2020 IEEE Energy Conversion Congress and Exposition (ECCE) 2020
DOI: 10.1109/ecce44975.2020.9236092
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LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters

Abstract: This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

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Cited by 3 publications
(2 citation statements)
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“…Evolution of LCOE is exhibited by the learning curve approach and discounted cash flow process for a PV-CSP combination for duration of 2010 to 2050 [35]. LCOE optimization by genetic algorithm with seeking the optimal values of three design variables for a transformer including the inductor cost for a 200kW PV generating technology is customized [36]. Economical performance analysis of organic Rankine turbines driven by the concentrated Photovoltaic thermal generation system is estimated by the LCOE evaluation [37], [38].…”
Section: ) Solar Photovoltaic Generationmentioning
confidence: 99%
“…Evolution of LCOE is exhibited by the learning curve approach and discounted cash flow process for a PV-CSP combination for duration of 2010 to 2050 [35]. LCOE optimization by genetic algorithm with seeking the optimal values of three design variables for a transformer including the inductor cost for a 200kW PV generating technology is customized [36]. Economical performance analysis of organic Rankine turbines driven by the concentrated Photovoltaic thermal generation system is estimated by the LCOE evaluation [37], [38].…”
Section: ) Solar Photovoltaic Generationmentioning
confidence: 99%
“…Inductors can be sized by using simulation-driven approach such as genetic algorithm (GA) and particle swarm optimisation. The GA is well suited to solve the problems where the decision variables are discontinuous, like transformers, inductors, and electric motors or generators, and is to be considered as the best tool to find an optimal solution, in the recent past [15][16][17][18]. The GA is a proven technique to solve both unconstrained and constrained optimisation problems, based on the natural selection (i.e., the process that drives biological evolution).…”
Section: Inductor Sizing Using Simulation-driven Approachmentioning
confidence: 99%