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 2 Issue 3
Jul.  2015

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Hepeng Li, Chuanzhi Zang, Peng Zeng, Haibin Yu and Zhongwen Li, "A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 296-303, 2015.
Citation: Hepeng Li, Chuanzhi Zang, Peng Zeng, Haibin Yu and Zhongwen Li, "A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 296-303, 2015.

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

Funds:

This work was supported by National Natural Science Foundation of China (61100159, 61233007), National High Technology Research and Development Program of China (863 Program) (2011AA040103), Foundation of Chinese Academy of Sciences (KGCX2-EW-104), Financial Support of the Strategic Priority Research Program of Chinese Academy of Sciences (XDA06021100), and the Cross-disciplinary Collaborative Teams Program for Science, Technology and Innovation, of Chinese Academy of Sciences-Network and System Technologies for Security Monitoring and Information Interaction in Smart Grid Energy Management System for Micro-smart Grid.

  • This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.

     

  • loading
  • [1]
    Ilic M D, Xie L, Khan U A, Moura J M F. Modeling of future cyber physical energy systems for distributed sensing and control. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, 2010, 40(4):825-838
    [2]
    Palensky P, Widl E, Elsheikh A. Simulating cyber-physical energy systems:challenges, tools and methods. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2014, 44(3):318-326
    [3]
    Ge Y Q, Dong Y W, Zhao H B. A cyber-physical energy system architecture for electric vehicles charging application. In:Proceedings of the 12th International Conference on Quality Software (QSIC). Xi'an, China:IEEE, 2012. 246-250
    [4]
    Jamshidi M M. Sustainable energy systems:cyber-physical based intelligent management of micro-grids. In:Proceedings of the 4th IEEE International Symposium on Logistics and Industrial Informatics (LINDI). Smolenice:IEEE, 2012. 11-12
    [5]
    Macana C A, Quijano N, Mojica-Nava E. A survey on cyber physical energy systems and their applications on smart grids. In:Proceedings of the 2011 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America). Medellin:IEEE, 2011. 1-7
    [6]
    Susuki Y, Koo T, Ebina H, Yamazaki T, Ochi T, Uemura T, Hikihara T. A hybrid system approach to the analysis and design of power grid dynamic performance. Proceedings of the IEEE, 2012, 100(1):225-239
    [7]
    Chakraborty S, Weiss M D, Simoes M G. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid. IEEE Transactions on Industrial Electronics, 2007, 54(1):97-109
    [8]
    Chen C, Duan S, Cai T, Liu B, Hu G. Smart energy management system for optimal microgrid economic operation. Renewable Power Generation, IET, 2011, 5(3):258-267
    [9]
    Kriett P O, Salani M. Optimal control of a residential microgrid. Energy, 2012, 42(1):321-330
    [10]
    Ahn S J, Nam S R, Choi J H, Moon S I. Power scheduling of distributed generators for economic and stable operation of a microgrid. IEEE Transactions on Smart Grid, 2013, 4(1):398-405
    [11]
    Tsikalakis A G, Hatziargyriou N D. Centralized control for optimizing microgrids operation. In:Proceedings of the 2011 IEEE Power and Energy Society General Meeting. San Diego, CA:IEEE, 2011. 1-8
    [12]
    Zhang D, Li S H, Zeng P, Zang C Z. Optimal microgrid control and power-flow study with different bidding policies by using powerworld simulator. IEEE Transactions on Sustainable Energy, 2014, 5(1):282-292
    [13]
    Birge J R, Louveaux F. Introduction to stochastic programming. Springer Series in Operations Research and Financial Engineering. New York:Springer-Verlag, 1997.
    [14]
    Deng R L, Yang Z Y, Chen J M, Chow M Y. Load scheduling with price uncertainty and temporally-coupled constraints in smart grids. IEEE Transactions on Power Systems, 2014, 29(6):2823-2834
    [15]
    Dupačová J, Gröwe-Kuska N, Römisch W. Scenario reduction in stochastic programming:an approach using probability metrics. Mathematical Programming, Series B, 2003, 95(3):493-511

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

    Article Metrics

    Article views (1074) PDF downloads(9) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return