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From: dpk (David P. Koester)
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\bigskip
{\bf IV. \hspace{2mm} Parallel Unit Commitment}
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	Syracuse University has previously performed research under contract to Niagra Mohawk
to investigate the feasibility of
utilyzing parallel dynamic programming techniques on shared-memory multiprocessors to perform
electrical power system unit commitment.  This research was performed by a graduate student
for the late Professor Yehuda Wallach in 1990.  This research illustrated that 
efficient parallel unit commitment algorithms exist for spared-memory multiprocessors:
efficiencies
ranging between 0.56 and 0.91 out of a perfect 1.00 were obtained.  It is anticipated that 
techniques used in parallel shared-memory multiprocessor algorithms would efficiently
implement on distributed-memory multiprocessors that exhibit either good broadcast communications
capabilities or the capability to perform communications while also performing floating point
operations.

	The parallel dynamic programming unit commitment prototype that had the best efficiency,
also was the most {\em coarse-grained}.  This implies that additional computations that
improve the accuracy of the unit commitment calculations would also improve the efficiency of
the parallel implementation.  For example, it is possible that the additional calculations from
adding new features such as environmental dispatch, may be absorbed by better utilization of
the available processors.  While there would be additional work being performed, there may be
minimal effect on wall-clock time to perform the more realistic unit commitment study.  

	In addition to the dynamic programming implementation of unit commitment, there are other
areas for research that could potentially improve the quality of unit commitment estimates
while also obtaining good utilization of multicomputers to significantly reduce the time to
perform the study.  Dynamic programming implementations
of unit commitment are effected by exponential growth in memory and computational time as the
number of generators increases, {\em the curse of dimensionality}, which significantly
limits the number
of generator combinations that can be examined throughout a unit commitment study.  
The careful structuring of the 
dynamic programming
implementation of unit commitment to overcome problems with intractable numbers of
potential generator combinations, could result in non-optimal solutions.  
One example of a parallel optimization technique that could
significantly reduce {\em the curse of dimensionality} is simulated annealing.  This
technique has efficient parallel implementations and does not require stringent limits on the
choice of generators examined throughout the unit commitment study time frame.  

	One algorithm for parallel unit commitment has already been demonstrated to be efficient,
and there are other parallel techniques that offer promise for efficient parallel
optimization of unit commitment.  Parallel unit commitment techniques will significantly
decrease the time to perform a unit commitment study, which in turn, may permit additional
fidelity to be included to the models to improve the performance.  Frequent unit commitment
with high fidelity models should significantly reduce generation cost by accurately
predicting the most efficient combinations of generators to supply adequate power for 
time-varying loads.  

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