Coordinated Optimization of On-Load Tap Changers and Shunt Capacitor Banks Using Evolutionary and Swarm Intelligence Techniques for Voltage Stability Enhancement

Abstract

The growing electricity demand and incorporation of the distributed energy resources have increased the burden of operations in the radial distribution networks. The instabilities in voltages and high power losses. The proposed study is based on a coordinated multi-objective optimization model to constrain the on-load tap changers (OLTCs) and shunt capacitor banks in parallel to optimize the voltage regulation and reduce system losses. IEEE 33-bus radial distribution system is taken as a standard and two sophisticated metaheuristic methods Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are applied to find optimal locations, sizes, and operating parameters of devices under the conventional voltage limitations (0.95 -1.05 p.u.).With base conditions, the system is characterized by a high level of performance degradation as 21 buses are exceeding the voltage limits with a minimum voltage of 0.9131 p.u. as well as by 202.7 kW of active and 135.1 kVAr of reactive power losses, respectively. The synchronized optimization plan is effective in reestablishing all bus voltages within allowable limits as well as minimizing active and reactive losses to 130.7 kW and 93.3 kVAr. Comparative analysis shows that coordinated control performs far better than individual deployment of OLTCs or capacitor banks showing better convergence behavior and near global optimum solutions.

The results substantiate the claim that the smartness of voltage regulation equipment application with the evolutionary and swarm intelligence methods can deliver a practical, scalable, and computationally economic technique concerning a higher voltage stability, lesser technical losses, and greater dependability of the operational characteristics of contemporary distributions.