Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

Reliability Modeling of Wind Farm Considering the Outages of Connection Cables

2013 ◽  
Vol 291-294 ◽  
pp. 461-466
Author(s):  
Guo Bing Qiu ◽  
Wen Xia Liu ◽  
Jian Hua Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Output ◽  
Complex Terrain ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Reliability Model ◽  
Wake Effect ◽  
Reliability Modeling ◽  
Wake Model

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.

scholarly journals Optimization Control Strategy for Large Doubly-Fed Induction Generator Wind Farm Based on Grouped Wind Turbine

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4848
Author(s):  
Shijia Zhou ◽  
Fei Rong ◽  
Xiaojie Ning
Keyword(s):  
Control Strategy ◽  
Power Output ◽  
Wind Farm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Active Power ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Optimization Control ◽  
Doubly Fed

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

Study on the Active Power Output Control of Wind Energy for Power Balance

2012 ◽  
Vol 608-609 ◽  
pp. 796-802
Author(s):  
Ming Song ◽  
Yuanyuan Su
Keyword(s):  
Power System ◽  
Power Control ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Control Mode ◽  
National Standard ◽  
Power Balance ◽  
Output Control ◽  
Active Power Control

This paper carried out a study on the contribution of wind farm to power balance of power system by wind power output control. The study mainly focuses on active power control capability of double feed induction generator (DFIG) based wind turbine, active power control mode of wind farm and capability of wind farm to participating in peak-load regulation. According to the National standard of China GB/T 19963-2011, a study case is given to simulate the impact of wind farm grid integration based on a real power system in order to validate the active power mode and control process presented in the paper.

A Probability Distribution Model to Simulate Wind Power Output Fluctuation

2014 ◽  
Vol 1070-1072 ◽  
pp. 171-176
Author(s):  
Chi Li ◽  
Chun Liu ◽  
Yue Hui Huang
Keyword(s):  
Probability Distribution ◽  
Wind Power ◽  
Power Output ◽  
Wind Farm ◽  
Probability Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Stable Operation ◽  
Power Fluctuation ◽  
Short Time

It is of great significance for the safe and stable operation of power system to master the fluctuation characteristics of wind power output. On the basis of analyzing a large number of field measured data, a weighted mixed Gaussian probability model is proposed to simulate short-time wind power fluctuation characteristics of wind farm cluster, that evaluation indices to reflect the short-time maximum fluctuation of wind power output and maximum likelihood estimation algorithm based on Expectation Maximization (EM) to estimate model parameters are put forward. This model is compared with various other kinds of probability distribution model and the simulation results show that the weighted mixed Gaussian probability model possesses the highest precision, so as the effectiveness of the weighted mixed Gaussian probability model is verified.

scholarly journals A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Riccardo De Blasis ◽  
Giovanni Batista Masala ◽  
Filippo Petroni
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
Wind Farm ◽  
Central Italy ◽  
High Order ◽  
Distribution Model ◽  
Spot Price ◽  
Model Parameters ◽  
Price Series ◽  
Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

Sign in / Sign up

Export Citation Format

Share Document