Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm

This paper presents a novel opposition-based self-adaptive modified gravitational search algorithm (OSAMGSA) for optimal reactive power dispatch and voltage control in power-system operation. The problem is formulated as a mixed integer, nonlinear optimization problem, which has both continuous and discrete control variables. In order to achieve the optimal value of loss, voltage deviation, and voltage stability index, it is necessary to find the optimal value of control variables such as the tap positions of tap changing transformers, generator voltages, and compensation capacitor. Therefore, this complicated problem needs to be solved by an accurate optimization algorithm. This paper solves the aforementioned problem by using the gravitational search algorithm (GSA), which is one of the novel optimization algorithms based on the gravity law and mass interactions. To improve the efficiency of this algorithm, the tuning of its parameters is accomplished using random generation, and by applying the self-adaptive parameter tuning scheme. Also, the proposed OSAMGSA of this paper employs the opposition-based population initialization and self-adaptive probabilistic learning approach for generation jumping and escaping from local optima. Since the proposed problem is a multiobjective optimization problem incorporating several solutions instead of one, we applied the Pareto optimal solution method in order to find all Pareto optimal solutions. Moreover, the fuzzy decision method is used for obtaining the best compromise solution between them.