Flexible wind speed generation model: Markov chain with an embedded diffusion process

Ma, Jinrui; Fouladirad, Mitra; Grall, Antoine

doi:10.1016/j.energy.2018.08.212

Cited by 27 publications

(24 citation statements)

References 31 publications

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“…To adapt the Monte Carlo sampling method [5][6][7][8][9][10][11][12][13][14][15][16][17] used in previous 2DSTPMs to the 3DSTPM, we extend it as follows: construct a 3D state transition cumulative probability matrix (STCPM) P 3dim cum from 3DSTPM and then sample it to obtain the SWPSTS.…”

Section: Monte Carlo Samplingmentioning

confidence: 99%

“…After the PDFs of the fluctuation quantity and noise, f fluc and f noise , have been obtained from (11)-(13), the noise-contained SWPTS can be generated by separately and sequentially adding the fluctuation quantity sampled from f fluc and noise sampled from f noise to the SWPSTS. Most previous MC-based wind simulation studies [5][6][7][8][9][10][11][12][13][14][15][16][17] not only lack the filtering process but also add either white noise or the noise-corrupted fluctuation quantity directly to the SWPSTS. Thus, the fluctuation characteristic is not replicated well.…”

Section: Addition Of the Fluctuation Quantity And Noisementioning

confidence: 99%

“…Forecast [2][3][4] and simulation [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] of wind power are two main ways to characterise wind power. The former is to predict wind power values in the future, while the latter is to generate synthetic wind power time series (SWPTS) that are highly consistent with the historical ones in characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…There are two main approaches in wind power simulation modelling: time series (TS) analysis methods and artificial intelligence (AI) techniques. The former includes the Markov chain (MC)-based method [5][6][7][8][9][10][11][12][13][14][15][16][17] and the auto-regressive and moving average (ARMA) method [18,19], while the latter includes neural networks [23], artificial neural fuzzy inference systems [24] and so on. AI techniques usually need massive data to build their intelligence, thus they are mainly used in big data scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The transition characteristic, depicted by the state transition probability and the state duration, reflects expected power steadiness; the fluctuation characteristic, described by the fluctuation quantity [16,17], provides information about ramp rates. At present, in terms of the conventional MCbased method involved in the above studies [5][6][7][8][9][10][11][12][13][14], the following problems mainly hinder its performance in reproducing those characteristics:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Markov chain‐based wind power time series modelling method considering the influence of the state duration on the state transition probability

Zhu

Zhang

Yan

et al. 2019

IET Renewable Power Generation

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Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variablemodelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.

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