The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling

dc.contributor.advisorRai, Varunen
dc.contributor.committeeMemberArima, Eugenioen
dc.contributor.committeeMemberZarnikau, Jayen
dc.creatorRobinson, Scott Austenen 2014en
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.description.departmentEnergy and Earth Resourcesen
dc.description.departmentPublic Affairsen
dc.subjectAgent-based modellingen
dc.titleThe solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modelingen