Active powers rescheduling og generators for congestion management in power systems by using metaheuristic optimization algorithms

Modern power systems must be operated and planned under optimal conditions. Recently, the power systems have moved from sole public ownership to the liberalized electrical energy market. The liberalized market creates a competitive environment and enables many companies to participate in this environment. The companies aim to both provide the most reliable electricity to consumers and maximize power transfer through transmission lines. This purpose leads to overload and congestion of transmission lines. The congestion problem can cause serious system damage to existing power systems. Therefore, the congestion management in modern power systems is of central importance to the electrical energy market. Different congestion management strategies are developed to eliminate the congestion problem. These strategies aim to ensure that the system operates in a reliable, stable and clean manner. Removing the congestion in transmission lines by reprogramming the active power production of generators is stated to be one of the most used strategies. The aim is to minimize the total congestion cost in rescheduling the production of generators. This reveals that the congestion problem is an optimization problem. This thesis reschedules the production of generators to eliminate the congestion problem in transmission lines by using the slime mold algorithm and the tasmanian devil optimization algorithm, which are metaheuristic optimization algorithms. The proposed optimization algorithms aim to minimize the total congestion cost under the specified equality and inequality constraints. Simulation studies were carried out on IEEE 30-bus and 57-bus test systems under four different congestion scenarios. The results of simulation studies obtained from the proposed approaches were compared with the results of other optimization algorithms in the literature. Comparison results show that the slime mold algorithm relieves the congestion of the system more effectively than other optimization algorithms by minimizing the total congestion cost.