Dynamic Data Driven Adaptive Simulation Framework for Automated Control in Microgrids

In this paper, we introduce a novel dynamic data driven adaptive simulation framework for the operation and control of microgrids (MGs) that significantly accelerates the real-time computation of the resource allocation, and controls decisions to optimize the operational cost, energy surety, as well as emissions per MW. The proposed framework includes a database receiving input from electrical and environmental sensors, a fault detection algorithm that discovers liabilities and potential hazards within the MG, an agent-based simulation of the MG system, an optimal computing budget allocation-based control selection algorithm that uses the agent-based simulation to decide the best control design of the MG, and a multiobjective algorithm for optimizing the decisions of the MG given the best control design. For validating our framework, we use the structure of a realistic MG that is simulated using real-historical data. The experiments reveal that the proposed framework significantly reduces the computational burden of a considerably complex multiobjective problem.