Microgrid design informed by genetic algorithms
Jessica L. Wert · Smith ScholarWorks (Smith College) · 2018
This project implements genetic algorithms (GAs) to optimize for both cost and emissions to determine microgrid design and optimal operation using the MATLAB Optimization Toolbox [1]. Thus, the use of GAs to perform an environmental economic dispatch (eED) is demonstrated as an achievable alternative to HOMER [2] as a method of microgrid design. This project considers a residential-scale microgrid with a peak load of 500kW and evaluates the recommended generation mix for loads at 30%, 60%, and 90% of this peak value. Generation types considered are microturbines, photovoltaic arrays, and wind turbines.