Optimization-based energy sharing among customers for enhanced resilience in a community microgrid

Islam, Sk. Razibul; Ratnam, Elizabeth L.; Chau, Chi-Kin; Ward, John

doi:10.1145/3396851.3403515

Cited by 1 publication

(2 citation statements)

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“…Predictions provide the facilitator with information to maximize energy-sharing system connectivity while minimizing loss and solar curtailment [16]. Participation and community engagement have been examined for business models [4,17], optimization [18,19], and demand side experience [20]. However, each of these approaches emphasizes the characteristics of the service provided to the end-user without consideration of end-user decision-making strategies, which may not be purely rational processes.…”

Section: Predicting Energy Sharing Participationmentioning

confidence: 99%

“…Using smart systems in the Internet of Things (IoT), an optimization model developed for an energy management system is used to increase the usage of renewable energy [24]. In order to encourage autonomous activity, Islam et al (2020) developed an optimization-based algorithm to improve grid resilience [18]. However, these studies fail to accurately represent the dynamics of consumer adoption and potential implications for the energy-sharing system.…”

Section: Predicting Energy Sharing Participationmentioning

confidence: 99%

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Comparing Behavioral Theories to Predict Consumer Interest to Participate in Energy Sharing

Morgan

Canfield

2021

Sustainability

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Consumer investment in distributed energy resources (DERs) is increasing the penetration of renewable energy in the grid. In some cases, DERs produce more electricity than needed by the owner and this excess electricity is sold to the utility (e.g., net metering). In contrast, energy sharing allows a facilitator, which may or may not be the utility, to redistribute excess renewable electricity to fellow community members directly. However, little is known about consumer interest in participating in this type of arrangement. This preregistered study uses structural equation modeling to compare two behavioral theories, Value-Belief-Norm and Diffusion of Innovation, to predict consumer interest in participating in energy sharing. Participants answered questions about energy sharing in the context of an energy-sharing community facilitated by the fictional company, E-topia. Survey data from 195 online participants suggest that Value-Belief-Norm is a better, although not quite acceptable, fit. This suggests that early adoption of energy sharing may be driven by appealing to core values rather than novelty-seeking. This study implies that individuals are more likely to participate in a new technology system such as energy sharing when the effects of participation align with individuals’ values.

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