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dc.contributor.advisorRai, Varunen
dc.creatorRobinson, Scott Austenen
dc.date.accessioned2015-11-17T21:08:00Zen
dc.date.available2015-11-17T21:08:00Zen
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.identifierdoi:10.15781/T28H0Qen
dc.identifier.urihttp://hdl.handle.net/2152/32555en
dc.descriptiontexten
dc.description.abstractThis thesis presents a step-by-step implementation of the Solar Energy Consumer Agent Decision (SECAD) model: an empirically-grounded multi-agent model of residential solar photovoltaic (PV) adoption with an integrated geospatial topology. Solar PV diffusion is a complex system with geographic heterogeneity, uncertain information, high financial risk, and important social interaction and feedback effects between consumers. A key limitation for agentbased models in human socio-technical systems is the integration of empirical patterns in the model structure, initialization, and validation efforts. This limitation is addressed though highly granular and interlocking data-streams from the geographic, social network, financial, demographic, and decision-making process of real households in the study. The fitted and validation model is used to simulate implementation of potential policies to inform decision-makers: i) Targeted informational dissemination campaigns, ii) Tiered rebates, iii) Locational pricing, and iv) Alternative rebate schedules. Informational campaigns can increase cumulative installations by as much as 12%, but vary greatly in their effectiveness based on which agents are targeted. Simulations suggest that by lowering the cost barrier to lower wealth households through a slightly higher rebate (+$0.25/Watt), the mean difference in wealth between solar adopters and non adopters could be reduced by 22.6%. Locational pricing can allow the utility more control over diffusion patterns with regard to load pockets--a $0.25 higher offering increased the percentage of adopters in the target area from less than 1% to over 10%. Relative to flatter rebate schedules, sharply decreasing schedules are effective in terms of motivating adoption but inefficient in small markets. It is our hope that this work will provide a working example for other agent-based models of human socio-technical systems as well as provide insight into the likely outcomes of novel policy-levers such as those described above.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectAgent-based modellingen
dc.subjectSolaren
dc.subjectDiffusionen
dc.subjectInnovationen
dc.subjectElectricityen
dc.subjectEnergyen
dc.subjectDecisionen
dc.subjectEconomicsen
dc.subjectPolicyen
dc.subjectSimulationen
dc.titleThe solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modelingen
dc.typeThesisen
dc.date.updated2015-11-17T21:08:00Zen
dc.contributor.committeeMemberArima, Eugenioen
dc.contributor.committeeMemberZarnikau, Jayen
dc.description.departmentEnergy and Earth Resourcesen
dc.description.departmentPublic Affairsen
thesis.degree.departmentEnergy and Earth Resourcesen
thesis.degree.departmentPublic Affairsen
thesis.degree.disciplineEnergy and Earth Resourcesen
thesis.degree.disciplinePublic Affairsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Artsen
thesis.degree.nameMaster of Public Affairsen


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