Optimal Prediction Intervals of Wind Power Generation

Wan, Can; Xu, Zhao; Pinson, Pierre; Zhang, Dong; Wong, Kit Po

doi:10.1109/tpwrs.2013.2288100

Cited by 288 publications

(112 citation statements)

References 34 publications

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“…In order to address the impacts of wind power forecast uncertainty, we make use of stochastic scenarios to fully account for the probability distribution of the wind power forecast errors [24,25].…”

Section: ) For Hour Hmentioning

confidence: 99%

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Wang

Zhou

Botterud

et al. 2016

J. Mod. Power Syst. Clean Energy

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Grid-scale battery energy storage systems (BESSs) are promising to solve multiple problems for future power systems. Due to the limited lifespan and high cost of BESS, there is a cost-benefit trade-off between battery effort and operational performance. Thus, we develop a battery degradation model to accurately represent the battery degradation and related cost during battery operation and cycling. A linearization method is proposed to transform the developed battery degradation model into the mixed integer linear programming (MILP) optimization problems. The battery degradation model is incorporated with a hybrid deterministic/stochastic look-ahead rolling optimization model of wind-BESS bidding and operation in the real-time electricity market. Simulation results show that the developed battery degradation model is able to effectively help to extend the battery cycle life and make more profits for wind-BESS. Moreover, the proposed rolling look-ahead operational optimization strategy can utilize the updated wind power forecast, thereby also increase the wind-BESS profit.

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Section: ) For Hour Hmentioning

confidence: 99%

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Wang

Zhou

Botterud

et al. 2016

J. Mod. Power Syst. Clean Energy

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show abstract

“…Wind power has been identified as one of the most important and efficient renewable energy and has been extensively utilized throughout the world [1][2][3]. With the rapid development of wind power, the proportion of wind power in the whole power system is becoming larger.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm

Peng

2016

Energies

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Abstract:Regarding the non-stationary and stochastic nature of wind power, wind power generation forecasting plays an essential role in improving the stability and security of the power system when large-scale wind farms are integrated into the whole power grid. Accurate wind power forecasting can make an enormous contribution to the alleviation of the negative impacts on the power system. This study proposes a hybrid wind power generation forecasting model to enhance prediction performance. Ensemble empirical mode decomposition (EEMD) was applied to decompose the original wind power generation series into different sub-series with various frequencies. Principal component analysis (PCA) was employed to reduce the number of inputs without lowering the forecasting accuracy through identifying the variables deemed as significant that maintain most of the comprehensive variability present in the data set. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by bat algorithm (BA) was established to forecast those sub-series extracted from EEMD. The forecasting performances of diverse models were compared, and the findings indicated that there was no accuracy loss when only PCA-selected inputs were utilized. Moreover, the simulation results and grey relational analysis reveal, overall, that the proposed model outperforms the other single or hybrid models.

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“…To the best of our knowledge, the only works that are independent of the point-forecast method are (Wan et al, 2014;Pinson and Tastu, 2014). Machine-learning methods capable of quantifying uncertainty bounds of point forecasts are presented in Wan et al (2014), Pinson and Tastu (2014).…”

Section: Introductionmentioning

confidence: 99%

Model-free computation of ultra-short-term prediction intervals of solar irradiance

2016

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We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities of this DIP are (i) its independence from the method used for the point forecast of solar irradiance, (ii) its independence from the error distribution of the point-forecast method. We compare the DIP with the most common prediction interval methods. By using significant data set covering several months of experimental observations, we have observed higher accuracy and lower width of the prediction intervals of the proposed DIP.

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Optimal Prediction Intervals of Wind Power Generation

Cited by 288 publications

References 34 publications

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm

Model-free computation of ultra-short-term prediction intervals of solar irradiance

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