Advanced computational approaches for power system operations considering wind power and emission problem

[Truncated abstract] Nowadays, the electric power systems which are electrical and mechanical controlled systems play the fundamental role in the modern society. No one can doubt the essential fact that the electric power industry is undergoing restructuring and the competitive markets will take place of the monopolistic industry structure. As a result, competitive markets pose severe challenges to power system. The first one is the electric power system stability. It is clear that the power system stability was spotlighted by many blackouts around the world. The second is that the conventional energy will tend to be exhausted and is the primary factor of the environmental pollution. Thirdly, lots of power operation constraints such as system security, emission reduction and associated government regulations need to be taken into considered. One consequence is that more advanced power system data analysis and system operational methods are required in the deregulated, market-oriented environment. In the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. As a result of deregulated markets and global warming, renewable energy and reliable energy supplies also play a key role in the government’s energy policy. In this thesis, the research work is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: (1). Advanced statistical approaches to wind power interval prediction; (2). A novel hybrid optimization algorithm connecting interior point method (IPM) and particle swarm optimization (PSO) for solving combined economic and emission dispatch (CEED) problem with valve point effects and stochastic wind power;