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Volume 2 Issue 3
Jul.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2015  >  2(3): 296-303
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.
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A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

  • Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

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.

    Abstract
  • 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.

     

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