Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties

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	Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties
	Li ZW(李忠文); Zang CZ(臧传治); Zeng P(曾鹏); Yu HB(于海斌); Li HP(李鹤鹏)
	2016
会议名称	International Conference on Probabilistic Methods Applied to Power Systems
会议日期	October 16-20, 2016
会议地点	Beijing
关键词	energy management microgrid model predictive control stochastic programming uncertainty
页码	1-6
通讯作者	李忠文
中文摘要	Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate intermittent renewable energy sources (RES), storage system, and local loads environmentally and reliably. Due to the randomness in RES and load, a great challenge lies in the optimal operation of MGs. Two-stage stochastic programming (SP) can involve the forecast uncertainties of load demand, photovoltaic (PV) and wind production in the optimization model. Thus, through two-stage SP, a more robust scheduling plan is derived, which minimizes the risk from the impact of uncertainties. The model predictive control (MPC) can effectively avoid short sighting and further compensate the uncertainty within the MG through a feedback mechanism. In this paper, a two-stage SP based MPC stratey is proposed for microgrid energy management under uncertainties, which combines the advantages of both two-stage SP and MPC. The results of numerical experiments explicitly demonstrate the benefits of the proposed strategy.
收录类别	EI ; CPCI（ISTP）
产权排序	1
会议录	2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5090-1971-7
WOS记录号	WOS:000392327900032
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/19515]
专题	沈阳自动化研究所_工业控制网络与系统研究室
推荐引用方式 GB/T 7714	Li ZW,Zang CZ,Zeng P,et al. Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties[C]. 见:International Conference on Probabilistic Methods Applied to Power Systems. Beijing. October 16-20, 2016.