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Power generation from wind generators is always associated with some intermittency due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient and effective operation. This can well support transmission and distribution system operators and schedulers to enhance the power network control and management in the smart grid context. This...
Reducing carbon emissions has accelerated the use of various renewable resources for electricity generation. Wind generation, in this context has seen increasing installations globally. Managing the intermittency of wind towards existing power system operation and control therefore becomes crucial. One effective solution is to predict the future values of wind power production. This paper focuses...
Wavelet neural network (WNN) is widely used in wind power prediction because of its good self-learning capacity and excellent performance to approach any nonlinear function. However it also has limitation in precision and operating speed. This paper proposes a new genetic algorithm of wavelet neural network (GAWNN) for short-term wind power forecasting in electrical power systems. GAWNN makes a good...
Accurate short-term wind speed forecasting is very important to improve the security and stability of power grid and to reduce the running cost. In this paper, a method based on Least squares support vector machine (LS-SVM) was proposed to the short-term forecasting. In order to avoid the blindness and inaccuracy of Parameter selection, Genetic algorithm is used to select the optimal regularization...
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
As wind power penetrations increase dramatically, wind power forecasting is increasingly becoming one of the fundamental strategies in hybrid power systems. In order to obtain higher accuracy, a new method-genetic algorithm neural network based on rough set theory is proposed in the paper. Considering many factors that influence wind speed forecasting, reduction algorithm of rough set theory is introduced...
The rapid growth of wind generation is introducing additional variability and uncertainty into power system operations and planning. These inherent characteristics of wind power have both technical and commercial implications for efficient planning and operation of power systems. As the penetration of wind power increases, the importance of accurate forecasting of this variable generation source over...
In this paper, a novel microgrid energy trading model (METM) is proposed to determine an optimal schedule of all available units over a planning horizon so as to meet all system, plant and unit constraints, as well as meet the load and ancillary service demands. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather,...
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