Renewable energy integration: An approach for micro-grid optimization

Nicolas Lopez, University of Texas at El Paso

Abstract

This thesis explores the Renewable Energy Integration Problem beginning with a literature review on the different sources of electrical energy classified as Non-Renewable and Renewable, stating that the integration of renewable sources within the pre-existing Power-Grids is a current worldwide problem that is being addressed with different approaches.^ The impossibility to exactly model the different equations relating the electric output of the renewable energy sources as they relate with meteorological factors, makes the renewable sector a stochastic component of the overall mathematical model, where it can be concluded that most of the problem is in the area of the forecast.^ A mathematical process flow is provided as an explanation referring to the general behavior of the renewable energy integration problem, and the obstacles associated with predicting the changes from cycle to cycle. The explanation continues as to conclude that the Renewable Energy Integration Problem can be viewed as a combinatorial problem if the forecasts: (1) Are ignored, or, (2) Are adequately handled.^ A mathematical model is proposed of a basic version of the problem in order to have a base for applying heuristic algorithms in order to provide answers for larger problems, and a computer program (Homer) is presented as a tool to solve small sized problems constrained to Micro-Grids. The results obtained are then analyzed giving detail of why special care is needed when handling the outputs of this type of problem, reaching conclusions regarding the overall knowledge the engineer must possess in order to adequately attack the Renewable Energy Integration Problem, and the decisions associated with it.^

Subject Area

Engineering, Industrial|Sustainability|Engineering, Environmental

Recommended Citation

Lopez, Nicolas, "Renewable energy integration: An approach for micro-grid optimization" (2010). ETD Collection for University of Texas, El Paso. AAI1487778.
http://digitalcommons.utep.edu/dissertations/AAI1487778

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