In recent year, the awareness of environmental protection has made the power dispatch problem not necessarily economy-oriented. This thesis proposed the application of Particle Swarm Optimization (PSO) algorithm to solve the Unit Commitment (UC) problem for 24 hours with maximum profit in the power and carbon market. Optimal Power Flow (OPF) is used to solve the UC problem for the interconnected power network that is comprised of three independent areas to optimize the dispatching strategy. The UC problem must satisfy the constraints of the load demand, generating limits, minimum up/down time, ramp rate limits, and also the limits of power flow, buses voltage and transmission line capacity. The other objective of this thesis is to employ the Static Synchronous Series Compensator (SSSC) to integrate with OPF based on Equivalent Current Injection (ECI) power flow model, and install it at interconnected lines between each independent area controlling the power flow to reduce emission. In order to avoid the local optimality problem, this thesis proposed the utilization of the Multiple Particle Swarm Optimization (MPSO), which can quickly reach the optimal solution with a better performance and accuracy. The Independent Power Producer (IPP) can get the maximum profit with installed SSSC from the power and carbon trading with the calculation of power wheeling expense and carbon forecasting data. Furthermore, it can also assess the need of participating in the trading market or not.

An Equivalent Current Injection¡]ECI¡^based Preventive Security- Constrained Optimal Power Flow¡]PSCOPF¡^is presented in this paper and a particle swarm optimization (PSO) algorithm is developed for solving non-convex Optimal Power Flow (OPF) problems. This thesis integrated Simulated Annealing Particle Swarm Optimization¡]SAPSO¡^ and Multiple Particle Swarm Optimization¡]MPSO¡^, enabling a fast algorithm to find the global optimum. Optimal power flow is solved based on Equivalent- Current Injection¡]ECIOPF¡^algorithm. This OPF deals with both continuous and discrete control variables and is a mixed-integer optimal power flow¡]MIOPF¡^. The continuous control variables modeled are the active power output and generator-bus voltage magnitudes, while the discrete ones are the shunt capacitor devices. The feasibility of the proposed method is exhibited for a standard IEEE 30 bus system, and it is compared with other stochastic methods for the solution quality. Security Analysis is also conducted. Ranking method is used to highlight the most severe event caused by a specific fault. A preventive algorithm will make use of the contingency information, and keep the system secure to avoid violations when fault occurs. Generators will be used to adjust the line flow to the point that the trip of the most severe line would not cause a major problem.