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Dynamic management of integrated res...

Muratori, Matteo.

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Dynamic management of integrated residential energy systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic management of integrated residential energy systems./
Author:	Muratori, Matteo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
Description:	343 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.
Contained By:	Dissertation Abstracts International76-12A(E).
Subject:	Transportation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3713793
ISBN:	9781321905243

Dynamic management of integrated residential energy systems.
Muratori, Matteo.

Dynamic management of integrated residential energy systems. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 343 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.

Thesis (Ph.D.)--The Ohio State University, 2014.

This study combines principles of energy systems engineering and statistics to develop integrated models of residential energy use in the United States, to include residential recharging of electric vehicles. These models can be used by government, policymakers, and the utility industry to provide answers and guidance regarding the future of the U.S. energy system.

ISBN: 9781321905243Subjects--Topical Terms:

555912
Transportation.

Dynamic management of integrated residential energy systems.
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100 1 $a Muratori, Matteo. $3 3426404
245 1 0 $a Dynamic management of integrated residential energy systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2014
300 $a 343 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.
500 $a Advisers: Giorgio Rizzoni; Michael J. Moran.
502 $a Thesis (Ph.D.)--The Ohio State University, 2014.
520 $a This study combines principles of energy systems engineering and statistics to develop integrated models of residential energy use in the United States, to include residential recharging of electric vehicles. These models can be used by government, policymakers, and the utility industry to provide answers and guidance regarding the future of the U.S. energy system.
520 $a Currently, electric power generation must match the total demand at each instant, following seasonal patterns and instantaneous fluctuations. Thus, one of the biggest drivers of costs and capacity requirement is the electricity demand that occurs during peak periods. These peak periods require utility companies to maintain operational capacity that often is underutilized, outdated, expensive, and inefficient. In light of this, flattening the demand curve has long been recognized as an effective way of cutting the cost of producing electricity and increasing overall efficiency. The problem is exacerbated by expected widespread adoption of non-dispatchable renewable power generation. The intermittent nature of renewable resources and their non-dispatchability substantially limit the ability of electric power generation of adapting to the fluctuating demand.
520 $a Smart grid technologies and demand response programs are proposed as a technical solution to make the electric power demand more flexible and able to adapt to power generation. Residential demand response programs offer different incentives and benefits to consumers in response to their flexibility in the timing of their electricity consumption. Understanding interactions between new and existing energy technologies, and policy impacts therein, is key to driving sustainable energy use and economic growth. Comprehensive and accurate models of the next-generation power system allow for understanding the effects of new energy technologies on the power system infrastructure, and can be used to guide policy, technology, and economic decisions.
520 $a This dissertation presents a bottom-up highly resolved model of a generic residential energy eco-system in the United States. The model is able to capture the entire energy footprint of an individual household, to include all appliances, space conditioning systems, in-home charging of plug-in electric vehicles, and any other energy needs, viewing residential and transportation energy needs as an integrated continuum. The residential energy eco-system model is based on a novel bottom-up approach that quantifies consumer energy use behavior. The incorporation of stochastic consumer behaviors allows capturing the electricity consumption of each residential specific end-use, providing an accurate estimation of the actual amount of available controllable resources, and for a better understanding of the potential of residential demand response programs.
520 $a A dynamic energy management framework is then proposed to manage electricity consumption inside each residential energy eco-system. Objective of the dynamic energy management framework is to optimize the scheduling of all the controllable appliances and in-home charging of plug-in electric vehicles to minimize cost. Such an automated energy management framework is used to simulate residential demand response programs, and evaluate their impact on the electric power infrastructure. For instance, time-varying electricity pricing might lead to synchronization of the individual residential demands, creating pronounced rebound peaks in the aggregate demand that are higher and steeper than the original demand peaks that the time-varying electricity pricing structure intended to eliminate.
520 $a The modeling tools developed in this study can serve as a virtual laboratory for investigating fundamental economic and policy-related questions regarding the interplay of individual consumers with energy use. The models developed allow for evaluating the impact of different energy policies, technology adoption, and electricity price structures on the total residential electricity demand.
520 $a In particular, two case studies are reported in this dissertation to illustrate application of the tools developed. The first considers the impact of market penetration of plug-in electric vehicles on the electric power infrastructure. The second provides a quantitative comparison of the impact of different electricity price structures on residential demand response. Simulation results and an electricity price structure, called Multi-TOU, aimed at solving the rebound peak issue, are presented.
590 $a School code: 0168.
650 4 $a Transportation. $3 555912
650 4 $a Civil engineering. $3 860360
650 4 $a Statistics. $3 517247
650 4 $a Sustainability. $3 1029978
650 4 $a Energy. $3 876794
650 4 $a Alternative Energy. $3 1035473
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710 2 $a The Ohio State University. $b Mechanical Engineering. $3 1684523
773 0 $t Dissertation Abstracts International $g 76-12A(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3713793