Probabilistic Load Flow Analysis for Power System Containing Wind Farms

Dong, Lei; Cheng, Wei; Bao, Hai; Yang, Yong

doi:10.1109/appeec.2010.5448900

Cited by 34 publications

(28 citation statements)

References 7 publications

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“…The parameters can be selected in terms of [29]. In this paper, the active power generation moments of WT can be calculated from the historical data record generated by (13) and (14). It is simpler than the mathematical approach of gaining WPG distribution and computing its moments.…”

Section: Probabilistic Model Of Power Injectionsmentioning

confidence: 99%

“…Generally, k and c are set to be 2.0 and 8.5 [21,29], or other values [14,16]. These will be further compared in case studies.…”

Section: Probabilistic Model Of Power Injectionsmentioning

confidence: 99%

“…Therefore, WPG is completely different from traditional generation units, and may cause severe fluctuation and skew on PDF curves of bus voltages and branch flows. So far, most researches obtained the PDF of WPG based on an assumption that the relationship between wind speed and WPG is linear [14][15][16]. This can introduce unacceptable errors for the cumulant calculation of WPG.…”

Section: Introductionmentioning

confidence: 99%

“…This method greatly enhances the calculation speed and is able to accurately approximate the PDF or CDF of output variables [13][14][15][16][17]. Based on the GC theory, several improved approaches were employed to address PLF analysis, such as the stochastic collocation interpolation [18], and polynomial chaos expansion [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Sui

Hou

Jia

et al. 2018

J. Mod. Power Syst. Clean Energy

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Distributed generation including wind turbine (WT) and photovoltaic panel increased very fast in recent years around the world, challenging the conventional way of probabilistic load flow (PLF) calculation. Reliable and efficient PLF method is required to take into account such changing. This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation, and then develops a maximum entropy based PLF (ME-PLF) calculation algorithm for power system integrated with wind power generation (WPG). Comparing to traditional Gram-Charlier expansion based PLF (GC-PLF) calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions (PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve the limitation of GC-PLF calculation method that mistakenly gaining negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.

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Section: Probabilistic Model Of Power Injectionsmentioning

confidence: 99%

“…Generally, k and c are set to be 2.0 and 8.5 [21,29], or other values [14,16]. These will be further compared in case studies.…”

Section: Probabilistic Model Of Power Injectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Sui

Hou

Jia

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…The utility function above can be used to quantify severity. Because of the stochastic fluctuation of wind power, the probability value is obtained by probabilistic power flow calculation [14]. Monte Carlo simulation method [15], widely used in power system, is accurate in calculating probabilistic power flow.…”

Section: Risk Assessment Of Wind Power Access To Power Systemmentioning

confidence: 99%

Wind Power System Risk Assessment Based on Fuzzy Clustering and Copula Function Modeling

Liu¹,

Zhao²,

Huang³

et al. 2017

EPE

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According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.

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Cost reduction in microgrid using demand response program of loads and uncertainty modeling with point estimation method

Rajamand

2019

Int Trans Electr Energ Syst

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Summary Uncertainty in microgrid causes degradation in load supporting and stability of the system. The origin of uncertainty may be from renewable distributed generators (DGs), market price, load demand, and other issues. To compensate for this problem, energy storage system (ESS) and demand response (DR) program are applied in microgrid energy management process. ESS is used as a reserve energy and DR is defined as cooperation of users on demand side of microgrid. DR is done in two forms: one is based on load reduction of users due to the price tariffs called price‐based DR and the second is DR based on incentive payment to customers for load reduction especially in peak times named as incentive‐based DR. In this article, price‐based DR and three types of load as industrial, commercial and house load are considered with three DR features to cooperate in energy management. Moreover, the uncertainty of renewable DGs, market price and load demand are modeled and estimated by point estimation method (PEM). Simulation results are performed in three scenarios including initial battery charge and the possibility of being off‐state of some DGs in stochastic medium with and without DR program and the results are compared in these scenarios. The effect of DR on microgrid cost is investigated using these scenarios where the influence of DR on the total cost reduction is observable in the studied microgrid.

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Probabilistic Load Flow Analysis for Power System Containing Wind Farms

Cited by 34 publications

References 7 publications

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Wind Power System Risk Assessment Based on Fuzzy Clustering and Copula Function Modeling

Cost reduction in microgrid using demand response program of loads and uncertainty modeling with point estimation method

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