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Your search keyword '"Yao, Jianfeng"' showing total 12 results
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12 results on '"Yao, Jianfeng"'

3. Research on Electric Vehicle Demand Response Strategy in Traffic-Grid Coupling Networks Based on Charging State Forecasting Model

Author

Qi Wang, Ding Leiming, Junjie Fan, Yao Jianfeng, Quan Yuan, and Guo Lei

Subjects
Coupling, Demand response, business.product_category, State of charge, Computer science, Electric vehicle, Node (circuits), business, Grid, Automotive engineering, Energy (signal processing), Voltage
Abstract

As the environmental concerns increase globally, electric vehicles (EVs) play an important role in energy saving and emission reduction perspectives. However, the integration of EV charging loads into power grid brings negative effects such as overload and voltage deviation. In order to solve these problems, an EV demand response (DR) strategy in traffic-grid coupling networks based on the charging state forecasting model is proposed. Firstly, the charging state forecasting model was established considering the traffic condition, which can capture the distribution characteristics of the aggregate EV charging loads. Then a DR strategy considering the power flow constraints was proposed. The DR potential of each coupling node are evaluated and the charging loads are rationally dispatched in the strategy. Finally, simulation results show that the proposed method can effectively alleviate the voltage deviation in the regional power grid.

Published
2020

4. Orderly dispatching strategy considering the impact of traffic information on electric vehicle charging

Author

Xiaocong Zhang, Junjie Fan, Yao Jianfeng, Qi Wang, Jie Wei, and Quan Yuan

Subjects
Demand response, Electric power transmission, business.product_category, Computer Science::Systems and Control, Computer science, Electric vehicle, Node (circuits), State (computer science), business, Grid, Automotive engineering, Voltage, Power (physics)
Abstract

Massive disordered charging loads of electric vehicles (EVs) integrating into power grid will bring negative effects such as node voltage deviation, power overload of transmission lines and so on. In order to solve the problem of large-scale EV disordered charging, this paper proposed an orderly dispatching strategy considering the impact of the traffic information on the EV charging, to accurately forecast and reasonably dispatch the EV charging loads. Firstly, the influence of the traffic information on EV charging state and the power grid operation is analyzed. Then the charging state prediction model is established to obtain the temporal and spatial distribution characteristics of the aggregate charging loads. Then the demand response (DR) orderly dispatching strategy considering the power flow constraints is proposed to accurately evaluate the DR potential of each grid node and reasonably allocate the response power. Finally, the actual map and 110kV power grid of a city are used for simulation. Results show that the proposed strategy can effectively reduce the voltage deviation and the power overload of transmission lines.

Published
2020

11. Fluctuations of an Improved Population Eigenvalue Estimator in Sample Covariance Matrix Models.

Author

Yao, Jianfeng, Couillet, Romain, Najim, Jamal, and Debbah, Mérouane

Subjects
*EIGENVALUES, *EIGENFUNCTIONS, *COVARIANCE matrices, *PARAMETER estimation, *CENTRAL limit theorem, *STIELTJES transform, *RANDOM matrices, *COMPUTER simulation, *MATHEMATICAL models
Abstract

This paper provides a central limit theorem for a consistent estimator of population eigenvalues with large multiplicities based on sample covariance matrices. The focus is on limited sample size situations, whereby the number of available observations is comparable in magnitude to the observation dimension. An exact expression as well as an empirical, asymptotically accurate, approximation of the limiting variance is derived. Simulations are performed that corroborate the theoretical claims. [ABSTRACT FROM AUTHOR]

Published
2013
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12. Eigenvalue Estimation of Parameterized Covariance Matrices of Large Dimensional Data.

Author

Yao, Jianfeng, Kammoun, Abla, and Najim, Jamal

Subjects
*EIGENVALUES, *RANDOM matrices, *COVARIANCE matrices, *MULTIPLICITY (Mathematics), *STIELTJES transform
Abstract

This article deals with the problem of estimating the covariance matrix of a series of independent multivariate observations, in the case where the dimension of each observation is of the same order as the number of observations. Although such a regime is of interest for many current statistical signal processing and wireless communication issues, traditional methods fail to produce consistent estimators and only recently results relying on large random matrix theory have been unveiled. In this paper, we develop the parametric framework proposed by Mestre, and consider a model where the covariance matrix to be estimated has a (known) finite number of eigenvalues, each of it with an unknown multiplicity. The main contributions of this work are essentially threefold with respect to existing results, and in particular to Mestre's work: To relax the (restrictive) separability assumption, to provide joint consistent estimates for the eigenvalues and their multiplicities, and to study the variance error by means of a Central Limit Theorem. [ABSTRACT FROM PUBLISHER]

Published
2012
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