Modeling wind speed using probability distribution function, Markov and ARMA models

Bizrah, Asad; Al-Muhaini, Mohammad

doi:10.1109/pesgm.2015.7286273

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…Finally, the test values were normalised, and the GOF index was calculated using (4). Table 2 lists the GOF values for several distributions [46]. As shown in Fig.…”

Section: Wind Speed Forecasting Using Pdfsmentioning

confidence: 99%

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Al-Muhaini

Bizrah

Heydt

et al. 2019

IET Renewable Power Generation

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Power utilities have shown great interest in utilising renewable energy resources for various environmental and economic reasons. However, the modelling and integration of conventional and renewable resources into electric microgrids will increase the complexity of microgrid reliability analyses. Therefore, an accurate reliability assessment of power distribution systems is expected to become a major challenge in the future. More extensive research must be conducted to improve system reliability assessment techniques and determine the impact of increased penetration of renewable resources. In this work, the reliability of wind-based electric microgrids was evaluated using a Markov model, taking the intermittent nature of wind speed into account. The effects of different wind speed modelling techniques based on the auto-regressive moving average method, Markov model, and probability distribution function on the reliability analysis of electric microgrids were assessed. The Roy Billinton test system was used to illustrate the analysis and evaluate the different load and system indices.

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“…Finally, the test values were normalised, and the GOF index was calculated using (4). Table 2 lists the GOF values for several distributions [46]. As shown in Fig.…”

Section: Wind Speed Forecasting Using Pdfsmentioning

confidence: 99%

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Al-Muhaini

Bizrah

Heydt

et al. 2019

IET Renewable Power Generation

Self Cite

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show abstract

“…In the studies of variable uncertainty, the common method is to obtain the probability distribution function of the research variable. Many methods are based on the probability distribution function of wind power output [11–13], but the probability distribution function of wind farm output is difficult to obtain. It is not suitable for the optimal model of the active power output of the wind farm.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy chance constraints model based power reference optimisation of wind farm in system restoration

Zhou

Huang

et al. 2019

J. eng.

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“…Hence, they have concluded that significant improvements to wind power prediction technology are still required, and are expected to have great research significance. Other researchers [21][22][23][24] have performed multi-step prediction for future wind speeds by calculating the Markov state transition matrix based on the Markov property in the wind speed change process and using historical wind speed data. The wind power at a wind farm is strongly correlated with the wind speed; thus, the Markov prediction model has also been applied for wind power prediction [25,26].…”

Section: Introductionmentioning

confidence: 99%

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

2018

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Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

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Modeling wind speed using probability distribution function, Markov and ARMA models

Cited by 6 publications

References 11 publications

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Impact of wind speed modelling on the predictive reliability assessment of wind‐based microgrids

Fuzzy chance constraints model based power reference optimisation of wind farm in system restoration

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

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