Optimal Positioning of Energy Assets in Autonomous Robotic Microgrids for Power Restoration

As the number of natural disasters and the duration of their aftermath continues to rise, the use of mobile and autonomous energy sources formed into a temporary and adaptive microgrid will improve response time and decrease overall recovery time. The optimal positioning of energy resources in the operating field is critical. There are many options for choosing an optimization technique but many of these assume specific connections between voltage source nodes and loads. This paper presents a brief overview of the genetic algorithm optimization in microgrid resource positioning in an operating field with obstacles. The indexing of the nodes and loads and the formulation for optimization of a general model of a microgrid system are presented. Next, the optimization of microgrids using the genetic algorithm approach is explained, as well as the shortest path algorithm used. The results show the success of optimization applied to a variety of test cases. Further research and expansions of optimization in the test cases are then explained.