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| 基于碳流理论的交通-电力融合系统能-碳综合定价方法 |
| An energy-carbon integrated pricing method for coupled traffic-power networks based on carbon flow theory |
| 投稿时间：2026-04-02 修订日期：2026-04-17 |
| DOI： |
| 中文关键词: 碳流理论 电动汽车 能-碳综合定价 协同调度 低碳运行 |
| 英文关键词: carbon flow theory electric vehicle energy-carbon integrated price collaborative scheduling low-carbon operation |
| 基金项目:河北省自然科学基金-青年科学基金项目(F2025501002)；中央高校基本科研业务费(N2523030)；国家自然科学基金(52577076). |
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| 中文摘要: |
| 随着全球碳减排进程的推进，电动汽车的普及正加速交通系统与电力系统的深度融合。然而，电力系统上游发电侧的碳排属性与存量燃油车的排放压力，仍是制约系统整体减排成效的关键。为此，本文提出一种面向交通-电力融合系统的能-碳综合定价方法及协同低碳调度模型。首先，基于碳流理论实现电网碳足迹向交通需求侧的溯源，为电动汽车制定差异化的节点碳价；对于燃油车，根据其排放模型实施直接碳定价。同时，构建基于交流最优潮流方程的电力系统模型，进而在跨网络维度构建能-碳综合价格调控体系，实现交通-电力融合系统协同低碳调度。其次，设计了高效迭代算法对协同调度方案进行优化求解。案例仿真表明，所提机制能精准识别排放责任主体并实现碳费的科学分摊。与传统调度方法相比，新方案在提升系统整体减排潜力的同时，有效兼顾了运行经济性，为城市能源互联网的低碳转型提供了关键理论支撑。 |
| 英文摘要: |
| As global carbon reduction efforts intensify, the rapid proliferation of electric vehicles (EVs) is accelerating the profound integration of traffic and power networks. However, the carbon-intensive nature of upstream power generation and the persistent emission challenges posed by conventional gasoline vehicles (GVs) remain fundamental bottlenecks restricting the overall decarbonization efficacy of the coupled network. To address these challenges, this paper de-velops an energy-carbon integrated pricing methodology and a synergistic low-carbon scheduling model tailored for coupled traffic-power networks (CTPN). Specifically, by leveraging carbon emission flow theory, the carbon footprints inherent in the power network are traced to the traffic side, enabling the formulation of spatiotemporally differentiated nodal carbon prices for EV charging. For GVs, a direct carbon pricing is implemented according to their emission models. Meanwhile, a power system model is constructed based on the alternating current optimal power flow equations, and an integrated ener-gy-carbon price regulation system is established at the cross-network level to achieve coordinated low-carbon scheduling of the CTPN. Furthermore, an efficient iterative solution algorithm is designed to resolve the complex syn-ergistic scheduling optimization problem. A case study demonstrates that the proposed mechanism accurately identifies carbon emission responsibilities and facilitates the scientific allocation of carbon-related costs among diverse stakeholders. Compared with conventional scheduling paradigms, the proposed scheme significantly enhances the decarbonization potential of the coupled network while effectively balancing operational economy. This research pro-vides essential theoretical insights and technical support for the low-carbon evolution of the modern urban energy internet. |
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