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To accelerate energy efficiency improvement and green transition in industrial parks while addressing energy utilization and carbon reduction requirements, this study proposes a low-carbon economic dispatch model for integrated energy systems (IES) based on an enhanced multi-objective artificial hummingbird algorithm (MOAHA). The main contributions are threefold: First, we establish an optimized dispatch model incorporating combined cooling, heating and power (CCHP) systems, a refined two-stage power-to-gas (P2G) conversion process, and carbon capture technologies. Second, a stepwise carbon trading mechanism is introduced to further reduce carbon emissions from the IES. Third, a multi-strategy enhanced MOAHA is developed through three key improvements: 1) Logistic-sine fused chaotic mapping for population initialization to enhance distribution uniformity and solution quality; 2) Elite opposition-based learning and adaptive spiral migration foraging mechanisms to optimize individual positions and population diversity; 3) Simplex method integration to strengthen local search capabilities and optimization precision. Comprehensive case studies demonstrate the model’s effectiveness, achieving an 82.9% reduction in carbon emissions and 17.3% decrease in operational costs compared to conventional approaches. The proposed framework provides a technically viable solution for sustainable energy management in industrial parks, effectively balancing economic and environmental objectives.

为加快工业园区能效提升与绿色转型，同时满足能源利用与碳减排要求，本研究提出一种基于改进多目标蜂鸟算法（multi-objective artificial hummingbird algorithm, MOAHA）的综合能源系统（integrated energy systems, IES）低碳经济调度模型。本研究的核心贡献主要有三点：其一，构建了整合冷热电联供（combined cooling, heating and power, CCHP）系统、精细化两阶电转气（power-to-gas, P2G）转换流程以及碳捕集技术的优化调度模型；其二，引入阶梯式碳交易机制，进一步降低综合能源系统的碳排放水平；其三，通过三项关键改进开发了多策略改进型MOAHA：1）采用逻辑斯谛-正弦融合混沌映射完成种群初始化，以提升种群分布均匀性与求解质量；2）融入精英反向学习与自适应螺旋迁徙觅食机制，优化个体位置并丰富种群多样性；3）集成单纯形法以强化局部搜索能力与优化精度。全面的算例分析验证了所提模型的有效性，相较于传统方法，该模型可实现82.9%的碳排放降幅与17.3%的运营成本降低。本研究提出的框架为工业园区可持续能源管理提供了技术可行的解决方案，可有效平衡经济与环境双重目标。