Distributed Optimization for Energy Allocation in Mictrogrid Power System

被引:0
作者
Wang, Tiancai [1 ]
He, Xing [1 ]
Huang, Junjian [2 ]
Sun, Miao [1 ]
He, Yachen [1 ]
Huang, Qin [1 ]
Feng, Dongyang [1 ]
Zhang, Wenming [1 ]
机构
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligen, Chongqing 400715, Peoples R China
[2] Chongqing Univ Educ, Key Lab Machine Percept & Childrens Intelligence, Chongqing 400067, Peoples R China
来源
2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2017年
关键词
ALGORITHM; CONSENSUS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a continuous-time distributed algorithms is proposed to solve the energy allocation problem in microgrid power system. Projected output feedback method is employed in the approach to deal with the local capacity constraints. Each agent in the multi-agent network has its own local objective function, local hound constraint and can access the information from the agent nearby. The agents determine the optimal power generation in the responsible area to minimize the sum of local generation cost function while reaching the global supply and demand balance. Also, the distributed method is applied to test on a six-agent system and the simulation result demonstrates that the algorithm proposed can successfully obtain the optimal power generation and the auxiliary variables of each generator agent can always reach consensus.
引用
收藏
页码:749 / 752
页数:4
相关论文
共 13 条
  • [1] Spiral Optimization Algorithm for solving Combined Economic and Emission Dispatch
    Benasla, Lahouaria
    Belmadani, Abderrahim
    Rahli, Mostefa
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 62 : 163 - 174
  • [2] Distributed Finite-Time Economic Dispatch of a Network of Energy Resources
    Chen, Gang
    Ren, Jianghong
    Feng, E. Ning
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2017, 8 (02) : 822 - 832
  • [3] Electrical Microgrid Optimization via a New Recurrent Neural Network
    Gamez Urias, Manuel E.
    Sanchez, Edgar N.
    Ricalde, Luis J.
    [J]. IEEE SYSTEMS JOURNAL, 2015, 9 (03): : 945 - 953
  • [4] A Second-Order Multi-Agent Network for Bound-Constrained Distributed Optimization
    Liu, Qingshan
    Wang, Jun
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2015, 60 (12) : 3310 - 3315
  • [5] Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment
    Morais, Hugo
    Sousa, Tiago
    Vale, Zita
    Faria, Pedro
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2014, 82 : 268 - 282
  • [6] Mukai T., 2015, ELECT ENG JAPAN, V194, P18
  • [7] Constrained Consensus and Optimization in Multi-Agent Networks
    Nedic, Angelia
    Ozdaglar, Asuman
    Parrilo, Pablo A.
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2010, 55 (04) : 922 - 938
  • [8] Reaching an Optimal Consensus: Dynamical Systems That Compute Intersections of Convex Sets
    Shi, Guodong
    Johansson, Karl Henrik
    Hong, Yiguang
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2013, 58 (03) : 610 - 622
  • [9] A projection neural network for optimal demand response in smart grid environment
    Yao, Yao
    He, Xing
    Huang, Tingwen
    Li, Chaojie
    Xia, Dawen
    [J]. NEURAL COMPUTING & APPLICATIONS, 2018, 29 (06) : 259 - 267
  • [10] Distributed Extremum Seeking for Constrained Networked Optimization and Its Application to Energy Consumption Control in Smart Grid
    Ye, Maojiao
    Hu, Guoqiang
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2016, 24 (06) : 2048 - 2058