Stochastic model predictive control for energy management optimization of an energy local network

被引：0
|
作者：
Zhang Y. [1 ]
Zhang T. [1 ]
Liu Y. [1 ]
Guo B. [1 ]
机构：
[1] College of Information System and Management, National University of Defense Technology, Changsha, 410073, Hunan Province
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2016年 / 36卷 / 13期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Energy internet; Energy local network; Mixed integer quadratic programming; Stochastic model predictive control;
D O I：
10.13334/j.0258-8013.pcsee.152491
中图分类号：
学科分类号：
摘要：
This paper proposed a stochastic model predictive control (SMPC) based energy local network (ELN) optimization and scheduling method for reducing the impacts introduced by the intermittent output of renewable energy resources, fluctuant of load demand and random real-time electricity price of a high renewable energy penetration level ELN. We constructed a mixed integer quadratic programming model as the objective function of the ELN operation by analyzing the features of all the elements in the ELN, and this optimization model can be online operated in a stochastic model predictive control (SMPC) framework. Forecast uncertainty for power production of renewable energy resources, real-time electricity price and load demand were described by scenarios, and a two-stage scenario cutting method was proposed to choose the typical scenarios. Simulation results show that the method proposed in this paper is effective and feasible by comparing with the open-looped operation method and MPC based operation method. © 2016 Chin. Soc. for Elec. Eng.
引用
收藏
页码：3451 / 3462
页数：11
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