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\title{}



\author[1,2]{So-Bin Cho}%
\author[2]{Rami M. Saeed}%
\author[1]{Todd Allen}%
\author[1]{Xiaodong Sun}%
\affil[1]{University of Michigan}%
\affil[2]{Idaho National Laboratory}%


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  \date{May 23, 2024}


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\begin{abstract}
This paper investigates the potential of coupling Thermal Energy Storage
(TES) with Advanced Reactors (ARs) to address uncertainties posed by
climate change in deep decarbonized power systems. The TES Use-case Team
at Idaho National Laboratory (INL) has examined the potential of storing
thermal energy from ARs during low demand periods and optimizing
discharge during peak-priced hours, in both steady-state and transient
conditions.~ Building on this groundwork, this study bridges the gaps in
optimal sizing of the sub-system of TES-coupled AR systems using Risk
Analysis Virtual Environment (RAVEN) and Holistic Energy Resource
Optimization Network (HERON), INL's framework for grid optimization. By
applying this framework, we present statistically-robust optimal charge,
discharge including balance of plant (BOP), and storage sizing for the
High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this
end, we generated synthetic price samples for 30 years using 2018 --
2021 real-time market data from ERCOT, PJM and MISO. Our results reveals
that the TES-coupled HTGR system is highly effective in maximizing
revenue from electricity sales. We observed a substantial increase of 40
\% in ERCOT and a noteworthy 15 \% increase in PJM and MISO when
compared to the conventional BOP without TES. This improvement is
achieved through regionally-tailored sub-system sizing, which ranges
from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for
the TES. We find that the average electricity price directly impacts the
overall economics, while price volatility influences storage size.
Additional sensitivity analyses were performed to access the impact of
key assumptions on system economics and sizing, differentiating the
optimization window (i.e., 24 -- 219 hours of chronological
observations) and by imposing storage continuity condition in tracking
TES cycles. We observed that at the 142-hour of the optimization window,
a reasonable balance between computation time and accuracy was achieved.
Our analysis also highlights the significance of conducting multi-day
cycle analysis (\textgreater{} 120-hour) for TES to capture interaction
between electricity prices and storage dynamics, providing a
comprehensive understanding of TES behavior that AR developers should
integrate into their plant designs.{}%
\end{abstract}%



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