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Stochastic optimization of energy systems. Cosmin Petra [email protected] Argonne National Laboratory. A) Project Overview. Real-time optimization (power dispatch and unit commitment) of power grid in the presence of uncertainty (renewable energy, smart grid, weather)

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a project overview
A) Project Overview
  • Real-time optimization (power dispatch and unit commitment) of power grid in the presence of uncertainty (renewable energy, smart grid, weather)
  • Stochastic formulations reduce both short-term (production) and long-term (reserve) costs, stabilize prices, and increase the reliability.
  • [email protected] team: MihaiAnitescu, Cosmin Petra, Miles Lubin (algorithms and implementation), Victor Zavala and Emil Constantinescu (modeling and data)
  • Funding: DOE Applied Math (2009-2012), DOE ASCR MMICC center (2012-2017)
  • DOE INCITE Award (2012-2013) - 10 mil core hours for 2012.
b science lesson
B) Science Lesson
  • What does the application do, and how?
  • Stochastic optimization = decisions taken now are influenced by future random conditions (multiple scenarios)
  • Unit Commitment: Determine optimal on/off schedule of thermal (coal, natural gas, nuclear) generators. Day-ahead market prices. (solved hourly)
  • Economic Dispatch:Set real-time market prices. (solved every 5-10 min.)
  • Scenario-based parallelization
  • The “now” decisions cause coupling
  • PIPS suite (PIPS-IPM, PIPS-S) - parallel implementations that exploits the stochastic structure at the linear algebra level.
c parallel programming model
C) Parallel Programming Model
  • MPI + OpenMP
    • Scenario computations accelerated with OpenMP (sparse linear algebra)
    • Inter-scenarios communication with MPI
    • Distributed dense linear algebra for the coupling (done with Elemental)
  • C++
  • Cmake build system
  • Runs on “Fusion” cluster, “Intrepid” BG/P
  • Asynchronous implementation may require new programming model (X+SMP).
  • Yeah, I know … 99.99% X will be MPI
d computational methods
D) Computational Methods
  • Standard interior-point method (PIPS-IPM) and dual simplex (PIPS-S)
  • In-house parallel linear algebra
  • Linear algebra kernels
    • Sparse: MA57, WSMP, PARDISO.
    • Dense: LAPACK, Elemental
  • Next: PIPS-L – Lagrangian decomposition for integer problems
    • “Dual decomposition” method
    • Based on multi-threaded integer programming kernels (CBC,SCIP) and PIPS-IPM
  • Asynchronous – master-worker framework to deal with load imbalance in scenarios
e i o patterns and strategy
E) I/O Patterns and Strategy
  • I/O requirements minimal, one file per MPI process at starting.
  • We end up with the optimal cost (a double) and decision variables (vectors of relatively small size)
  • Restarting done by saving the intermediate iterates (vectors)
  • Future plans: Parallel algebraic specification of the problem
    • Generating the input data IN PARALLEL given an algebraic/mathematical description of the problem (AMPL-like script)
    • Currently done in serial
f visualization and analysis
F) Visualization and Analysis
  • Output is small, no special analysis required
  • less
g performance
G) Performance
  • Bottlenecks to better performance?
    • SMP sparse kernels (PIPS-IPM)
    • memory bandwidth (PIPS-S)
  • Bottlenecks to better scaling?
    • Dense kernels (PIPS-IPM)
    • load imbalance(PIPS-S, PIPS-L)
  • Collaboration with Olaf Schenk - PARDISO – SMP sparse rhs
  • PIPS-L – asynchronous optimization algorithms
h tools
H) Tools
  • How do you debug your code?
    • cerr, cout
i status and scalability
I) Status and Scalability
  • PIPS-IPM scaling
  • Efficiency likely to decrease with faster SMP scenario computations
  • Factors that adversely affect scalability
    • Serial bottlenecks: dense linear algebra for the “now” decisions
    • Using Elemental improves scaling for some problems
i status and scalability1
I) Status and Scalability
  • PIPS-S scaling efficiency is
    • 31% on Fusion from 1 to 256 cores
    • 35% on Intrepid from 2048 to 8192 cores
  • Factors that adversely affect scalability
    • Serial bottleneck (“now” decisions)
    • Communication ( 10 collectives per iteration, cost of 1 iteration=O(ms) )
    • Load imbalance
  • Intended to be used on up to few hundred of cores
  • PIPS-S is the first HPC implementation of simplex
j roadmap
J) Roadmap
  • 2 years from now?
  • Solve grid optimization models with
    • Better resolution and larger time horizon
    • Larger network: continental US grid
    • More uncertainty
    • Integer variables