NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Accelerating Computation of 

the Unit Commitment 

Problem

Marissa Hummon

Team: Clayton Barrows & Wesley Jones

INFORMS 2013 Annual Meeting

Minneapolis, Minnesota

October 6-9, 2013

NREL/PR-6A20-60623
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Executive Summary

Unit commitment (UC) and economic dispatch (ED) problems are the fundamental problem 

that system operators solve in order to minimize the costs associated with reliably operating 

electricity grids. We use UC/ED modeling in renewable integration studies to study the changes 

in system operation, emissions production, and system reliability with the addition of variable 

and uncertain renewable energy technologies. Simulating operations in the Eastern 

Interconnect for a calendar year with hourly resolution requires up to 45 days of computation. 

Many UC/ED studies avoid this problem by reducing the simulation horizon and/or making 

model simplifications (e.g. simpler network topology, consolidated generators, fewer 

constraints, etc.). We propose a method to decompose the inherently chronological UC/ED 

problem into many distinct simulation horizons for parallel computation. Decomposition 

enables the application of long term simulations of grid operations on high performance 

computation environments. This talk summarizes our foundational research on the persistence 

of unit commitment decisions. We demonstrate that the persistence of UC decisions are related 

to the operational parameters of different generators: combustion turbines (CT) operate for 1-4 

hours per day to meet peak daily demand, and thus the persistence of the CT UC is about 4 

hours. This talk concludes with an exploration of the uncertainty reduction enabled by allowing 

simulations time to recover from uninformed start conditions.
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Unit commitment problem

PLEXOS is a grid modeling software package from 

Energy Exemplar that has multiple modeling 

horizons including capacity expansion, unit 

commitment, and sub-hour dispatch. Our research 

focuses on parallelizing the unit commitment and 

economics dispatch phases.
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Unit commitment and economic dispatch

optimization horizon:

48 hours
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Unit commitment and economic dispatch

rolling forward in

24 hour increments
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Unit commitment and economic dispatch

The state of the system at time t=0 is 

dependent on:

1. Generator commitment status: on/off

2. If “on”: hours of continuous operation; 

current ramp rate

3. If “off”: hours since last operation 

(minimum shut down duration)
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Unit commitment and economic dispatch

Each optimization problem takes 

between 2 and 30 minutes to solve.

Annual solutions can range from hours 

to weeks.
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Changes to generator configuration...

• Annual solutions are important because load and renewable generation have seasonal 

variations.

• Increasing renewable penetration increases the number and magnitude of short duration ramps.

• Investment decisions are based on both investment costs and long–term operational costs & 

savings. Production cost models are the choice tool for evaluating the majority of operational 

costs & savings, however they do not estimate make investment decisions.
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Changes to generator configuration...

Increasing renewable 

penetration increases 

reserve requirements 

for the system.

The cost of timevarying operating 

reserves is captured in 

a production cost 

model.
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Idea: Parallelize in the time domain

Hypothesis: a decision at time t is not dependent on the state of 

the system at previous time intervals, given a delay of n time 

periods.

Plotted here: Autocorrelation of the 

generator unit commitment 

decision variable for a group of 

generators.

The duration of the lag necessary 

for the autocorrelation of the Unit 

Commitment to reach a local 

minimum is called the Unit 

Commitment Decision Persistence, 

or just Persistence
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UC Persistence

Changes with type of generator (which are distinct because of the operation restrictions: startup 

time, minimum up/down time, and short run marginal cost) and time of year. The “time to reach 

zero autocorrelation” is a measure of the how long a set of generators “hold” a UC decision.
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UC on NREL’s HPC

Peregrine Characteristics:

• 11520 Intel Xeon E5-2670 "SandyBridge" cores

• 14400 next-generation Intel Xeon "Ivy Bridge" 

core

• 576 Intel Phi Intel Many Integrated Core (MIC) 

core co-processors with 60+ cores each

• 32 GB DDR3 1600Mhz memory per node

• Peregrine will deliver a peak performance of 1 

petaFLOPS

NREL PIX 24580
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Change in UC/ED with no overlap

Started UC/ED on January 31 at 12:00 am

Started UC/ED on January 1 at 12:00 am
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Effect of Overlap on Dispatch

Normalized root mean square difference (NRMSD) in generation dispatch, by type of generator, 

relative to the annual solution. This calculation is made each day and plotted relative to the 

number of overlap days (number of days since the start of the optimization).
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Seasonal Overlap

NRMSD decays significantly after 2-days of overlap throughout the year.
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Day of Week Overlap

For the Colorado test system, model start days with least NRMSD error are Sunday and 

Wednesday, corresponding to taking the data on Tuesday and Friday, respectively, with 

a 2-day overlap.
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ST Solution Time

9-day simulations: weekly with 2-days of overlap.
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Speedup in hourly UC simulation

Annual solution takes 131.7 minutes. 

With 52 partitions (with increasing overlap days): 52 parallel 7-day simulations 52 parallel 

8-day simulations

52 parallel 

9-day simulations

(4.3 minutes)

(4.4 minutes)

(5.1 minutes)

(5.3 minutes)

(6.0 minutes)

(6.6 minutes)
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Future Work

• Explore overlap on different systems

• Seed UC partitions with a “guess” at the 

commitment state variable in order to decrease 

overlap period

• Explore overlap on hour-ahead and 5-minute 

dispatch models
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Thanks and Questions?

Marissa Hummon

303-275-3269

Marissa.hummon@nrel.gov