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 will present 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 will be presented. Next, the optimization of microgrids using the genetic algorithm approach will be 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.

The concept of hidden genes was recently introduced in genetic algorithms to handle variable-size design space optimization problems. This paper presents new developments in hidden genes genetic algorithms. Mechanisms for assigning (selecting) the hidden genes in the chromosomes of genetic algorithms are presented. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not, while they evolve over generations using stochastic operations. These mechanisms are tested on mathematical optimization problems and on a trajectory optimization problem for a space mission to Jupiter. In the conducted tests, one of the proposed hidden genes assignment mechanism has enabled the hidden genes genetic algorithms to find better (lower cost) solutions, while other mechanisms has shown to be able to find close solutions.

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions.

The dynamic model of Wave Energy Converters (WECs) may have nonlinearities due to several reasons such as a nonuniform buoy shape and/or nonlinear power takeoff units. This paper presents the Hamiltonian Surface-Shaping (HSS) approach as a tool for the analysis and design of nonlinear control of WECs. The Hamiltonian represents the stored energy in the system and can be constructed as a function of the WEC’s system states, its position, and velocity. The Hamiltonian surface is defined by the energy storage, while the system trajectories are constrained to this surface and determined by the power flows of the applied non-conservative forces. The HSS approach presented in this paper can be used as a tool for the design of nonlinear control systems that are guaranteed to be stable. The optimality of the obtained solutions is not addressed in this paper. The case studies presented here cover regular and irregular waves and demonstrate that a nonlinear control system can result in a multiple fold increase in the harvested energy.