CEC2022 and CEC2017 benchmark functions.

The integrated energy systems (IES) in China face a dual challenge under the “dual-carbon” targets: maximizing renewable energy utilization while minimizing carbon emissions. Traditional tiered carbon markets often lack the flexibility to dynamically incentivize low-carbon operation. To address this, a coordinated framework is proposed, integrating a dynamic carbon emission trading (CET) mechanism with green certificate trading (GCT) and a Multi-Strategy Ameliorated Goose Optimization (MSAGOOSE) algorithm. The GCT-CET mechanism introduces exponential reward–penalty coefficients based on real-time renewable consumption rates, enabling adaptive carbon pricing. MSAGOOSE combines adaptive parameter adjustment, multimodal distribution-guided exploration, and population-aware reverse learning to improve optimization robustness in high-dimensional, nonlinear scheduling problems. Benchmark evaluations on CEC2017 and CEC2022 show that MSAGOOSE achieves an order-of-magnitude improvement in accuracy over seven state-of-the-art algorithms. In a 24-hour IES scheduling case in Anhui Province, the proposed method reduces carbon emissions by 27.3% (5,121 kg/d), increases renewable energy share to 88%, and cuts operating costs by 24.8% (6,151 CNY/d). Parametric analysis further confirms the framework’s effectiveness in balancing economic and environmental goals under decentralized energy scenarios. This study presents a policy-algorithm co-design paradigm that offers both theoretical and practical support for low-carbon IES transitions, enabling scalable, flexible, and economically viable scheduling strategies.我国综合能源系统（integrated energy systems, IES）在“双碳”目标下面临双重挑战：既要最大化可再生能源消纳规模，又要最大限度降低碳排放。传统分级碳市场往往缺乏灵活性，无法对低碳运行进行动态激励。为解决这一问题，本研究提出一种协同框架，将动态碳排放权交易（carbon emission trading, CET）机制与绿色证书交易（green certificate trading, GCT），以及多策略改进鹅优化算法（Multi-Strategy Ameliorated Goose Optimization, MSAGOOSE）相结合。该GCT-CET机制基于实时可再生能源消纳率引入指数型奖惩系数，可实现自适应碳定价。MSAGOOSE算法融合自适应参数调整、多模态分布引导探索与种群感知反向学习，以提升高维非线性调度问题中的优化鲁棒性。在CEC2017与CEC2022基准测试集上的评估结果表明，MSAGOOSE的求解精度较7种前沿算法提升了一个数量级。在安徽省开展的24小时综合能源系统调度案例中，所提方法将碳排放降低27.3%（日均减排5121 kg），可再生能源占比提升至88%，同时将运行成本降低24.8%（日均节约6151元人民币）。参数敏感性分析进一步验证了该框架在分布式能源场景下平衡经济与环境目标的有效性。本研究提出了一种政策-算法协同设计范式，可为综合能源系统的低碳转型提供理论与实践支撑，助力形成可扩展、灵活且经济可行的调度策略。