Abstract

Integrated Energy Systems (IES) are emerging as critical infrastructures that synergize renewable and conventional energy sources for efficient, reliable, and sustainable energy distribution. The design and operation of such systems involve complex trade-offs between economic cost, environmental emissions, and operational efficiency, necessitating robust multi-objective optimization strategies. Traditional optimization algorithms often fail to balance convergence speed, global search capability, and solution diversity in high dimensional, multi-objective design spaces. This limitation affects the real-world applicability of IES in dynamic environments. To overcome these challenges, we propose a novel Cascaded Hierarchical Gray Wolf Optimizer (CHGWO). CHGWO enhances the standard Gray Wolf Optimizer (GWO) by incorporating a multi-level search hierarchy and cascaded convergence-control strategies. The population is organized into elite, exploration, and exploitation tiers, allowing global exploration and local refinement simultaneously. A dynamic weight adaptation scheme is used to fine-tune convergence behavior. Simulation results on a hybrid IES combining solar PV, wind turbines, battery energy storage, and diesel generators show that CHGWO achieves a 12.7% lower Levelized Cost of Energy (LCOE), a 17.5% improvement in system reliability, and a 14.3% reduction in carbon emissions compared to state-of-the-art methods like NSGA-II, MOPSO, and MO-GA. CHGWO also exhibited superior convergence speed and robustness across multiple runs. The results validate CHGWO as an effective and scalable tool for real-time, multi-objective energy system design.

Authors

K. Anbumani¹, K. Kayalvizhi²
Sri Sairam Engineering College, India

Keywords

Integrated Energy Systems, Multi-objective Optimization, Gray Wolf Optimizer, Renewable Energy, Energy Management