Community-Centric Multi-Criteria Assessment Framework for Energy Transition
By: Jayashree Yadav , Ingemar Mathiasson , Bindu Panikkar and more
Potential Business Impact:
Helps towns switch to clean energy affordably.
The transition to low-carbon energy systems demands comprehensive technical, economic, environmental, and social evaluation tools. While numerous studies address specific aspects of energy transition, few provide an integrated framework to capture the full spectrum of impacts. This work developed a community-collaborative assessment framework that integrates intelligent energy devices with optimization-based coordination of energy assets. The proposed framework uses techno-economic, environmental, and social criteria to evaluate transition pathways. A detailed case study is performed for a remote community in Alaska to assess its applicability, where the feasibility of renewable energy transitions remains underexplored. Three distinct pathways, including heat pump and battery integration, resource coordination, and expanded community solar PV, are analyzed using a year-long dataset of demand, renewable energy, and transformer data. The analysis revealed that using heat pumps lowers the overall energy costs by 30% and carbon emissions by 28%. In addition, the share of the population spending more than 10% of their income on energy falls from 74% in the existing scenario to 40% with heat pump adoption, indicating significant affordability improvements. By combining a general, community-centric assessment framework with a data-driven case study, this work offers a practical tool for utilities, community stakeholders, and policymakers to work toward equitable and sustainable energy transitions.
Similar Papers
On the Complementarity of Shared Electric Mobility and Renewable Energy Communities
Systems and Control
Electric cars help power homes and save money.
Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy
Systems and Control
Improves rural energy systems for better, cleaner power.
Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions
Machine Learning (CS)
Helps plan energy changes without hurting people or nature.