王晓晖, 张粒子, 程世军, 等. 基于关联节点的含风电系统改进多场景随机机组组合模型[J]. 现代电力, 2014, 31(3): 1-6.
引用本文: 王晓晖, 张粒子, 程世军, 等. 基于关联节点的含风电系统改进多场景随机机组组合模型[J]. 现代电力, 2014, 31(3): 1-6.
WANG Xiaohui, ZHANG Lizi, CHENG Shijun, et al. An Improved Multi-scenario Stochastic Unit Commitment Model for System With Wind Power Based on Connection Node[J]. Modern Electric Power, 2014, 31(3): 1-6.
Citation: WANG Xiaohui, ZHANG Lizi, CHENG Shijun, et al. An Improved Multi-scenario Stochastic Unit Commitment Model for System With Wind Power Based on Connection Node[J]. Modern Electric Power, 2014, 31(3): 1-6.

基于关联节点的含风电系统改进多场景随机机组组合模型

An Improved Multi-scenario Stochastic Unit Commitment Model for System With Wind Power Based on Connection Node

  • 摘要: 多场景机组组合模型是一种常用的解决系统调度决策不确定性的模型。目前的研究大多通过设置场景时段变量构建多场景机组组合模型,但是这种方法产生的约束和变量数量较多,导致计算速度较慢,对于规模较大的实际系统适用性较差。本文提出了基于关联节点的建模方法,通过构建节点时段和节点节点关联矩阵,设置各节点的优化变量。该方法降低了求解复杂度,提高了计算速度。算例测试验证了所提方法的可行性和适用性,以及相对于传统建模方法在计算速度上的优势。针对大规模、多场景的实际电力系统,本文提出的基于关联节点的多场景机组组合模型具有较高的适用性。

     

    Abstract: Multi-scenario unit commitment model is a typical method to solve the uncertainty problem of power system scheduling. At present, multi-scenario models are built by setting scenario-stage variables. But this modeling approach generates too many constraints and variables, which slows the calculation speed and reduces the practicability in a large-scale power system. In this paper, a new and efficient modeling approach is proposed, and the optimized variables of each node are set through establishing node-stage and node-node incidence matrices. The proposed method reduces the model complexity and accelerates the calculation speed. The case study testifies the feasibility and applicability of the proposed method, and its advantage of calculation speed over traditional method. For large-scale and multi-scenario practical power systems, the node-based multi-scenario unit commitment model has better applicability.

     

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