Techno-Economic Optimization and Environmental Life Cycle Assessment (LCA) of Microgrids Located in the US using Genetic Algorithm
A methodology was developed that assessed the techno-economic and environmental performance of a small scale microgrid located in US cities of Tucson, Lubbock and Dickinson. Providing uninterrupted power the microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H2 tanks. Firstly, detailed mathematical models that predicted the hourly energy generation for each of the components were developed and validated. Secondly, based on an electricity dispatch strategy, configurations having lowest LCOE (Levelized Cost of Energy) were determined using an evolutionary optimization technique - Genetic Algorithm (GA). Results for a single home microgrid were verified using an exhaustive search technique that scanned the entire design space to find the lowest LCOE configuration. The microgrid size was subsequently increased to satisfy power requirements for 10 and 50 homes and new lowest LCOE configurations were determined using GA to examine the economies of scale effect on sustainability. The LCOEs obtained were in the range of $0.32-0.42/kWh and were also compared with similar economic analyses available in the literature. The carbon footprint (LCA GHG emissions - CO2 eq.) was extremely low and was approximately 1/10th as that of an equivalent conventional electric grid.
P. Nagapurkar and J. D. Smith, "Techno-Economic Optimization and Environmental Life Cycle Assessment (LCA) of Microgrids Located in the US using Genetic Algorithm," Energy Conversion and Management, vol. 181, pp. 272-291, Elsevier Ltd, Feb 2019.
The definitive version is available at https://doi.org/10.1016/j.enconman.2018.11.072
Chemical and Biochemical Engineering
Keywords and Phrases
Environmental assessment (LCA); Genetic algorithm; Hybrid systems; Microgrid; Optimization; Techno-economic assessment
International Standard Serial Number (ISSN)
Article - Journal
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