽  
Shan Ying Li ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farms ◽  
Frequency Stability ◽  
Frequency Deviation ◽  
Intrinsic Mode Functions ◽  
Power Fluctuation ◽  
System Frequency ◽  
The Impact

The output power of wind farms has significant randomness and variability, which results in adverse impacts on power system frequency stability. This paper extracts wind power fluctuation feature with the HHT (Hilbert-Huang Transform) method. Firstly, the original wind power data was decomposed into several IMFs (Intrinsic Mode Functions) and a tendency component by using the EMD (Empirical Mode Decomposition) method. Secondly, the instantaneous frequency of each IMF was calculated. On this basis, taking a WSCC 9-bus power system as benchmark, the impact on power system frequency caused by wind power fluctuation was simulated in a real-time simulation platform, and the key component which results in the frequency deviation was found. The simulation results validate the wind power fluctuation impacts on frequency deviation, underlying the following study on power system frequency stability under the situation of large-scale intermittent generation access into the grid.

Analysis on the Fluctuation of Wind Power

2015 ◽  
Vol 733 ◽  
pp. 199-202
Author(s):  
Rui Hao Wang
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Wind Power ◽  
Time Scales ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Power Fluctuation ◽  
Power Fluctuations ◽  
Different Time Scales

This paper is aimed at exploring the characteristic fluctuation of wind power based on samples from a certain wind farm. First, the paper is to analyze fluctuations of wind power at different time scales. According to a sliding difference algorithm to build wind power fluctuations evaluation. Wind power fluctuation index for different time scales are used to fit probability distributions, indicating that the best form of distribution of wind power fluctuations is t location scale distribution. Secondly, considering the wind power has the characteristics of non-linear, non-stationary signal of the data, it fully meets the wavelet neural network analysis of the characteristics of the data. Therefore, select wavelet neural network training and testing so as to make predictions about the future of the total power of wind farm. It points out the differences between different regions covered by the index from the fluctuation characteristics of wind power, thus further understanding the fluctuation characteristics of wind power: Influenced by the time and space distribution and other factors, there is a big difference between the output power fluctuation characteristics of single wind generator and wind farm, which is because of the different wind machine in the field by the wind energy differences, and the wake effect of organic groups, making frequent fluctuations in power distribution; the fluctuation of wind is gentle, i.e. with increasing spatial distribution scale, so gentle effect occurs to wind power fluctuations. Finally, through the analysis of the fluctuation characteristics of power, power factor and analyses the influence of the characteristics of fluctuation, the paper draws a conclusion of the following improvement programs to overcome the adverse effects of wind power fluctuation of power grid operation: the rational allocation of energy storage devices, expanding the coverage area of a wind farm, or improving the design of the windmill, which will make wind farms adapt to different wind directions, thus eliminating the impact of fluctuations on the power grid from the wind farm power output by the energy storage device, and covering the area of large wind farms can adapt to different wind directions, and with power complementary, it has achieved the amount of stable power transmission into the grid.

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