一种基于管状分布MPC的外源扰动低碳能源网络求解方法

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Yubin Jia;Zhao Yang Dong;Changyin Sun;Ke Meng
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引用次数: 0

摘要

随着可再生能源越来越多地融入电力系统,低碳能源网络出现了两个关键挑战:分布式能源(DERs)导致的分布式拓扑结构,以及可再生能源(RES)间歇性造成的波动。本文提出了一种分布式模型预测控制(MPC),用于包括传统发电机(包括水力和燃气轮机发电厂)和风力涡轮机的低碳能源网络的频率调节。首先,在考虑约束条件的同时,基于协作的各子系统分布式模型预测控制器考虑了子系统与全局控制目标之间的通信;其次,提出了一种包含两个级联mpc的管状控制器来处理系统的外源干扰,如风速波动。仿真实例验证了该方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Tube-Based Distributed MPC Based Method for Low-Carbon Energy Networks With Exogenous Disturbances
With the increasing integration of renewable energy into power systems, two key challenges emerge in low-carbon energy networks: the distributed topology resulting from distributed energy resources (DERs), and the fluctuations caused by the intermittency of renewable energy sources (RES). This paper proposes a distributed model predictive control (MPC) for the frequency regulation of low-carbon energy networks that encompass both conventional generators (including hydro and gas turbine power plants) and wind turbines. First, the cooperation based distributed model predictive controller of each subsystem accounts for the communication between the subsystems and global control objectives while the constraints are considered. Second, a tube-based controller containing two cascaded MPCs is proposed to deal with the system exogenous disturbance such as wind speed fluctuation. The simulation cases illustrate the efficiency and the advantages of the proposed method.
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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