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This paper develops a community resilience optimization method subject topower flow constraints in the Cyber-Physical-Social Systems in PowerEngineering, which is solved using a multi-agent-based algorithm. The tool thatmakes the nexus between electricity generation on the physical side and theconsumers and the critical loads on the social side is the power flowalgorithm. Specifically, the levels of emotion, empathy, cooperation, and thephysical health of the consumers, prosumers are modeled in the proposedcommunity resilience optimization approach while accounting for the electricpower system constraints and their impact on the critical loads, which includehospitals, shelters, and gas stations, to name a few. The optimization accountsfor the fact that the level of satisfaction of the society, the livingstandards, and the social well-being are depended on the supply of energy,including electricity. Evidently, the lack of electric energy resulting fromload shedding has an impact on both the mental and the psychical quality oflife, which in turn affects the community resilience. The developed constrainedcommunity 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 anincrease in load shedding, which in turn results in a decrease in communityresilience. In contrast, an increase in the initial values of cooperation,empathy, physical health, the capacity of microgrids and distributed energyresources results in a decrease in the load shedding, which in turn induces anenhancement of the community resilience.

Community Resilience Optimization Subject to Power Flow Constraints in Cyber-Physical-Social Systems in Power Engineering