【目的】在“双碳”目标与新型电力系统建设背景下，高比例新能源并网与低碳减排任务使传统电力系统规划面临碳计量粗放、市场机制与规划决策脱节以及负荷侧低碳引导不足等问题。为实现源网荷多维协同低碳优化，亟需构建能够刻画节点碳责任并计及绿证交易与碳排放权交易机制的电力系统规划方法。本文以区域输电系统为对象，构建计及碳排放流与绿证碳交易双重市场机制的源荷协同双层低碳规划模型。【方法】上层从系统规划者视角出发，将年化投资成本、供应侧运行成本以及碳配额与绿证净交易项统一纳入目标函数，实现电源结构与市场机制的协同优化；基于碳排放流理论建立节点碳势计算模型，刻画碳责任在网络中的时空分布。下层以节点碳势为信号构建差异化需求响应模型，引导负荷在时空维度上进行低碳转移，并通过迭代实现源荷耦合求解。【结果】算例分析结果表明，相较于传统规划，所提协同优化方案使系统年碳排放降低7.40%，系统侧年综合成本降低1.13%，新能源渗透率由51.45%提高至54.98%；负荷在节点碳势引导下呈现高碳时段削减、低碳时段转移的响应特征，负荷侧碳响应评价成本降低5.11%。【结论】绿证与碳交易机制从投资激励与排放约束两方面重塑电源结构，节点碳势信号实现了碳责任由系统层向节点层的精细传导，二者与需求响应机制形成协同作用，可在保障经济性的同时有效提升系统低碳水平。

[Objective] In the context of carbon peaking and carbon neutrality goals and the ongoing construction of new-type power systems， the high penetration of renewable energy and stringent emission reduction requirements have revealed several critical deficiencies in conventional power system planning， including coarse-grained carbon accounting， poor coordination between market mechanisms and planning decisions， and insufficient low-carbon incentives on the demand side. To achieve coordinated low-carbon optimization across source-grid-load dimensions， it is imperative to develop a planning approach that can characterize nodal carbon responsibility while incorporating both green certificate trading and carbon emission trading mechanisms. Taking a regional transmission system as the research subject， this paper proposes a source-load coordinated bi-level low-carbon planning model that integrates carbon emission flow with the dual market mechanisms of green certificate trading and carbon trading. [Methods] From the perspective of the system planner， the upper-level model incorporates annualized investment costs， supply-side operating costs， and the net trading terms of carbon allowances and green certificates into a unified objective function， thereby coordinating generation expansion decisions with market signals. On the basis of carbon emission flow theory， a nodal carbon potential model is developed to characterize the spatiotemporal distribution of carbon responsibility across the network. The lower-level model formulates a differentiated demand response scheme driven by nodal carbon potential signals， which guides load shifting in both temporal and spatial dimensions toward low-carbon patterns； the source-load coupling is then solved through iterative coordination between the two levels. [Results] Case study results demonstrate that， compared with a conventional planning scheme， the proposed coordinated optimization reduces annual carbon emissions by 7.40% and total annual system cost by 1.13%， while increasing the renewable penetration rate from 51.45% to 54.98%. Under the guidance of nodal carbon potential， load is reduced during high-carbon periods and shifted to low-carbon periods， resulting in a 5.11% decrease in the demand-side carbon response assessment cost. [Conclusions] Green certificate trading and carbon emission trading reshape the power supply structure through investment incentives and emission constraints， respectively， whereas nodal carbon potential signals enable refined allocation of carbon responsibility from the system level to individual nodes. In conjunction with demand response， these mechanisms constitute a synergistic framework that effectively enhances system decarbonization performance while maintaining economic efficiency.