Optimal replacement strategy for residential solar panels using monte carlo simulations and nonlinear optimization methods
| dc.contributor.advisor | Adomaitis, Raymond | |
| dc.contributor.author | Poissant, Andrew | |
| dc.date.accessioned | 2017-06-06T17:45:15Z | |
| dc.date.available | 2017-06-06T17:45:15Z | |
| dc.date.issued | 2017-05 | |
| dc.description.abstract | The purpose of this analysis is to determine the optimal replacement strategy for a residential photovoltaic (PV) array. Specifically, the optimal year and number of solar modules that should be replaced on a residential solar panel system. This analysis aims at saving the stakeholder, a homeowner with a residential PV array, money. A Monte Carlo simulation and nonlinear mixed-integer programming are the analytic techniques used in determining the replacement strategy. Localized cost of electricity (LCOE) is the objective function in these analyses. Modular, environmental, and market factors are all variables that can affect the LCOE. University of Maryland’s LEAFHouse was the basis of these analyses because it is a house equipped with an aging PV array and readily accessible data. Based on the findings in this report, it was determined that 0 ± 0 solar modules should be replaced after 1.42 ± 0.32 years with a reference year of initial installation being 2007. While the analysis results were not expected, they were proven to be reasonable based on cost trends for solar panels and the calculated monetary value of the power production lost from the PV array. | en_US |
| dc.identifier | https://doi.org/10.13016/M2QZ97 | |
| dc.identifier.uri | http://hdl.handle.net/1903/19229 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | Institute for Systems Research | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
| dc.relation.ispartofseries | ISR;TR_2017-02 | |
| dc.title | Optimal replacement strategy for residential solar panels using monte carlo simulations and nonlinear optimization methods | en_US |
| dc.type | Other | en_US |
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