线性近似方法被广泛应用于分布式电力调度模型构建，以降低非线性调度优化模型的求解复杂度。现有线性化研究更多关注分布式电力调度模型中约束函数本身的近似误差。基于分布式电力调度模型的最优性条件，研究指出约束线性化不仅会在约束中引入误差，还会在调度模型目标函数中产生一个与拉格朗日乘子相关的偏置项。基于此，研究提出一种基于最优性条件的分布式电力调度模型线性化方法，利用典型分布式算法迭代过程中获得的拉格朗日对偶信息，推导了拉格朗日乘子相关偏置项的表达式。根据算例分析表明，在调度模型目标函数中补偿该对偶乘子相关偏置项，即提高分布式电力调度优化线性模型的求解精度和计算效率分别约43.7%和40.1%。

Linear approximation methods are widely used for constructing distributed power dispatch models to reduce the computational complexity of nonlinear dispatch optimization. Existing studies on linearization mainly focus on the approximation errors in the constraints of distributed power dispatch models. Based on the optimality condition of the distributed power dispatch model, it is pointed out that constraint linearization not only introduces errors into the constraints but also generates a Lagrange-multiplier-related term in the dispatch objective function. Based on this, a linearization method of distributed power dispatch model is proposed. By utilizing the Lagrangian dual information iteratively obtained in typical distributed algorithms for solving distributed power dispatch models, the formulation of the proposed Lagrange-multiplier-related term is derived. The case study shows that, by compensating for this Lagrange-multiplier-related bias term in the objective function of the dispatch model, the solution accuracy and computational efficiency of the linear model for distributed power dispatch optimization are improved by approximately 43.7% and 40.1%, respectively.