Details. Check. Abstract. This study presents a new improved differential scalable IDE algorithm to optimize reactive power management. This planning strategy mainly focused on RPP t reactive power planning. Optimal reactive power planning in a power transmission system considering FACTS devices and, this paper presents a novel approach to address the reactive power VAr planning problem using multi-objective EA evolutionary algorithms . Specifically, the strength, this paper presents an approach to solve the reactive power planning problem using the hierarchical analytical process and differential evolution, evolutionary programming approach to reactive power planning. JT Ma, L. Lai. Engineering computer Science. TLDR. The multi-objective optimal power flow OPF with FACTS devices, including TTC and penalty functions, is used to evaluate the achievable TTC value within the real and reactive power generation limits. Optimal planning of reactive power is one of the major and important problems in the operation and control of electric power systems. It is nothing more than a multi-objective and non-linear minimization problem. The GA genetic algorithm based strategy is applied to determine the optimal reactive power output values ​​of generators, shunt capacitor sizes and transformer tap settings before minimizing system operating costs due to loss of active power. SUMMARY In a competitive electric power market, RPP reactive power planning is an essential element. A new improved differential scalable IDE algorithm is presented in this paper to optimize reactive power management RPM problems. The objective function of the RPM problem is considered to be the minimization of active power losses. E−The proposed method is used to find the optimal value of control variables including RPP reactive power problem in power systems which was solved using an effective bio-inspired meta-heuristic method called enhanced algorithm of IKHA krill herd, in this article. IKHA improves the original Krill Herd KH algorithm by incorporating an elitism framework and a new distribution of sampling flights. Optimal ORPD reactive power distribution is one of the most challenging optimization problems with respect to power system operation, which is strongly related to system stability. To overcome the ORPD problem, in general, the total real power loss is reduced by determining the power system control parameters, such as generator, RPP reactive power scheduling, and improving voltage stability. VSI considers two of the most important issues to address a major challenge. of the electrical system. In this work, a multi-objective genetic algorithm MOGA for RPP with objectives of minimizing power loss costs, new sources of reactive power VAR, extended evolutionary algorithms for solving the optimal distribution of reactive power are presented, based on evolutionary programming and evolution strategies, mutations in standard deviations were controlled using a dynamic bounds strategy. The extended evolutionary algorithms for solving the optimal distribution of reactive power are,