Samuel G. Dotson
Candidate for Doctor of Philosophy
The Grainger College of Engineering
Department of Nuclear, Plasma, and Radiological Engineering
Date: Thursday, October 23, 2025
Time: 1:00PM
Location: Talbot Laboratory 101A
Zoom information:
https://illinois.zoom.us/j/86948984833?pwd=y9bQdOwayv7zJPdQgUrnTrKbu5hBQN.1
Meeting ID: 86948984833
Passcode: 140942
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. An economy-wide transition presents a challenge unto itself due to the spatial, temporal, and topological complexities of energy systems. Energy system optimization models (ESOMs) are a class of tools designed to optimize this transition used by energy planners and decision-makers to generate insights that inform policy. However, ESOMs have a few critical gaps. First, current ESOMs exclusively optimize on cost, yet real world decisions are also informed by non-financial priorities, such as sustainability and social benefits. Since these objectives cannot be neatly captured by a cost metric, ESOMs fail to optimize for these goals. Second, ESOMs struggle to meaningfully incorporate concepts of justice. Some studies model distributive justice — related to the way benefits and burdens are shared among society’s members — but do so in an ex post fashion. Procedural justice and recognition justice— aspects of justice related to the policymaking process and context in which decisions are made — are frequently sidelined. Indeed, ESOMs may be misused during a policymaking process to dismiss public input for lack of rigor. This thesis attends to these flaws in ESOM tools by developing the first multi-objective energy system optimization framework, Osier.
Rather than returning a single optimal solution, multi-objective optimization generates a set of co-optimal solutions called a Pareto front, where no objective can be improved without making another objective worse. The existence of a Pareto front evinces tradeoffs which can only be resolved through dialogue in a participatory process. Osier allows users to optimize arbitrarily many objectives and define new objectives to create a bespoke, contextualized, model. This thesis recognizes that some structural uncertainty will persist regardless of the number of objectives. Thus, Osier leverages genetic algorithms for their ability to sample complex Pareto fronts and because their search methods automatically sample sub-optimal space. Further, this thesis develops a novel algorithm to calculate a subset of maximally different solutions within the sub-optimal space to address this uncertainty related to unmodeled objectives. 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’s suitability for energy modeling problems with several in silico experiments. The first set of experiments demonstrate that Osier produces results that are internally consistent within its suite of available methods. The next set of experiments compare Osier to a more mature ESOM, Temoa, to verify that Osier produces results consistent with known methods. The results for a least-cost optimization with Osier and Temoa show strong agreement, with 0.5% of each other. In addition to benchmarking exercises, this thesis applies Osier to two timely examples. The first uses Osier to reanalyze a set of nuclear fuel cycle options through the lens of Pareto optimality. The second shows how Osier optimizes a novel objective, energy-return-on-investment, for a hypothetical data center. Finally, this thesis validates earlier claims of Osier’s usefulness for energy planning through a qualitative study of municipal and state-level energy planners in Illinois. The results of thirteen expert interviews demonstrate enthusiasm for a new tool that can optimize objectives beyond cost. However, this study surfaced structural barriers to ESOM usage at the municipal level which must be addressed before ESOMs, like Osier, can be adopted. Lastly, I recommend that the state of Illinois develop a participatory process for its energy modeling exercises.
Samuel Dotson is a Ph.D. candidate in the Nuclear, Plasma and Radiological Engineering (NPRE) department at the University of Illinois Urbana-Champaign, co-advised by Professors Kathryn Huff and Madicken Munk. His multidisciplinary research focuses on the intersection between energy system design and energy justice. Prior to joining NPRE, he earned his B.Sc. in Engineering Physics, also at the University of Illinois Urbana-Champaign