A dynamic data-driven approach for operation planning of microgrids

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.