考虑天然来水随机性的水火电系统机组检修计划

如何合理安排机组检修是水火电系统调度运行中的一项重要任务。在长时间尺度下，天然来水的随机性使机组检修计划本质上成为随机优化问题，通常采用场景法描述随机性，但其形成的高维优化问题难以直接求解。建立多场景耦合的水火电系统机组检修优化模型，利用多学科协同优化(Multidisciplinary Collaborative Optimization, MCO)方法将各场景间的非预期性约束及检修变量耦合约束解耦，实现了原问题的降维，且MCO结构具有内在的并行性。此外，在基于MCO的系统级优化问题中，用绝对值惩罚项替代二次惩罚项，保证该问题是一个混合整数线性规划问题，有利于提高计算效率。最后以某省级实际水火电系统为算例进行仿真分析，验证了所提模型和算法的有效性。

Generator maintenance schedule of hydro-thermal power systems considering randomness of natural water inflow

How to rationally arrange maintenance of generators is an important task in the dispatch and operation of hydro-thermal power systems. On a long timescale, the randomness of natural water inflow makes the generator maintenance schedule (GMS) essentially a stochastic optimization problem. The scenario-based method is usually used to describe the randomness, but it is difficult to solve efficiently the high-dimensional optimization problem with this method. This paper establishes a coupled multi-scenario GMS model of hydro-thermal power systems, applies a multi-disciplinary collaborative optimization (MCO) method to decouple the nonanticipative and the coupling constraints on maintenance variables between scenarios. Thus, the dimension of the multi-scenario GMS model is reduced and the MCO-based structure has inherent parallelism. In addition, in the MCO-based system-level optimization problem, an absolute value penalty term is introduced to replace the quadratic penalty term to ensure that the problem is a mixed integer linear programming model. This helps improve computational efficiency. Finally, a simulation calculation on a real provincial hydro-thermal power system is carried out to verify the effectiveness of the model and algorithm proposed. This work is supported by the National Natural Science Foundation of China (No. 52077083).