DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS

DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS

Bibliographic Details
Main Author: Muratori, Matteo
Language:English
Published: The Ohio State University / OhioLINK 2014
Subjects:
Engineering
Energy
Statistics
Sustainability
Transportation
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1397476747
id ndltd-OhioLink-oai-etd.ohiolink.edu-osu1397476747
record_format oai_dc
collection NDLTD
language English
sources NDLTD
topic Engineering
Energy
Statistics
Sustainability
Transportation
spellingShingle Engineering
Energy
Statistics
Sustainability
Transportation
Muratori, Matteo
DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
author Muratori, Matteo
author_facet Muratori, Matteo
author_sort Muratori, Matteo
title DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
title_short DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
title_full DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
title_fullStr DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
title_full_unstemmed DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS
title_sort dynamic management of integrated residential energy systems
publisher The Ohio State University / OhioLINK
publishDate 2014
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1397476747
work_keys_str_mv AT muratorimatteo dynamicmanagementofintegratedresidentialenergysystems
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu13974767472021-08-03T06:23:49Z DYNAMIC MANAGEMENT OF INTEGRATED RESIDENTIAL ENERGY SYSTEMS Muratori, Matteo Engineering Energy Statistics Sustainability Transportation 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 towards a sustainable energy future.Nowadays, fossil fuel dependency, the need for greater energy security, macroeconomic considerations, and concern about climate changes call for a paradigm shift in the energy industry. 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 nondispatchability substantially limit the ability of electric power generation of adapting to the fluctuating demand.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.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.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.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. 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. 2014-06-24 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1397476747 http://rave.ohiolink.edu/etdc/view?acc_num=osu1397476747 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

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