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dc.contributor.advisorDavid H. Marks, John Sterman, Ingo Vogelsang, Marija Ilic and Richard de Neufville.en_US
dc.contributor.authorBlack, Jason W. (Jason Wayne)en_US
dc.contributor.otherMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.date.accessioned2006-02-02T18:54:52Z
dc.date.available2006-02-02T18:54:52Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/31168
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.en_US
dc.descriptionIncludes bibliographical references (v. 2, p. 311-316).en_US
dc.description.abstractThe electric power system in the US developed with the assumption of exogenous, inelastic demand. The resulting evolution of the power system reinforced this assumption as nearly all controls, monitors, and feedbacks were implemented on the supply side. Time invariant, averaged retail pricing was a natural extension of the assumption of exogenous demand and also reinforced this condition. As a result, the market designs and physical control of the system exclude active participation by consumers. Advances in information and communications technologies enable cost effective integration of demand response. Integrating demand into the US electricity system will allow the development of a more complete market and has the potential for large efficiency gains. Without feedbacks between supply and demand, attempts to develop competitive markets for electricity will suffer from a greater potential for market power and system failure. This thesis provides an analysis of the technical, regulatory, and market issues to determine a system structure that provides incentives for demand response. An integrated, dynamic simulation model is utilized to demonstrate the effects of large scale adoption of demand response technologies. The model includes distributed decision making by both consumers and investors in generation capacity, the effects of their decisions on market prices, and the feedbacks between them. Large scale adoption of demand response technology is simulated to quantify the potential benefits of responsive demand. The effects of technology improvement via learning, long term demand elasticity, and policies to promote adoption are considered.en_US
dc.description.abstract(cont.) The simulations show that diminishing returns for adopters and free rider effects limit the attractiveness of individual adoption. A subsidy to alleviate the costs to adopters can be justified by the significant system level savings from widespread participation. Several pernicious effects can emerge from large scale demand response, however, including increased price volatility due to reductions in generation capacity reserve margin, increases in long term demand, and increased emissions from the substitution of peak generation capacity, such as natural gas and renewables by intermediate capacity. Significant rent transfers will also occur, and stakeholder analysis is conducted to determine interests and distributional effects of large scale demand response.en_US
dc.description.statementofresponsibilityby Jason W. Black.en_US
dc.format.extent2 v. (432 p.)en_US
dc.format.extent22567190 bytes
dc.format.extent22627288 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectEngineering Systems Division.en_US
dc.titleIntegrating demand into the U.S. electric power system : technical, economic, and regulatory frameworks for responsive loaden_US
dc.title.alternativeIntegrating demand into the United States electric power systemen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.identifier.oclc61233007en_US


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