Research

1. How are evolving local and regional climate patterns intensifying challenges within energy infrastructure and demand, especially in vulnerable communities?
2. What are the most effective methods for integrating sociodemographic factors into energy planning considering heightened climate change risks?
3. How can the integration of AI analytics with digital twin technology enhance our capacity to forecast, plan, and operate energy systems amid dynamic climate scenarios?

4. What new community-centric models and methodologies can be developed to proactively address energy equity and resilience in the context of the changing climate?
5. How can vulnerability assessments focused on climate change impacts inform the development of energy policies that are both community-focused and climate-resilient?
6. What are the essential indicators for assessing the effectiveness of AI-driven digital twin technologies in improving resilience and addressing energy inequity in the face of climate change?

Explore each of the research questions in more detail below.

Research Overview

Task 1.1

Construct cross-scale data compilation from long-term regional and national weather observations and high-resolution historical and future climate simulations.

The researchers assigned to this task are Dr. Hu, Dr. Junod, Dr. Dubois, and Dr. Spangler.

This task will create an open-source geodatabase to integrate long-term regional and national weather observations with high-resolution historical and future climate simulations. Observations will be collected with collaboration from state climatologists and use citizen science programs to include rural and urban communities.

Task 1.2

Identify historical and future shifting of climate at multiple scales in shaping the energy sector

Researchers assigned are Dr. Hu, Dr. Dubois, Dr. Christy, Dr. Ashokkumar, and Dr. Shi.

This task focuses on identifying how historical and future climate changes at various scales affect energy systems by synthesizing long-term climate observations and simulations from 1980 to 2060. Results will be integrated into task 1.1 to provide more comprehensive testing of climate hypotheses.

Task 1.3

Assess the roles of land-atmosphere interactions in diversifying climate change-induced risks in energy systems

Researchers assigned are Dr. Ashokkumar, Dr. Dubois, Dr. Hu, Dr. Zhang, and Dr. Spangler.

This task examines the impact of land-atmosphere interactions on climate change-induced risks to energy systems. The task focuses on how extreme weather events, intensified by climate change, and influenced by human activities like urbanization and agriculture

Task 1.4

Predict climate-driven change in renewable energy production.

Researchers assigned are Dr. Hu, Dr. Dubois, Dr. Junod, Dr. Zhang, Dr. Wang, and Dr. Cui.

This task will develop methods to predict the impact of climate change on renewable energy production. Utilizing the comprehensive climate database developed in Tasks 1.1-1.2 to analyze historical weather patterns and project future VRE outputs considering changing climate trends.

Task 2.1

Conduct a socioeconomic status (SES) analysis to inform the development of equitable energy systems, and guide community outreach and workforce development needs in New Mexico’s diverse communities.

Researchers assigned to this task include Dr. Haggerty, Dr. Sullivan, Dr. Han, Dr. Zhao, and Dr. Shanahan.

This task involves a deep dive into the social and economic dimensions of the three targeted communities. Then utilize this analysis to prepare the research team and collaborators for effective community engagements and outreach, and help craft tailored energy solutions that inform strategies for addressing the unique challenges and needs of each community

Task 2.2

Utilize proven strategies for conducting community conversations to effectively engage the broader communities in identifying and co-developing equitable, relatable, and relevant strategies for energy systems management and to inform opportunities for future energy infrastructure investment and guide prioritization of vulnerable communities.

Researchers assigned are Dr. Sullivan, Dr. Surova, Dr. Han, Dr. Shanahan, Dr. Haggerty, Dr. Kirksey, and Dr. Shi.

This task focuses on fostering co-development and effective collaboration in energy planning through engaged community conversations. The task employs a stakeholder analysis approach to thoroughly understand the roles and influences of various stakeholders.

Task 2.3

Community-centric energy system design and implementation.

Researchers for this task are Dr. Han, Dr. Haggerty, Dr. Zhao, Dr. Wang, and Dr. Miller.

This task uses the insights from Tasks 1 and 2 to produce tangible energy solutions for communities like Kit Carson, Mora-San Miguel, and the Navajo Nation. By developing and deploying customized pilot energy systems that are attuned to the unique needs and expressed preferences of each community we will integrate successful elements from these community-specific energy systems into broader energy policies for the local communities and the states of the region.

Task 2.4

Developing context-sensitive energy justice measures for the digital twin.

Researchers include Dr. Han, Dr. Zhao, Dr. Haggerty, Dr. Zhang, and Dr. Surova.

By comprehensively studying the socioeconomic context of communities and recognizing the limitations of existing measures, which predominantly focus on the percentage of minority and low-income groups we are developing an integrated set of energy justice measures that will extend existing ones. By extending the existing ones, proposed by EPA and DOE, it will account for a broader spectrum of socioeconomic attributes.

Task 3.1

Develop community-centric and location-based metrics.

Research driven by Dr. Shi, Dr. Dadgostari, Dr. Spangler, Dr. Hu, and Dr. Sullivan.

By focusing on creating key metrics for climate-aware resilience in energy systems, this task addresses service reliability, environmental resilience, energy burden and equity, and adaptability

Task 3.2

Model interactions and impact dynamics.

Researchers include Dr. Shi, Dr. Zhang, Dr. Cui, Dr. Dadgostari, and Dr. Spangler.

This task aims to develop impact assessment models for energy systems, utilizing baselines and metrics from Task 3.1 to analyze “what-if” scenarios. Then using an algorithm that is designed to estimate resilience levels across multiple scales, from individual households to entire communities.

Task 3.3

Develop a framework for climate-adaptive multi-timescale infrastructure optimization for renewable energy systems.

Researchers for this task are Dr. Wang, Dr. Dadgostari, Dr. Shi, Dr. Spangler, and Dr. Zhang.

This task develops a hierarchical optimization model to manage uncertainties from climate change and weather extremes by focusing on renewable energy infrastructure planning,

Task 3.4

Model community-centric load restoration in the aftermath of power outages.

Researchers are Dr. Zhang, Dr. Spangler, Dr. Hu, Dr. Haggerty, and Dr. Wang.

This task aims to develop models for load restoration in communities following power outages, filling critical gaps in current smart grid technologies. It addresses two main challenges: the under-researched area of resilience infrastructure at the community level, and the tendency of existing restoration models to overlook the needs of low-income communities

Task 4.1

Community-focused energy demand forecasting

Researchers are Dr. Zhang, Dr. Wang, Dr. Shi, Dr. Spangler, and Dr. Cui.

This task is dedicated to developing community-level load forecasting models. Utilizing historical energy usage data from utility billing systems and additional insights from the American Time Use Survey, we will create predictive models for community-level energy demand employing recurrent neural networks (RNNs).

Task 4.2

AI-powered spatial-temporal renewable forecasting.

Researchers are Dr. Shi, Dr. Junod, Dr. Zhang, Dr. Dadgostari, and Dr. Hu.

This task focuses on advancing the adaptivity and accuracy of renewable energy forecasting within the digital twin, a critical aspect for sustainable energy planning and operation. The task addresses the integration of long-term climate changes and weather extremes into the behavior modeling of energy prosumers.

Task 4.3

High-fidelity future scenario generation.

Researchers are Dr. Zhang, Dr. Cui, Dr. Shanahan, Dr. Shi, Dr. Spangler.

This task focuses on creating high-fidelity power system operating scenarios, particularly under extreme weather conditions, to enhance the digital twin’s predictive capabilities.

Task 4.4

Development of an integrated climate-energy-community digital twin platform.

Researchers are Dr. Cui, Dr. Zhang, Dr. Shi, Dr. Wang, Dr. Hu, Dr. Han, Dr. Sullivan, and Dr. Christy.

This task aims to create an AI-driven digital twin framework that transcends mere power systems replication to encompass the entire climate-energy-community nexus. By integrating physical power system models with state-of-the-art climate solution simulators like En-ROADS and the C-ROADS climate policy model, the framework will be capable of conducting comprehensive simulations.

Task 5.1

Develop and refine digital twins for the three communities.

Research driven by Dr. Zhang, Dr. Sullivan, Dr. Surova, Dr. Zhao, and Dr. Wang.

This task focuses on the development and refinement of digital twin models for Kit Carson, Mora-San Miguel, and the Navajo Nation. This involves integrating diverse datasets, including demographics, climate patterns, and energy system specifics, to accurately represent each community’s unique energy infrastructure.

Task 5.2

Evaluate energy infrastructure upgrade options.

Research done by Dr. Wang, Dr. Spangler, Dr. Han, Dr. Haggerty, and Dr. Sullivan.

This task involves simulating and analyzing the impacts of specific energy infrastructure upgrade strategies on climate resilience and energy burden within each community.

Task 5.3

Analyze policy impact and energy scenarios with digital twins.

Research done by Dr. Junod, Dr. Han, Dr. Kirksey, Dr. Sullivan, and Dr. Wang.

The task aims to merge forward-looking policymaking (FLPM) with the capabilities of digital twin technology for scenario analysis and policy impact evaluation. This task will use comprehensive simulations via digital twins to project various energy policy and infrastructure scenarios, analyzing their impacts on community resilience, energy equity, and sustainability.

Task 5.4

Report, recommend, and disseminate knowledge.

Task driven by Dr. Zhao, Dr. Han, Dr. Junod, Dr. Sullivan, Dr. Ashokkumar, and Dr. Hu.

This task centers on compiling and synthesizing data and insights from digital twin simulations and pilot studies into comprehensive reports. A critical aspect of this task is the broad dissemination of these findings. The aim is to engage policymakers, community leaders, and other stakeholders, fostering informed discussions on climate resilience, energy equity, and sustainability.

Task 6.1

Integrate LLMs for enhanced feedback processing in digital twin system.

Research done by Dr. Shi, Dr. Cui, Dr. Zhang, Dr. Sullivan, Dr. Haggerty, and Dr. Dadgostari.

This task is dedicated to incorporating advanced LLMs into the digital twin system, specifically to process feedback from community leaders, stakeholders, and users. This interface will allow diverse stakeholders to effectively interact with the digital twin, ensuring their insights are promptly reflected in real-time updates and simulations.

Task 6.2

Enhanced data interpretation and visualization through LLM integration.

Researchers are Dr. Shi, Dr. Sullivan, Dr. Wang, Dr. Hu, Dr. Dadgostari, Dr. Zhang, Dr. Cui, Dr. Dubois, and Dr. Haggerty.

This task aims to integrate LLMs into the digital twin system to enhance data interpretation and visualization, making complex outputs understandable to a wide range of users. By translating technical data into clear, actionable insights, LLMs simplify the digital twin’s outputs and highlight key trends in an accessible manner.