(193f) Nonconvex Robust Optimization for the Design and Operation of Advanced Energy Systems Using Pyros

Due to economic, environmental, and sociopolitical factors, energy systems with reduced carbon footprints are becoming increasingly prominent worldwide [1–3]. Integrated systems featuring renewable energy generation, carbon capture and storage, and/or energy storage technologies are considered key to minimal cost pathways for reducing net CO₂ emissions to meet established climate change mitigation targets while maintaining grid reliability and resiliency [1–3]. To this end, the United States Department of Energy has spearheaded initiatives for developing computational tools to accelerate the widespread industrial deployment of novel energy systems technologies [4].

Mathematical models for the design and operation of advanced energy systems are increasingly becoming subject to uncertainty stemming from economic volatility, the intermittency of renewable energy generation technologies, extreme weather events, and novel physicochemical property models [3–8]. Consequently, system design and operational policies prescribed by deterministic optimization under a nominal realization of the uncertain model parameters may be suboptimal or infeasible under off-nominal realizations [6, 8]. It follows that an optimization under uncertainty framework is required to obtain minimal cost designs that are feasible in light of the parametric uncertainty.

Two-stage and multi-stage robust optimization (RO) is particularly useful for addressing the various uncertainties present in the mathematical modeling of energy systems. In the most general case, these mathematical models feature nonconvexities in the objective and/or constraints, equality constraints that cannot be reformulated out of the model, and both design and recourse variables. Methods for obtaining robust solutions to such models have been recently developed. In [8], the utility of a generalized cutting set algorithm for nonconvex two-stage RO problems with uncertain equality constraints was demonstrated for nonlinear chemical process systems models. The algorithm has been implemented in PyROS, an open-source meta-solver for models written in the Pyomo algebraic modeling language [9, 10].

Recent implementation advances in the PyROS solver are reviewed, and the reliability and computational performance of the solver are demonstrated via the results of a computational study on a library of over 8,500 small-scale benchmark instances. We also discuss the performance of PyROS on an ensemble of medium-scale and large-scale models stemming from applications of advanced energy systems, including point-source carbon capture, scheduling of thermal power plants integrated with battery storage, and optimal power flow transmission. Our results demonstrate that the PyROS solver, including recent extensions to handle multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainties.

Disclaimer

This abstract was prepared as an account of work sponsored by the 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, 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.

Acknowledgements

The authors graciously acknowledge funding from the U.S. Department of Energy, Office of Fossil Energy and Carbon Management, through the Carbon Capture Program and Simulation-based Engineering/Crosscutting Research Program. CEG and JAFS also gratefully acknowledge support from the Department of Energy’s Carbon Capture Simulation Impact for Industry (CCSI²) program.

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