Contextualizing Renewable Energy Adoption: An Examination of the Role of Community Choice Aggregation
"The rapid expansion of renewable energy generation in the U.S., both through distributed and utility-scale facilities, is largely driven by top-down policy measures and the growing engagement of residential consumers on both individual and community levels. Previous studies on motives behind residential renewable energy adoption have examined procurement options in isolation and within a static context, primarily focused on intrinsic attributes like economic incentives, emission reductions, and peer popularity. This research introduces a novel context, assessing renewable procurement options in the presence of Community Choice Aggregation (CCA), a more prevalent and accessible alternative. This dissertation makes four pivotal contributions, offering insights into the importance of contextual factors when examining renewable energy adoption. The first contribution identifies shared attributes across various renewable procurement options using a discrete-choice experiment, and consumer classes in residential renewable market. The second contribution evaluates the impact of these attributes on procurement decisions when the default electricity supply either meets or surpasses state mandates. The third contribution employs an empirically-grounded agent-based model to forecast PV adoption rates under two scenarios: with and without the CCA context. The fourth contribution delves into how a greener default electricity supply within the CCA context influences the foundational beliefs prompting PV adoption. The insights gleaned from these contributions enables policymakers with valuable information to design targeted incentives and engineering managers to devise a strategic plan for the future development of the U.S. electricity grid with a focus on renewable generation infrastructure"-- Abstract, p. iii
Agent-based modeling, choice framing, community choice aggregation, discrete choice experiment, renewable energy, solar pv, Engineering, Operations Research, Systems Engineering and Industrial Engineering
Missouri University of Science and Technology
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