García Cooper, Robert A.

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    Distributing resilience: Rethinking renewable energy system sizing towards the communally affordable
    (2023-07-07) García Cooper, Robert A.; Castro Sitiriche, Marcel; College of Engineering; Irizarry Rivera, Agustín A.; Andrade Rengifo, Fabio; Department of Electrical and Computer Engineering; Delgado Acosta, Bernadette
    Modern civilization has grown exponentially because of electric power fueling goods, services, interests, and sustaining the many different communal and social infrastructures in our world. Our reliance is such that prolonged service interruptions can lead to staggering individual, household, or communal economic losses, which could also lead to human loss of life. Despite the importance, there are places in the world that lack access to electric power or are burdened with unreliable electrical utilities that force consumers to evaluate more electrically resilient alternatives, which are more costly than published electric utility rate but less than the Customer Electric Service Interruption Costs (CESIC) to supply stated goods and services without electricity. This thesis focuses on redefining how to estimate the levelized costs of energy (LCOE) from renewable energy systems (RES) and other sources by 1) developing a RES sizing costs optimization algorithm for single and aggregate would-be prosumers, 2) establishing a framework of levelized costs reduction for potential microgrids through ideal RES component allocation, and 3) compare single and aggregate distributed generation LCOE with those of the electric power supply of a given utility, factoring any costs incurred during electric service interruptions. Results yield hourly CESIC estimates, depending on the occupation or household income, with projected annual losses in combination with resiliency metrics or measured interruptions. Findings reveal that the LCOE objective function for single or aggregate RES sizing is a concave function, whose minimum is determined via an optimization algorithm. Finally, this study shows how microgrids can be more cost-effective via the ideal allocation of RES components and point of common coupling (PCC). The designs for the hourly and annual CESIC model, the RES LCOE optimization algorithm, and the microgrid LCOE minimizer were programed for and validated by actual case studies.