No abstract

No abstract

The high penetration of wind energy in electrical power systems presents challenges for all operators. For the wind farm (WF) planners, one of these challenges is optimizing its layout with a set of constraints. This paper proposes a bi-hierarchy optimization scheme to determine the capacity and layout of a grid-connected WF. The environmental impacts involved by the installation of a WF have been taken into consideration in the problem. The first-layer model optimizes the WF capacity and configuration with minimized comprehensive generation cost of wind energy and two sets of constraints. The sound pressure level ( ) limit of the noise emitted by the wind turbines (WTs) is handled to be one of the constraints of the first-layer model. The second-layer model determines the generation schedule of other conventional generators. A Gaussian wake model is applied to calculate the effective wind speed for each WT. For the simulations, the WF is supposed to be integrated in the IEEE 30-bus test system. The wild goats algorithm (WGA) and the quadratic programming (QP) method are used to solve the problem. The simulation results validate the effectiveness of the proposed model and prove that environmental influences of WFs should not be ignored during the planning stage.Siyu Tao (GS'18) received the B.Sc degree in electrical engineering from Zhejiang University, China, in 2013 and the M.Sc degree in control systems from Imperial College London, U.K., in 2014. She is currently pursuing the Ph.D degree at the

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.