Research focuses on Microgrid Energy Management utilizing Model-Driven Engineering and autonomic software systems. More specifically it utilizes a model driven approach to run-time realization of optimal configuration under safety and resilience concerns. This allows for a lay person to specify high level objectives to the system which would generate multiple scenarios to achieve said objectives based on the internal state of the system and the environment. The system then is capable of executing an optimal scenario (Runtime Model) and reactively reconfigure based on activities in the real world. This research raises some interesting challenges such as the incorporation of electric vehicles and near-future forecasting of energy demand and supply.