Optimal Allocation Research of Distribution Network with DGs and SCs by Improved Sand Cat Swarm Optimization Algorithm

The contemporary paradigm for developing novel distribution grids is centered on the integration of distributed generations (DGs) and shunt capacitors (SCs) within the distribution infrastructure. Unreasonable DG and SC configurations may have a negative impact on grid line loss and voltage quality. The goal of optimal allocation research of distribution network (OARDN) is to allocate distributed generators (DGs) and shunt capacitors (SCs) in the distribution network in a rational manner to minimize power losses, enhance voltage quality, and ensure secure and stable power supply to the loads. Aiming at the critical OARDN problems, such as minimizing power loss and improving voltage quality, this paper proposes an improved sand cat swarm optimization algorithm (ISCSO). To address the issue of the original SCSO being prone to local optima and low search accuracy, this method introduces a tent map-based chaotic strategy and reverse learning approach to augment the precision of the optimization, and proposes a cross-learning mechanism to expand the global search ability, and this method is used to solve the single-objective problem. Based on ISCSO, the sensitivity analysis method and pareto non-inferior sorting method are introduced, which is extended to the MOISCSO algorithm to solve multi-objective OARDN problem. Simulation experimental results on IEEE33, IEEE69, and IEEE119 test systems and comparison with other scholars' research results show that the proposed method is practical and competitive for solving single-objective and multi-objective OARDN problems.