引用本文:王锐,张彦,王冬,等.基于随机模型预测控制的含大规模风电接入的电力系统优化调度[J].控制与决策,2019,34(8):1616-1625
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基于随机模型预测控制的含大规模风电接入的电力系统优化调度
王锐1, 张彦1, 王冬2, 张涛1, 刘亚杰1
(1. 国防科技大学系统工程学院,长沙410073;2. 陆军勤务学院国防经济系,重庆400041)
摘要:
风电是重要的清洁可再生能源,将其引入智能电网中对节能减排有着重要的意义.为降低大规模风电不确定性给电网调度带来的影响,提出一种基于随机模型预测控制的风电与传统机组协调调度方法.考虑了部分传统机组需要人工调度而无法频繁、连续操作的情况,并引入可调负荷以增加系统可调度能力.构建基于混合整数二次规划(MIQP)的风电调度目标函数,以及包括机组最大可调节次数、最小运行/停机时间、可调度负荷总能量需求一致性、风电切负荷比例等约束.提出两阶段场景缩减方法以实现典型场景的快速筛选.通过与传统开环调度方法的性能对比表明所提出方法的可行性与有效性,并在此基础上进一步分析机组启停次数和可调度负荷对系统运行的影响.
关键词:  风力发电  随机模型预测控制  离散化约束  混合整数二次规划
DOI:10.13195/j.kzyjc.2018.1418
分类号:TP273
基金项目:国家自然科学基金项目(61773390, 71571187);湖南省自然科学杰出青年基金项目(2017JJ1001);湖南省湖湘青年英才计划项目(2018RS3081);国防科技大学科研计划-重点项目(ZK18-02-09).
Optimization and scheduling of power system stochastic model predictive control based optimization and scheduling for power system with large scale wind integrated
WANG Rui1,ZHANG Yan1,WANG Dong2,ZHANG Tao1,LIU Ya-jie1
(1. College of System Engineering,National University of Defense Technology,Changsha410073,China;2. Department of National Defense Economy,Army Logistics University of PLA,Chongqing400041,China)
Abstract:
Wind power is an important clean and renewable energy. Integrating it into smart grid is significant to the energy conversion and emission reduction. In order to reduce the negative impact introduced by uncertainties and randomness of large scale wind power integration, a stochastic model predictive control (SMPC) based optimization and scheduling approach is proposed to coordinate to the wind power and traditional fossil generators. The discrete generation regulation constraints of some traditional generators without the automatic generation control(AGC) function are considered, and schedulable loads are introduced to make the system more flexible. A mixed integer quadratic programming (MIQP) based energy management model is constructed, and the regulation frequency constraints, minimum up/down time constraints and discrete output constraints are all considered. A two-stage scenario cutting method is proposed to efficiently choose typical scenarios. Experimental results show that the approach proposed is flexible and efficient by comparing with the traditional scheduling approach. Furthermore, the impact of start-up/shut-down times and schedulable loads is discussed.
Key words:  wind power  stochastic model predictive control  discretized constraints  mixed integer quadratic programming

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