基于动态场景集和需求响应的二阶段随机规划调度模型

发布时间：2018-03-17 22:03

  本文选题：风力发电　切入点：动态场景法　出处：《电力系统自动化》2017年11期 　论文类型：期刊论文

【摘要】：风电及负荷场景集作为随机规划调度的输入,对决策结果影响大。为使场景集刻画出风电及负荷的波动性,采用考虑随机变量相关性的动态场景法生成风电、负荷及净负荷的场景集,并从与调度接轨的角度提出相应的指标对场景质量进行评价。建立了一种基于here-and-now和waitand-see(HN-WS)的二阶段随机规划调度模型。该模型包含了日前与实时的关联性,决策过程融入了对实时场景已实现情况下的考虑,优化目标涵盖了日前阶段的燃料成本及实时阶段的平衡矫正期望成本。此外,为减少弃风,在调度模型中引入了激励型需求响应,与火电机组的备用容量进行协同优化。最后,采用爱尔兰电网的风电数据、Elia电网的负荷数据及改进的IEEE-118节点系统验证了所提调度模型的有效性。
[Abstract]:As the input of stochastic programming scheduling, wind power and load scene sets have a great influence on the decision results. In order to depict the fluctuation of wind power and load, the dynamic scene method considering the correlation of random variables is used to generate wind power. The scene set of load and net load, and the corresponding indexes to evaluate the scene quality are put forward from the point of view of the connection with scheduling. A two-stage stochastic programming scheduling model based on here-and-now and waitand-seeHN-WSis is established. The model includes the relationship between pre-day and real-time. The decision-making process incorporates consideration of the situation where the real-time scenario has been achieved, and the optimization objectives cover the fuel cost of the pre-day phase and the desired cost of balancing the correction in the real-time phase. The incentive demand response is introduced into the dispatching model to optimize the reserve capacity of thermal power units in cooperation. Finally, The effectiveness of the proposed dispatching model is verified by using the wind power data of Ireland power grid and the load data of Elia power network and the improved IEEE-118 node system.
【作者单位】： 武汉大学电气工程学院;广东电网有限责任公司电力调度控制中心;
【分类号】：TM73
，

本文编号：1626669