(36e) Co-Optimizing the Design and Operation Strategy of Solid Oxide Fuel Cell-Based Hydrogen-Electricity Co-Production Systems

Due to an ever growing population and pushes towards decarbonizing the U.S. economy, such as more widespread usage of electric cars, electricity use is expected to rise [1]. The U.S. Energy Information Administration’s 2022 Annual Energy Outlook reports that renewable energy is the fastest growing generation type [2]. The increased penetration of variable renewables, such as wind and solar, into energy infrastructures and markets will result in more volatile market prices, requiring more flexible energy generation technologies. Increased flexibility allows energy systems to benefit from faster market time scales while keeping supply and demand in balance [3]. Integrated energy systems (IES) can offer this flexibility by exploiting synergies between multiple technologies,e.g., natural gas combined cycles, CO2 capture, solid oxide fuel cells, energy storage, and renewables.

Solid oxide fuel cells (SOFC) present unique advantages for integration with other technologies. They show excellent promise as an energy conversion technology utilizing natural gas, as they have a higher efficiency and are more environmentally friendly than competitive generation types [4-5]. Their high operating temperature, while posing operating challenges, allows waste heat from electricity generation to be collected and used in other integrated processes such as cogeneration, biofuels processing, and gas turbines [6-8]. While studies on SOFC-based IES are being conducted, very limited work is currently being done on techno-economic analysis (TEA) of such systems in the context of the modern wholesale market. This detailed market analysis is a challenging task, calling for accurate technical models of system operations (e.g., ramping rates) as well as capturing the complex economic interactions that these markets involve.

In this work, we present a framework for conducting optimized-based market-informed TEA of IES. The framework allows co-optimization of design and operation of these IES by varying system size as well as optimal operating conditions under different locational marginal price (LMP) signals. The problem is formulated as a generalized disjunctive programming (GDP) model and implemented in Pyomo. Detailed equation-oriented process models are developed in the IDAES modeling platform [9-11]. We then use ALAMO to generate algebraic surrogates for operating costs, capital costs, and co-production constraints. Using these surrogates embedded in the GDP optimization model, we can directly compare the economic performance of different IES concepts. Here, we compare several IES concepts involving SOFC’s that co-produce hydrogen and electricity. Integrated technologies include natural gas combined cycles, SOFC, solid oxide electrolyzer cells (SOECs), and general electricity storage. By directly comparing different IES concepts, we can demonstrate that the additional flexibility these systems provide makes them great candidates to enter the increasingly volatile electricity market. We find that SOFC systems offer significant cost and technical advantages over alternatives, which is consistent with the finding of prior more traditional, e.g., levelized cost of electricity, TEA [12]. Moreover, we also identify the market conditions, i.e., electricity and H₂ prices, in which co-production offers significant benefits.

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.

References

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[11] “Institute for the Design of Advanced Energy Systems (IDAES) — IDAES v1.13.0.” https://idaes-pse.readthedocs.io/en/stable/.

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