In this dissertation, we leverage predictive and prescriptive analytics to develop decision support systems to promote the use of renewable energy in society. Since electricity from renewable energy sources is still relatively expensive, there are variety of financial incentive programs available in different regions. Our research focuses on financial incentive programs and tackles two main problem: 1) how to optimally design and control hybrid renewable energy systems for residential and commercial buildings given the capacity based and performance based incentives, and 2) how to develop a model-based system for policy makers for designing optimal financial incentive programs to promote investment in net zero energy (NZE) buildings.
In order to customize optimal investment and operational plans for buildings, we developed a mixed integer program (MIP). The optimization model considers the load profile and specifications of the buildings, local weather data, technology specifications and pricing, electricity tariff, and most importantly, the available financial incentives to assess the financial viability of investment in renewable energy. It is shown how the MIP model can be used in developing customized incentive policy designs and controls for renewable energy system.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7439 |
| Date | 08 April 2016 |
| Creators | Ghalebani, Alireza |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
Page generated in 0.0018 seconds