research-article

Scalable stochastic optimization of complex energy systems

Authors:

Cosmin G. Petra,

Mihai Anitescu,

Victor ZavalaAuthors Info & Claims

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 64, Pages 1 - 64

https://doi.org/10.1145/2063384.2063470

Published: 12 November 2011 Publication History

Abstract

We present a scalable approach and implementation for solving stochastic programming problems, with application to the optimization of complex energy systems under uncertainty. Stochastic programming is used to make decisions in the present while incorporating a model of uncertainty about future events (scenarios). These problems present serious computational difficulties as the number of scenarios becomes large and the complexity of the system and planning horizons increase, necessitating the use of parallel computing. Our novel hybrid parallel implementation PIPS is based on interior-point methods and uses a Schur complement technique to obtain a scenario-based decomposition of the linear algebra. PIPS is applied to a stochastic economic dispatch problem that uses hourly wind forecasts and a detailed physical power flow model. Solving this problem is necessary for efficient integration of wind power with the Illinois power grid and real-time energy market. Strong scaling efficiency of 96% is obtained on 32 racks (131,072 cores) of the "Intrepid" Blue Gene/P system at Argonne National Laboratory.

References

[1]

P. R. Amestoy, I. S. Duff, J.-Y. L'Excellent, and J. Koster. A fully asynchronous multifrontal solver using distributed dynamic scheduling. SIAM J. Matrix Anal. Appl., 23:15--41, January 2001.

Digital Library

[2]

P. R. Amestoy, A. Guermouche, and S. Pralet. Hybrid scheduling for the parallel solution of linear systems. Parallel Computing, 32:136--156, 2006.

Digital Library

[3]

J. R. Birge and F. Louveaux. Introduction to stochastic programming. Springer-Verlag, New York, 1997.

[4]

J. R. Birge and L. Qi. Computing block-angular Karmarkar projections with applications to stochastic programming. Manage. Sci., 34(12):1472--1479, 1988.

Digital Library

[5]

L. S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley. ScaLAPACK Users' Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA, 1997.

Digital Library

[6]

J. R. Bunch and L. Kaufman. Some stable methods for calculating inertia and solving symmetric linear systems. Mathematics of Computation, 31(137):163--179, 1977.

[7]

J. R. Bunch and B. N. Parlett. Direct methods for solving symmetric indefinite systems of linear equations. SIAM Journal on Numerical Analysis, 8(4):639--655, Dec. 1971.

[8]

I. Chowdhury and J.-Y. L'Excellent. Some experiments and issues to exploit multicore parallelism in a distributed-memory parallel sparse direct solver. Technical Report RR-7411, INRIA, October 2010.

[9]

E. M. Constantinescu, V. M. Zavala, M. Rocklin, S. Lee, and M. Anitescu. A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Transactions on Power Systems, 26:431--441, 2010.

[10]

E. M. Constantinescu, V. M. Zavala, M. Rocklin, S. Lee, and M. Anitescu. A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Transactions on Power Systems, 26(1):431--441, 2010.

[11]

J. Gondzio and A. Grothey. Direct solution of linear systems of size 10⁹ arising in optimization with interior point methods. In PPAM, pages 513--525, 2005.

Digital Library

[12]

J. Gondzio and A. Grothey. Parallel interior-point solver for structured quadratic programs: Application to financial planning problems. Annals of Operations Research, 152(1):319--339, July 2007.

[13]

J. Gondzio and A. Grothey. Exploiting structure in parallel implementation of interior point methods for optimization. Computational Management Science, 6(2):135--160, May 2009.

[14]

J. Gondzio and R. Sarkissian. Parallel interior point solver for structured linear programs. Mathematical Programming, 96:561--584, 2003.

[15]

A. Gupta. WSMP: Watson Sparse Matrix Package. Technical report, IBM Research Report, 2000.

[16]

J. Linderoth and S. Wright. Decomposition algorithms for stochastic programming on a computational grid. Comput. Optim. Appl., 24(2--3):207--250, 2003.

Digital Library

[17]

M. Lubin, C. G. Petra, and M. Anitescu. On the parallel solution of dense saddle-point linear systems arising in stochastic programming. Optimization Methods and Software, in press, 2011.

Digital Library

[18]

S. Mehrotra and M. G. Ozevin. Decomposition based interior point methods for two-stage stochastic convex quadratic programs with recourse. Oper. Res., 57(4):964--974, 2009.

Digital Library

[19]

C. G. Petra and M. Anitescu. A preconditioning technique for Schur complement systems arising in stochastic optimization. Technical report, Preprint ANL/MCS-P1748-0510, Argonne National Laboratory, 2010.

[20]

J. Poulson, B. Marker, and R. A. van de Geijn. Elemental: A new framework for distributed memory dense matrix computations (FLAME Working Note #44). Technical report, Institute for Computational Engineering and Sciences, The University of Texas at Austin, June 2010.

[21]

O. Schenk and L. Gartner. On fast factorization pivoting methods for symmetric indefinite systems. Elec. Trans. Numer. Anal., 23:158--179, 2006.

[22]

A. Shapiro, D. Dentcheva, and A. Ruszczyński. Lectures on Stochastic Programming: Modeling and Theory. MPS/SIAM Series on Optimization 9, Philadelphia, PA, 2009.

[23]

R. A. van de Geijn. Using PLAPACK. MIT Press, March 1997.

[24]

S. J. Wright. Primal-Dual Interior-Point Methods. Society for Industrial and Applied Mathematics, Philadelphia, PA, 1997.

Digital Library

[25]

V. M. Zavala, A. Botterud, E. M. Constantinescu, and J. Wang. Computational and economic limitations of dispatch operations in the next-generation power grid. IEEE Conference on Innovative Technologies for an Efficient and Reliable Power Supply, 2010.

[26]

V. M. Zavala, C. D. Laird, and L. T. Biegler. Interior-point decomposition approaches for parallel solution of large-scale nonlinear parameter estimation problems. Chemical Engineering Science, 63(19):4834--4845, 2008.

Cited By

Pacaud FSchanen MShin SMaldonado DAnitescu M(2024)Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architecturesOptimization Methods and Software10.1080/10556788.2024.232964639:4(874-897)Online publication date: 8-Apr-2024
https://doi.org/10.1080/10556788.2024.2329646
Zheng ZLiu MXie MZhu J(2022)A multidiscipline collaborative optimization approach with acceleration strategies for generator maintenance scheduling in large hydrothermal power systems considering uncertainty of natural inflowsSustainable Energy, Grids and Networks10.1016/j.segan.2022.10095732(100957)Online publication date: Dec-2022
https://doi.org/10.1016/j.segan.2022.100957
Joshi GMohagheghi S(2021)Optimal Operation of Combined Energy and Water Systems for Community Resilience against Natural DisastersEnergies10.3390/en1419613214:19(6132)Online publication date: 26-Sep-2021
https://doi.org/10.3390/en14196132
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Index Terms

Scalable stochastic optimization of complex energy systems

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Published In

cover image ACM Conferences

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

November 2011

866 pages

ISBN:9781450307710

DOI:10.1145/2063384

Conference Chair:
Scott Lathrop
University of Chicago
,
Program Chairs:
Jim Costa
Sandia National Laboratories
,
William Kramer
National Center for Supercomputing Applications

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 November 2011

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SC '11: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2011

Washington, Seattle

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SC '11 Paper Acceptance Rate 74 of 352 submissions, 21%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Pacaud FSchanen MShin SMaldonado DAnitescu M(2024)Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architecturesOptimization Methods and Software10.1080/10556788.2024.232964639:4(874-897)Online publication date: 8-Apr-2024
https://doi.org/10.1080/10556788.2024.2329646
Zheng ZLiu MXie MZhu J(2022)A multidiscipline collaborative optimization approach with acceleration strategies for generator maintenance scheduling in large hydrothermal power systems considering uncertainty of natural inflowsSustainable Energy, Grids and Networks10.1016/j.segan.2022.10095732(100957)Online publication date: Dec-2022
https://doi.org/10.1016/j.segan.2022.100957
Joshi GMohagheghi S(2021)Optimal Operation of Combined Energy and Water Systems for Community Resilience against Natural DisastersEnergies10.3390/en1419613214:19(6132)Online publication date: 26-Sep-2021
https://doi.org/10.3390/en14196132
Tanneau MAnjos MLodi A(2021)Design and implementation of a modular interior-point solver for linear optimizationMathematical Programming Computation10.1007/s12532-020-00200-8Online publication date: 8-Feb-2021
https://doi.org/10.1007/s12532-020-00200-8
Engelmann AJiang YBenner HOu RHouska BFaulwasser T(2021)ALADIN‐—An open‐source MATLAB toolbox for distributed non‐convex optimizationOptimal Control Applications and Methods10.1002/oca.281143:1(4-22)Online publication date: 22-Nov-2021
https://doi.org/10.1002/oca.2811
Rao VKim KSchanen MMaldonado DPetra CAnitescu M(2019)A Multiperiod Optimization-Based Metric of Grid Resilience2019 IEEE Power & Energy Society General Meeting (PESGM)10.1109/PESGM40551.2019.8974137(1-5)Online publication date: Aug-2019
https://doi.org/10.1109/PESGM40551.2019.8974137
Boland NChristiansen JDandurand BEberhard AOliveira F(2019)A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problemsMathematical Programming: Series A and B10.1007/s10107-018-1253-9175:1-2(503-536)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10107-018-1253-9
Sampathirao ASopasakis PBemporad APanos Patrinos P(2018)GPU-Accelerated Stochastic Predictive Control of Drinking Water NetworksIEEE Transactions on Control Systems Technology10.1109/TCST.2017.267774126:2(551-562)Online publication date: Mar-2018
https://doi.org/10.1109/TCST.2017.2677741
Zhao WFu YZheng ZChen BLiao QXie WYang B(2018)Correlation Analysis of Wind Power Based on Mixed Copula Model and Its Application into Stochastic Dispatch2018 International Conference on Power System Technology (POWERCON)10.1109/POWERCON.2018.8602354(1062-1069)Online publication date: Nov-2018
https://doi.org/10.1109/POWERCON.2018.8602354
Sopasakis PSampathirao ABemporad APatrinos P(2018)Uncertainty-aware demand management of water distribution networks in deregulated energy marketsEnvironmental Modelling & Software10.1016/j.envsoft.2017.11.035101:C(10-22)Online publication date: 1-Mar-2018
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