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Bachelor of Science
Power systems, Microgrids, Renewable, Distributed energy resources, Energy, Genetic altorithms, Machine learning, Electric power systems, Photovoltaic power systems, Microgrids (Smart power grids)-Design, Renewable energy sources, Algorithms
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 . Thus, the use of GAs to perform an environmental economic dispatch (eED) is demonstrated as an achievable alternative to HOMER  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.
2018 Jessica Leigh Wert.Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Wert, Jessica Leigh, "Microgrid design informed by genetic algorithms" (2018). Honors Project, Smith College, Northampton, MA.
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