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A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach

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  • Parvizimosaed, M.
  • Farmani, F.
  • Monsef, H.
  • Rahimi-Kian, A.

Abstract

Smart Energy Management Systems (SEMS) have become indispensable in Micro-Grid (MG) infrastructure for saving energy usage costs and system control considering the time-varying parameters. In this paper, a new multi-stage SEMS architecture is proposed for optimal energy management in MGs considering various resource uncertainties. The proposed SEMS performs various tasks such as data acquisition/mining/refinement, pattern recognition, learning parameters and offline/online decision making. To meet the energy consumption suitably, the multi-objective SEMS operates in multi-stage scheduling problem, i.e. day-ahead, hour-ahead, and real-time markets. Moreover, some data mining algorithms have been applied to reduce the huge amount of raw data, to recognize patterns for analysis, and to learn the given parameters. From the stochastic point of view, the proposed architecture also takes into account the uncertainties of weather conditions, energy consumption and the spot market price in the risk analysis. To handle these uncertainties, a stochastic scheduling approach which includes the mean and variance of energy cost is considered in the optimization process. The simulation results illustrate the efficiency of the proposed SEMS in different case studies.

Suggested Citation

  • Parvizimosaed, M. & Farmani, F. & Monsef, H. & Rahimi-Kian, A., 2017. "A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 178-189.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pa:p:178-189
    DOI: 10.1016/j.renene.2016.10.021
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    3. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.
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    7. Jin, S.W. & Li, Y.P. & Nie, S. & Sun, J., 2017. "The potential role of carbon capture and storage technology in sustainable electric-power systems under multiple uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 467-480.

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