【目的】建设完善省间电力中长期市场是促进绿色电力发展、落实“双碳”目标的基础环节之一。然而，绿电出力随机性将影响省间电力中长期交易出清方案的物理可执行性，且大规模绿电的参与将加重出清模型求解负担。对此，提出了一种计及绿电低碳价值与随机特性的省间电力中长期交易快速出清方法，以提高出清方案的可执行性以及出清计算效率。【方法】首先，构建了计及绿电低碳价值与随机特性的省间电力中长期交易出清模型，在目标函数中添加了发电碳排放成本以及基于绿电出力分布特性所建立的不可执行电力惩罚，使清洁低碳的绿电在市场竞争中占据优势，同时提升了绿电中标电力的可执行性。然后，提出了基于交易变量固定的加速策略，以时段解耦模型的计算结果为依据，固定了大概率不会成交的交易变量，从而构建了规模更小的出清模型，提高了出清计算效率。【结果】基于我国实际交易数据的算例仿真表明，所提出清模型可兼顾绿电中标量及中标电力的可执行性。此外，所提加速策略能够在保证可行性与精确性（多个算例中相比最优解的平均相对误差仅为0.4%）的同时，将出清计算效率平均提升1.91倍。【结论】所提方法通过内嵌“低碳价值衡量、出力随机性惩罚”以及高效求解策略，能够有效兼顾绿电竞争力与出清方案的可执行性，显著提升省间绿电交易市场资源配置效果和效率。

[Objective] Building and improving the mid-long-term electricity market between provinces is one of the fundamental links in promoting the development of green electricity and implementing the “dual carbon” goals. However，the randomness of green power output affects the physical feasibility of medium- and long-term clearing schemes for inter-provincial electricity transactions，and the participation of large-scale green power increases the burden of solving the clearing model. This paper proposes a fast-clearing method for mid- to long-term inter-provincial electricity transactions that considers the low-carbon value and stochastic characteristics of green electricity to improve the feasibility of clearing schemes and the efficiency of clearing calculations. [Methods] This paper constructs an inter-provincial electricity mid- to long-term transaction-clearing model that considers the low-carbon value and stochastic characteristics of green electricity. The objective function includes the cost of carbon emissions from power generation and the non-executable electricity penalty established based on the distribution characteristics of green electricity output，enabling clean and low-carbon green electricity to gain an advantage in market competition and improve the enforceability of green electricity-winning bids. Subsequently，an acceleration strategy based on fixed transaction variables is proposed. Based on the calculation results of the period decoupling model，transaction variables with a high probability of not closing are fixed，thereby constructing a smaller-scale clearing model and improving the efficiency of the clearing calculation. [Conclusions] Simulation examples based on actual transaction data from China show that the proposed model can balance the feasibility of the green electricity winning bid quantity and winning bid electricity. In addition，the proposed acceleration strategy can improve the computational efficiency by an average of 1.91 times while ensuring feasibility and accuracy （with an average relative error of only 0.4% compared with the optimal solution in multiple examples）. [Conclusions] This translation accurately reflects the technical meaning and structure of the original Chinese sentence while being fluent and appropriate for an academic context.