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% \underline{\large \bf DRAFT}
% \bigskip

{\large \bf
ACTION-NYS \\}
\bigskip
{\large \bf
High Performance Computing and Parallel \\
Processing in the Utility Industry \\}
% 
\medskip
\bigskip
Geoffrey Fox, Paul Coddington, \\
Alok Choudhary, Salim Hariri, \\
Nancy McCracken, Michael Nilan, Sanjay Ranka \\
%
\bigskip
Northeast Parallel Architectures Center \\
Syracuse University
\end{center}
% 
\bigskip
% 
\noindent
{\bf Executive Summary}

\medskip
We propose a major study and implementation project which addresses
the role in the utility industry of High Performance Computing (HPC),
and in particular, parallel processing.
The project has two phases:

\begin{description}

\item[\quad 1)]  A study phase to evaluate the use of parallel computing 
	  for unit commitment, contingency evaluation and transient
	  stability analysis. 
	  This will include a survey of available sparse 
	  matrix algorithms and an analysis of their performance on
	  power systems applications.
	  At the end of this phase we will have incorporated a 
	  parallel sparse matrix solver into a prototype transient 
	  stability analysis program, and 
	  will make recommendations on the appropriate hardware, 
	  software, and user interface for a production system. 
	  We will also investigate the creation of a consortium of
	  interested corporations for developing this prototype into a 
	  product in a second phase of the project.
          This phase will also include a more general survey on the role 
	  of HPC and parallel processing in the utility industry.

\item[\quad 2)]  A production phase to produce a commercial package for 
	  unit commitment and on-line contingency evaluation and transient
	  stability analysis with a high-level user interface
	  incorporating a knowledge-based expert system.
	  The package will include hardware, software, documentation, 
	  and training.

\end{description}

\noindent
Phase 1 is proposed to run from January 11, 1993 to October 10, 1993.
The implementation of Phase 2 will be dependent on the results of 
Phase 1, and discussions between NPAC, NMPC and other possible commercial
partners. Phase 2 is expected to run for approximately 18 months.
%, from September 1, 1993 to February 28, 1995.
 
\bigskip
\noindent
The project will be part of our ACTION-NYS industrial outreach program
and substantial cost sharing will be available due to state funding of 
this program.

\end{titlepage}

%

\begin{document}

\noindent
{\large \bf Background}
\bigskip

% **** 
% Refer to IEEE report on parallel processing in the power industry
% as well as the TASC report?
% What about the report Sanjay mentioned?
%

    The rapid increase in computer performance has led to increased
opportunities in the application of computing to the power systems industry.
The report, ``Comprehensive Study of Supercomputing and its Applicability
to the Utility Industry'' by TASC, concludes that by 1995 even small
improvements in operations, peak reduction, transmission, and avoided
customer interruption ``can translate into total annual dollar savings that 
are nearly an order of magnitude greater than the annual cost of operating a
supercomputer.''  
Bill Merritt of the Operations group of Niagara Mohawk Power Corporation
(NMPC) has suggested that
improved contingency evaluation and transient stability analysis could save 
NMPC on the order of \$1.7 million per year without sacrificing 
system reliability by intelligently reducing the present operating margins.  
The magnitude of this additional analysis will require supercomputing 
capabilities in order to produce near real-time modeling to enable 
operations staff to make better informed decisions.  

    It is widely recognized within the computer industry that increased
supercomputer speeds will necessarily involve parallel computing.  This is
evidenced by the plans for parallel computing by such traditional
supercomputing companies as Cray, Digital and IBM and by the success of
parallel supercomputers like the Connection Machine.  The Federal High
Performance Computing and Communication Initiative (FHPCCI) both
endorses this concept and will accelerate progress.  The Syracuse University
team is at the leading edge of this initiative and currently funded by DARPA
% **** ??
to produce portable software systems for parallel machines.  Furthermore,
considerable experience has been gained in recent years on a variety of
parallel computing architectures.  For example, the Caltech Concurrent
Computation Program, while it was under the direction of Geoffrey Fox, was
able to successfully implement 50 large parallel application codes from all
areas on the hypercube architecture.

    Parallel computing has now been developed to the point where it is
possible to examine computationally intense computer applications,
for example
those computer programs required to support daily power utility operations, 
to determine what parallel algorithms can implement those applications and 
what is the most effective parallel computer architecture to run those
algorithms. 
We note that TASC acknowledges the 
advances of parallel processing but does not consider the implication of this 
in their report.  Parallel processing will be needed to achieve the performance 
levels quoted by TASC ($0.5 \times 10^{12}$ operations per second) 
by the year 2000,
and we expect at least an order of magnitude better performance than this.  
% Fast, accurate and extensive contingency evaluation and transient
% stability analysis will require these impressive computing capabilities.
However, parallel processing requires the algorithms and software to be
rethought and often reimplemented.  This critical issue
was not addressed in the TASC report and is a key point of our proposal.
    
    We expect that the use of high performance parallel computers will permit
faster and more accurate system modeling during daily operations, and this 
additional knowledge of the system status and security will have direct
benefits for NMPC.
Daily planning activities that examine stability analysis were previously 
impossible due to long run-times and the time required for engineers to 
interpret the results.
Significantly faster computational capabilities and an improved user interface
for presenting the output data would enable near real-time system analysis,
which should allow a reduction in the conservative operating margins currently
required. 
Reducing excessive operating margins would reduce fuel consumption, thus 
saving money and reducing environmental impact while maintaining the present
standards for reliable operations, or even improving system reliability.

    The Syracuse team has excellent qualifications, indicated by the DARPA 
project mentioned above and by our membership in the prestigious NSF Science
and Technology Center devoted to making parallel computation practical.  
In the latter project, we are involved in parallel software and direct all the 
parallel application work at the participating institutions (Caltech, Rice
and Syracuse Universities and Los Alamos National Laboratory).  
%
Dr. Paul Coddington will be the project leader, and will work full time on 
co-ordination and technical aspects of the project, and liasing with 
NMPC staff. He has 8 years experience in computational physics and 
parallel computing. 
%
Our proposed project for NMPC also benefits from the State funded
ACTION-NYS program, 
which provides infrastructure support and cost sharing for the parts 
of the project which have generic value to the power industry.

\bigskip
\bigskip
\noindent
{\large \bf Project Description}
\bigskip

We now detail the components of the project, which has two phases:

\begin{description}

\item[\quad 1)] A study phase lasting 9 months, which will consist of
           research, study and evaluation in the areas listed below.  
           This phase will also include a more general survey on the role 
	   of parallel processing in the utility industry. \\
           This phase to run from January 11, 1993 to October 10, 1993.

\item[\quad 2)] A production phase, in which we will develop hardware and
	   software investigated in the study phase into a product for use
	   at NMPC, in collaboration with commercial partners. \\
           This phase is expected to take approximately 18 months.

\end{description}

The plan for the production phase will be dependent on the results 
obtained in the study phase. Work on a deliverable product will involve
collaboration with corporate partners.
During the study phase we will investigate the creation of a consortium of
interested parties, including NPAC, NMPC and other companies from the 
utilities, software and computer industries, for developing the prototype
system into a product in a second phase of the project.
We are aware of at least two companies that would be interested in 
helping to develop this product. At the end of the study phase we will
submit a proposal for the development of a product in the second
phase of the project.


\newpage
\noindent
{\large \bf 1) \hspace{1mm} Study Phase}
\bigskip

The study phase will consist of study, research, evaluation and prototype
development in the following interrelated research areas:
\begin{enumerate}
\item Unit Commitment
\item Contingency Analysis
\item Transient Stability Analysis
\item Parallel Sparse Matrix Algorithms
\item User Interface and Expert Systems
\item Parallelism and Computational Issues
\item Survey on HPC and Parallel Processing in the Utility Industry
\end{enumerate}

\medskip\noindent
Previous research into parallel unit commitment has shown promising results
for shared memory multiprocessors.
We will extend this work and investigate new optimization techniques such
as simulated annealing, and implement and benchmark different algorithms 
on distributed memory parallel computers.

\medskip\noindent
We will examine the present contingency analysis performed 
at the NMPC Operations center, and improve upon it by the application
of parallel processing to provide more computing power, and
by adding the capability to perform transient stability analysis.  

\medskip\noindent
A key research area required to implement efficient parallel transient 
stability analysis programs is parallel differential-algebraic equation
(DAE) solvers. There has been much recent research into sequential DAE
solvers, and we plan to capitalize upon that work as we identify the best of
those algorithms to solve the transient stability equations concurrently.  

\medskip\noindent
Critical to any DAE solver are sparse matrix solvers. Extensive 
research will be required in this area because sparse matrix computations
are some of the most challenging problems for parallel computers.  

\medskip\noindent
Another key research area for transient stability analysis is the user 
interface and the analytical software to classify generator stability.
Introduction of transient stability into the security analysis functions 
in Operations will require computer assisted analysis of simulation 
output to allow timely interpretation of results.

\medskip\noindent
Finally, computational issues related to the development of this work in
the production phase will also be analyzed. We will also conduct a more
general survey of High Performance Computing and parallel processing in the 
utility industry. This survey will extend the TASC report by discussing
more carefully the computer architecture and software issues needed to
implement HPC in utility applications.

\newpage
% \bigskip

{\bf I. \hspace{2mm} Unit Commitment}
\medskip

Syracuse University has previously performed research under contract to NMPC
to investigate the feasibility of utilizing parallel dynamic programming 
techniques on shared memory multiprocessors to perform electrical power 
system unit commitment.  This research was performed by one of our graduate
students for Professor Yehuda Wallach in 1990
\footnote{``Parallel Implementation of the Unit Commitment Problem on 
Shared-Memory Multiprocessors'', by D.P. Koester, Fall 1990}.
This work illustrated that efficient parallel unit commitment algorithms 
exist for shared memory multiprocessors: efficiencies ranging from 56\% to
91\% were obtained.  
It is anticipated that these techniques can also be implemented efficiently 
on distributed memory multiprocessors.

The parallel dynamic programming unit commitment prototype that had the best
efficiency was also the most 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, so that little extra time will be
required to perform a more valuable and 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.
Dynamic programming implementations of unit commitment are affected by
exponential growth in memory and computational time as the number of
generators increases, which significantly limits the number of generator 
combinations that can be examined and may result in non-optimal solutions.  
One example of an optimization technique that could significantly
reduce this problem is simulated annealing, which has efficient 
parallel implementations.

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 into the models. 
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.  

\medskip
For this application we will: 
\begin{itemize}
\item Implement the existing parallel unit commitment algorithms using
      dynamic programming on a distributed memory parallel computer.
\item Carry out a research and implementation program for parallel unit 
      commitment using simulated annealing.
\item Investigate the use of higher fidelity models incorporating additional
      features such as environmental dispatch.
\item Benchmark the performance of these different algorithms on parallel
      machines.
\end{itemize}


\bigskip
{\bf II. \hspace{2mm} Contingency Evaluation}
\medskip

Contingency evaluation is currently done on a large number (of order 200) of
independent cases. Contingency screening is first performed by running a fast
contingency evaluation program which uses very approximate techniques. This
orders the contingencies by increasing severity, and around 30 of the most
severe cases are evaluated using a much more accurate program. Speeding up
the contingency evaluation program would allow the use of a more accurate
screening process, or even the elimination of screening if the program could 
be made fast enough to allow a full evaluation of all the contingencies.

Since the different contingencies are all independent, this problem is
{\em embarassingly parallel}, meaning that different cases can be sent to
different processors and processed in parallel with optimum efficiency, 
by just using the sequential algorithm on each processor.
However before the contingency evaluation can be performed, the state
estimator program must be run. To get near real-time performance for 
contingency evaluation, we may need to develop parallel algorithms to 
speed up state estimation.

\medskip
For this application we will: 
\begin{itemize}
\item Study the current contingency evaluation software used at the NMPC
      Operations center.
\item Evaluate methods for incorporating parallelism into the state 
      estimator and security screening process.
\item Demonstrate the feasibility of incorporating a parallel on-line
      transient stability analysis capability. 
\end{itemize}


\bigskip
{\bf III. \hspace{2mm} Transient Stability Analysis}
\medskip

Transient stability analysis involves the study of the electrical network
and generator dynamics for several seconds following a disturbance. 
The dynamics of each generator in the system is described by multiple 
differential equations, while the coupling of the generators to the 
electrical network is described by algebraic equations. 
Together these lead to a system of differential-algebraic equations (DAEs) 
that must be solved simultaneously. Since a large system of DAEs needs to 
be solved at small time increments to minimize the errors in numerical 
integration of the differential equations, the transient stability analysis
is a highly compute intensive problem.

Traditional approaches to solving transient stability DAEs, such as
partitioning and linearization, have used simple trapezoidal integration
techniques that require very small step sizes during the integration steps.
Meanwhile, there has been significant research in the numerical analysis
community on fast, accurate, and efficient DAE solvers. 
However, very little work has been done to date on using these more recent
public domain DAE solvers for the transient stability problem. 
Numerous public domain DAE solvers exist, based on different computational
techniques. These include: 

% \begin{enumerate}
% \item DASSL -- a multistep, backward differentiation formulas technique 
% \item DASRT -- a variant of DASSL that handles discontinuities 
% \item CDASSL -- a parallel variant of DASSL 
% \item RADAU5 -- a multistep, implicit Runge-Kutta based technique 
% \item RODAS -- a Rosenbrock technique 
% \item LIMEX -- an extrapolation technique 
% \item SEULEX -- an extrapolation technique 
% \end{enumerate}

\medskip
1. DASSL -- a multistep, backward differentiation formulas technique 

\medskip
2. DASRT -- a variant of DASSL that handles discontinuities 

\medskip
3. CDASSL -- a parallel variant of DASSL 

\medskip
4. RADAU5 -- a multistep, implicit Runge-Kutta based technique 

\medskip
5. RODAS -- a Rosenbrock technique 

\medskip
6. LIMEX -- an extrapolation technique 

\medskip
7. SEULEX -- an extrapolation technique 

\medskip
DASRT may be suitable for this problem, since it has the capability of
locating discontinuities, which is a condition encountered in transient 
stability analysis. A parallel version of DASSL, CDASSL, has shown promising
results in a petrochemical engineering application.  
Inherent in all DAE solvers are sparse matrix solvers. 
In general, most of the calculations -- excluding the solution of the
sparse matrices -- are highly parallel.  The research required in this task 
is to identify the most efficient DAE solver for the specific nature of the
transient stability equations.

An additional source of parallelism in the power system
transient stability problem is the examination of multiple, independent
fault events simultaneously.
Such a technique could significantly improve speed-up and efficiency
on parallel computers by matching the simulation requirements at NMPC
to available computer architectures.  

\medskip
For this application we will: 
\begin{itemize}
\item Investigate the use of various DAE solvers and parallel sparse
      matrix algorithms, and the interfacing of these methods to create 
      fast parallel DAE solvers.
\item Benchmark the performance of these different algorithms on transient
      stability analysis using sample data from the NMPC power network and 
      larger regional networks, since the performance of DAE and sparse
      matrix solvers can be highly problem dependent.
\item Benchmark these programs on different parallel computers, in order to 
      determine the most productive and cost-effective hardware for
      this problem.
\item Use the most promising parallel DAE solver to produce a prototype 
      on-line transient stability analysis program.
\item Study the current PTI transient stability software used at the NMPC
      Planning department, and investigate the problems of interfacing
      this software with our parallel DAE solver and the contingency 
      evaluation software. 
\item Develop the basis for an expert system and improved user interface to 
      classify and display network output from the stability analysis program. 
\end{itemize}


\bigskip
{\bf IV. \hspace{2mm} Parallel Sparse Matrix Algorithms}
\medskip

    Sparse matrix calculations are some of the most challenging problems 
for parallel machines, but they are key to many power system applications,
including load flow and transient stability analysis,
for which they represent the majority of the computation.
Various organizations have efforts in progress to develop general concurrent
sparse matrix solvers.  There has already been significant progress in
this area, especially by Michael Heath at Oak Ridge National Laboratory, 
Ian Duff in Europe and groups at Sandia National Laboratory.

However, specialized parallel sparse matrix solvers which can capitalize on 
any regular structure in the sparse matrices for a particular problem will
generally give better performance than general solvers.
In-house research at Syracuse University is currently focussing on the
development of specialized sparse matrix solvers that exploit hierarchical
structure in the underlying power systems problems.  

There are distinct portions of the matrix that represent the 
generator equations and the power distribution network. 
Diakoptics theory and the actual matrix sparsity structure (the power 
distribution network structure) will be used to develop algorithms to 
reorder the sparse matrices resulting from power system applications 
into block-bordered diagonal form. 
The result of this reordering is that many of the operations that would be
sequential in a general solver are now highly parallel.  
This will simplify the development of scalable, portable algorithms.
Reordering can also be used to improve load balancing for both direct and
iterative techniques for matrix solvers.  

One of our graduate students is presently working on parallel sparse matrix
techniques especially suited for power systems applications. 
Another graduate student in our team has worked with a group at Lawrence 
Livermore National Laboratory on a related petrochemical simulation using
parallel DAE and sparse matrix solvers.  

Parallel sparse matrix solvers are critically dependent on a proper match 
between the particular problem and the machine architecture and ``general 
approaches'' are often less than optimal.
It is essential to have matrices of representative sizes to 
successfully research this real-life problem. 
We will work with NMPC to obtain the actual structure of the 
transmission network for three interconnected power grids:
 
\medskip
1. the NMPC network 
 
\smallskip
2. the New York State Power Pool network 
 
\smallskip
3. the Eastern US regional power network
 
\medskip
\noindent
% the NMPC network, the New York State Power Pool network, and
% the Eastern US regional power network.
It would not be surprising if our research indicates that different sparse 
matrix techniques are applicable for the different size networks. 

The problem size will also influence the applicable hardware.  
We expect that the sparse matrix solver for a system the size of the NMPC 
network, with around 300 generators and 2000 buses, would perform
well on the 32 node MIMD parallel computers currently available at NPAC.
However, a ``small'', say 10 generator and 50 bus network, system would not
have enough parallelism to easily make use of such machines.
Meanwhile, the large 10 teraflop ($10^{13}$ floating point operations
per second) parallel supercomputers available in the year 2000 will probably
contain thousands of nodes. This will be too much parallelism for a simulation
of the NMPC system, due to size of the sparse matrices, but could be
well suited for the transmission system covering a large geographic area.
The coordination of grids over a large region may be an exciting possibility 
opened up by the parallel supercomputers of the future.  

\medskip
For this application we will: 
\begin{itemize}
\item Monitor work by Heath and others on general sparse matrix solvers. 
\item Continue in-house research at Syracuse University to develop 
      specialized sparse matrix solvers that exploit the hierarchical structure
      in power systems applications.  
\item Compare the performance of direct and iterative methods, and investigate
      the use of composite direct/iterative methods.
\item Benchmark different parallel sparse matrix solvers for different sized
      power networks and parallel computers.
\item Interface the parallel sparse matrix solvers with the DAE solvers 
      also being investigated.  
\end{itemize}


\bigskip
{\bf V. \hspace{2mm} User Interface and Expert Systems}
\medskip

The time required for performing transient stability analysis includes the
time needed to interpret the large quantity of data produced by the program.
Currently, stability classification requires lengthy scrutiny by a trained 
engineer. 
In order to obtain maximum benefit from an on-line transient stability 
analysis program, this data analysis procedure must also be sped-up so as
to work in near real-time. 
This can only be achieved with some form of computerized tool to help with
the complex task of interpreting the output of the stability program.
We propose to implement a high-level user interface incorporating expert
system and possibly neural network technology to greatly reduce the time 
required for this task.

Expert systems have been under intense study by research organizations for
the last 10 years, and thousands of expert systems are now in use in a wide
variety of applications in many large corporations.
There has already been a considerable amount of research into the use of 
expert systems in the power industry, including transient stability analysis.
Some experimental transient stability expert systems have been implemented,
with promising results.

Professor Mike Nilan of the School of Information Studies at Syracuse 
University is an expert in cognitive science, user interfaces, 
data representation, and expert systems, 
and has worked successfully with Niagara Mohawk on previous projects. 
Under the direction of Professor Nilan, we will develop the basis of
a high-level user interface for the transient stability analysis program.
This will include an expert system to reduce the amount 
of program output which requires human interpretation, for example by 
filtering out ``obviously'' transient stable cases, and to represent the data 
which does require analysis by the operator in a form which can be more easily
interpreted. This will reduce the training overhead for users of the program.
Dr Salim Hariri of NPAC has experience at implementing expert systems and
will be working on this aspect of the project.

The benefits of such a high-level user interface are not just in allowing
near real-time analysis, but also simplification of operator training, 
minimization of human error, and time independent expertise, since the expert
system is available on-line at any time.
The system can also be improved by accumulating additional knowledge over time.

\medskip
For this application we will: 
\begin{itemize}
\item Work with NMPC engineers and operators in the Planning and Operations 
      departments who are currently using transient stability analysis, 
      or hope to use it in the developed product, in order to
      develop a user-based decision process model and undertake knowledge 
      acquisition and analysis for the development of an expert system
      and its user interface.
\item Demonstrate the feasibility of such a system for fast analysis of
      transient stability data.
\item Study the recent research on the application of neural networks to 
      the power industry, in particular to stability classification, 
      and exploit the expertise in neural networks at Syracuse University to 
      investigate their application to this problem. 
\end{itemize}


\bigskip
{\bf VI. \hspace{2mm}  Parallelism and Computational Issues}
\medskip

STAG, the vendors of the software currently used by the Operations group
at NMPC, are currently porting this software to a Local Area Network of 
IBM RS/6000 workstations. 
This environment appears to be more viable for use with parallel processing
than the current STAG software run on IBM mainframes, 
since the new software will be more up-to-date and portable.
Software running on a distributed workstation environment should
allow easier interfacing to parallel machines, and 
both the software and hardware of such a system should also be easier to 
maintain and more cost-effective, robust, and reliable than the current system.
This should thus allow a substantial reduction in system down-time, which is 
a major concern of the Operations group. 

The new STAG contingency evaluation program is expected to be available in 
the third or fourth quarter of 1993, and most other features of their
Energy Management System will be available before then.
This release schedule will allow us to evaluate much of the new software
during the study phase, and allow us the possibility of using their new
software in the production system.

The parallel hardware and software used in a production system should be
readily interfaced to existing and future systems, and needs to be scalable. 
This will allow the initial product to be used by NMPC, but should
it become desirable, enable the system to be scalable in both hardware
(the number of processors) and software to accommodate larger networks.  
Larger networks might incorporate additional detail by including lower voltage
buses, the network for the New York Power Pool, or the network for the entire
North East power grid.

\medskip
For the power systems applications to be studied in this phase, we will: 
\begin{itemize}
\item Conduct an extensive evaluation of the current software used at NMPC,
      as well as the new STAG EMS software and other available power systems
      software, to determine how easily these programs can be interfaced and
      modified to include parallel processing capability.
\item Evaluate the relevant architecture and software issues for using 
      parallel computers, including scalability and portability.
\item Provide recommendations for hardware and software for the systems to 
      be developed in the production phase.
      This will include the type of hardware to be used, the number of
      processors required, and an appropriate parallel software environment.
\end{itemize}

\bigskip
{\bf VII. \hspace{2mm} Survey of High Performance Computing and Parallel} \\
{\bf \hspace*{18mm} Processing in the Utility Industry}
\medskip

During the study phase, we also propose to conduct a detailed
survey of High Performance Computing and parallel processing in the 
utility industry.
We will extend the TASC report mentioned above in two ways.  
Firstly, we will work with NMPC, especially in the load planning 
area and perhaps also the nuclear plant simulation problem, to extend and 
improve the analysis of the applications described in that report.  
Secondly, we will discuss more carefully the computer architecture and 
software issues needed to implement HPC in the TASC applications and 
further areas suggested by NMPC.

We have provisionally defined eight key HPC technologies of interest.
These are:

\begin{enumerate}
\item  Sparse Matrix Algebra
\item  Signal Processing
\item  Numerical Optimization 
\item  Component Modeling
\item  Expert Systems
\item  Neural Networks 
\item  Visualization and Virtual Reality 
\item  Computational Fluid Dynamics (CFD) and Continuum Simulations 
\end{enumerate}

\noindent
% ****  Add the table?
% In the table, we indicate a preliminary identification of technologies 
% with application area.  
We are familiar with the state-of-the-art in all the above technologies. 
Each technology has rather different computer architecture and software 
requirements. For instance, signal processing, 
neural networks and CFD are naturally suitable for massively parallel SIMD 
systems.  Sparse matrix manipulation and component modeling requires the 
flexibility of the MIMD architecture.  Expert systems, numerical optimization 
and virtual reality are naturally implemented in a heterogeneous network 
involving high performance workstations.

For this project, we will quantify the comments above and provide a survey 
relating utility applications to the most appropriate technologies, both
hardware (SIMD, MIMD, number of processors, etc.) and software (existence
or feasibility of parallel algorithms, etc.). We will 
indicate where it is realistic to use HPC and parallel processing 
in the electric utility industry over the next ten years.


% \newpage
\bigskip
\bigskip
\noindent
{\large \bf 2) \hspace{1mm} Production Phase}

% \bigskip
% {\bf I. \hspace{2mm} Product  Development}
\medskip

% 
% **** Elaborate on this?? ****
%

During the study phase, we will talk with NMPC and other companies in 
the power utilities, software and computer industries about developing
the prototype systems developed in the study phase into a commercial 
product for use at NMPC. We envisage forming a consortium of interested
corporations to fund and develop such a product.

Based on these negotiations with NMPC and other corporate partners, 
and the results obtained in the study phase, we will submit a further
proposal for a second phase of this project, to develop a commercial 
product. We anticipate that this will include a system of parallel
hardware and software for unit commitment and an on-line near real-time 
security model incorporating contingency evaluation and transient stability
analysis. 
This system will include an improved user interface with an expert system 
for fast analysis of stability data. We expect that this production 
phase will take on the order of 18 months.

During this phase we will also continue our research into more efficient 
parallel algorithms, particularly for sparse matrix problems. 
We will also continue research into the application of neural networks to 
the project,
and work with NMPC operators and engineers to develop and improve the user
interface and the knowledge base for the expert system. 

\newpage

{\large \bf Summary of NPAC and NMPC Deliverables}

\medskip
\bigskip
{\bf Meetings}

\begin{itemize}
\item NPAC faculty, research staff and graduate students associated with this
      work will meet with NMPC staff and consultants for one to three meetings,
      as required, each month to review progress, discuss problem areas and
      discoveries, and for general information exchange.

\item NMPC will arrange for appropriate application area personnel to meet with
      NPAC researchers in order to conduct the study and evaluation program,
      acquire knowledge for the expert system,
      and to provide software, data, and other requirements for the 
      contingency evaluation and transient stability analysis package.
\end{itemize}


\bigskip
{\bf Reports}

\begin{itemize}
\item Progress reports will be delivered by NPAC to NMPC at the end of
      each month. These reports will outline the status of each of the 
      five main research and development areas:

      \begin{enumerate}
      \item Unit Commitment
      \item Contingency Evaluation
      \item Transient Stability Analysis
      \item Parallel Sparse Matrix Algorithms
      \item User Interface and Expert System
      \end{enumerate}
 
      We will also report on the status of our research into computational
      issues for the above areas, and on the survey of applications of HPC
      in the utility industry.

      A work plan and goals for the following month will also be included.

\item A detailed status report for the study and evaluation phase 
      will be delivered by NPAC to NMPC at the end of 6 months. 

\item A final report on the results of the study phase will be delivered by
      NPAC to NMPC at the end of 9 months. The report will also include 
      detailed descriptions of the following items for each of the five
      research and development areas listed above:
      \begin{enumerate}
      \item Theoretical background. 
      \item Research issues and new results.
      \item Methods and algorithms used. 
      \item Computational issues, including parallel algorithms, \\
	    languages, software and hardware.
      \item Design and implementation of the system. 
      \item Evaluation of the performance of the system. 
      \end{enumerate}
      %
      The report will also contain:
      \begin{itemize}
      \item A comparison of different DAE and sparse matrix solvers.
      \item Documentation for the prototype software, including a user \\
	    guide and reference manual detailing the major subroutines.
      \item An analysis of a possible consortium of corporate partners \\
	    for development of the prototype system into a product.
      \item Recommendations for the design, hardware and software for \\
	    the system to be developed in the production phase. 
      \item A general survey of the role of High Performance Computing \\
	    and parallel processing in the utility industry.
      \end{itemize}

\end{itemize}


\bigskip
{\bf Software}

\begin{itemize}

\item NMPC will have unlimited access to the software developed for
      the prototype system.
      % **** How do we say this politically correctly? ****

\item NPAC will retain rights to the use of the parallel DAE and sparse 
      matrix solvers developed during the project.

\end{itemize}

\newpage

\noindent
{\bf Appendix 1. \qquad \large \bf ACTION-NYS}
\bigskip

This project is part of our new industrial economic development program
ACTION-NYS, which is partially funded by New York State. We hope that this 
project will be one of our showcase industrial applications. The State 
funding will allow us to reduce the level of funding from Niagara Mohawk,
since ACTION will cover a substantial part of the costs of the proposal.

ACTION-NYS is the technology transfer program of the Northeast
Parallel Architectures Center (NPAC).  It introduces and integrates
parallel computing into New York State industry by enabling NPAC
scientists and affiliated Syracuse faculty to apply their parallel
computing knowledge to large, complex industrial applications.  ACTION
also provides industrial access to the latest parallel machines, as
well as education and training in high performance parallel computing.

Direction for the ACTION program is provided by Professor Geoffrey Fox
with guidance from the ACTION High Performance Computing Industrial
Advisory Board consisting of 17 top executives from New York State
industries and the ACTION Regional Economic Development Committee
comprised of representatives from Central New York economic
development agencies. 
Niagara Mohawk is represented on the Advisory Board.

The ACTION Program operates by reaching out to New York State industry
to explore opportunities in parallel computing.  In cooperation with
high level technical executives of targeted corporations, ACTION
initially prepares a survey of potential for parallel computing for
that industry.  The experienced NPAC staff then determines the most
effective methodology for integration of parallel computing into the
industry.  Typically, small ``proof-of-concept'' projects are initiated
to demonstrate that parallel computing is beneficial to the industry.
The next step is to identify a large applications project that
actually begins to integrate parallel computing technology into the
processes of the corporation.  
A number of industrial projects are already completed or underway, with
such companies as IBM, General Electric, and Sonnet Software.

Other activities of ACTION include the quarterly publication of the
newsletter, Parallel Computing News, produced to inform NPAC's clients
and advisors of current developments in the field of high performance
parallel computing.  Education and training are also offered to
prepare the next generation of workers and to upgrade skills of the 
present workforce.
Geoffrey Fox and other ACTION-affiliated
experts often address groups of industrial executives to introduce
them to issues and uses of parallel computing.

High performance parallel computing can play an important role in the
development of industries and the advancement of technologies that
lead to industrial development.  The ACTION program contributes to
this effort through the dissemination, application and integration of
parallel computing to help industries to develop new consumer and
industrial products, and the development of new processes to increase
productivity and the competitiveness of existing products.

\end{document}

