An Activity-based Spatial-Temporal Analysis for Community Energy Vulnerability Assessment

Restricted (Penn State Only)
- Author:
- Xia, Chen
- Graduate Program:
- Architectural Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 06, 2024
- Committee Members:
- Julian Wang, Professor in Charge/Director of Graduate Studies
Guangqing Chi, Outside Field Member
Yuqing Hu, Chair & Dissertation Advisor
Wang-Chien Lee, Outside Unit & Field Member
Wangda Zuo, Major Field Member - Keywords:
- Energy Vulnerability
Activity Simulation
Markov Chain Model
Bayesian Spatial Mapping
Energy Vulnerability
Activity Simulation
Markov Chain Model
Bayesian Spatial Mapping - Abstract:
- The power system is among the most important critical infrastructures in communities and is becoming increasingly essential in supporting people’s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Energy vulnerability, therefore, forms a crucial basis for community resilience. The definition of community energy vulnerability falls into two categories: potential damage caused to the power system when exposed to hazards and the power outage impact on potential economic damage or human mortality and morbidity in the community. Generally, while there are widely studied and reviewed methodologies for power infrastructure vulnerability analysis, there is not one established body of literature that addresses community vulnerability to power outages in terms of its potential impact on occupancy when it inevitably happens. This research aims to develop a comprehensive community energy vulnerability framework to facilitate the enhancement of community energy resilience with the distribution of spatial-temporal energy vulnerability levels. To reach this goal, the research proposes a community energy vulnerability assessment framework that incorporates human and building characteristics, occupancy activities, and energy consumption patterns. The framework involves indicators measured by both direct assessments and tailored measurements for a precise analysis of each dimension's impact on energy usage and vulnerability. This involves using demographic and socioeconomic data, building characteristics information, and multiple modeling techniques, such as the Markov Chains model, agent-based model, and Bayesian-based mapping methods. The proposed framework is validated through case studies in New York and Philadelphia. Finally, the spatial-temporal distribution of energy vulnerability is visualized by a GIS-based platform. The proposed community energy vulnerability framework will enhance the understanding of the community’s overall vulnerability level against power outages, which improves community energy planning and community resilience analysis against disasters. For research methodology contribution, the proposed assessment framework demonstrates a way of integrating multi-dimensional big data to do the community-scale assessment with the application of various modeling techniques, including Markov chains, agent-based modeling, and Bayesian-based mapping. Key findings of case studies underscore the multidimensional nature of community energy vulnerability and illustrate the dynamic nature of energy vulnerability that fluctuates with daily activities. It also identifies areas with low thermal resilience, suggesting a need for retrofitting older buildings to improve energy efficiency. The results serve as a foundation for stakeholders to implement targeted interventions, prioritize retrofitting efforts, and support equitable access to energy resources, thereby enhancing urban sustainability and resilience. Despite its comprehensive scope, the research acknowledges limitations in terms of its focus on typical patterns of indoor activities. Future studies could expand this scope to include dynamic outdoor human activities with behavior changes before, during, and after disasters. Another area for improvement lies in the validation of simulation results. Currently, validation is conducted stepwise—for instance, checking occupancy mapping results and mobility patterns—due to the unavailability of comprehensive energy consumption data. Access to more detailed actual data in the future would enable a more thorough validation process. Moreover, the research paves the way for exploring interventions at the building, microgrid, and urban levels aimed at improving energy resilience and sustainability. These future directions hold the promise of not only advancing theoretical understanding but also informing practical strategies to mitigate energy vulnerabilities and support community well-being.