Optimization Methodologies for Large-Scale Power Systems back


The objective of this project is to push the state-of-the-art in optimization algorithms and software to enable the use of high-performance computing in power grid planning and operations. This will enable decision-making under uncertainty and the incorporation of high-fidelity power system models. 


Sponsored by the Department of Energy (DOE) Office of Electricity, we
reached and demonstrated a high level of technical maturity of our stochastic optimization solver, PIPS. We have used PIPS to solve challenging problems arising in power systems. The solver is now capable of solving a stochastic unit commitment problem over the entire Illinois transmission system with a prediction horizon of 24 hours, 32,000 scenarios, a full physical DC network model, and up to billion variables in less than one hour on a variety of high-performance computing platforms such as BG/P, XE6, XK7 and XC30. This is a drastic reduction in computational time from previous versions. This enables us, for the first time, to consider operational solutions with full physical network models (we do not need to make assumptions on uncongested lines as is done in practice) and to capture spatio-temporal wind power uncertainty in deployable times.  The tests took place in the BlueGene/P system and in Oak Ridges' Titan system. The solution of these problems required significant effort in extending PIPS to integrate the cutting-edge linear algebra solver PARDISO with Schur assembling capabilities capable of exploiting hypersparsity. In a related activity, we also implemented and tested network partitioning strategies in interior point solvers (see Figure 1) and have been capable of reducing computing times by a factor of 10 in the Illinois system using Argonne's Fusion cluster. This is an important step in addressing ISO-sized transmission systems.

We are also currently developing new models to capture reliability constraints and to capture emerging phenomena from the interconnection of natural gas and power grid systems.   Finally, we are exploring the use of the the advanced scripting language SWIFT to perform computationally intensive simulations using heterogeneous computing systems (see Figure 2).

  • Cosmin Petra [link]
  • Mihai Anitescu [link]
  • Michael Wilde [link]
  • Olaf Schenk  [link]
  1. Cao, Y.; Laird. C.D. and Zavala, V. M. Clustering-Based Preconditioning for Stochastic Programs. Under Review,  2014. [pdf]
  2. Chiang, N. and Zavala, V. M. An Inertia-Free Filter Line-Search Algorithm for Large-Scale Nonlinear Programming. Under Review,  2014. [pdf]
  3. Zavala, V. M.; Anitescu, M. and Birge, J. A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties.  Under Review,  2014. [pdf]
  4. C. G. Petra, O. Schenk, M. Anitescu. Real-time Stochastic Optimization of Complex Energy Systems on High Performance Computers. Submitted, 2013 [pdf]
  5. M. Lubin, J. A. J. Hall, C. G. Petra, and M. Anitescu. Parallel distributed-memory simplex for large-scale stochastic LP problems. Computational Optimization and Applications, 2013 [pdf]
  6. Lubin, M.; Petra, C. G.; Anitescu, M., and Zavala, V.M. Scalable Stochastic Optimization of Complex Energy Systems. Supercomputing, 2011. [pdf]
  7. Cioaca, A.; Zavala, V.M. and Constantinescu, E.M.  Adjoint Sensitivity Analysis for Numerical Weather Prediction: Applications to Power Grid Optimization. International Workshop on High Performance Computing, Networking and Analytics for the Power Grid, 2011. [pdf]
  8. Robbins, B.; and Zavala, V.M. Convergence Analysis of a Parallel Newton Scheme for Dynamic Power Grid Simulations. International Workshop on HPC, Networking and Analytics for the Power Grid, 2011. [pdf]
  9. Zavala, V. M.; Botterud, A.; Constantinescu, E. M. and Wang, J.  Computational and Economic Limitations of  Dispatch Operations in the Next-Generation Power Grid. IEEE Conference on Innovative Technologies for and Efficient and Reliable Power Supply, 2010. [pdf]

Figure 1. Partitioning of Illinois Network using Metis

Figure 2. Time Evolution of Locational Marginal Prices Generated with Swift