Investigating the Wind Power Smoothing Effect Using Set Pair Analysis

Yang, Ming; Zhang, Luobin; Cui, Yang; Zhou, Yi; Chen, Yulin; Yan, Gangfeng

doi:10.1109/tste.2019.2920255

Cited by 71 publications

(33 citation statements)

References 31 publications

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“…VMD was first proposed by Dragomiretskiy and Zosso in 2014 [41]- [44]. The decomposition process of VMD consists of two parts: the construction process and the solution process.…”

Section: Theretical Backgrounds a Evmd 1) Basic Principles Of Vmdmentioning

confidence: 99%

Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Huang

et al. 2019

IEEE Access

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Wind power combination probability prediction can effectively describe the uncertainty of wind farm output power and reduce the negative impact of this uncertainty on grid dispatching and operation. However, there is a conflict between coverage and interval width in probability prediction that affects the construction of the optimal prediction model. To resolve the conflicts between different probabilistic evaluation indicators, this paper proposes a new method for wind power combination probability prediction based on area gray correlation decision. First, the original wind power output data is reconstructed using energy-optimized variational mode decomposition to reduce the randomness of the original wind power signal. Processed wind power output data is used to establish an input feature set containing 96-dimensional historical wind power output data. Then, different Gaussian process regression prediction models are established based on 10 covariance functions. The area gray correlation method is used to calculate the area gray correlation degree of the five evaluation indicators, and a comprehensive evaluation of multiple indicators is carried out to eliminate the conflict between indicators. Finally, the weights of the prediction results of the 10 GPR models are determined according to the area gray correlation closeness, and the prediction interval and mean are reconstructed by combining the calculation results of each model. Simulation results show that the model determined by the new method has a more reliable prediction interval and faster prediction speed and can therefore provide decision support for wind power probability prediction and for the safe and stable operation of wind power grid connections.

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“…VMD was first proposed by Dragomiretskiy and Zosso in 2014 [41]- [44]. The decomposition process of VMD consists of two parts: the construction process and the solution process.…”

Section: Theretical Backgrounds a Evmd 1) Basic Principles Of Vmdmentioning

confidence: 99%

Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Huang

et al. 2019

IEEE Access

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“…In order to reduce the amount of reserve capacity in the system, the uncertainty in the output power of the WF system should be decreased by optimizing the set-point of the WF system. Various methods have been proposed to determine the set-points of WTGs and the WF system with different objectives [18][19][20]. The authors in [18] have proposed an operation strategy to optimize the set-point of each WTGs for maximizing the total output power of the WF system.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [18] have proposed an operation strategy to optimize the set-point of each WTGs for maximizing the total output power of the WF system. The authors in [19] have investigated the wind power smoothing effect considering the different number of WTGs and the operation of WTGs in the WF system. The authors in [20] have developed an operational strategy to minimize the power deviation in the WF system by optimizing the set-point for each WTGs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System

et al. 2021

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Due to the uncertainty in output power of wind farm (WF) systems, a certain reserve capacity is often required in the power system to ensure service reliability and thereby increasing the operation and investment costs for the entire system. In order to reduce this uncertainty and reserve capacity, this study proposes a multi-objective stochastic optimization model to determine the set-points of the WF system. The first objective is to maximize the set-point of the WF system, while the second objective is to maximize the probability of fulfilling that set-point in the real-time operation. An increase in the probability of satisfying the set-point can reduce the uncertainty in the output power of the WF system. However, if the required probability increases, the set-point of the WF system decreases, which reduces the profitability of the WF system. Using the proposed method helps the WF operator in determining the optimal set-point for the WF system by making a trade-off between maximizing the set-point of WF and increasing the probability of fulfilling this set-point in real-time operation. This ensures that the WF system can offer an optimal set-point with a high probability of satisfying this set-point to the power system and thereby avoids a high penalty for mismatch power. In order to show the effectiveness of the proposed method, several case studies are carried out, and the effects of various parameters on the optimal set-point for the WF system are also analyzed. According to the parameters from the transmission system operator (TSO) and wind speed profile, the WF operator can easily determine the optimal set-point using the proposed strategy. A comparison of the profits that the WF system achieved with and without the proposed method is analyzed in detail, and the set-point of the WF system in different seasons is also presented.

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“…At present, the energy structure of the world and the problems caused by it force mankind to find new energy solutions, so clean and emission-free new energy is widely developed and utilized. Wind energy is one of the most potential clean energy resources for large-scale development and utilization [1][2][3][4]. Due to the randomness of wind energy and the limitation of forecasting technology, the accuracy of wind power forecasting is unsatisfactory, so improving the accuracy and applicability of wind farm power forecasting methods has become the focus of current research [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance

Sun

et al. 2020

IEEE Access

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Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultrashort-term prediction accuracy of wind power and verify the effectiveness of the proposed method.

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Investigating the Wind Power Smoothing Effect Using Set Pair Analysis

Cited by 71 publications

References 31 publications

Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System

Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance

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