Simulation of Microgrid and Mobile Power Transfer System interaction using Distributed Multiobjective Evolutionary Algorithms

Optimization of a microgrid interacting with mobile power transfer systems is a multiobjective problem that grows to be computationally expensive as components and fidelity are added to the simulation. In previous work [17] we proposed an optimization strategy relying on evolutionary computing. With an evolutionary computing approach, seeking a well-distributed set of solutions on the entire optimal frontier necessitates a large population and frequent evaluation of the aforementioned simulation. With these challenges, and inspiration from Roy et al. [14] distributed pool architecture, we propose an architecture for distributed pool evolutionary computing that differs from the Roy et al. design. We use this strategy with a microgrid and mobile power transfer system simulation to optimize for cost and relaibility. We find that the distributed approach achieves increased performance in raw system execution time, and in some cases converges faster than a non distributed version of the same evolutionary strategy.