Bi-Level Optimal Planning of Wind-Based Distributed Generation and Battery Energy Storage in Microgrids Under Uncertainty Using an Improved Cheetah Optimizer

Due to the rising share of renewable energy sources within distribution systems, there is a need for planning methods that can accommodate wind uncertainty. This paper introduces a holistic bi-level optimization approach for the optimal planning of wind-based distributed generation (WBDG) and battery energy storage system (BESS). At the higher level, the optimal location and size of WBDG and BESS are selected based on minimizing power loss, improving voltage stability, and minimizing the cost of electricity production. Meanwhile, at the lower level, the BESS charging–discharging operations and power transmission between different wind situations are scheduled. Wind uncertainty is considered in the model by applying the Weibull probability density function in combination with the Two-Point Estimation Method (2m-PEM). The resulting complicated bi-level optimization issue is addressed by creating a new Improved Cheetah Optimizer (ICO) that incorporates four enhancements to the Cheetah Optimizer (CO) to improve its explorative and exploitative capabilities. Simulations conducted on the IEEE 33-bus system show the superiority of the proposed method compared to other methods. The ICO outperforms particle swarm optimization (PSO), genetic algorithm (GA), and the basic CO by achieving up to 69.07% daily energy savings, raising the lowest bus voltage from 0.9440 per unit to 0.9610 per unit, and providing an expected operating cost of $5449.92.