考虑风电并网和电量分解的月度机组组合模型  

Monthly Combination Model of Thermal Power Units Considering Wind Power Absorption and Electricity Contract Decomposition

作　　者：姜立超 刘洋[1] 沈晓东[1] 杨军峰[2] 李旻[3] 张宏图[3] 王长浩 JIANG Lichao;LIU Yang;SHEN Xiaodong;YANG Junfeng;LI Min;ZHANG Hongtu;WANG Changhao(College of Electrical Engineering,Sichuan University,Chengdu 610065,China;Dispatching and Control Center of State Grid Corporation of China,Beijing 100031,China;State Grid Sichuan Electric Power Company,Chengdu 610041,China)

机构地区：[1]四川大学电气工程学院,成都市610065 [2]国家电网公司调度控制中心,北京市100031 [3]国网四川省电力公司,成都市610041

出　　处：《电力建设》2019年第12期30-30,31-37,共8页Electric Power Construction

基　　金：国家电网有限公司总部科技项目“中长期电量安全校核关键技术研究及其应用”(521999180005)~~

摘　　要：合理的月度机组组合(unit commitment,UC)可以在更长时间尺度上进一步拓展风电消纳空间,但大规模风电入网造成的较强不确定性会给机组组合带来很大挑战。另外,由于基于比例分配法编制的月度合同电量调度实施难度较大,文章提出一种结合改进月度合同电量分解方法与考虑源荷不确定性的月度火电机组组合模型。将风电历史数据应用于月度机组组合决策,采用基于数据驱动的分布鲁棒方法结合随机优化技术求解机组组合与电量分解方案。计算结果表明,该模型能有效减少弃风量,提高火电机组负荷率.Reasonable monthly pow er unit commitment( UC) can further expand the space of w ind pow er absorption in a longer time scale,but the strong uncertainty caused by large-scale w ind pow er w ill bring great challenges to pow er unit commitment. In addition,because the implementation of monthly contract pow er dispatch based on proportional distribution method is difficult,this paper presents a monthly combined model of thermal pow er units w hich combines w ith improved monthly contract pow er decomposition method and the uncertainty of source and load. The historical data of w ind pow er is applied to monthly unit commitment decision-making, and the data-driven distributed robust method combined w ith stochastic optimization technology is used to solve the unit commitment and pow er decomposition scheme. The calculation results show that the model can effectively reduce the amount of abandoned w ind pow er and improve the load rate of thermal pow er units.

关 键 词：大规模风电并网 合约电量分解 月度机组组合(UC) 分布鲁棒 经济调度 

分 类 号：TM61[电气工程—电力系统及自动化]