On the limited observability of energy community members: An uncertainty-aware near-optimal bilevel programming approach

Energy communities offer various socio-economic advantages to their members, such as competitive internal electricity prices. Nevertheless, the efficacy of these competitive prices might be influenced by the bounded rational behavior of the members. This study explores the concept of limited observability as a paradigm of bounded rational behavior toward the internal pricing strategies of the community manager who is exposed to incomplete information regarding the real-time consumption of community members as well. Stochastic robustness near-optimality bilevel programming problem is proposed for energy sharing management in centralized energy communities. The proposal enables the users to mitigate the decision uncertainties arising from limited observability, as well as data uncertainties related to PV power generation. The investigation focuses on understanding the near-optimal decision-making of community members due to limited observability and potential impacts on community energy exchanges. Simulation results show that mitigating limited observability and PV generation uncertainties leads members to reduce their total consumption by an average of 14.64%. Therefore, the CM lowers internal purchasing prices by 24.82% to promote energy purchases by the members. Additionally, limited observability improves user social welfare by an average of 12.65% and reduces the CM's daily power purchases from the external supplier by approximately 17%.