In recent years, with the energy shortage, the use of renewable energy is inevitable. With CO2 the most important greenhouse gas causing global warming as well as the increase of population, renewable energy is one way to save energy and reduce carbon emissions. The traditional capacity investment for serving the load in distribution systems usually considered the addition of new substations or expansion of the existing substation and associated new feeder requirement. Nowadays, there are a lots of distributed generations (DG¡¦s) to be chosen. Factors of the choice taken into account will include lower pollution, higher efficiency, higher return rate for construction of distributed power generation systems. This thesis assumes that the distributed generation can be invested for long-term power plant planning. The planning of DG would be investigated from the perspectives of the independent investors. The modified Particle Swarm Optimization is proposed to determine the optimal sizing and sit of DG¡¦s addition in distribution systems with the constrains of CO2 limitation and addition of distributed generation to maximize profits. This thesis deals with discrete programming problem of optimal power flow, which includes continuous and discrete types of variables. The continuous variables are the generating unit real power output and the bus voltage magnitudes, the discrete variables are the shunt capacitor banks and sit problems. The Miaoli-Houlong system of Taiwan power will be used in this thesis for the verification of the feasibility of the proposed method.

The wind turbine generation system (WTGS) exhibits a nonlinear characteristic and its maximum power point varies with changing atmospheric conditions. In order to operate the WTGS at maximum power output under various wind speeds and to avoid using speed encoder in practical applications, it is necessary to improve the controller system to operate the maximum power points in the WTGS. There are three factors to influence wind generator, the wind speed, power coefficient and the radius of blade. The power coefficient depends on the blade pitch angle and tip speed ratio (TSR). The objective of the dissertation is to develop an intelligent controlled wind energy conversion system (WECS) using AC/DC and DC/AC power converters for grid-connected power application. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of a fuzzy neural network (FNN), a recurrent fuzzy neural network (RFNN), a wilcoxcon radial basis function network (WRBFN) and a improved Elman neural network (IENN) for MPPT. Furthermore, the parameter of the developed FNN, RFNN, WRBFN and IENN are trained on-line using back-propagation learning algorithm. However, the learning rates in the FNN, RFNN, WRBFN, and IENN are usually selected by trial and error method, which is time-consuming. Therefore, modified particle swarm optimization (MPSO) method is adopted to adjust the learning rates to improve the learning capability of the developed RFNN, WRBFN and IENN. Moreover, presents the estimation of the rotor speed is based on the sliding mode and model reference adaptive system (MRAS) speed observer theory. Furthermore, a sensorless vector-control strategy for an induction generator (IG) operating in a grid-connected variable speed wind energy conversion system can be achieved. On the other hand, a WRBFN based with hill-climb searching (HCS) maximum-power-point-tracking (MPPT) strategy is proposed for permanent magnet synchronous generator (PMSG) with a variable speed wind turbine. Finally, many simulation results are provided to show the effectiveness of the proposed intelligent control wind generation systems.