摘要
中国许多地区已经开展火电机组深度调峰辅助可再生能源消纳的试点工作,但基于场景的时序模拟方法在分析其调峰效果时受到各类不确定因素影响,难以覆盖所有的运行场景,导致中长期评估结果准确性不高。因此,时序方法不适用于缺少详细时序数据的电源容量规划。为此基于随机生产模拟方法,面向规划应用提出了一种火电深度调峰提升系统可再生能源消纳效果的评估方法。该方法建立了包括非供热火电机组、热电联产机组和风电等电源在内的供给侧概率模型,分析了在不同火电深度调峰方式下,系统负荷、可再生能源发电的概率特性变化,给出了弃风率等指标的计算方法。以北方某省级电网为仿真系统进行数据实验,研究了不同深度调峰配置方案下的弃风率变化情况,证明了该方法的有效性。
The trial of thermal units with regulation capacity developed has been carried out in many regions. Most of the existing sequential production simulation methods are difficult to effectively deal with various uncertainties, which are not applicable to the effect evaluation, especially the power capacity planning without detailed data. Based on the probabilistic production simulation, this paper proposes an assessment method for renewable energy accommodation, which considers participation of conventional units with deep peak shaving. The method establishes a probabilistic model of supply side, including non-heating thermal power units, CHP generator units, and wind power. This paper analyzes the variation of probability characteristics of system load and renewable power source under different thermal power peak-shaping modes. The calculation method of the curtailment indicators is given. The data experiment has been carried out with the data of a provincial power grid in northern China. The variation of the curtailment rate under different deep peak-shaving configuration schemes has been studied, and this method proves to be effective.
作者
马彦宏
姜继恒
鲁宗相
乔颖
叶一达
MA Yanhong;JIANG Jiheng;LU Zongxiang;QIAO Ying;YE Yida(State Grid Gansu Electric Power Company,Lanzhou 730030,Gansu province,China;State Key Lab of Control and Simulation of Power Systems and Generation Equipment,Dept. of Electrical Engineering, Tsinghua University,Haidian District,Beijing 100084,China)
出处
《全球能源互联网》
2019年第1期35-43,共9页
Journal of Global Energy Interconnection
基金
国家重点研发计划项目(2017YFB0902200)
国网甘肃省电力公司科技项目(SGGSKY00FJJS1700007)~~
关键词
随机生产模拟
概率模型
多维联合随机变量
probabilistic production simulation
probabilistic model
multidimensional joint probability distribution
