Enhancing energy efficiency in distributed systems with hybrid energy storage

This paper presents a pioneering approach to enhance energy efficiency within distributed energy systems by integrating hybrid energy storage. Unlike prior research, our study focuses on synergizing two distinct energy storage systems while concurrently optimizing system configuration and operational strategies to enhance performance and reduce costs. The employed distributed energy system incorporates hybrid energy storage, merging thermal energy storage with power storage technologies such as supercapacitors and lithium batteries. We conduct a comprehensive investigation into the impact of this innovative system on distributed energy systems, employing a dual-objective cooperative optimization method that addresses energy efficiency and economic factors alongside environmental considerations. This approach is applied to an almost zero-energy community, demonstrating its efficacy in improving load profiles and optimizing energy management through demand-side management. Additionally, we incorporate stochastic energy management, renewable energy resource planning, and dynamic storage to account for uncertainties in energy sources and demand, utilizing the Two-Point Estimate Method (2P + E PEM). To address the complexity of the problem, we introduce an advanced multi-objective optimization algorithm, the Horse Herd Optimization Algorithm (HOA), inspired by the movement behavior of horses in a herd. This algorithm exhibits superior performance with various operators including elite selection and crossover. In conclusion, our contributions include the introduction of a distributed energy system with hybrid storage, a dual-objective cooperative optimization method, and the application of advanced algorithms. Our results demonstrate significant reductions, with primary energy consumption decreasing by nearly 54.8 % and equivalent pollutant emissions by 63.6 %, highlighting the significance and effectiveness of our proposed approach.