(29h) Conceptual Design Via Superstructure Optimization in Advanced Energy Systems Using Idaes | AIChE

(29h) Conceptual Design Via Superstructure Optimization in Advanced Energy Systems Using Idaes


Rawlings, E. - Presenter, Sandia National Laboratories
Susarla, N., National Energy Technology Laboratory
Ghouse, J., McMaster University
Zamarripa, M. A., National Energy Technology Laboratory
Laird, C., NA
Siirola, J., Sandia National Laboratories
Bynum, M., Sandia National Laboratories
Miller, D., National Energy Technology Laboratory
Given current electricity markets, renewable energy penetration, and limited availability of resources, the energy generation systems are required to operate more flexibly and efficiently. To address these issues, the use of advanced mathematical programming methods and algorithms with more rigorous models are required to identify new opportunities for the integrated design of these energy systems, without affecting their performance.

The conceptual design of advanced energy systems is a challenging problem that involves the solution of multiple alternatives to find an optimal configuration. Currently, most computational tools focus on the simulation and optimization of different processes, but offer limited capabilities for supporting discrete design decisions. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) (Lee et al., 2021) is built for the simulation, optimization, and conceptual design of different chemical processes and energy systems, enabling the use of advanced solvers and a built-in library of unit models that can be used for the construction of traditional and innovative flowsheet configurations. IDAES takes advantage of Generalized Disjunctive Programming to formulate conceptual design problems, in which the discrete design decisions that are provided by the user, are included in the model as logical constraints using a Disjunction modeling object (Bynum et al., 2021). To solve the GDP formulation within IDAES, the solver GDPopt (Chen et al., 2018; Türkay and Grossmann, 1996), a logic-based decomposition algorithm, is used. This solver performs the automatic reformulation of the logical constraints to linear constraints, a capability that is currently not supported by other optimization solvers (Chen et al., 2020). For the solution of the GDP model, GDPopt solves a set of MIP master problems and NLP sub-problems using commercial and/or open source solvers (i.e., for the MIP master problem: Cbc, Cplex, Gurobi; NLP solvers: Conopt, Ipopt, etc.).

In this work, we demonstrate the use of IDAES for the solution of conceptual design problems using rigorous models applied to advanced energy systems. As a case study, we present the retrofit of a supercritical power plant with an integrated thermal energy storage (TES) system. In this case study, we develop two superstructure models to identify the optimal location of the TES system in the power plant. The first superstructure considers 20 alternative configurations for charging the integrated TES system, whereas the second superstructure considers 15 different alternative configurations for discharging the storage system. We then optimize the process design and operating conditions of the charge and discharge superstructures to minimize the total cost of the system. The total cost includes variable and fixed costs associated to the design of the thermal energy storage system, including pumps, heat exchangers, and hot/cold storage tanks. For the charge superstructure, the solution corresponds to the selection of a medium pressure steam source, while for the discharge superstructure, the condensed steam source is extracted from the low pressure feed water heaters in the power plant. The solution for the charge superstructure was found after about 460 seconds, while the solution for the discharge system is found after about 182 seconds using a commodity laptop.

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-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the USDOE or the United States Government. IDAES is supported through the Simulation-Based Engineering, Crosscutting Research Program within the U.S. Department of Energy’s Office of Fossil Energy.

Bynum, M. L., Hackebeil, G. A., Hart, W. E., Laird, C. D., Nicholson, B., Siirola, J. D., Watson, J.-P., and Woodruff, D. L. (2021). Pyomo - Optimization modeling in Python, volume 67. Springer Science & Business Media, third edition.

Chen, Q., Johnson, E. S., Bernal, D. E., Valentin, R., Kale, S., Bates, J., Siirola, J. D., and Grossmann, I. E. (2020). Pyomo.GDP: an ecosystem for logic based modeling and optimization development. In preparation.

Chen, Q., Johnson, E. S., Siirola, J. D., and Grossmann, I. E. (2018). Pyomo.GDP: Disjunctive models in Python. Proceedings of the 13th International Symposium on Process Systems Engi-

Lee, A., Ghouse, J. H., Eslick, J. C., Laird, C. D., Siirola, J. D., Zamarripa, M. A., Gunter, D., Shinn, J. H., Dowling, A. W., Bhattacharyya, D., Biegler, L. T., Burgard, A. P., and Miller, D. C. (2021). The IDAES process modeling framework and modeling library - Flexibility for process simulation, optimization and control. Accepted.

Türkay, M. and Grossmann, I. (1996). Logic-based MINLP algorithms for the optimal synthesis of process networks. Computers and Chemical Engineering, 20:959–978.