A decoupling strategy for protecting sensitive process information in cooperative optimization of power flow

AIChE Journal, vol. 68

Abstract

Abstract Optimal power flow (OPF) with close cooperation between the power grid and flexible electricity‐intensive chemical processes can reduce costs for the grid and for electricity users. However, this would require sharing detailed chemical process models, which may reveal confidential information and potentially jeopardize competitive advantages for a chemical process operator. We propose an algorithm that enables economically advantageous cooperation without the need to exchange sensitive information. Low‐order linear models are used to represent the dynamic behavior of electricity‐intensive processes. We integrate these models into the OPF problem and solve the problem using a decoupling strategy based on Benders‐type cuts. The cuts introduce limited communication between the chemical processes and the grid without exchanging explicit information pertaining to the process dynamics and performance. Our results reproduce solutions obtained by sharing detailed process models up to a user‐defined optimality gap for several test cases that reflect both normal and congested grid states. We also investigate the trade‐off between the value of the optimality gap and computational effort. Finally, we study the scaling behavior of the iterative procedure with respect to flexible loads at multiple grid locations.

Authors 6

1. Tim Varelmann Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

2. Joannah I. Otashu

   The University of Texas at Austin

   Affiliation as printed

   McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA

   Process Systems Enterprise Houston TX USA

   McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas, USA

   Process Systems Enterprise, Houston, TX, USA

3. Kyeongjun Seo

   The University of Texas at Austin

   Affiliation as printed

   McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA

   McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas, USA

4. Adrian W. Lipow Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

5. Alexander Mitsos Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute of Energy and Climate Research, Energy Systems Engineering (IEK‐10), Forschungszentrum Jülich GmbH Jülich Germany

   JARA‐CSD Aachen Germany

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   JARA-CSD, Aachen, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

6. Michael Bâldea corresponding

   The University of Texas at Austin

   Affiliation as printed

   McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA

   Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin TX USA

   McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas, USA

   Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA