A dynamic data-driven approach for operation planning of microgrids

Xiaoran Shi, Haluk Damgacioglu, Nurcin Celik

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the realtime operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; and 3) a multiobjective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstrate the power of the proposed approach in real-time assessment and control of operation in microgrids.

Original languageEnglish (US)
Pages (from-to)2543-2552
Number of pages10
JournalProcedia Computer Science
Issue number1
StatePublished - 2015
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: Apr 21 2002Apr 24 2002


  • Dynamic data driven
  • Microgrid operation
  • Multi-objective optimization
  • Scale identification

ASJC Scopus subject areas

  • Computer Science(all)


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