Irregular distribution of wind power prediction

Yuan, Kun; Zhang, Kaifeng; Zheng, Yaxian; Li, Dawei; Wang, Ying; Yang, Zhenglin

doi:10.1007/s40565-018-0446-9

Cited by 23 publications

(11 citation statements)

References 25 publications

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“…However, approaches in [10]- [16] rely on a centralized framework, that is questionable in the case of largescale systems. Other references [17] and [18] propose the use of a normal distribution aimed at considering the wind speed variability. For instance, in [19], a Monte Carlo simulation is employed to generate a scenario tree to predict wind power availability.…”

Section: A Literature Reviewmentioning

confidence: 99%

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Scarabaggio

Grammatico

Carli

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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In this article, we propose a distributed demandside management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.

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Section: A Literature Reviewmentioning

confidence: 99%

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Scarabaggio

Grammatico

Carli

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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“…The UAV model is fully detailed in [45]. A simplified normally distributed wind model was supposed based on [46][47][48], and average wind velocity and direction values in the operation area were employed (see Table A1 and Equations ( 1) and ( 2)) [49,50]. The atmospheric parameters were provided by the International Standard Atmosphere (ISA) [43,51,52].…”

Section: Problem Definitionmentioning

confidence: 99%

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

2021

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Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness.

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“…However, wind power has strong randomness and volatility and is difficult to accurately predict [3][4][5][6][7][8], thus making wind power prediction a current hotspot in research. Most current studies of wind power prediction focus more heavily on point prediction [9][10][11][12][13][14] and less on interval prediction [15]. Because the point prediction approach causes the wind power prediction results to greatly deviate from the actual values, it is difficult to implement real-time scheduling [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective interval prediction of wind power based on conditional copula function

Zhang

et al. 2019

J. Mod. Power Syst. Clean Energy

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Interval prediction of wind power, which features the upper and lower limits of wind power at a given confidence level, plays a significant role in accurate prediction and stability of the power grid integrated with wind power. However, the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function, which neglects the correlations among various variables, leading to decreased prediction accuracy. Therefore, in this paper, we improve the multiobjective interval prediction based on the conditional copula function, through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function. We use the multi-objective optimization method of nondominated sorting genetic algorithm-II (NSGA-II) to obtain the optimal solution set. The particular best solution is weighted by the prediction interval average width (PIAW) and prediction interval coverage probability (PICP) to pick the optimized solution in practical examples. Finally, we apply the proposed method to three wind power plants in different Chinese cities as examples for validation and obtain higher prediction accuracy compared with other methods, i.e., relevance vector machine (RVM), artificial neural network (ANN), and particle swarm optimization kernel extreme learning machine (PSO-KELM). These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

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Irregular distribution of wind power prediction

Cited by 23 publications

References 25 publications

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

Multi-objective interval prediction of wind power based on conditional copula function

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