PSO-TOPSIS-Based Comprehensive Evaluation Framework for CHP Integrated Energy Systems

Under the impetus of the “dual-carbon” strategic objectives, integrated energy systems demand synergistic optimization schemes that comprehensively balance economic efficiency, low-carbon performance, and operational flexibility. To address the inherent limitation of traditional TOPSIS methods in subjective weight assignment leading to decision-making biases, this study proposes an enhanced multi-attribute decision-making model that integrates Particle Swarm Optimization (PSO) with TOPSIS. Focusing on the comprehensive benefit evaluation of electric boiler and heat pump configurations for Combined Heat and Power (CHP) units, we establish three core evaluation indices: total system cost, carbon emissions, and renewable energy accommodation capacity. The research framework encompasses two main phases: First, a low-carbon dispatch model for CHPbased electro-thermal systems is developed, incorporating electric boilers and heat pumps as auxiliary heat supply components. Subsequently, the PSO algorithm is employed to dynamically optimize indicator weights through iterative swarm intelligence search, thereby constructing an objective-weighted decision matrix. The relative closeness to ideal solutions is systematically calculated to achieve scheme prioritization. This optimized methodology demonstrates enhanced objectivity in weight determination through its dynamic weighting mechanism, effectively mitigating subjective interference. Case validation confirms its capability to provide robust decision support for lowcarbon configuration planning of CHP units. Furthermore, the successful application substantiates the methodological superiority and practical value of PSO-TOPSIS in addressing multi-objective energy planning challenges.