Quantum Predator-Prey Brain Storm Optimization for Robust Optimal Allocation of D-STATCOM Devices
Yusuke Kawauchi, Naohisa Someya, Hiroyuki Mori, Hsiao‐Dong Chiang · 2024
This paper presents a ground breaking solution to minimize distribution network losses by optimizing D-STATCOM (Distribution Static Compensator) device allocation. The proposed method uses Quantum Predator-Prey Brain Storm Optimization (QPPBSO), which integrates Predator-Prey and Quantum-Computing superposition strategies to Brain Storm Optimization (BSO). The optimization problem is expressed as a mixed integer nonlinear programming (MINLP) problem, where the location and output variables of D-FACTS are optimized. With the rise of renewable energy, uncertainties in active distribution networks need to be considered in optimization problems. To address this issue, the proposed method employs Robust Optimization (RO), which ensures that the optimal allocation of D-STATCOM devices is robust to uncertainties. The simulation results indicate the effectiveness of the proposed method through testing it on the 69-node distribution system.