高比例可再生能源电力系统中计及灵活调节产品的实时调度

并网运行的间歇性可再生能源发电（intermittent renewable energy generation， IREG）出力的波动性和不确定性对电力系统运行灵活性提出了更高的要求，系统灵活性匮乏时有可能导致切负荷和/或弃风弃光等问题。在此背景下，灵活调节产品（flexible ramping product， FRP）的概念应运而生并逐步引起广泛关注。FRP为电力系统消纳IREG提供了一种新思路。文章首先概述了电力系统灵活性的定义、特点与灵活性资源的获取问题。接着，提出了针对发电机组与需求响应（demand response， DR）资源提供FRP情形下的电力系统实时调度模型。该调度模型可容纳不同类型的需求响应资源，并考虑了未来时段机组的启停约束以及上行爬坡/下行滑坡速率约束。之后，基于负荷水平和IREG出力水平的预测值，采用拉丁超立方抽样获得多个相应的不同实际值的运行场景，并通过对实际调度过程的仿真来评价所获得的调度方案。最后，采用算例系统说明了FRP在提升系统灵活性以及消纳IREG方面的作用，以及不同因素对系统运行灵活性的影响。

Real-Time Dispatch Considering Flexible Ramping Products in A Power System with High Proportion of Renewable Energy Generation

The variability and uncertainty brought about by intermittent renewable energy generation （IREG） impose higher requirements on the operational flexibility of the power system. The scarcity of flexibility may result in load shedding and/or curtailment of wind or solar power. Given this background， the concept of flexible ramping product （FRP） thus comes into being and has gradually attracted extensive attentions， since FRP provides an alternative for enhancing the accommodation capability for IREG. Firstly， the definition and characteristics of power system flexibility and the procurement of flexible resources are clarified. Then， a real-time dispatch model considering FRP provided by generation units and demand response resources is presented. In the developed real-time dispatch model， multiple types of demand response resources are taken into account， and some operating constraints are respected including the ones associated with unit start-up/shut-down in future periods and ramping up/down rates. Next， scenarios with various corresponding actual load demands and power outputs from IRES are generated by the Latin Hypercube Sampling method based on forecasted values of load demands and power outputs from IREG， and the dispatch schemes are evaluated by simulating the actual dispatch process. Finally， the role of FRP in enhancing system flexibility and in enhancing the accommodation capability for IREG and the impacts of different factors on the system operational flexibility are investigated through case studies.