Session: 08-05-03: Energy-Related Multidisciplinary III

Paper Number: 145897

145897 - An Integrative Framework to Support Energy Transition in Disadvantaged Communities 

The paper presents an integrative framework to support energy transition in middle-class neighborhoods by addressing barriers such as financial constraints, limited access to information, and insufficient infrastructure. The framework, referred to as the cyber-physical social (CPS) infrastructure, integrates community insights with data analysis, statistical modeling, decision support systems, and artificial intelligence (AI). This approach aims to describe, organize, and apply computational tools to data, exploring the causes of barriers and the impact of mitigation programs. The adaptability and scalability of the CPS framework allow its application across diverse middle-class neighborhoods.

The motivation for this research is to improve middle-class participation in energy transition, which advances societal goals of sustainability and inclusion. The project engages diverse neighborhoods, enhancing broad applicability and relevance. Partnerships with government agencies, utilities, and communities will bolster research and education infrastructure, fostering interdisciplinary knowledge and community empowerment. Dissemination of findings will advance scientific and technological understanding of energy transition barriers and mitigation strategies, contributing to the development of innovative solutions with societal benefits.

The research examines the effective integration of qualitative and quantitative data within the CPS framework. Mathematical approaches are explored for integrating community insights with data. Foundational elements ensure that this integration contributes to sustainable and adaptable solutions. Methodologies for analyzing qualitative data, such as community insights, are crucial. Strategies for integrating complex data support decision-making in diverse neighborhoods. The research addresses uncertainties inherent in integrating diverse data types, with specific methods to tackle uncertainties from qualitative data integration. Scalability strategies ensure the CPS framework accommodates varying neighborhood scales and characteristics, with technological and infrastructural adaptations enabling this scalability.

The methodology includes the development of a mathematical program based on two assumptions: finite federal investment in energy transition programs and surmountable goals for these programs. The program provides local or semi-local governments with initial budgetary allocations. As government initiatives take effect, coefficients in the program can be adjusted and the program recomputed on a yearly or quarterly basis. The mathematical program can provide optimal solutions, allowing budget administrators to implement or adjust the proposed schemes based on new information.

Preliminary results from the application of the CPS framework in two different communities demonstrate the effectiveness of the integrative approach. In the first community, resources are diverted to the most effective programs based on their Community Importance (CI) values, resulting in improved resource allocation and community satisfaction. In the second community, the same principles are applied, showing flexibility in budget allocation while maintaining program effectiveness.

The results highlight the potential of the CPS framework to provide initial allocation strategies for government programs supporting energy transition in communities. By combining qualitative infrastructure status with quantitative models and minimum program needs, the framework aligns resource allocation with community improvement goals. Repeated application of linear programs allows for iterative adjustments, ensuring that programs achieve their objectives. The results emphasize the importance of human interaction in decision-making, reminding users of the flexibility and adaptability required in real-world applications.

Presenting Author: Andrew Pangia University of North Carolina at Charlotte

Presenting Author Biography: Andrew Pangia is a Postdoctoral Fellow in Machine Learning for Trustworthy AI at the University of North Carolina at Charlotte. He holds a Ph.D. in Mathematics from Clemson University, where his research focused on the use of optimization and theoretical machine learning to solve complex mathematical problems. Andrew has also taught courses in statistics and calculus. He has held leadership roles in professional societies that include the Society of Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), and the Multi-Criterion Decision Making (MCDM) Society.

Authors:

Andrew Pangia University of North Carolina at Charlotte
Maryam Yaghoubirad Iran University of Science and Technology
Hailie Suk University at Buffalo
Taufiquar Khan University of North Carolina at Charlotte
John Hall University of North Carolina at Charlotte