(623g) Technoeconomic Assessment of Coupling an Existing Nuclear Power Plant with a Low Temperature Electrolysis Unit

Diversification of an electric grid with intermittent renewables often tends to increase the volatility in the price of electricity. This motivates the need to analyze energy and power systems in the context of a larger system, such as an electric grid. DISPATCHES [1], a collection of various unit models and tools for integrated, multiscale process and market analysis, aids in carrying out such an analysis. Recent work by Frick et al. [2] demonstrated the techno-economic feasibility of producing hydrogen with an existing nuclear power plant in the PJM market using high temperature steam electrolysis (HTSE). This work considers a similar analysis with three primary differences: (1) Low temperature electrolysis (LTE) is considered, instead of HTSE since it has a higher near-term technology readiness level; (2) DISPATCHES uses an equation-oriented approach to solve the problem as opposed to the multi-level hybrid simulation black-box approach used in [2], which may enable more rigorous optimization of larger systems; and (3) this system considers a hydrogen peaking turbine to convert stored hydrogen into electricity during high pricing hours.

The analysis considers a generic nuclear power plant coupled with an LTE process, a hydrogen storage unit, and a hydrogen peaking unit. The nuclear plant is assumed to operate at its baseload power due to ramping limitations. Depending on the price of electricity, the produced power can either be sold to the grid and/or it can be used to produce hydrogen via LTE. The produced hydrogen can then either be sold to the market or stored in a tank to produce electricity during periods of high demand. Using DISPATCHES [1], we formulate a stochastic multiperiod optimization problem to determine the optimal size of the LTE unit, storage tank, combustion turbine, and the optimal operation of the entire system maximizing the net present value. The distinct stochastic realizations of market signals needed for the formulation of the price-taker problem, are generated using the RAVEN [3] plugin within FORCE [4] based on historical hourly price data. We solve the resulting optimization problem for price signals corresponding to different regions and compare the results.

Disclaimer:

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.

References:

1. DISPATCHES: Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems. https://github.com/gmlc-dispatches/dispatches
2. Frick, K., Wendt, D., Talbot, P., Rabiti, C. and Boardman, R., 2022. Technoeconomic assessment of hydrogen cogeneration via high temperature steam electrolysis with a light-water reactor. Applied Energy, 306, p.118044.
3. RAVEN: Risk Analysis Virtual ENvironment. https://github.com/idaholab/raven
4. FORCE: Framework for Optimization of ResourCes and Economics ecosystem (FORCE). https://github.com/idaholab/FORCE