Graduation Year

2016

Document Type

Thesis

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Tapas K. Das, Ph.D.

Committee Member

Yogi D. Goswami, Ph.D.

Committee Member

Michael Fountain, Ph.D.

Committee Member

Andrei Barbos, Ph.D.

Committee Member

Bo Zeng, Ph.D.

Committee Member

Hui Yang, Ph.D.

Keywords

Integer Programming, Analytics, Power Systems, Net Zero Energy Building, Financial Incentives

Abstract

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.

Download

Share

COinS