چكيده به لاتين
The restructuring of the electricity industry has created new challenges and problems which did not exist or matter in traditional power systems. Congestion in transmission networks is one of the most important among these challenges. In this thesis, a method of congestion management in operation time by using generation rescheduling and load shedding has been proposed, in which the power system loads are modelled as voltage-dependent, and the congestion management problem is modelled as a three-objective optimization problem. In previous references which have used the constant load model, the amount of load shedding and costs have usually been minimized; But in this research, due to the usage of the voltage-dependent load model, the load served error index (the square of the difference between the load served at nominal voltage and the actually supplied load) is also minimized due economic issues and avoid voltage reduction in load buses. Also in this research, for the reason of time limitation to clear transmission congestions in operation time, the maximum allowed time for congestion clearing has been added to the problem constraints. Furthermore, the proposed congestion management algorithm can clear congestions in non-integrated power systems. In this thesis, the second version of the non-dominated sorting genetic algorithm (NSGA-II), and the fuzzy min-max decision making method have been utilized in order to carry out the multi-objective optimization and choose the best compromised solution between the approximated Pareto solutions, respectively. With the consideration of the voltage-dependent load model, a load flow program has been coded whose results are verified using the results of its counterparts. To analyze the effect of the voltage-dependent load model on the transmission congestion management, simulation results have been compared to those of the constant-load model. The particle swarm optimization algorithm and the interior point method have been used for single-objective optimizations, and the proposed congestion management algorithm has been tested on IEEE 30-bus test system in order to confirm the results.
