Power grids around the world are evolving with increased demand, sustainability goals, and the need for resilience against disruptions. Integrated models of distributed resources break the grid into networks of microgrids that combine storage and generators, operate in connected or disconnected modes, and serve critical infrastructure under real-world disruption scenarios. Optimizing these systems requires modeling thermal generation with higher-fidelity cubic cost functions and evaluating performance across multiple simulated scenarios. Participants will use quantum computing methods, including entropy quantum computing for optimizing cubic cost functions, to improve cost efficiency and resilience in microgrid networks while benchmarking performance against classical approaches.