A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 3 Issue 4
Oct.  2016

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Bonan Huang, Yushuai Li, Huaguang Zhang and Qiuye Sun, "Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet," IEEE/CAA J. Autom. Sinica, vol. 3, no. 4, pp. 357-364, Oct. 2016.
Citation: Bonan Huang, Yushuai Li, Huaguang Zhang and Qiuye Sun, "Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet," IEEE/CAA J. Autom. Sinica, vol. 3, no. 4, pp. 357-364, Oct. 2016.

Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet

Funds:

This work was supported by National Natural Science Foundation of China 61433004, 61603085

the China Postdoctoral Science Foundation 2015M570253

and the Fundamental Research Funds for the Central Universities N150403004

More Information
  • Unlike conventional power systems, the upcoming energy internet (EI) emphasizes comprehensive utilization of energy in the whole power system by coordinating multi-microgrids, which also brings new challenge for the energy management. To address this issue, this paper proposes a novel consensus-based distributed approach based on multi-agent framework to solve the energy management problem of the energy internet, which only requires local information exchange among neighboring agents. Correspondingly, two consensus algorithms are presented, one of which drives the incremental cost of each distributed generator (DG) converge to the state of the leader agent-energy router, and the other one is used to estimate the global power mismatch, which is a first-order average consensus algorithm modified by a correction term. In addition, in order to meet the supply-demand balance, an effective control strategy for the energy router is proposed to accurately calculate the power exchange between the microgrid and the main grid. Finally, simulation results within a 7-bus test system are provided to illustrate the effectiveness of the proposed approach.

     

  • loading
  • [1]
    Sun Q Y, Han R K, Zhang H G, Zhou J G, Guerrero J M. A multiagentbased consensus algorithm for distributed coordinated control of distributed generators in the energy internet. IEEE Transactions on Smart Grid, 2015, 6(6): 3006-3019 doi: 10.1109/TSG.2015.2412779
    [2]
    Huang A Q, Crow M L, Heydt G T, Zheng J P, Dale S J. The future renewable electric energy delivery and management (FREEDM) system: the energy internet. Proceedings of the IEEE, 2011, 99(1): 133-148 doi: 10.1109/JPROC.2010.2081330
    [3]
    Lin C E, Viviani G L. Hierarchical economic dispatch for piecewise quadratic cost functions. IEEE Transactions on Power Apparatus and Systems, 1984, PAS-103(6): 1170-1175 doi: 10.1109/TPAS.1984.318445
    [4]
    Lin C E, Chen S T, Huang C L. A direct Newton-Raphson economic dispatch. IEEE Transactions on Power Systems, 1992, 7(3): 1149-1154 doi: 10.1109/59.207328
    [5]
    Gaing Z L. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems, 2003, 18(3): 1187-1195 doi: 10.1109/TPWRS.2003.814889
    [6]
    Xin H H, Qu Z H, Seuss J, Maknouninejad A. A self-organizing strategy for power flow control of photovoltaic generators in a distribution network. IEEE Transactions on Power Systems, 2011, 26(3): 1462-1473 doi: 10.1109/TPWRS.2010.2080292
    [7]
    Chang T H, Nedić A, Scaglione A. Distributed constrained optimization by consensus-based primal-dual perturbation method. IEEE Transactions on Automatic Control, 2014, 59(6): 1524-1538 doi: 10.1109/TAC.2014.2308612
    [8]
    Sui X C, Tang Y F, He H B, Wen J Y. Energy-storage-based lowfrequency oscillation damping control using particle swarm optimization and heuristic dynamic programming. IEEE Transactions on Power Systems, 2014, 29(5): 2539-2548 doi: 10.1109/TPWRS.2014.2305977
    [9]
    Zhang Z, Chow M Y. Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid. IEEE Transactions on Power Systems, 2012, 27(4): 1761-1768 doi: 10.1109/TPWRS.2012.2188912
    [10]
    Mudumbai R, Dasgupta S, Cho B B. Distributed control for optimal economic dispatch of a network of heterogeneous power generators. IEEE Transactions on Power Systems, 2012, 27(4): 1750-1760 doi: 10.1109/TPWRS.2012.2188048
    [11]
    Yang S P, Tan S C, Xu J X. Consensus based approach for economic dispatch problem in a smart grid. IEEE Transactions on Power Systems, 2013, 28(4): 4416-4426 doi: 10.1109/TPWRS.2013.2271640
    [12]
    Binetti G, Davoudi A, Lewis F L, Naso D, Turchiano B. Distributed consensus-based economic dispatch with transmission losses. IEEE Transactions on Power Systems, 2014, 29(4): 1711-1720 doi: 10.1109/TPWRS.2014.2299436
    [13]
    Xu Y L, Zhang W, Liu W X. Distributed dynamic programming-based approach for economic dispatch in smart grids. IEEE Transactions on Industrial Informatics, 2015, 11(1): 166-175 doi: 10.1109/TII.2014.2378691
    [14]
    Xu Y L, Li Z C. Distributed optimal resource management based on the consensus algorithm in a microgrid. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2584-2592 doi: 10.1109/TIE.2014.2356171
    [15]
    Rahbari-Asr N, Ojha U, Zhang Z A, Chow M Y. Incremental welfare consensus algorithm for cooperative distributed generation/demand response in smart grid. IEEE Transactions on Smart Grid, 2014, 5(6): 2836-2845 doi: 10.1109/TSG.2014.2346511
    [16]
    Zhang W, Xu Y L, Liu W X, Zang C Z, Yu H B. Distributed online optimal energy management for smart grids. IEEE Transactions on Industrial Informatics, 2015, 11(3): 717-727 doi: 10.1109/TII.2015.2426419
    [17]
    Zhang W, Liu W X, Wang X, Liu L M, Ferrese F. Online optimal generation control based on constrained distributed gradient algorithm. IEEE Transactions on Power Systems, 2015, 30(1): 35-45 doi: 10.1109/TPWRS.2014.2319315
    [18]
    Loia V, Vaccaro A. Decentralized economic dispatch in smart grids by self-organizing dynamic agents. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 44(4): 397-408 doi: 10.1109/TSMC.2013.2258909
    [19]
    Binetti G, Davoudi A, Naso D, Turchiano B, Lewis F L. A distributed auction-based algorithm for the nonconvex economic dispatch problem. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1124-1132 doi: 10.1109/TII.2013.2287807
    [20]
    Zhang C H, Chang L, Zhang X F. Leader-follower consensus of upper-triangular nonlinear multi-agent systems. IEEE/CAA Journal of Automatica Sinica, 2014, 1(2): 210-217 doi: 10.1109/JAS.2014.7004552
    [21]
    Wang C R, Wang X H, Ji H B. A continuous leader-following consensus control strategy for a class of uncertain multi-agent systems. IEEE/CAA Journal of Automatica Sinica, 2014, 1(2): 187-192 doi: 10.1109/JAS.2014.7004549
    [22]
    Hengster-Movric K, You K Y, Lewis F L, Xie L H. Synchronization of discrete-time multi-agent systems on graphs using Riccati design. Automatica, 2013, 49(2): 414-423 doi: 10.1016/j.automatica.2012.11.038

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(1)

    Article Metrics

    Article views (1187) PDF downloads(34) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return