Comparative analysis of the particle swarm optimization and mixed integer linear programming methods for transmission congestion management in deregulated power systems

Abstract

Restructuring has taken over all aspects of human activity, including the electric power industry, due to the massive rise in population and industrialization over the last few years. Due to highly competitive market needs among participants, the electric power industry's restructuring has resulted in significant changes, including overloading critical portions of the transmission networks, leading to the inevitable congestion of the transmission lines. Congestion can be defined as a violation of transmission line capacity constraints that endanger the system's dependability and security. Furthermore, an open-access transmission network configuration in the contemporary deregulated electricity market has exacerbated congestion difficulties. As a result, congestion management (CM) in deregulated power networks is essential to the efficient and productive operation of the modern electricity power market. Significantly, generator rescheduling has been widely viewed as an approach towards alleviating the network congestion difficulty resulting from the ever-increasing volume of power/energy transactions in the power industry. Thus, this research aims to develop an efficient approach for managing transmission network congestion in a deregulated environment. Significantly, the goal of the study is to describe and define the appropriate mathematical optimization approach that lowers the cost of active and reactive power of the generators, thereby reducing the deviation of rescheduled active and reactive power from scheduled values using particle swarm optimization (PSO) and mixed integer linear programming (MILP), comparatively. Including reactive power rescheduling and voltage stability consideration in this research is innovative compared to other existing methodologies that solely examine active power rescheduling. This research yielded the subsequent contributions: developed a reliable multi-objective function for managing congestion in an electric transmission network; derived suitable generator sensitivity factors to detect overloaded lines and determine the generators that will be participating in congestion management; solving the formulated congestion management problems with a comparative optimal analysis using PSO and MILP algorithms. The developed CM problem solutions were validated using three IEEE standard test system networks (14, 30, and 118). The simulation results prove that the developed approaches in this study achieved better performance in the system’s generator rescheduling, resulting in the inexpensive cost of both active and reactive powers compared to other approaches, with MILP showing better strength when the problem is linearized. The active power losses for each of the considered IEEE 14, 30, and 118 cases with PSO are 4.7%, 11.03%, and 10.87%, respectively, and the reactive power losses are 3.67%, 15.39%, and 12.31%, respectively. Meanwhile, MILP has 5%, 15.5%, and 12.5% for active power losses and 5%, 24%, and 13% for reactive power losses, respectively. Furthermore, the developed approaches significantly enhanced voltage stability and voltage profile while reducing the transmission system operation cost.