A costing framework for fusion power plants

TL;DR

This paper documents a long-running ARPA-E effort (2017–2024) to build a transparent, auditable costing framework for fusion power plants that can support portfolio-level comparisons and decision-making. It traces the evolution from ARIES-based scaling to bottom-up, subsystem-cost models aligned with IAEA/GIF/EPRI cost accounts, culminating in the open-source pyFECONs framework. The methodology emphasizes a physics-to-economics workflow, explicit treatment of indirect costs, and design-for-cost strategies, enabling like-for-like comparisons across fusion concepts and against other low-carbon options. The work culminates in a mature, standards-aligned toolchain with broad applicability, including future extensions for safety integration, Sankey visualization, and materials-cost realism, to support credible, instrumented optimization of fusion plant designs.

Abstract

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.

Figures (2)

• Figure 1: Cost categories used in this work. The capital-cost taxonomy separates direct costs into Category 10 (pre-construction) and Category 20 (construction), and groups indirect and capitalized ancillary costs into Categories 30--60.
• Figure 2: Sequential physics-to-economics workflow implemented in the pyFECONs example: power balance and net output establish the normalization basis; radial build and containment sizing determine heat-island geometry; NETL 2019-derived rules size the turbine island and BOP; plant buildings and layout are generated around major elements; direct costs are assembled into OCC; overhead and capitalized indirect accounts are rolled up bottom-up; annualized costs and LCOE are then computed and written to LaTeX tables.