Innovative multi-objective optimization for financially viable and efficient residential μCHP system planning

Residential micro-combined heat and power (mu CHP) systems have gained prominence for their dual roles in heat and electricity generation. This study introduces a multi-objective optimization framework for mu CHP system planning that integrates financial considerations, exergy efficiency, and thermal comfort, while addressing uncertainty through a novel estimation approach. The framework aims to maximize profits and minimize investment costs to attract investors, with additional emphasis on optimizing system efficiency and occupant comfort. A developed salp algorithm (DSA), enhanced with chaos theory, is employed to prevent local optima and ensure robust convergence. The optimization incorporates the Pareto criterion and a fuzzy mechanism for effective trade-offs among competing objectives, delivering balanced and practical solutions. Simulation results demonstrate the framework's effectiveness, achieving a 25% reduction in overall costs compared to traditional models, along with a 4% improvement in priority rankings and a 40% decrease in computation time relative to the closest competitor. The proposed approach offers a robust and efficient solution for sustainable and cost-effective mu CHP system planning, addressing real-world complexities and advancing residential energy management strategies.