Multi-objective decision-making for nuclear-integrated energy hub planning and operation for industrial heat and power supply

Meeting the need for large-scale geographically concentrated industrial heat demand poses a unique challenge. Unlike the power sector, which can transmit electricity over long distances via established grids, the heat distribution faces significant infrastructure costs and heat losses at high temperatures. Enhancing energy security in adjacent industrial parks provides a dual benefit: reduced exposure to volatile fossil fuel prices and improved economic viability, largely driven by economies of scale in energy supply and distribution. This study presents a comprehensive decision maker-ready framework for high-temperature gas-cooled reactors (HTGR) coupled with thermal energy storage (TES) to supply combined heat and power. Synthetic load profiles for chemical, refinery, and steel plants are generated with autoregressive moving-average models, and a multi-objective optimization traces Pareto-optimal trade-offs between electricity sales and on-site self-sufficiency. The findings establish actionable sizing rules that bridge engineering modeling and practical investment decisions: Pareto-efficient reactor capacity remains below 60 % of peak site demand in balancing net profits and energy self-sufficiency, and TES sized at 1–2.5× the reactor capacity effectively reduces natural gas consumption. The analysis highlights that the entire heat demand with nuclear energy alone is costly, particularly for industries with seasonally fluctuating heat demand. Achieving 99 % self-sufficiency in electricity supply is feasible, representing virtually no grid imports during modeling. However, this increases reliance on natural gas backup boilers. This study informs the early-stage planning of nuclear-integrated energy hubs for industrial sites and the findings remain applicable to industries beyond those examined in this study, as long as these idiosyncratic differences are considered.