Towards a holistic integration of energy justice and energy system engineering

Solving climate change will require our globalized society to transition from fossil fuel infrastructure to clean energy infrastructure. This transition must also be done equitably and justly to prevent entrenching further injustices to marginalized and vulnerable communities. Current energy planning tools optimize for a singular cost objective which challenges decision-makers’ ability to balance competing priorities such as sustainability, employment, or land use. This thesis develops the first multi-objective energy system optimization framework, Osier, to enhance the democratic engagement necessary for a just transition. Osier stores information about different energy technologies (e.g., wind, solar, nuclear), including their costs, emissions, and other data. Users provide energy demand data and define one or more goals for their energy system, such as minimizing cost, maximizing renewable energy, or minimizing land use. Osier then presents a set of co-optimal energy portfolios that prescribe how much of each technology should be built. Further, Osier recognizes that some objectives resist quantification. Rather than claiming to model the unmodelable, Osier samples near-optimal solution space using a novel algorithm developed herein. Together, these solution sets expose tradeoffs and allows communities, planners, and decision-makers to deliberate over priorities. Beyond planning, Osier serves as an accountability tool that allows communities or non-profit organizations to evaluate the alignment between implemented policies and stated values. By producing multiple solutions, Osier gives modelers and decision-makers the tools to meaningfully engage with public stakeholders and learn their preferences, thereby attending to issues of procedural and recognition justice. This thesis verified Osier through a set of benchmarking experiments, demonstrating comparable results for a least-cost optimization within 0.5% of the mature modeling framework, Temoa. In addition to benchmarking exercises, this thesis applies Osier to two timely examples related to nuclear fuel cycle options and powering new data centers. Finally, this thesis presents results from thirteen expert interviews with Illinois energy planners which support Osier’s utility for enhancing democratic engagement by enabling stakeholders to explore tradeoffs and articulate their values, though structural barriers to energy modeling adoption at the municipal level persist.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Nuclear, Plasma, Radiolgc Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Dotson, Samuel G.

Contributors dc:contributor

• Huff, Kathryn D
• Munk, Madicken
• Stubbins, James F
• Singer, Clifford
• Johnson, McKenzie F
• Djokić, Denia

• Multi-objective optimization
• many-objective optimization
• energy system modeling
• energy justice
• structural uncertainty
• genetic algorithms
• evolutionary algorithms

Statement dc:rights

• Copyright 2025 Samuel G. Dotson

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132555

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132555