Optimization of Charging or Discharging Strategy of Energy Storage in Multi-Objective Market Transactions Based on Quantum Genetic Algorithm

Abstract

Multi-objective energy optimization is critical for ensuring stable and power system operation securely. Though, multi-objective energy optimization is difficult because of an interdependence and opposing goals. To address conflicting objectives, a multi-objective optimization model is required, hence Monarch Butterfly African Vulture Optimization Algorithm (MBAVOA) is newly proposed for resolving multi objective issues. MBAVOA is the hybridized of two optimization algorithms, which includes Monarch Butterfly Optimization (MBO), and African Vulture Optimization Algorithm (AVOA). Here, charging cost, distance, and the user convenience are optimized while taking Renewable Energy Sources (RES) into consideration using MBAVOA. In addition, a load is computed using a Quantum Genetic Algorithm (QGA), describes intermittent and uncertain RES, such as wind and solar. The QGA-MBAVOA outperformed with the least charging cost 63%, fitness 0.010, and user convenience 0.819.