(692e) Conceptual Design and Analysis of a Power Generator with Integrated Thermal Energy Storage and CO2 Capture

Conceptual design of an integrated energy system (IES) involving a thermal power generator (TPG), a thermal energy storage (TES) system, and a CO₂ capture system (CCS) is studied for its operation under fluctuating electricity demand. Determining an efficient and economic design for such an IES (i.e., TPG + TES + CCS) is increasingly important for improving operational flexibility, decarbonizing the electricity grid, and reducing the curtailment of intermittent renewable power. However, the design and integration of TES and CCS systems with a TPG is non-trivial and requires the evaluation of several discrete design choices and their effect on the overall power plant operation [1, 2]. Thus, the focus of this study is to use equation-oriented rigorous process models and advanced design optimization methods to determine the optimal IES design using historical electricity price signals with a price taker assumption. The modeling and optimization are done using the Institute for the Design of Advanced Energy System’s (IDAES) open-source computational platform [3, 4].

In this work, a 2-step approach is proposed for the IES design. In step 1, a superstructure model is presented, which includes both discrete and continuous design decisions. The discrete decisions include selection of a storage medium for the TES system, steam/condensate sources for operating charge and discharge cycles of the TES system, and a steam source for operating the CCS reboiler. The continuous decisions include heat exchanger areas, fluid flowrates in the TES system, and steam flow rates to CCS. This superstructure model for conceptual design is implemented as a generalized disjunctive programming (GDP) problem with an objective of minimizing the total annualized cost and is solved using GDPopt [5] in IDAES. In step 2, the performance of the optimal IES design is evaluated by solving an operational scheduling problem using a nonlinear model, which is formulated by fixing the optimal design decisions from step 1 in the design superstructure. Additional operating constraints for power plant ramping, monitoring storage inventory, and updating current state of charge are added to the scheduling model. The optimal operating schedule under a price taker assumption is determined by solving the nonlinear problem using IPOPT in IDAES to maximize the operating profit. In our preliminary analysis, for a TES system with a storage capacity of 900 MWh, the optimal IES design is identified from 40 alternatives in step 1. The results from step 2 provide insights into storage utilization, including salt inventory used and maximum heat duties of storage heat exchangers over the scheduling horizon and their sensitivity to the electricity prices. These insights are used to evaluate the assumptions made in the design stage (i.e., step 1).

Acknowledgements

This research was conducted as part of the Institute for the Design of Advanced Energy Systems (IDAES) and Design and Optimization Infrastructure for Tightly Coupled Hybrid Systems (DISPATCHES) projects. It was supported by (1) the Simulation-Based Engineering, Crosscutting Research Program within the U.S. Department of Energy’s Office of Fossil Energy and Carbon Management and (2) the Grid Modernization Initiative of the U.S. Department of Energy as part of its Grid Modernization Laboratory Consortium, a strategic partnership between DOE and the national laboratories to bring together leading experts, technologies, and resources to collaborate on the goal of modernizing the nation’s grid.

Disclaimer

This project was funded by the U.S. Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, expressor 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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