This paper proposes a method to obtain the optimal placement of wind turbines (WTs) in an offshore wind farm (WF). The optimization objective is to minimize the levelized average cost per net electric power generated by a WF with a fixed number of WTs while the distance between WTs is not less than the allowed minimal distance in the far wake region. The WT wake losses have been taken into account, with the Frandsen-Gaussian (F-G) wake model and the optimization problem is subsequently solved by the Hybrid Grey Wolf Optimization (HGWO) algorithm. Synthesis methods which contain a special WT ranking strategy for multiple WTs are described in detail. Both the F-G model and Jensen's model are applied in the offshore WF optimization simulation platform for comparison. Simulation results demonstrate that the F-G model is more consistent with real wakes and thus the optimization result is more accurate than the commonly used Jensen's model.

Wind farms (WFs) are important components of smart grid. The modeling and optimal planning of the WF is preliminary before its construction. In this paper, a bi-level multi-objective optimization framework is presented, with the aim of simultaneously designing the configuration of wind turbines (WTs) as well as the topology of electrical collector system in an offshore WF. The installation capacity of the WF, the positioning of the WTs and the planning scheme of the electrical system are balanced to achieve a better performance of the WF. In this proposal, there is an outer layer along with two inner layers. The objectives of the outer-layer model are the maximization of the WF’s daily profit rate, the daily average capacity factor, and power quality. It is tackled by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The objectives of the two inner layer models are to determine the topology of the electrical system and the generation schedule of other generators, and are solved by means of the Binary Particle Swarm Optimization (BPSO) algorithm and the quadratic programming (QP) method respectively. The WF is assumed to be connected to the IEEE-24 bus test system. The simulation results validate the adaptability and effectiveness of the proposed approach with the main factors that affect the WF layout being analyzed.

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.

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 (SPL) 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.

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.

In order to make full use of the potential of wind resources in a specific offshore area, this paper proposes a new method to simultaneously optimize the number, hub height and layout of a wind farm. The wind farm is subdivided by grids, and the intersection points are set as the potential wind turbine positions. The method adopts a genetic algorithm and encodes wind farm parameters into chromosomes in binary form. The length of chromosomes is decided by the number of potential positions and the hub heights to be selected. The optimization process includes selection, crossover, and mutation, while the efficiency of wind farm is set as the optimization objective. The proposed method is validated by three benchmark cases. It has proven to be effective in deciding the number of turbines and improving the efficiency of the wind farm. Another advantage of the proposed method is that it can be widely applied to wind farms of any shape. A case study applying the new method to an irregularly shaped wind farm in Hong Kong is demonstrated. By comparing the results with the original regularly shaped wind farm, the new method can improve power generation by 6.28%. Therefore, the proposed model is a supportive tool for designing the best number, hub heights and positions of wind turbines.