  This paper develops a community resilience optimization method subject to
power flow constraints in the Cyber-Physical-Social Systems in Power
Engineering, which is solved using a multi-agent-based algorithm. The tool that
makes the nexus between electricity generation on the physical side and the
consumers and the critical loads on the social side is the power flow
algorithm. Specifically, the levels of emotion, empathy, cooperation, and the
physical health of the consumers, prosumers are modeled in the proposed
community resilience optimization approach while accounting for the electric
power system constraints and their impact on the critical loads, which include
hospitals, shelters, and gas stations, to name a few. The optimization accounts
for the fact that the level of satisfaction of the society, the living
standards, and the social well-being are depended on the supply of energy,
including electricity. Evidently, the lack of electric energy resulting from
load shedding has an impact on both the mental and the psychical quality of
life, which in turn affects the community resilience. The developed constrained
community resilience optimization method is applied to two case studies,
including a two-area 6-buses system and a modified IEEE RTS 24-bus system.
Simulation results reveal that a decrease in the initial values of the emotion,
the risk perception, and the social media platform effect factor entails an
increase in load shedding, which in turn results in a decrease in community
resilience. In contrast, an increase in the initial values of cooperation,
empathy, physical health, the capacity of microgrids and distributed energy
resources results in a decrease in the load shedding, which in turn induces an
enhancement of the community resilience.
