EES revenue through customer DSM projects ,
 ---------------------- Forwarded by Vince J Kaminski/HOU/ECT on 04/19/2001 =
04:28 PM ---------------------------


Jeff Gray@EES
04/17/2001 12:30 PM
To:=09Vince J Kaminski/HOU/ECT@ECT
cc:=09Michael Moore/HOU/EES@EES=20
Subject:=09EES revenue through customer DSM projects

Vince:

Have you or a member of your group had a chance to look over the forwarded =
e-mail yet?  We have a meeting on Thursday with another potential client wh=
o plans to use his own capital.  His capital budgeting issues are similar t=
o those of the client who prompted my original e-mail.

The more DSM projects that we can help the client get approved, the more DS=
M savings we can share with the client.  These DSM projects can turn a reta=
il commodity contract with low profitability into a substantially more prof=
itable contract for us.

Would one of your GARCH analyses provide us with a quantitative solution?  =
One of your experts in differential equations might have devised something.=
  We'd really like to have something quantitative if you think it's at all =
possible.

Thanks,

Jeff

---------------------- Forwarded by Jeff Gray/HOU/EES on 04/17/2001 12:16 P=
M ---------------------------


Jeff Gray
04/09/2001 04:41 PM
To:=09Vince J Kaminski/HOU/ECT@ECT
cc:=09Michael Moore/HOU/EES@EES, Jay Sparling/HOU/EES@EES=20
Subject:=09DSM projects as real options  =20

Vince:

Could you spare a few moments to look over the attached three-page Power Po=
int presentation?  At a minimum, we're trying to demonstrate the following:=
  In high-volatility electricity environments, for those EES customers who =
employ their own capital, their usual capital-project IRR hurdles may be lo=
wered slightly in the special case of DSM projects, to allow for the extra =
value derived from reduced risk exposure stemming from reduced power consum=
ption/dependence.

I'm pushing the boundaries of real-option theory (and good sense) and viewi=
ng a DSM project as a series of options, the values of which are additive. =
 First is the option to engage in the DSM project itself; which, if we exer=
cise the option immediately, can be valued intrinsically through a simple N=
PV analysis.  Second is a strip of put options to generate "negawatts," ext=
ending over the useful life of a DSM installation, the cumulative value of =
which is an extrinsic value associated with the DSM project.  These options=
 are exercised sequentially--over the life of the DSM installation--wheneve=
r the customer experiences a dramatic, stochastic spike in electricity pric=
e, associated with a lognormal price distribution.  I'm calling the value o=
f this strip a "residual time value" associated with the original DSM optio=
n.  If we can add this residual time value to the NPV and derive a total va=
lue that is quantitatively higher than a simple NPV alone,  we may be able =
to help the customer get more projects approved, even at the original high =
IRR hurdle.

Alternatively, and more feasibly, we'd like to give these same customers a =
"qualitative" tool with which they can convince their finance gatekeepers t=
o lower--for DSM projects--their standard capital-project IRR hurdle .  The=
 attached presentation is a first attempt at this qualitative argument.

We intend to use this only as a sales and marketing tool, to allow at least=
 one of our customers who has an onerously high hurdle rate to manage aroun=
d his company's internal capital-budgeting requirements.  He has set aside =
a large sum of money for DSM projects, but will only be able to spend a sma=
ll portion of it under his company's current capital budgeting methodology,=
 which does not take into account forward commodity price volatility.

In your opinion, is there any hope in devising a quantitative justification=
, or will we have to stick with the qualitative argument as described in th=
e attached presentation?  In other words, is it possible to quantify "resid=
ual time value" as I've defined it above? Or, even better, are you aware of=
 a more practical way of conceptualizing this problem?

Thanks,

Jeff Gray

=20

=20



Vince J Kaminski@ECT
01/05/2001 03:26 PM
To:=09Jeff M Gray/NA/Enron@ENRON
cc:=09Vince J Kaminski/HOU/ECT@ECT, Stinson Gibner/HOU/ECT@ECT, Alex Huang/=
Corp/Enron@ENRON, Gary Hickerson/HOU/ECT@ECT, Michelle D Cisneros/HOU/ECT@E=
CT=20
Subject:=09Power Plant Model

Jeff,

A few comments on the model:

1. We have a few reservations about some features of the model but would li=
ke to
discuss it internally and make the improvements without giving the benefit =
of our insights to the consultant.
In general, the model is not unreasonable but the devil is always in the de=
tails and in the inputs and
calibration. The same model may produce drastically different results depen=
ding
on the quality of inputs.=20

2. We don't have a separate pool of programmers in the Research  Group. We =
were told that you
would provide an IT resource. Alex would supervise this person.


Vince








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