Multiobjective Optimization in the Problem of SVC Placement Using N-1 Approach and Population-Based Algorithms

The comprehensive integration of key components within power systems and the interconnectedness of stakeholders involved in electricity generation, transmission, distribution, and consumption necessitates consideration at both macro and micro levels. Reactive power compensation devices play a crucial role in managing reactive power and have a substantial impact on various operational parameters of power systems. Consequently, it is essential to establish a set of optimization criteria for determining the optimal placement and parameters of these devices. This optimization problem is inherently multiobjective in nature and requires a systematic approach for resolution. This study focuses on the optimization of static var compensator placement using elitist genetic algorithms, particle swarm optimization, and full enumeration methods. The selection of optimal location and parameters for a static var compensator is based on power flow analysis of entire power systems within test schemes. The consideration of $\mathbf{N}-1$ contingencies in the optimization algorithms has been shown to significantly influence the results, as demonstrated in previous research by the authors. Optimization criteria include the expected value and standard deviation of power excess, current capacity excess, and absolute voltage deviation. The results of the tests indicate that the formulation and number of objective functions play a critical role in determining the effectiveness of the solution to the optimization problem.