Optimal Allocation of Distribution Static Synchronous Compensators in Distribution Networks Considering Various Load Models Using the Black Widow Optimization Algorithm

Abstract

Incorporating Distribution Static Synchronous Compensator (DSTATCOM) units into the radial distribution network (RDN) represents a practical approach to providing reactive compensation, minimizing power loss, and enhancing voltage profile and stability. This research introduces a unique optimization technique called the Black Widow Optimization (BWO) algorithm for strategically placing DSTATCOM units within the RDN. The primary objective is to minimize power loss while simultaneously evaluating various techno-economic parameters such as the voltage profile index (VPI), voltage stability index (VSI), and annual cost savings. The analysis of optimal DSTATCOM allocation, employing the proposed BWO algorithm, encompasses different load models, including constant impedance (CZ), constant current (CI), constant power (CP), and composite (ZIP) models. These analyses consider three distinct scenarios: single and multiple DSTATCOM integration. To gauge the effectiveness of the proposed BWO technique, it is applied to the IEEE 33-bus and 69-bus RDNs as test cases. Simulation results confirm the efficiency of the proposed approach across all four load models. Notably, in the case of the constant power model, the percentage reduction in power loss is substantial, with a reduction of 34.79% for the IEEE 33-bus RDN and 36.09% for the IEEE 69-bus RDN compared to their respective base cases.

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

“…Evolutionary algorithms, grounded in Darwin's theory, facilitate the gradual discovery of optimal solutions as the individuals within a population evolve through iterations during the search process. Typical examples include genetic algorithms [4], biogeography-based optimization algorithms [5], artificial algae algorithm [6], widow optimization search algorithm [7], and taboo search algorithm [8]. In a population of organisms, each individual has its own role, and communication among individuals enables the acquisition of superior information, ultimately completing the population's evolution.…”