PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
COMPREHENSIVE LONG TERM MODELING OF THE DYNAMICS OF
INVESTMENT AND GROWTH IN ELECTRIC POWER SYSTEMS
A. Dimitrovski, M. Gebremicael and K. Tomsovic, School of Electrical Engineering and Computer Science
A. Ford and K. Vogstad, Program in Environmental Sciences and Regional Planning
Washington State University, Pullman
ABSTRACT
This paper discusses our recent research on the interplay between the economic,
technical, social and environmental factors that influence the production, transmission
and consumption of electric energy. We are developing computer models of these
interactions suitable for use by both policy makers and researchers. The main effort is
focused on the development of simulation models, which extend beyond traditional
disciplinary boundaries. Such models will enable us to simulate short-term behavior, such
as, electricity prices and congestion in the near term, and long-term behavior, such as,
investment in new generation and transmission. Our emphasis in this report is on models
for tradeable green certificates, transmission network models in system dynamic studies
and our benchmark systems for the Western US and West African electric power
systems.
KEY WORDS
Interdisciplinary modeling, power plant construction, power system planning, system dynamics, transmission congestion,
tradeable green certificates.
1
INTRODUCTION
Arguments of the benefit for the deregulation of the
electric power system have been largely based on
conventional and general wisdom regarding separate topics
such as competition, service reliability, economic
efficiency and environmental protection. Few have looked
carefully at the interplay between the economic, technical,
social and environmental factors that influence the
production, transmission and consumption of electric
energy. Further, no one has carefully investigated the long
term dynamics of the process that includes an
understanding of the necessary engineering. Our research
is developing models to study the long terms effects of
deregulation, including interactions between regulatory
policy, investor behavior, environmental impact and
system engineering.
We have developed models with data from the Western
power grid (WECC) and under a supplemental award a
West African Power Pool system, which we have selected
as our Benchmark Test Systems. This paper outlines our
recent work on the long term interactions between
financial markets and electric power system development.
Figure 1 shows the spatial and temporal boundaries of our
research on modeling the electric system. The system
security modeling is represented by the 1st of three boxes
located at the base of the diagram. The security model
represents the power flow and system dynamics, which
operate in seconds on a spatially complex grid system.
Loads are described at the level of sub-stations, while the
scope of the model extends to cover the entire WECC. The
model calculates power flows, real and reactive reserves
and system limits for a specified scenario. The grid
structure of the WECC is represented in explicit fashion,
so the system security model provides the foundation for
proposed research on power networks. We highlight the
system security “box” in Figure 1 with a double boundary
to emphasize the extra challenges of representing the grid
network in explicit fashion.
Our demand size research is depicted as the 2nd of three
boxes. The research was launched to explain the response
of California electricity consumers during 2000 and 2001.
The study makes use of billing data from distribution
companies to determine the extent and factors behind the
surprising reduction in electricity consumption in the
summer of 2001. Figure 1 depicts the spatial dimension
ranging from individual service areas to cover an entire
state. This work is not being expanded under EPNES but is
shown here for completeness.
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
3. Wholesale Market Model
Decades
Years
Quarters
Months
Weeks
Days
Hours
Minutes
Seconds
2. Demand Response
Research
Explains the reduction of
electricity demands in
California in the year
2000-2001. Data from
individual service
territories combined for
state-wide impact, both on
energy consumption and
peak demand.
Calculates construction of new generating
capacity over a 10-15 year period,
long enough to see the cycles of boom and
bust in construction.
Loads, generation and market prices are
simulated for 24 hours in a typical day for
each quarter. The four areas of the WECC
are combined into a single market for
electric energy and ancillary services.
1. System Security Model
Calculates power flows, real and reactive reserves, and system limits
given a scenario on loads and resources.
Sub-stations Service Areas
States
Regions
WECC
Figure 1. Spatial and temporal dimensions of previous research at WSU.
The 3rd box in Figure 1 depicts the WSU model of the
western electricity market. The model operates with load
and resource data from the four regions of the WECC. The
model simulates hourly operations for a typical 24 hour
day in each quarter of a year. We assume adequate
interconnections between all loads and all resources in the
west, so the wholesale market is treated as a single market.
The simulations begin in 1998 and run for a decade or
more to allow sufficient time to see the patterns of power
plant construction.
These models are being constructed using basic concepts
from system dynamics, a simulation method pioneered by
Forrester [1] and explained in texts by Ford [2] and
Sterman [3]. System dynamics has its origins in control
theory and has been defined by [4] as that
branch of control theory which deals with socio-economic
systems and that branch of management science which
deals with problems of controllability.
Such an approach is valuable in a rapidly changing electric
industry with high uncertainty and high risk [5].
2
SUMMARY OF RESEARCH RESULTS
2.
3.
Development of computer models suitable for use by
both policy makers and researchers to study the
impact of transmission systems on electric energy
supply;
Creation of test systems for the Western US and a
Western Africa Power Pool system.
For a summary of our earlier results, the reader is referred
to [6].
2.1
RPS and TGCs in electricity markets
In the US, Renewable Portfolio Standards (RPS) have been
adopted in several states [7]. An RPS requires utilities to
include a specified amount of renewables in their portfolio
of electricity generation, typically an increasing share over
a time horizon as shown in Figure 2. Australia and several
EU countries have adopted an RPS standard.
A Tradeable Green Certificate (TGC) is a market
instrument that allows trading to meet RPS obligations.
TGCs are financial assets issued to producers of certified
green electricity and can be regarded as a market-oriented
environmental subsidy. An issuing body issues green
α %
The discussion in this paper focuses on the following
areas:
1.
Study of Tradeable Green Certificates (TGC) as an
incentive for investment in renewable resources;
2003
2020 t
Figure 2 RPS target
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
certificates when generators registered in their database
report monthly generation. Retailers must comply with
their RPS obligations (renewable targets) every year by
presenting the certificates to the redemption body, at which
time the certificates are withdrawn from circulation.
Between issuing and withdrawing, the certificates are
monitored.
Pe
q
demand
TGC
TGC annual
demand
TGC monthly
penalties
DisCos Actual
Coverage
Hb
TGC
holding
buyer
b
indicated frac
demand satisfied
desired TGCs
held by Discos
<Price Cap>
DisCos Desired
Coverage
TGCs held by
Discos
DisCo monthly fraction
for shortfall correction
c
Hs
purchase
sales
TGC
issued
TGC
holding
seller
Figure 4 shows the market structure of our TGC market
<Calendar Year One
Yr Ahead>
Fundamental Price Exogenous
Lookup for
Fundamental Price
TGC price
Fundamental
Price
<max value of
price effect>
Lookup for
Fundamentals Multip
Effect on Fundamental Price
from Wind Adequacy Ratio
TGC price ratio
actual annual
TGC Sales
TGC monthly
transfers
Disco indicated
purchases
DisCos TGC
Shortfall
g gene ra tion
s
Genco Price
Elasticity Effect
on Sales
GenCos
Desired Sales
GenCos price
elasticity of sales
Effect on Sales from
Low Coverage
GenCo's Actual
Coverage
TGCs Turned In
Lookup for Indicated
Fr Satisfied
Sales
strategy?
CF
model builds on the previous work by Vogstad [14] who
developed a system dynamics analysis of the Swedish
TGC market that has been in operation since May 2003.
Vogstad is visiting WSU in the Summer of 2004 to assist
us in developing a TGC market model for our WECC
system. Figure 3 shows Vogstad’s representation of a
TGC market with buyers (retailers/utilities with not
enough renewables) and sellers (generators with surplus of
renewables). To test market design, model has been
converted into interactive experimental economics
laboratory models, to test how trading strategies influence
market performance.
excess demand
fraction
DisCos
Desired
Purchases
Indicated TGCs
to Turn in
Monthly
<TGC price>
d depre cia tion
Figure 3 System dynamics representation of the TGC
market with buyers and sellers
monthly fractional
change in Price
Discos Price
Elasticity
Effect on
Purchases
C apa cit y
L L ife time
Q
chg in certificate price
max value of
price effect
K
dp
P urchase
strategy ?
let price change?
max plausible fr
change
inve stme nts
Pc
2020 t
Price Cap
<Demand for
Electricity>
Desired Wind
Gen from
RPS
i
Investment
loop
Price
α
%
Using a system dynamics approach, we have started
building a TGC market model appropriate for the western
system. The model uses the system dynamics approach to
analyse the performance of various market designs. The
<excess demand
fraction>
Ieq
Price expectations
Numerous studies of TGCs have been performed already,
but, with a few exceptions, these studies are based on
comparative static analysis and partial equilibrium models.
The dynamics of the TGC market can pose problems that
are not captured by comparative statics and equilibrium
approaches [NYSD].
DisCos price
elasticity of
purchases
Cost
profitabili ty rp
TGC markets have already been introduced in some
countries: Sweden, UK, Netherlands, Italy and Australia
[8]. Within Europe, the EU considers a community-wide
market to reach renewables targets, from 320 to 640
TWh/yr in 2010 (large scale hydro included), and have
conducted a series of studies to establish a TGC market [912].
Flexible mechanisms for trading are seen as
necessary in order to achieve an efficient deployment of
renewables in trans-national electricity markets. In the US,
Texas has a TGC market in place for their RPS standard
[13], and such a trading scheme is also relevant for other
states.
<RPS>
C
DisCo monthly
purchases to correct
shortfall
TGCs held
by Gencos
TGCs Issued
GenCo Indicated
sales
GenCo monthly
purchases to correct
shortfall
Figure 4 View of the market portion of the TGC model
GenCos TGC
Shortfall
<Wind Adequacy
Ratio>
Lookup for Effect of
Effect of Low Coverage
<expected wind
generation>
GenCos Desir
Coverage
<actual wind
generation>
desired TGCs
held by Gencos
GenCo monthly fraction
for shortfall correction
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
Retirements
Annual
Fraction
Initial Demand for
Electricity
Initial Demand in
aGW
electricity dem
monthly growth
aGW of
Retirements
Market Share S Curve
Shape Parameter
aGW per month to
replace retirements
indicated fract
cap starts wind
Required Wind
Cap by Next Year
capacity starts in
aGW per month
wind cap starts
<Time>
<wind average
capacity factor>
Wind Adequacy
Ratio
Wind Cap
needed next
Year
electricity demand
annual growth rate
Random Number
Year
Ramdon Wind?
(Annual)
<wind average
capacity factor>
YbyY
Initial Wind
Capacity
Months to build
wind Generators
Wind cap under
construction
wind starts in
aGW per month
fraction of cap
starts by wind
<Cost CC>
<Cost W>
<RPS Nx Yr>
Demand in aGW
Demand growth in
aGW per month
L
Demand for
Electricity
Random
Number by
month
Monthly Counter
<RPS>
Required Wind Cap
from RPS
wind new cap on line
actual CF
Wind installed
capacity
expected
wind
generation
wind average
capacity factor
Exogenous
Fraction?
actual wind
generation
<Calendar Year>
Lookup for
Fractional Starts
exogenous
fractional starts
Calendar Year One
Yr Ahead
fraction of demand
generated by wind
<Demand for
Electricity>
Figure 5 View of the wind capacity construction portion of the TGC model
TGC Price and DisCo Obligations
RPS Ramp Pattern
10
0.4
7.5
0.3
5
0.2
2.5
0.1
0
2000
2005
2010
2015
Calendar Year
2020
2025
Renewable Portifolio Standard : current
fraction of demand generated by wind : current
0
2000
2005
2010
2015
Calendar Year
2020
2025
TGC monthly penalties : current
TGCs Turned In : current
TGC price : current
Price Cap : current
Figure 6 (left) shows the fraction of demand generated by wind compared to the Renewable Portfolio Standard (RPS).
The RPS is 1% for the first 5 years based on the amount of wind generation in California. We then ramp up the RPS to
20% by the year 2015. The red line shows that wind generation would be slightly below the ramp in the first few years of
the program. Because of high TGC prices, however, wind generation will exceed the ramp by around 2010.
Figure 7 (right) shows the TGC price climbing quickly to the price cap, a result which we expect to see. This model
simulates a highly idealized market in which investors and market participants have extremely good information and base
their trades on the “fundamental” value of a TGC. Even under such idealized conditions, we see the price shooting up to
the cap in the early years. The high prices trigger somewhat more investment in wind than is needed to meet the RPS
standard. The DisCos pay the penalty in 2005-2006 rather than turning in the required certificates. The required TGCs
are turned in after 2008.
model adapted for the WECC system, and Figure 5 shows
the wind capacity construction portion of the TGC model.
We focus on wind generation as the renewable resource
to meet the RPS.
Results from the analysis of the Swedish TGC system
suggests that the delays in capacity construction and
expectation formation of the TGC market will pose a
problem for price formation. Moreover, the possibility of
storing certificates from year to year enables speculation
and withholding in the TGC market. These were the
results of laboratory experiments, both at NTNU and from
the EU studies [11]. The experiments match the simulated
behavior of the TGC models, showing that prices are likely
to reach the price cap/penalty price upon introduction.
Furthermore, persisting high TGC prices may lead to
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
overinvestment in capacity, leading to a later price collapse
in the market. Such a market behavior will probably lead
to an inefficient allocation of investments during the boom.
Moreover, to achieve the RPS targets increasing over time,
the expected price received by developers of renewables
must be much higher than the long run marginal costs of
new generation. Preliminary results from the TGC model
for the WECC model show a similar pattern as illustrated
in Figures 6 and 7.
Finally as shown in Figure 8, one years’ experience form
the Swedish TGC market so far, shows that prices did
settle on the price cap. While there are many alternative
explanations for this, they are not mutually excluding each
other. By conducting experiments and model simulations,
it is possible to identify mechanisms that cause the
observed behavior, and suggest improvements for efficient
market designs.
Fundamental questions to be addressed are:
•
•
•
what are the likely consequences of various market
designs (i.e., allowing banking, borrowing and levels,
price caps)?
which design of a TGC market should be preferred?
how does the TGC market interact with the spot
market and other markets?
These are questions that can be addressed when the TGC
market model is integrated with the WECC system
dynamics model.
2.2
Expanding the WECC Wholesale Market Model
to Include Transmission System Effects
Capturing longer-term trends in power plant and
transmission line construction requires modeling not only
the engineering and economic concerns, but also social,
environmental and regulatory issues. Modeling such a
complex system with all the inherent interactions among
these factors requires a synergy of different approaches.
One approach is the classical detailed planning approach
used in engineering studies. Another approach is the higher
level, and less frequently used, of system dynamics
studies. To bring these approaches together and create
complex models that encompass all the relevant
interactions endogenously we need appropriate tools.
Unfortunately, currently there are no such tools readily
available. To overcome this problem, we have been
investigating different ways of combining the most
frequently used tools in engineering and system dynamics
modeling.
The greatest limitations in this effort are imposed by the
system dynamics tools. These tools are relatively few in
number and, generally, not amendable to specific user
requirements as the engineering ones. The one exception is
the Vensim simulation environment [15] which allows for
calling of external functions during the process of
simulation. The gateway for these external functions in
Vensim is provided via a dynamic link library (DLL). The
library is usually created in a C/C++ developing
environment, but other languages may also be used. This
DLL can contain any number of functions and it can also
call other DLLs. Calling other DLLs is used when it is not
possible to directly compile required functions within the
environment used to create the gateway DLL.
On the engineering side, there are a vast variety of tools
that can be used but, in recent times, Matlab/Simulink [16]
has become the de facto standard in the academic circles.
Its ease of use, versatility, and the very large library of
functions make it the preferred choice. Matlab code can
Figure 8 TGC price formation showing boom and bust.
Bottom: Results from laboratory experiments, Top:
Simulated behavior from system dynamics models.
Figure 9 Linking Matlab code in Vensim simulations
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
also be compiled into stand alone DLL which can then be
used as a link with Vensim’s DLL. This process of linking
the Matlab with Vensim is depicted on Figure 9.
The combined approach shown in Fig. 9 enables us to
solve easily the issue which occurs frequently when
performing simulations with more complex models algebraic constraints. The transmission system equations
are an example of such constraints. These constraints are
extremely difficult to satisfy within the simulation
environment except in some trivial cases. However, they
are easy to handle by calling an external function for
solution.
Although it is possible to represent every technical detail
of interest using the above approach, there needs to be a
balance in the depth within the overall system dynamics
framework. For example, accurately modeling a
transmission network cannot be easily incorporated into
market models. Even for daily operations, where specific
details of the interconnections are known, most power
exchanges use a simplified linearized transmission model
to avoid computational problems.
For the broader analysis proposed here, the data problem is
even more difficult as longer term changes are nearly
impossible to map into the detailed models of the network
and generators. Instead, a simplified approach is being
pursued. The WECC system is divided into 5 main areas
interconnected with each other by means of equivalent ties.
This is shown on Figure 10.
The areas are based on the WECC regional division as
follows: Area 1 is the North West Power Pool – NWPP;
Area 2 is the Rocky Mountain Power Area - RMPA; Area
3 represents the Arizona New Mexico Southern Nevada
Power Area; Area 4 represents Southern California and
Area 5 represents the Northern California. Breaking the
California region in two areas enables us to explicitly
model the infamous path 15. The generators and loads
within each area are lumped and the whole area is
considered a single unit. The modeling is consistently done
using this unit as the smallest building block. For example,
Figure 11 shows a sub-model of the generating capacity
from a generic “thermal technology.” The parameters of
the equivalent ties between the areas are derived from the
explicit network structure. Corresponding to the broader
scope of the analysis carried out, we use the DC optimal
power flow model for simplified calculation of marginal
prices, tie loadings and congestions. The call to the
external Matlab function that does this within Vensim
simulation is shown on Figure 12.
Figure 10 The WECC five areas and equivalent ties
2.3
Benchmark System for West Africa
The original proposal focused on the Western US as the
benchmark system. As part of supplemental work, we are
applying our developed methodology in order to
understand the long term benefits of a West African Power
Pool. The following describes the status of these modeling
efforts.
Methodology
The general building blocks of the model to perform the
dynamic system of competitive electric market require:
1.
2.
3.
Initialization of parameters for each area (CCs total
levelized cost, permit shelf life, developers goal for
permits, initial peak annual demand, demand annual
growth rate, investors weight given to CCs in the
construction pipeline, permit gap adjustment time,
generating capacity from all units, variable cost, gas
prices and transmission network topology and line
parameters) and calculate the peak demand for each
area over the study period.
Creation of a typical 24 hour demand curve for each
month of the year.
Computation of the price and power generated for
each demand hour of a given day that represents an
entire month and which can be used to determine the
forecasted profit for each generator.
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
We have six types of Thermal Technology "TT": Nuclear, Coal, Combined Cycle, Gas
Steam, Combustion Turbines and Economic Withholding
<Demands>
Existing Generating Capacity:
5 areas & 6 types of thermal
capacity: The 30 values
entered from the CEC boom
bust revisited model
Existing Generating
Capacity
<Gas Steam Low
Var Cost>
Generating
Capacity
<Availability
Factors>
<Coal Low Var
Cost>
<Gas CC Low
Var Cost>
New Generating
Capacity
<Hydro Other &
Wind Gen>
Market Gen Total in Area
Var Cost Range
Generation
Fraction of Gen
Cap in Operation
<Prices>
<Gas Steam
High Var
Cost>
Gen from Nuclear
Gen from Coal
Gen from CCs
<PriceR>
Gen from Gas Steam
Gen in NW
<PricesC>
Gen from CTs
Gen in RMt
Use "Prices" if five separate areas;
use "PriceR" if entire region totally
interconnected; use PricesC if
interties with possible Congestion
Gen from Economic
Withholding
Gen in SW
Gen in SoCA
Gen in NoCA
Peak Exports
Total
Export Total
Fr Op
Price Cap
<Gas CC
High Var
Cost>
Off Peak Exports
from Area
Off Peak Exports
Peak Exports
Total
from Area
Export from Area
Gen Cap Available
to Generate
VarCostLow
VarCostHigh
<Coal High
Var Cost>
<New CCs on
Line>
<Generating
Capacity>
Exports Check: If we
use Prices 1, there will
be zero exports from
each area.
Exports Check: If we
use Prices 2, total export
should be zero.
Note: Region wide exports can
be positive if the wind and hydro
gen exceeds the demand. This
would be a time of spill.
Total Gen Cap in
Each Area
WSCC Thermal
Gen Capacity
Figure 11 A view of the “Thermal Generating Capacity” in the working version of the WECC model
<VarCostHigh>
<Generating
Capacity>
<VarCostLow>
<Transmission
Capacities>
<Demands to
Market>
<TieTos>
PricesC
Flows
<Price Cap>
<Trans Line
Reactances>
<TieFroms>
Flags
Check if
Optimization OK
<TieStatus>
Figure 12 Call to the external OPF function from Vensim
New power plants are built when prices are sufficient to
make them profitable. The optimal amount of capacity to
be built is determined as a function of the demand profile
in each region and the mix of capacity in each region. As
the need for the new capacity increases, the competitive
price will rise until capacity expansion become profitable.
At this point in the development, there are several
limitations. The model does not consider the capacity
factor and decay rate of each generating unit. It also
assumes constant hydropower supply during wet and dry
seasons. Practically, the only way in which demand and
supply can be kept in balance during extremely high
demand periods would be through an increase in the price
to a level that would encourage some customers to reduce
their usage. The price adjustment during periods of high
demand is not implemented in this model.
Validity tests have been conducted with arbitrary test data
for the Western USA power network with five regions.
Demand of electricity varies monthly during the year and
hourly during the day. As a result, the cost of electricity
production and the competitive price follows the same
pattern as the demand does.
The first test shows the effect of monthly and hourly
demand change on electricity price. From Figures 13-14, it
can be inferred that high demand during June and July
results in a higher hourly price, especially for the peak load
hours. These two plots also demonstrate that there is
congestion because the transmission capacity is limited for
this analysis. Notice that setting the transmission line
capacity high enough to allow unlimited power exchange
between neighboring regions would yield identical prices
for all areas as shown in Figure 15. Together, these
examples demonstrate that the model can simulate
competitive pricing of electricity.
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
.
Figure 13 WECC Area Prices in Jan. with Congestion
Figure 15 WECC Area Prices in Jan. with no Congestion
Figure 14 WECC Area Prices in July with Congestion
Figure 17 WAPP Area Prices with Congestion
Figure 16 West African Power Pool (WAPP)
Key: #1 - # 14
Countries of WAPP; _ _ _ _ _ New/proposed international transmission lines; ______ Old/existing international transmission lines.
PRESENTED AT THE 2004 EPNES WORKSHOP, MAYAGUEZ, PUERTO RICO, JULY 12-14, 2004.
WAPP (West Africa Power Pool)
Our model can be adapted to any number of areas and any
possible power network interconnections with simple
modifications (this is quite different than a typical system
dynamics implementation). Fourteen countries of the
WAPP system are being treated as a separate area as
shown in Figure 16. Since the available data for WAPP
dose not have a pattern of hourly distribution the parameter
for hourly distribution is adapted from the Western US
model. Simulating the model for prices in each area with
specific information is shown in Figure 17.
3
EDUCATION IMPACTS
We have learned in previous experiments with computer
based learning systems that students can spend valuable
lab time learning the basic protocol of the experimental
system. Some students may arrive in the lab ready for
serious experimentation and reflection; others may arrive
with a few minutes of preparation. One response to the
uneven preparation of students is to allow them to work in
asynchronous fashion and assume that they will all
eventually learn some of the lessons from the experimental
system. In previous research at the WSU Center for
Teaching and Learning, we have discovered that webbased tutorial materials can be used to bring the students to
a minimum level of understanding. This idea was tested
with “The Idagon”, a computer simulation model of a
hypothetical river [17].
at the Norwegian University of Science and Technology in
TGCs [14], and the work by the experimental economics
group at the University of Arizona [20]. We will select
appropriate ideas from these experimental electricity
markets for inclusion in our courses.
Our teaching modules will be designed for integration into
syllabi for senior/graduate level courses in environmental
science and engineering. At this stage, students from
engineering have been taking courses within
environmental science but our plan is to develop teaching
modules that will be used with students from several
disciplines learning in the same classroom. The
supplemental grant to support research cooperation with a
group of West African scholars will also lead to interesting
insights on systems in developing countries where weak
transmission systems exacerbate the difficulty of designing
functioning electricity markets.
4
FURTHER WORK
The proposed modeling approach needs further
developments in several areas that will be supported by
case studies in order to highlight the value of the work.
Specifically, the following is planned:
•
•
completion of the TGC model and integration into
WECC system models,
full integration of transmission line model into the
system dynamic models,
studies of various transmission investment incentives
and the impact on boom-and-bust cycles,
incorporation of uncertainty into modeling [21-22],
detailed study of the benefits of the WAPP on electric
power system development in West Africa, and
major modification of senior power systems analysis
course in Electrical Engineering and graduate course
modeling course in Environmental Science.
The Idagon website provides students with visual and
technical information to learn the names, units, jargon and
concepts of a complex river system that is to be managed
to serve multiple and conflicting objectives. We plan to
develop a similar web-based preparation system to provide
students with the requisite introductory material. We will
include interactive tests, so the participants can verify that
they are sufficiently informed to begin experimentation
with the simulation model. The preparatory materials will
require significant time and effort, but the time is well
spent if the participants are to have a meaningful
experience in the lab. The attention to preparations will
help ensure the validity of our proposed assessment
process since we can require a certain minimum level of
understanding before proceeding into the course materials.
•
Our plan is for students to study the preparatory material
on the web and experiment with the interactive simulation
model on separate computers in asynchronous fashion. The
asynchronous operation allows students to operate at
different speeds and has proven useful in student
simulations of fishery management and promotion of
electric vehicles [17] and in executives’ experiments with
models of electricity markets [18]. However, several
groups report useful experiments in which subjects operate
simulated markets from a web-based model. These include
the Cornell University’s PowerWeb [19], the experiments
[1] Jay W. Forrester, Industrial Dynamics, Pegasus
Communications, Waltham, MA.
[2] Andrew Ford, Modeling the Environment, Island
Press.
[3] John Sterman, Business Dynamics, Irwin McGrawHill, 2000.
[4] Geoffrey Coyle, Management System Dynamics,
John Wiley, 1977.
•
•
•
ACKNOWLEDGEMENT
The work reported in this paper has been supported in part
by the NSF and the Office of Naval Research under the
NSF grant ECS-0224810.
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TABLE OF ACRONYMS
DLL
NWPP
NTNU
OPF
RMPA
RPS
TGC
WAPP
WECC
WSU
Dynamic Link Library
North West Power Pool
Norwegian University of Science and
Technology
Optimal Power Flow
Rocky Mountain Power Area
Renewable Portfolio Standard
Tradeable Green Certificate
West Africa Power Pool
Western Electricity Coordinating Council,
Washington State University