Exhibit 99.468

CLEARING PRICES COMPUTATION FOR INTEGRATED GENERATION, RESERVE AND TRANSMISSION
MARKETS

Shangyou Hao Dariush Shirmohammadi
Perot Systems Corporation
Los Angeles, CA, USA

Abstract: In a competitive electricity market, trading and scheduling for
electric energy, transmission and reserve related services are often the
centerpieces of market functions. The operation of these three markets may
reside in centralized pools, system operators or power exchanges. However,
they are often conducted separately and sequentially in practice.

This paper sets forth a new formulation to determine schedules and clearing
prices for integrated electric energy, transmission and reserve markets
simultaneously. The formulation yields locational market clearing prices when
transmission congestion is present. Example results based on the new
formulation are presented.

I. INTRODUCTION

Complex interactions between daily generation schedules, transmission
constraints and reserve requirements need to be accounted for in determining
schedules and clearing prices of a competitive electricity market. The
delivery of electric energy is not possible without the use of transmission
system and capacity reserves. In many established electricity markets energy
scheduling, transmission congestion and reserve management are decomposed into
component products, allowing these products to be traded or scheduled in
centralized power pools, system operators or power exchanges.

However, these component products are often traded and scheduled separately
and sequentially in practice. For example, in the California [1], day-ahead
energy schedules are determined by Scheduling Coordinators; Transmission
congestion is managed by the Independent System Operator (ISO); and unloaded
capacities along with capacity price bids are then submitted into regulation,
spinning, non-spinning and replacement reserve markets. Each market is
independently priced and scheduled at different time. Similar arrangements
exist in the electricity markets of New York [2], PJM [3] and others around
the world.

In the power pool of England and Wales, dispatch orders and prices for energy
are determined using unconstrained system marginal price that is set at the
highest offer price of generating units being [4]. However, when transmission
constraints are detected, constrained dispatch program is executed and schedules
are adjusted. The rescheduling costs are then charged to consumers as
transmission service uplift.

Many have studied formulations and algorithms of generation scheduling and
pricing methodologies. Ma[5] reviewed different dispatch algorithms such as
merit order dispatch, sequential and joint dispatch of energy and reserve. He
proposed a joint dispatch algorithm that minimized costs and incorporates
several zonal interface constraints to account for inter-zonal trading. Hybrid
method of solving for multi-commodity schedules is investigated by Cheung [6]
for the interim ISO New England. Post [7] investigated how energy schedules
can be determined by a central power pool to minimize total generation costs
while individual bidders construct their bids to maximize profits. Guan [8]
discussed the limitation of using ramp rates in Unit Commitment algorithm and
proposed a concept of realizable schedules. Auction design and schedule issues
related to using different objectives were studied in [9]. Sheble [10] proposed
a framework for coordinating financial and physical electricity markets.

In [11], Cadwalader recognized the need for security-constrained, economic
dispatch methodologies for computing market equilibrium of these component
markets. He pointed out that pricing is the integral part of the market
equilibrium along with he schedules that maximize the bidders' profits or
utility. However, in the proposed formulation, clearing prices are still the
by-products of an OPF cost minimization problem and derived from the dual
variables of the solution. Chattopadhyay [12] discussed the modeling
requirement for price based generation scheduling and examined the importance
to add transmission constraints in scheduling energy and reserves.

The formulations studied so far are based on variations of unit commitment of
OPF models. While traditional unit commitment or dispatch techniques can be
used to minimize bidding costs, locational clearing prices for generation
capacity and transmission are difficult to compute. On the other hand, while
OPF techniques have been deployed to compute transmission prices, it has
difficulties for scheduling reserves and enforcing clearing pricing rules.

Our objective is to set forth a new formulation that solves for schedules and
clearing prices for electric energy, transmission and reserve capacity markets
simultaneously. This should lead to better economic efficiency and reduced
transaction costs due to the integration of the component markets and
simplification of market processes.

The paper has five main sections. After the introduction, Section II lists the
notations used in this paper. The proposed

formulation is described in detail in Section III. We will also discuss the
features and properties of the new formulation. Section IV presents application
examples. Section V contains a summary of the paper.

II. NOTATIONS

K: Number of generators.
X: Generation energy output vector, dim(X) =K.
Y: Generation reserve capacity vector, dim(Y) =K.
C: Bidding price (function of X and Y) vector, dim(C) =K.
Qmax: Maximum generation vector, dim(Qmax) =K
N: Number of buses in the network model.
M: Number of branches in the network model.
Ox: Voltage angle vector due to energy schedule only, dim(0) =N.
Oy: Voltage angle vector due to reserve use, dim(Oy) =N.
Dx: Energy demand vector, dim(Dx) =N.
Dy: Reserve demand vector, dim(Dy) =N.
Px: Energy clearing price vector at each bus, dim(Px) =N.
Py: Reserve capacity clearing price at each bus, dim(Py) =N.
Px(b): Energy clearing price for generators, dim(Px(b)) =K.
Py(b): Reserve capacity clearing price for generators, dim(Py(b)) =K.
Fx: Branch flow vector due to energy schedule only, dim(Fx) =M.
Fy: Branch flow vector due to reserve schedule only, dim(Fy) =M.
Fxy: Branch flow vector due to both energy schedule and reserve used,
dim(F(xy) =M.
Fmax: Branch flow maximum limit vector, dim(Fmax) =M.
Bx: Branch congestion cost vector of energy schedule, dim(Bx) =M.
By: Branch congestion cost vector of reserve schedule, dim(By) =M.
B: DC network admittance matrix (symmetric) ignoring branch resistance,
dim(B) =NxN.
A Network incidence matrix, dim(A) =NxM.
I: Diagonal matrix with diagonal elements being the reciprocal of branch
admittance, dim(IB) =MxM.
J: Indices for identifying balanced generation and demand resources.

III. FORMULATION

A. Market Structures

Various pricing and scheduling rules have been developed in order to address
unique issues facing each market. However, there are some common attributes for
the established electricity markets. To set up our new formulation, we make a
few assumptions about the market structures used in this paper. These
assumptions are intended to elaborate the proposed algorithm and solution;
however, the algorithm and solution are not necessarily limited by the
assumptions.

Specifically, we consider the following market rules:

- One reserve market is assumed and the impact of ramp rate is ignored.
- The DC transmission system model is uded and the B matrix is
symmetric (no phase shifters).
- No double auction is considered for simplicity. This means that
demands are treated as inelastic.
- Schedules of different time periods are independently calculated. This
means that hourly generation schedules are determined independently.
- Both the energy and capacity demands are know.
- Market operators are payment neutral. This means that the net payment
is zero for market operators. The congestion related network surplus,
if any, is paid to transmission owners.
- Clearing prices principles apply to the markets. When there is no
congestion, uniform clearing prices are used for all bidders.

We assume that bidders submit to the market operator a monotonically increasing
and non-negative price curve as a function of output quantity. This curve
specifies the minimum price that the bidders will accept for the unit to
operate at that level. The curve will also be used by the market operator to
schedule reserve capacity and to compute reserve clearing prices. Further, the
energy payment from the reserved capacity, when being called upon during
real-time operation, will be equal to or more than that of the forward energy
clearing prices.

As shown in Figure 1, a generator can potentially receive three
payments:forward energy, reserved capacity and real-time energy if being
called. For a marginal unit whose forward energy schedule is x, total schedule
is x+y, and real-time instructed output is z, the three payments are
represented by areas of OECX, ABCD and CGZX, respectively.

[GRAPH]

B. Proposed Formulation
2

The proposed formulation is a minimization problem as follows:

Solve for P(x1) P(y1) X(1) Y(3) 0(x3) B(x) and B(y) that
Minimize (P(x1) D(z) + P(y1) D(y)) (1)

Subject to:
B 0(z) = X - D(z) (2)
B 0(y) = Y - D(y) (3)
0(z1) = 0 (4)
0(y1) = 0 (5)
X(1) Y(1) P(x1) P(y), B(x3) B(y) > 0 (6)
-
F(0(x)) - I(B) A(t) 0(z) [ILLEGIBLE] (7)
F(0(z)+0(y)) = I(B) A(1) [ILLEGIBLE] F(max) (8)
C(X) [ILLEGIBLE] (9)
C(X+[ILLEGIBLE]C(X) [ILLEGIBLE] (10)
X + Y [ILLEGIBLE] (11)
B P(z) - [ILLEGIBLE] B(z) = 0 (12)
B P(y) - A[ILLEGIBLE] = 0 (13)
E(kcj(k)) (x(k) - d(k)) = 0 (optional) (14)
E(kcj(k)) (y(k) - a(k)) = 0 (optional) (15)

C. Discussions

1. Objective

The objective in (1) represents the total payment by consumers. This payment is
computed as the inner product of the demand vectors with the clearing price
vectors. The same clearing prices are used to pay suppliers and charge
consumers. Consequently, the total payment from consumers is the same as the
payment credited to generators when transmission congestion is not present. The
use of total consumer payment minimization tends to reduce both generation
payment and transmission congestion costs.

2. Constraints

The DC network model for the transmission system is represented by (2) and (3)
which describe network balance for energy and reserve capacity schedules,
respectively. Voltage angels of reference bus are set using (4) and (5). Eq.(6)
ensures that all price and schedule variables are positive.

Transmission branch flow limits are enforced by (7) and (8). These limits are
applicable to both energy schedules and total schedules (energy and reserve).
For simplicity, we assume flow direction is known and flow constraints are not
bi-directional.

Behaviors of profit maximizing bidders are modeled by (9) and (10). These
constraints ensure that the energy and reserve clearing prices are no less than
their bids. Consequently, all generators are sufficiently compensated with the
final clearing prices. Eq.(11) is used to enforce generation output limit.

We use (12) and (13) as network price constraints. They are critically
important in that they link the clearing prices at different buses to satisfy
the network surplus requirements. Network surplus is defined as the difference
of total payment received from demand users and credited for suppliers. When
there is no transmission congestion, constraints (12) and (13) will ensure that
the clearing prices at all buses are identical and the network surplus is zero.
If B(x)=0, (12) becomes:

B P(x)=0

The rank of B admittance matrix in n-1. Thus the solution for P(x) must lie in
the null space of B. Using the property of B matrix, we can obtain:

P(x) = p [1,1,1, ...1](t) (16)

Where p is a scalar. Hence, all clearing prices are identical. Without these
constraints, the solution can lead to different clearing prices when there is
no transmission congestion.

When transmission lines are constrained, we will show later that network
surplus is maintained and market operator receives adequate revenue from the
resulting clearing prices.

The motivation for introducing the network price constraints stems from the
solution process used in traditional OPF formulation. The gradient vector of
Langrangian with respect to the bus angel yields the necessary optimality
condition to an OPF problem. This condition is the basis of locational marginal
prices in the OPF formulation and sets forth many desired properties between the
locational prices and branch congestion prices. In order to inherit these
desired properties for the computed prices, we therefore impose this optimality
condition in the original OPF problem as the new network price constraints.

Eqs. (14) and (15) are optional market separation constraints. These
constraints, if enforced, ensures that a set of energy schedules or capacity
reserves are balanced for market participants. These can be used in modeling
bilateral trading arrangement. In California and Texas, for example, energy
schedules for each scheduling coordinator must be balanced.

Additional constraints can be incorporated to deal with the requirements of
specific market rules. For instance, when a unit participants only in the
reserve or energy market, the energy variable (x) or reserve variable (y) can
be constrained to be zero.

D. Properties of the New Formulation

PRICE CLARITY AND SIMPLICITY: The explicit use of pricing variables provides
price clarity to markets. With the traditional method of scheduling, prices are
often computed as the by-products of the solution process. In contrast, the new
formulation uses prices as control variables for scheduling and no separate
process is needed for clearing price computation. With these prices, each
bidder's profit is maximized. In addition, opportunity costs are often needed
for compensation for redispatched or constrained generators by market
operators. Use of opportunity costs has been very controversial and often adds
to price ambiguity. With the new formulation, there is no need for computing
these opportunity costs.

COMPETITION FOR TRANSMISSION USAGE: The formulation allocates transmission to
energy and reserve use according to the economic bid data rather than
heuristics. Therefore, market operators no longer need to guess the amount of
transmission that sets aside for reserve use.

REDUCED TRANSACTIONS COSTS: A large amount of transactional costs are incurred
for market participants and operators as the process of market operations
becomes more complex. With this formulation, transactions for market operators
and participants are straightforward and associated transaction costs can be
reduced. For instance, market participants need only submit one bid curve for
all three markets, thus simplifying the data processing and management.
Furthermore, iterations between the constrained and unconstrained market prices
can be eliminated.

MODELING OF DISTRIBUTED RESERVE REQUIREMENT: The proposed formulation allows
modeling of the distributed reserve requirements at different locations.
Although the reserve requirements are often proportional to energy demand,
there are cases that some areas may have more reserve requirements.

GENERATOR PAYMENT ADEQUACY: The total payment received by any generator is
equal to or more than its costs computed using its bid curve. Since on-marginal
units are paid at higher clearing prices, it suffices to show that marginal
units can fully recover their bidding costs. As shown earlier in Figure 1, the
total payment credited to the marginal unit is represented by areas of OECX,
ABCD and CGZX, and is greater than its bidding cost (area OCFZ).

NETWORK SURPLUS ADEQUACY LEMMA: The network surplus is always positive and is
exactly the sum of branch congestion prices multiplied by their flows.

PROOF: Taking the inner product of (2) with Px leads to:

[FORMULA OMITTED] (17)

Substituting (12) into (17),

[FORMULA OMITTED] (18)

Substituting (7) into illegible

[FORMULA OMITTED] (19)

Similarly, we can show:

[FORMULA OMITTED] (20)

COROLLARY: When there is no congestion, the total consumer payment is the
same as that total payment credited to generators. This can be derived by
setting Bx=O and By=O in the above lemma.

IV. APPLICATIONS

This section presents numerical results of applying the new formulation for
computing clearing prices and schedules for integrated electricity markets.

The looped network used by the example is shown in Figure 2. The network has 3
buses and 5 generators. For simplicity, all three branches have equal
impedances. The bid price curve is represented as co + c1*q where q is the
output quantity. The bid parameter, energy and capacity demands used in the
example are listed in Tables 1 and 2.

Table 3 shows 5 result sets with different demand and network parameters. The
parameter changes from the base case are in bold fonts. These results are
discussed below. No market separation constraints are applied in the example.

[GRAPH]

Table 3: Example Results

DESCRIPTION BASE CASE CASE A CASE B CASE C CASE D
- ----------------------------------------------------------------------------------------------------------------------------

Energy price at Bus 1 (Ps1) 27.5325 26.5185 27.6481 27.5325 27.6481
Energy price at Bus 2 (Ps2) 27.5325 27.7778 28.4722 27.5325 28.4722
Energy price at Bus 3 (Ps3) 27.5325 29.0370 29.2963 27.5325 29.2963
Reserve price at Bus 1 (Py1) 0.7792 0.6704 0.6704 1.0823 0.3741
Reserve price at Bus 2 (Px2) 0.7792 0.8056 0.8056 1.1898 1.3611
Reserve price at Bus 3 (Py3) 0.7792 0.9407 0.9407 1.2972 2.3481
Energy schedule of Unit 1 (X1) 30.0000 30.0000 30.0000 30.0000 30.0000
Energy schedule of Unit 2 (X2) 37.6623 32.5926 38.2407 37.6623 38.2407
Energy schedule of Unit 3 (X3) 18.4416 19.2593 21.5741 18.4416 21.5741
Energy schedule of Unit 4 (X4) 5.0649 5.5556 6.9444 5.0649 6.9444
Energy schedule of Unit 5 (X5) 8.8312 12.5926 13.2407 8.8312 13.2407
Reserve schedule of Unit 1 (Y1) 0.0000 0.0000 0.0000 0.0000 0.0000
Reserve schedule of Unit 2 (Y2) 3.8961 3.3519 3.3519 5.4117 1.8704
Reserve schedule of Unit 3 (Y3) 2.5974 2.6852 2.6852 3.9658 4.5370
Reserve schedule of Unit 4 (Y4) 1.5584 1.6111 1.6111 2.3795 2.7222
Reserve schedule of Unit 5 (Y5) 1.9481 2.3519 2.3519 3.2429 5.8704
Energy demand at Bus 3 (D3) 50.0000 50.0000 60.0000 50.0000 60.0000
Reserve requirements at Bus 3 (A3) 3.0000 3.0000 3.0000 8.0000 8.0000
Energy branch flow from 1 to 2 (Fx2) 14.7186 12.5926 13.2407 14.7186 13.2407
Energy branch flow from 2 to 3 (Fx2) 13.2251 12.4074 16.7593 13.2251 16.7593
Energy branch flow from 1 to 3 (Fx3) 27.9437 25.0000 30.0000 27.9437 30.0000
Reserve use from 1 to 3 (Fx3) 0.3160 0.0000 0.0000 2.0563 0.0000
Flow limit from 1 to 3 (Fmax3) 30.0000 25.0000 30.0000 30.0000 30.0000
Congested branch energy cost (Bx3) 0.0000 3.7778 2.4722 0.0000 2.4722
Congested branch Reserve cost (Bx3) 0.0000 0.4056 0.4056 0.3222 2.9611
Energy network surplus 0.0000 94.4444 74.1667 0.0000 74.1667
Capacity network surplus 0.0000 0.0000 0.0000 0.6626 0.0000
Total consumer payment 2761.0390 2817.1796 3168.7074 2771.5227 3185.1519

In Base Case, transmission constraints are inactive, resulting in uniform
clearing prices for both energy and reserve markets, as well as a zero network
surplus. Although the transmission constraints are inactive, the network price
constraints are still playing an important role. Before solving for the
schedules, transmission flows are unknown and congestion is undetermined.
Simply allowing different locational prices are computed without any
transmission limitation. On the other hand, if we use pre-defined congestion
and restrict tradings between locations, we may not fully capture the economic
efficiency due to the inter-locational trading. To confirm this, we remove the
network price constraints in the base case and solve for the prices and
schedules. In the new solution (not shown in Table 3), the objective is reduced
to be 2707.89, and the different energy clearing prices at the three buses are
computed as (26.00, 28.37, 28.81). If 28.81 is chosen as the clearing price,
the total energy payment from consumers alone will be 2881 -- a less optimal
solution.

In case A, we derate the flow limit of Branch 3 from 30 to 25. Branch 3 becomes
congested since the unconstrained flow is 27.9437. This leads to reduction of
output in Bus 1 and increase of more expensive units in Buses 2 and 3.
Consequently, higher zonal clearing prices at Buses 2 and 3 are computed.

Let's examine the energy price at Bus 3. When energy is delivered from Bus 1 to
Bus 3, two thirds flow through Branch 1 and one third goes through the parallel
path on Branches 1 and 2. At market equilibrium, we note that [FORMULA OMITTED].
The clearing price at Bus 2 is a combination of the clearing price at Bus 1 and
the transportation cost from Bus 1 to Bus 3. The network surplus 94.4444 is the
difference between demand payment and generator credit payment. It can also be
computed by multiplying the total flow of Branch 3 with the branch congestion
price.

In Case B, we examine the solution behavior when the energy demand at Bus 3
increases from 50 to 60. the objective function is increased by 407.0084. This
increase is due to three factors: network surplus, cost increase for additional
generation, and additional payment due to the price increases. Because of the
increased demand, energy clearing prices at all buses have increased and the
largest increase occurs at bus 3. However, reserve prices remain the same as in
Case A.

Case C is rather interesting. We increase the reserve demand at Bus 3 from 3 to
8. Branch 3 is unconstrained for energy schedules. Hence, uniform energy
clearing prices are computed for all three buses. However, there is not enough
transmission to support the reserve use from 1 to 3. Therefore, a congestion
price of 0.3222 is assessed to the capacity reservation for this branch.
Consequently, different

clearing prices for reserve are computed and a network surplus of 0.6626 (the
product of reserve usage 2.0563 and congestion price 0.3222) is computed due to
the insufficient transmission for reserve.

Finally, we simultaneously increase the reserve demand and energy demand at Bus
3 as shown in Case D. Branch is constrained for energy delivery. This
results in different energy and reserve clearing prices for all three buses.
The largest price increases for reserve is also at Bus 3. Because the energy
demand is the same as in Case B, the energy clearing prices are the same as in
case B.

V. CONCLUSIONS

In this paper we proposed a new formulation that solves for schedules and
clearing prices for integrated electric energy, transmission and generation
capacity reserve markets simultaneously. The direct use of price control
variables and innovative network price constraints are the most significant
aspects of the formulation. Moreover, we incorporated a DC transmission
network models and yields locational clearing prices when transmission
congestion is present.

The new formulation has several advantages over traditional prices and
scheduling methods. We overcome the shortcomings of clearing price computation
in the traditional unit commitment and optimal power flow techniques by using
price based dispatch methods and innovative price constraints. Because prices
are used as control variables, post processing for price calculation is no
longer required and there are no price ambiguity to market participants. All
bidders can easily verify that their profits are maximized. Furthermore,
competition for transmission allocation between energy and reserve market is
addressed. The new formulation offers the ability to integrate three
different, but inter-related, markets for better economic efficiency as well as
for reducing market transactions costs. The formulation can be adopted for
real market applications.

VI. REFERENCES

[1] H. Singh and A. Papalexopoulos, "Competitive Procurement of Ancillary
Services by an Independent System Operator," IEEE Trans. on Power Systems,
Vol. 14, No. 2, May 1999, pp. 498-504.

[2] New York Independent System Operator, Day Ahead Scheduling Manual, New York
Independent System Operator, Sep. 1999,//www.nyiso.com.

[3] PJM Interconnection, L.L.C., Operating Agreement Accounting -- Manual M-28,
June 01, 2000,//www.pjm.com.

[4] NGC Settlement Limited, "Introduction to Pool Rules," Electricity Pool of
England and Wales, April 1993.

[5] X. Ma, D. Sun, and K. Cheung, "energy and Reserve Dispatch in a Multi-zone
Electricity Market," IEEE Trans. on Power Systems, Vol. 14, No. 3, Aug.
1999, pp. 913-919.

[6] K.W. Cheung, P. Shamsollahi, D. Sun, J. Milligan and M. Potishnak, "Energy
and Ancillary Service Dispatch for the Interim ISO New England Electricity
Market," Proceeding of Power Industry Computer Application Conference, May
1999, Santa Clara, pp. 47-53.

[7] L. Post, S.S. Coppinger, and G.B. Sheble, "Application of Auctions as a
Pricing Mechanism for the Interchange of Electric Power," IEEE Trans. on
Power Systems, Vol. 10, August 1996, pp. 1580-1584.

[8] G. Xiaohong, G. Feng, and A. Svoboda, "Energy Delivery Capacity and
Generation Scheduling in the Deregulated Electric Power Market," Proceeding
of Power Industry Computer Application Conference, May 1999, Santa Clara,
California, pp. 25-30.

[9] S. Hao, A. Papalexopoulos, G. Angelidis and H. Singh, "Consumer Cost
Minimization in Power Auctions," Proceeding of Power Industry Computer
Application Conference, Columbus, Ohio, May 1997.

[10]G.B. Sheble, "Price Based Operation in an Auction Market Structure,"
Presented at the IEEE/PES Winter Meeting, New York, New York, Feb. 1996.

[11]M.D. Cadwalader, S. Harvey, S.L. Pope and W.W. Hogan, "Reliability,
Scheduling Markets, and Electricity Pricing," Putnam, Hayes & Barlett, inc.,
Cambridge, Massachusetts, May,
1998,//ksgwww.harvard.edu/people/whogan.

[12]D. Chattopadhyay, "Daily Generation Scheduling: Quest for New Models," IEEE
Trans. on Power Systems, Vol. 13, No.2.,May 1998,pp.624-629.

VII. BIOGRAPHIES

SHANGYOU HAO received his B.S. degree from Wuhan Institute of Hydraulic and
Electrical Engineering, China, in 1982, and his M.S. and Ph.D. degrees in
Electrical Engineering from Ohio State University in 1984 and 1988,
respectively. He was with the Pacific Gas and Electric Company from 1988 to
1997, working on development of analytical methodologies for California
electricity industry restructuring. He has been with Perot Systems Corporation
since 1997, developing information system and business protocols for the
California ISO and Power Exchange.

DR. DARIUSH SHIRMOHAMMADI is the Director of energy Infrastructure Services
with Perot Systems Energy Group. He has been with Perot Systems since 1996
where he has been developing and integrating information technology solutions
for market operations and settlements of emerging energy market players
worldwide including the California Independent System Operator and California
Power Exchange. Dariush has around 25 years of experience with the electric
utility industry mainly in the development and implementation of methodologies
and computer models for operations, planning, costing, pricing and automation
of electric power systems. His career path includes positions as researcher for
two years with Hydro Quebec, transmission planner for three years with Ontario
Hydro, systems engineer, transmission planner, automation engineer, and the
Director of the Energy Systems Automation Organization with Pacific Gas and
electric Company for 11 years and the principal consultant with Shir Power
Engineering Consultants, Inc., for more than a year. Dariush has authored
numerous technical paper and reports all around the world. Dariush has a Ph.D.
in Electric Power Engineering from the University of Toronto.