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Jaber Valinejad
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Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health. Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, information-seeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.
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Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health. Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, information-seeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.
According to the Department of Energy, demand response provides an opportunity for end-users to play a significant role in the efficiency, reliability, resilience, and sustainability of a power grid. This is made possible owing to the existence of storage devices and diversity of energy sources at the customer level and the advent of the Internet of Things. Social influences and psychological traits of consumers affect their behavior and decision-making. Consequently, there is a necessity to bring the influences of humans, organizations, and societies on the power system together through computational social science into a cyber-physical-social system. Hence, in this brief, we introduce our development of an artificial society of the social demand response of a power system, a well-known approach in computational sociology based on a bottom-up approach, starting from theory. We assume that consumers can engage in demand response to fulfill two aims: save their cost or enhance the sustainability of a power system. The literature concerning sustainability-based demand response is limited to only considering CO2, NOX, and SO2. In addition to NOX, and SO2, we examine the impact of power systems on water pollution, disability-adjusted loss of life year, and exergy in demand response, and provide an environomic-based social demand response. We show that when the level of satisfaction and cooperation of end-user is low, the marginal level of load shaving and improvement in sustainability cannot be fulfilled.
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According to the Department of Energy, demand response provides an opportunity for end-users to play a significant role in the efficiency, reliability, resilience, and sustainability of a power grid. This is made possible owing to the existence of storage devices and diversity of energy sources at the customer level and the advent of the Internet of Things. Social influences and psychological traits of consumers affect their behavior and decision-making. Consequently, there is a necessity to bring the influences of humans, organizations, and societies on the power system together through computational social science into a cyber-physical-social system. Hence, in this brief, we introduce our development of an artificial society of the social demand response of a power system, a well-known approach in computational sociology based on a bottom-up approach, starting from theory. We assume that consumers can engage in demand response to fulfill two aims: save their cost or enhance the sustainability of a power system. The literature concerning sustainability-based demand response is limited to only considering CO2, NOX, and SO2. In addition to NOX, and SO2, we examine the impact of power systems on water pollution, disability-adjusted loss of life year, and exergy in demand response, and provide an environomic-based social demand response. We show that when the level of satisfaction and cooperation of end-user is low, the marginal level of load shaving and improvement in sustainability cannot be fulfilled.
Conference paper
(2021)
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Jaber Valinejad, Lamine Mili, C. Natalie van der Wal, Michael Von Spakovsky, Yijun Xu
Power systems serve social communities that consist of residential, commercial, and industrial customers. As a result, the disaster resilience of a power system should account for social community resilience. The social behavior and psychological features of all stakeholders involved in a disaster influence the level of power system preparedness, mitigation, recovery, adaptability, and resilience. Hence, there is a need to consider the social community's effect on the power system and the dependence between them in determining a power system's resilient to human-made and natural hazards. The social community, such as a county, city, or state, consists of various stakeholders, e.g., social consumers, social prosumers, and utilities. In this paper, we develop a multi-dimensional output-oriented method to measure resilience. The three key ideas for measuring power system resilience are the multi-dimensionality, output-oriented, and degraded functionality aspects of the power system. To this end, we develop an artificial society based on neuroscience, social science, and psychological theories to model the behavior of consumers and prosumers and the interdependence between power system resilience, comsumer and prosumer well-being, and community capital. Both mental health and physical health are used as metrics of well-being, while the level of cooperation is used to measure community capital resilience.
...
Power systems serve social communities that consist of residential, commercial, and industrial customers. As a result, the disaster resilience of a power system should account for social community resilience. The social behavior and psychological features of all stakeholders involved in a disaster influence the level of power system preparedness, mitigation, recovery, adaptability, and resilience. Hence, there is a need to consider the social community's effect on the power system and the dependence between them in determining a power system's resilient to human-made and natural hazards. The social community, such as a county, city, or state, consists of various stakeholders, e.g., social consumers, social prosumers, and utilities. In this paper, we develop a multi-dimensional output-oriented method to measure resilience. The three key ideas for measuring power system resilience are the multi-dimensionality, output-oriented, and degraded functionality aspects of the power system. To this end, we develop an artificial society based on neuroscience, social science, and psychological theories to model the behavior of consumers and prosumers and the interdependence between power system resilience, comsumer and prosumer well-being, and community capital. Both mental health and physical health are used as metrics of well-being, while the level of cooperation is used to measure community capital resilience.