arXiv subjects

Kevin Daley

Publications and source records attributed to Kevin Daley.

3 recordsLinked to original sources

arXiv2026

A Framework for Evaluating the Siting of Fusion Power: Case Study on the Retired Coal Sites in the United States

As fusion advances toward commercialization, systematic siting approaches are needed to identify locations that meet technical, economic, and infrastructural requirements, while also ensuring public acceptance and avoiding the socio-political challenges that have historically hindered fission deployment. Therefore, this study introduces a comprehensive, first-of-its-kind fusion siting framework and applies it to 85 retired (2020-2025) U.S. coal power sites as a case study. The framework evaluates 21 sub-criteria under four key attributes: State Policies, Federal Policies, Risk and Hazard Metrics, and Connectivity and Spatial Factors. Sub-attributes weights are derived using the Fuzzy Full Consistency Method with input from five fusion experts, and site rankings are determined using the Measurement Alternatives and Ranking According to COmpromise Solution method. Results indicate that federal incentives, transportation, substation, and energy prices are the most important factors for fusion siting. Sensitivity analysis reveals that landslide hazards have the greatest effect on rank stability, while fault lines is the least influential. A separate comparative assessment of the fusion deployment sites proposed by Type One Energy, Zap Energy, and Commonwealth Fusion Systems is also conducted using results from our proposed framework. This framework provides a transparent, stakeholder-inclusive decision-making tool that clarifies how sites are evaluated using weighted criteria and distinguishes inflexible policy-responsive factors, thereby enabling targeted regional and federal strategies.

physics.soc-ph↗

arXiv2025

A Multi-Criteria Evaluation Framework for Siting Fusion Energy Facilities: Application and Evaluation of U.S. Coal Power Plants

This paper proposes a comprehensive methodology for siting fusion energy facilities, integrating expert judgment, geospatial data, and multi-criteria decision making tools to evaluate site suitability systematically. As a case study, we apply this framework to all currently operational coal power plant sites in the United States to examine their potential for hosting future fusion facilities at a time when these coal plants are shut down on reaching their end of life - timelines which are expected to coincide with the potential deployment of fusion energy facilities. Drawing on 22 siting criteria - including state and federal policies, risk and hazard assessments, and spatial and infrastructural parameters - we implement two MultiCriteria Decision-Making (MCDM) methods: the Fuzzy Full Consistency Method (F-FUCOM) to derive attribute weights and the Weighted Sum Method (WSM) to rank sites based on composite suitability scores. By focusing on fusion-specific siting needs and demonstrating the framework through a coal site application, this study contributes a scalable and transparent decision-support tool for identifying optimal fusion energy deployment locations.

eess.SY↗

arXiv2024

Multi-objective Combinatorial Methodology for Nuclear Reactor Site Assessment: A Case Study for the United States

As clean energy demand grows to meet sustainability and net-zero goals, nuclear energy emerges as a reliable option. However, high capital costs remain a challenge for nuclear power plants (NPP), where repurposing coal power plant sites (CPP) with existing infrastructure is one way to reduce these costs. Additionally, Brownfield sites-previously developed or underutilized lands often impacted by industrial activity-present another compelling alternative. This study introduces a novel multi-objective optimization methodology, leveraging combinatorial search to evaluate over 30,000 potential NPP sites in the United States. Our approach addresses gaps in the current practice of assigning pre-determined weights to each site attribute that could lead to bias in the ranking. Each site is assigned a performance-based score, derived from a detailed combinatorial analysis of its site attributes. The methodology generates a comprehensive database comprising site locations (inputs), attributes (outputs), site score (outputs), and the contribution of each attribute to the site score. We then use this database to train a neural network model, enabling rapid predictions of nuclear siting suitability across any location in the United States. Our findings highlight that CPP sites are highly competitive for nuclear development, but some Brownfield sites are able to compete with them. Notably, four CPP sites in Ohio, North Carolina, and New Hampshire, and two Brownfield sites in Florida and California rank among the most promising locations. These results underscore the potential of integrating machine learning and optimization techniques to transform nuclear siting, paving the way for a cost-effective and sustainable energy future.

cs.CE↗