This thesis is focused on development of an integrated decision making support framework to assist with the design of a sustainable community that has access to secure clean energy and utilizes innovative technologies and strategies. Innovative technologies include but not limited to renewable generation/storage resources, Plug-In Electric Vehicle (PEV), Building Monitoring Systems (BMS), Programmable Communication Devices (PCD), and etc. This framework has 3 unique components: i. energy dynamic demand modeling, ii. investment strategies for Distributed Energy Resources (DER), and iii. demand side energy management in uncertain markets. In the existing litrature and industry practices, energy load profile is considered as an input to a decision making support tools and its dynamics is ignored which can lead to unreliable and less cost effective investment decisions in the long run. The analysis of such dynamics is not possible with existing demand forecast models, which are built based on time series forecasts relying on historical data. Therefore, in this thesis a bottom-up demand forecasting model entitled High Resolution Adaptive Model (Hi-RAM) is integrated with DER investment model. Hi-RAM provides compelling results concerning the potential load shifting of PEVs, as well as how advanced energy management systems enable response to Electric Distribution Companies (EDC) price signals. Hi-RAM is a bottom-up stochastic demand model consisting of: 1- Markovian stochastic process for simulating human activities, and buildings occupany profiles. 2- Probabilistic Bayesian and Logistic technology adoption models and 3- Optimization and rule-based energy management models for building end-uses e.g., Heating, Ventilating, and Air Conditioning (HVAC), Lighting, and PEV charging which enable them to respond to EDC price signals without compromising users' comfort. The DER investment model is built as a non-linear stochastic mixed integer programming to maximize the cash flow over the planning horizon considering long-term market variations, short-term operational volatilities, and the dynamics of underlying demand. Integration of Hi-RAM and the DER investment model offers a novel analytical framework which can be used at the design stage of new products to assess their effectiveness. Such a framework can also assist decision makers to investigate investment strategies on community's DER e.g., PV solar, wind turbines, electric storages, and etc. taking into account the specifics of building behavioral and physical characteristics as well as the emergence of new end-uses and Demand-Side Management (DSM) capabilities. While developing this tool is an essential step towards sustainable and efficient energy solutions in the planning stage of new buildings and communities, attention must also be paid to the existing building stock where the U.S. buildings sector alone accounted for 7% of global primary energy consumption. Recognizing its importance, this thesis also investigates advanced energy magement and control policies for buildings within a constrained peak demand envelope while ensuring that custom climate conditions are facilitated. This will mitigate service disruption and high cost of energy production and distribution.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = ETD-LCSH)
Topic
Sustainable development
Subject (authority = ETD-LCSH)
Topic
Energy development
Subject (authority = ETD-LCSH)
Topic
Energy industries
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7484
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 148 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Farbod Farzan
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. Graduate School - New Brunswick
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Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.