A costing framework for fusion power plants

This paper summarizes and consolidates fusion power-plant costing work performed in support of ARPA-E from 2017 through 2024, and documents the evolution of the associated analysis framework from early capital-cost-focused studies to a standards-aligned, auditable costing capability. Early efforts applied ARIES-style cost-scaling relations to generate Nth-of-a-kind (NOAK) estimates and were calibrated through a pilot study with Bechtel and Decysive Systems to benchmark balance-of-plant (BOP) costs and validate plant-level reasonableness from an engineering, procurement, and construction (EPC) perspective. Subsequent work, informed by Lucid Catalyst studies of nuclear cost drivers, expanded the methodology to treat indirect costs explicitly and to evaluate cost-reduction pathways for non-fusion-island systems through design-for-cost practices, modularization, centralized manufacturing, and learning. As ARPA-E’s fusion portfolio expanded, these methods were applied across BETHE and GAMOW concepts (and select ALPHA revisits), including enhanced treatment of tritium handling and plant integration supported by Princeton/PPPL expertise. In 2023 the capability was refactored to align with the IAEA-GEN-IV EMWG-EPRI code-of-accounts lineage, while key ARIES-derived scaling relations were replaced by bottom-up subsystem models for dominant fusion cost drivers (e.g., magnets, lasers, power supplies, and power-core components) coupled to physics-informed power balances and engineering-constrained radial builds. These developments were implemented in the spreadsheet-based Fusion Economics code (FECONs) and released as an open-source Python framework (pyFECONs), providing a transparent mapping from subsystem estimates to standardized accounts and a consistent computation of LCOE.

💡 Research Summary

The paper presents a comprehensive accounting framework for estimating the cost of fusion power plants, documenting the evolution of the methodology from early ARIES‑based scaling relations to a modern, standards‑aligned, auditable, and extensible toolset. The work was carried out under the U.S. Department of Energy’s ARPA‑E program between 2017 and 2024, supporting a growing portfolio of private‑sector fusion concepts (BETHE, GAMOW, and selected ALPHA revisits).

In the initial 2017 pilot, the authors used the ARIES cost‑scaling equations to generate Nth‑of‑a‑Kind (NOAK) capital‑cost estimates. These were calibrated against an engineering‑procurement‑construction (EPC) benchmark provided by Bechtel and Decysive Systems, confirming that the balance‑of‑plant (BOP) cost breakdowns were directionally consistent with real‑world construction data. Sensitivity analyses highlighted the importance of plant layout, building size, and major equipment dimensions.

The 2019 phase incorporated insights from Lucid Catalyst’s nuclear‑cost‑driver studies, explicitly separating indirect costs (engineering, construction management, owner’s costs, contingencies, schedule‑related financing) from direct equipment costs. The authors evaluated design‑for‑cost strategies, standardized plant layouts, modularization, and centralized manufacturing, quantifying learning‑curve benefits. A key finding was that non‑core BOP and indirect costs could dominate total capital expenditure, often exceeding 50 % of CAPEX, thereby shifting the focus from pure physics performance to system‑level cost optimization.

From 2022 to 2023 the framework was applied across ARPA‑E’s BETHE and GAMOW awardees, as well as revisited ALPHA concepts. The analysis distinguished cost drivers by architecture: magnetic‑fusion (MFE) concepts were dominated by superconducting magnet systems and power supplies; inertial‑fusion (IFE) concepts were driven by high‑energy lasers, repetition‑rate targets, and associated optics; hybrid MIFE concepts showed mixed dominance. Collaboration with Princeton Plasma Physics Laboratory (PPPL) enriched the treatment of tritium handling, fuel‑cycle logistics, and safety, embedding decades of operational experience into the cost model.

In 2023 the authors refactored the entire cost‑account structure to align with the International Atomic Energy Agency (IAEA) 2001 guidelines, the Generation‑IV Economics Modeling Working Group (GEN‑IV EMWG) 2007 taxonomy, and the Electric Power Research Institute (EPRI) 2024 code‑of‑accounts. Legacy ARIES scaling equations were replaced with bottom‑up subsystem models for the dominant fusion‑specific cost drivers: (i) magnets (including structural supports and cryogenic systems), (ii) lasers (energy, pulse‑rate, efficiency, facility footprint), (iii) power supplies (peak demand profiles, converter sizing), and (iv) power‑core components (blanket, vacuum vessel, thermal‑shield volumes) derived from physics‑informed radial‑build calculations. These models are tightly coupled to power‑balance equations, ensuring that engineering constraints (heat flux, structural limits) directly influence cost outputs.

Implementation was first realized in a spreadsheet‑based tool called Fusion Economics (FECONs). To improve transparency, reproducibility, and extensibility, the authors released an open‑source Python package, pyFECONs, which maps subsystem inputs to the standardized IAEA‑GEN‑IV‑EPRI accounts and produces a consistent Levelized Cost of Electricity (LCOE). All code, data, and documentation are publicly available, enabling peer verification and facilitating integration with other energy‑system models.

The paper’s contributions are fourfold: (1) it documents the methodological shift from high‑level ARIES scaling to detailed bottom‑up costing, providing higher resolution and traceability; (2) it demonstrates that indirect and BOP costs are often the primary cost levers, justifying modular, standardized, and learning‑based cost‑reduction pathways; (3) it aligns fusion cost accounting with internationally recognized standards, allowing like‑for‑like comparisons across fusion concepts and against competing low‑carbon generation technologies; and (4) it delivers an open‑source, auditable software platform that can be adopted by researchers, industry, and policymakers to assess the economic viability of future fusion power plants.

Overall, the work establishes a robust, transparent, and extensible costing framework that is essential for credible economic assessments, technology‑roadmapping, and investment decisions as fusion moves from experimental demonstrations toward commercial deployment.