1
Web-based Transaction Operational Feasibility
Analysis in Electricity Markets
H. Chen, Student Member, IEEE, C. A. Cañizares, Senior Member, IEEE¸
and A. Singh, Member, IEEE
Abstract
Transaction Operational Feasibility (TOF) analysis
is important in electricity markets to ensure reliable and secure
operation of power systems. This paper presents a web-based
design and implementation of TOF analysis, based on three-tier
client/server architecture and up to date web technologies, so that
the TOF analysis can be easily and effectively accessed by all
market participants for competitive decision making. The
proposed TOF analysis is based on approximate ATC
computations for efficient real-time market operation. A 3-area
system example is given for illustration purposes.
Index Terms
Electricity markets,
Capability (ATC), Web computing,
client/server architecture.
Available Transfer
Java programming,
I. INTRODUCTION
B
ECAUSE of overriding power system security
requirements, the transfer capabilities of transmission
systems are limited. In electricity markets, transactions
proposed by the market forces are hence limited by these
security requirements. Thus, transactions are operationally
feasible only when they are within the transmission system
transfer capability, i.e. these are dispatched only when
transmission security limits are not violated. After bidmatching, potential transactions need to be checked by
market/system operators for their operational feasibility [1],
[2]; market participants can also use the feasibility analysis to
estimate the possibilities of the acceptances of their bids for
strategically bidding in the markets to achieve higher profits.
Since market participants are geographically widely distributed
and have different software and hardware platforms,
implementing Transaction Operational Feasibility (TOF)
analysis in a web-based platform would allow them to easily
and widely carry out these types of analysis through
heterogeneous hardware and software platforms without any
configuration, installation or update operation.
With the evolution and widespread deployment of the
World Wide Web (WWW), accelerated by the rapid adoption
of web browsers, web-based applications have been developed
for a variety of applications in electricity markets. Most of
these applications use web techniques for information access
This work was partially supported by NSERC, Canada, and the
Department of Electrical and Computer Engineering at the University of
Waterloo.
All the authors are with the Department of Electrical and Computer
Engineering at the University of Waterloo, 200 University Av. W., Waterloo,
ON, Canada, N2L-3G1, c.canizares@ece.uwaterloo.ca
and exchange. For example, in [3], Internet-based client/server
concepts are used to monitor transmission substations; in [4],
the authors introduce an Internet-based energy trading system
which allows buyers and sellers of energy to freely engage in
trading activities over a wide range of energy sources,
products and geographical regions. With the help of high
performance CPU and fast network communications, more
sophisticated applications have also been built on the web,
using it as a widely distributed environment to share data and
resources for parallel computing application [5]. Other
examples of web-based computing in power systems are [6] to
[11]. The authors in [6] apply Java for the interactive learning
of power system stability on the web. In [7] and [8], remote
power flows are introduced as a simulation environment for
power system analysis and for educational purposes via the
Internet. The design of a web-based system to support
Electronic Commerce solutions for deregulated electricity
markets by reengineering legacy EMS software is introduced
in [9]. The authors in [10] implement a web-based SCADA
display system. Finally, in [11], a web-based electricity market
simulator that can be used as a decision-making tool for
market participants is described. In most of these cases, Java is
used for the implementation.
In this paper, the prototype Java implementation of a threetier client/server design for a web-based TOF analysis is
presented. The underlying TOF analysis procedure is based on
approximate ATC computations, as suggested in [12].
The paper is structured as follows: In Section II, the
supporting web technologies used in the implementation are
briefly reviewed. The TOF analysis is discussed in Section III.
In Section IV, the web-based architecture design and prototype
implementation of the TOF analysis are presented. Section V
discusses the application of the developed prototype to a 3area system example. The main contributions of this paper and
future research directions are presented in Section VI.
II. SUPPORTING WEB TECHNOLOGIES
A. The Web
The web is not only used as a medium to share data,
information, and knowledge, but also used as general
distributed computing environment for complicated business
applications. Web environment offers common user interface
(the browser), richer interaction possibilities, and platform
independent features.
The typical data communications in the web are based on a
2
client-server model, which involves three basic components:
client (browser), server and network. The communication
between web clients and servers is always in the form of
request-response pairs. Thus, the user initiates a request for
information or action through the client software. This request
then travels over the network to the server, which interprets the
requests and takes some desired action. The results of the
requested transaction are then sent back to the client and
displayed [13].
Web browsers and web servers communicate by using the
Hypertext Transfer Protocol (HTTP), this way objects are
addressed using a special form of universal resource identifier,
i.e. the Universal Resource Locator (URL). On the client's
side, the central part is the web browser, such as Netscape's
Communicator, Microsoft's Internet Explorer, etc., for
displaying Hypertext Markup Language (HTML)-content.
B. Java
Java is a general-purpose, high-level programming language
and a powerful software platform [14]. It is object-oriented,
robust, secure, architecture neutral, portable, and
multithreaded. Its executable code (called bytecode) runs on
any hardware and software platforms, provided that a Java
runtime system called Java Virtual Machine (JVM) is present.
Hence, Java becomes a good platform for writing client/server
web-based applications.
1) Servlet
Servlets can provide secure protocol- and platformindependent, server-side web-enabled software components,
written in Java [15]. They provide a general framework for
services built using the request-response paradigm. The Java
Servlet API provides the way to build JavaServer technology
in networked applications. Instead of serving only static web
pages, a servlet-enabled web server can invoke servlet
methods to dynamically generate content at runtime. A servlet
instance goes through a simple life cycle, initialized once,
requested multiple times, and then finally destroyed. Servlets
remain alive between requests.
Servlets offer an efficient platform-independent replacement
for Common Gateway Interface (CGI) scripts. It has less startup cost, executes more efficiently, and can be used on any
platform that supports Java. Servlet instances can persist
across client requests, whereas, for CGI scripts, web servers
need to throw a separate process for processing each request
received, thus when requests increase, servers are loaded with
too many simultaneous processes and hence system
performance deteriorates.
2) Java Database Connection (JDBC)
JDBC is the Java standard specification for accessing and
manipulating relational databases [15]. It allows programming
to proceed quickly and design software interface easily. JDBC
consists of a set of classes and interfaces written in the Java
programming language that provide a standard API for
database developers, making it possible to write database
applications using a pure Java API. Through JDBC, it is
possible to establish a connection with a database, send SQL
statements, and receive results.
JDBC has two layers: The JDBC API that provides a Java
interface to a relational database, and the JDBC driver API
that executes/implements this interface. The basic function of a
JDBC driver is to provide certain mechanism to make Java
applications able to talk to a Database Management System
(DBMS) that is usually not written in Java.
JDBC is a cross-platform, non-database specific approach
for database access from Java programs, and can be used for
database manipulation in the web.
III. TRANSACTION OPERATIONAL FEASIBILITY ANALYSIS
TOF analysis consists of checking if the dispatching of
transactions violates system security limits, which essentially
consists on checking if transactions are within the Available
Transfer Capability (ATC) of transmission systems. The
acceptance, rejection and/or adjustment of transactions is then
based on this particular analysis.
A. Available Transfer Capability (ATC)
ATC, as defined by NERC, is “a measure of the transfer
capability remaining in the physical transmission network for
further commercial activity over and above already committed
uses” [16]. It is expressed as:
ATC = TTC -TRM – ETC
where TTC stands for the Total Transfer Capability of the
network, and ETC, which includes the Capacity Benefit
Margin (CBM), represents the Existing Transmission
Commitments. The Transmission Reliability Margin (TRM)
and CBM are included to account for uncertainties in system
operation. Accurate and speedy ATC computation is very
important for real-time electricity market operation, since both
system operators and market participants need to know the
network's capability to transfer power reliably and securely,
while making the most out of the existing transmission
network. Different methods have been used for on-line and
off-line ATC calculation. Among them, the bifurcation-based
Continuation Power Flow (CPF) methods can be used for
adequate estimation of ATC [12]; therefore, this technique is
used here for ATC computation.
B. Analysis Procedure
A TOF analysis procedure based on a CPF-based ATC
computation is shown in Fig. 1. The main steps are:
1. The base system conditions, which includes projected
customer demand or base load, generation dispatch pattern,
system configuration and base scheduled transfer, determine
the base power flow condition, which are modeled as the
already committed transmission usage, i.e. ETC.
2. The load and generation increase patterns determine the
power increase above the base power flow, and are defined by
potential transactions obtained from bid matching, i.e.
matching offers with bids and forecasted loads.
3. The ATC computation is the core part of the TOF
analysis, and it is performed by a CPF method, taking into
account thermal limits, voltage limits and voltage stability
3
load
affecting
factors
historical
Load
Client
(GUI)
Web Browser
HTML document
Load
Forecasting
forecasted
load
HTTP
Presentation Tier
Internet
Bids Offers
Application
Server
Bid
system
generation
configuration pattern base load
Matching
HTTP server
Servlet container
Servlets
potential
Middle Tier
JDBC
transactions
SQL
Load & Generation
Increasing pattern
TRM
power
increase
Base condition
Modeling
Internet
Database
Server
base power
flow
DBMS
Backend Tier
ATC Computation
Fig. 2. Three-tier client/server architecture.
ATC
ATC >= transactions
N
Infeasible
Y
Feasible
Fig. 1. TOF analysis procedure.
limits [12]. The value of TRM is assumed known in this case.
Thus, the ATC is calculated from the load and generation
patterns and base system conditions, and it basically consists
on determining the maximum power transfer for the power
system, considering all credible contingencies.
4. If potential transactions are within the calculated ATC,
they are considered as feasible transactions and can be
accepted; otherwise, they are deemed infeasible and hence
need to be adjusted. Techniques for properly adjusting these
proposed transactions will be included in the next prototype.
IV. WEB-BASED DESIGN AND IMPLEMENTATION
A. Architecture
The web-based TOF is designed using a 3-tier client/server
architecture, as shown in Fig. 2. The application logic is
separated from the Graphic User Interface (GUI) and the
backend database, so that changing any one of the layers can
be done without the need to change other layers.
The top layer is the presentation layer, where the client
machine is located. Data is presented by a thin client solution,
using a web browser and the HTML standard file format. It is
the GUI of the application. The parameters of the application
are controlled by the client side using HTML forms. The user
sets the parameters and then activates the process on the server
side via the network. All the client sees is an abstract operation
request which takes input and output parameters. Since web
browsers are available for almost all platforms, using them as
GUI eliminates the need for designing different application
interfaces across different platforms, and also allows to
adequately present the application to the user without too
much coding effort.
The middle layer is the application server, which
implements the application logic to process data requests. An
HTTP web server is running on the machine to receive HTTP
request from clients, and send HTTP response back to clients.
A servlet container and servlet API must be installed to allow
the servlets to run. JDBC API and JDBC driver associated
with the database should be installed to communicate with
DBMS by SQL statements. The main control logic is
encapsulated at the server side through Java servlets, which
represent different modules of the application. Using modular
design enables modifications and enhanced features to be
added easily to the system to properly respond to specific user
requirements.
The backend layer is the Database Server, which uses a
typical relational database that stores and manipulates the data
at the backend. The connectivity to the database from the
middle layer is through the JDBC drivers, this allows the
application logic to be written with little dependency on the
type of database used.
The way the system works is as follows: The user fills in a
HTML form and clicks the Submit button, which posts the
request to a Java servlet. The servlet reads the input
parameters and performs the business logic, and the same time
uses JDBC to communicate with a database to obtain the
content; the dynamic response is then generated and given
back to the client for display, depending on the user inputs.
B. Implementation
A prototype of the web-based TOF was implemented on
Sun machines, using the Solaris 2.6 operating system. A Sun
Enterprise 450 machine is used in the middle tier as
application server and web server, whereas a Sun Ultra 10
machine is used in the backend tier as database server. The
client can be any machine which has a web browser installed
and has the access to the Internet. The prototype was tested
using a PC running Windows 2000 with both Internet Explorer
and Netscape as a client.
In the middle tier, Apache HTTP Server 1.2.6 was installed
as the web server. JavaServerTM Web Development Kit
(JSWDK) 1.0.1 is installed for the support of the Java servlet
4
Public class basecon extends HTTPServlet
{
...
Public void doPost(HttpServletRequest request,
HttpServletResponse response)
throws ServletException, IOException
{
PrintWriter out=response.getWriter( );
response.setContentType(“text/html”);
...
//parse the parameters of HTML form and
//save the uploaded file
//encapsulate UWPFLOW to generate base
//power flow using Java Runtime.exec( )
Public class atc extends HTTPServlet
{
...
Public void doPost(HttpServletRequest request,
HttpServletResponse response}
throws ServletException, IOException
{
PrintWriter out=response.getWriter( );
response.setContentType(“text/html”);
...
//Database Interface: retrieve data from tables
//use UWPFLOW to calculate loading margins
//under normal condition and N-1 contingencies
//Database Interface: update tables
//Database Interface: update tables
//generate response web page
}
//generate response web page
}
}
}
Fig. 3. Pseudocode of basecon servlet.
Fig. 5. Pseudocode of atc servlet.
Public class direction extends HTTPServlet
{
...
Public void doPost(HttpServletRequest request,
HttpServletResponse response)
throws ServletException, IOException
{
PrintWriter out=response.getWriter( );
response.setContentType(“text/html”);
...
//parse the parameters of HTML form and
//save the uploaded file
//Database Interface
//update tables
//generate response web page
}
}
Fig. 4. Pseudocode of direction servlet.
//loading driver
try{
class.forName(“org.gjt.mm.mysql.Driver”);
}catch(ClassNotFoundException e){
System.err.print(“ClassNotFoundException:”);
System.err.println(e.getMessage( ));
}
try{
//making the connection
Connection con=DriverManager.getConnection(
Dburl, “login”, “password”);
//creating JDBC statements
Statement stmt=con.createStatement( );
//create or update database
stmt.executeUpdate(“....”);
//retrieve information from database
ResultSet rs=stmt.executeQuery(“...”);
//retrieve values from ResultSet
while(rs.next( )){
float p=rs.getFloat(“Pg”);
...
}
}catch(SQLException e){
System.err.println(“SQLException:”+e.getMessage( ));
}
Fig. 6. Database interface.
API. Java JDK-1.2.2 is installed as the Java runtime
environment. An MM JDBC Driver (mm.mysql) is used to
access MySQL relational database. MySQL resides on the
backend tier.
Three main modules, implemented as three servlets, run on
the application server. These are:
1. basecon (Fig. 3): Receives base system conditions from
the client and calculates the base power flow.
2. direction (Fig. 4): Receives load and generation
increase patterns from the client.
3. atc (Fig. 5): Calculates the ATC value based on the
loading margin computations for normal conditions and
contingencies.
UWPFLOW [17], a robust CPF program, is used as the
computing engine for base power flow and loading margin
calculations. It is treated as legacy software and was not
changed, thus avoiding extra programming costs and errors;
this way, the engine used for ATC calculation can be readily
changed. This package is thus encapsulated in servlets
basecon and atc, using Java Runtime.exec, as shown
in Figs. 3 and 5.
A database interface is included in each module to retrieve
and update information from the database. Its main code is
shown in Fig. 6.
V. EXAMPLE
The 3-area system shown in Fig. 7 is used here to illustrate
the web-based TOF analysis prototype. Each area has its own
generation and demand; transmission losses are not considered
here; buses 2 and 3 are modeled as PQ buses.
Market participants, who are assumed here to be represented
by suppliers G1, G2 and G3, and buyers L2 and L3, send pricequantity bids to the market operator (see Fig. 8). If there is no
demand–side bidding, i.e. no buying bids, the quantity of the
load is then determined by load forecasting, which corresponds
to the cases of inelastic load.
5
G1
150MW
G2
L1
150MW+j80MVar
V1=1.02
100MW
L2
150MW+j70MVar
0
V2
80MVar
X =0.12p.u.
12
X =0.12p.u.
X =0.12p.u.
23
13
V3
100MW
G3
50MVar
L3
50MW+j30MW
Fig. 7. A 3-area test system.
Fig. 9. Part of the input GUI.
Fig. 8. High-low bid matching with demand-side bidding.
The TRM may be assumed differently for different systems
using either a fix amount or a fix percentage of the maximum
load. In this example, the TRM is assumed to be zero.
Part of the input GUI is depicted in Fig. 9. There are 3 more
input forms presented to the client to provide the server with
all the required data. These are just quickly developed GUIs,
with not much attention given to the presentation format.
A. Case 1: Demand-side Bidding
If the “standard” high-low method is employed for bid
matching, which consists of matching the highest buying bids
with the lowest selling offers, as shown in Fig. 8, then the
potential transactions in this case are: G1 sells 150 MW, L2
buys 50 MW and L3 buys 100 MW, and the uniform market
clearing price is 30 $/MWh. These potential transactions
define the load and generation increase “direction” for the
ATC computations.
The loading margin results for normal conditions and for
each possible contingency are shown in the GUI generated by
the prototype, as depicted in Fig. 10. In this case, the outage of
line 1-2 is the critical contingency, which yields an ATC value
of 101.38 MW, and hence the transactions are not feasible.
Fig. 10. Demand side bidding results.
B. Inelastic Load
Assuming that L2 and L3 are inelastic loads and determined
by load forecasting to be 100 MW each, the resulting high-low
bid matching is then shown in Fig. 11. The potential
transactions are: G1 sells 150 MW and G3 sells 50 MW to
satisfy the total load of 200MW, and the uniform market
clearing price is 32 $/MWh. The loading margin results in this
case are shown in Fig. 12, with the resulting ATC of 95.35
MW. Once more, the proposed transactions are not feasible,
and hence need to be adjusted.
VI. CONCLUSIONS
A web-based TOF analysis prototype, based on a 3-tier
client/server architecture and up to date web technologies, is
proposed and discussed in detail in this paper. The underlying
TOF analysis is based on adequate ATC calculation, which
uses a CPF program as the computational engine. A 3-area
system example is used to demonstrate the presented
prototype.
6
[7]
[8]
[9]
[10]
[11]
Fig. 11. High-low bid matching for inelastic load.
[12]
[13]
[14]
[15]
[16]
[17]
T. Ueda, “Remote Power Flow Analysis,” Kitami Institute of
Technology, Japan, 1999.
Available: http://power.elec.kitamiit.ac.jp/~ueda/java/pf/
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USA, 1996.
C. A. Cañizares, “UWPFLOW,” University of Waterloo, November
2001. Available: http://www.power.uwaterloo.ca
Hong Chen received her Bachelors (1992) and Masters (1995) degree in
Electrical Engineering from Southeast University in China. She worked in
NARI (Nanjing Automation Research Institute, P.R.China) from 1995 to
1998, and was engaged in developing EMS power application software. She
is currently a Ph.D. candidate in the Department of Electrical & Computer
Engineering at the University of Waterloo, pursuing research on electricity
markets and computer applications in power systems.
Fig. 12. Inelastic load results.
The proposed web-based implementation of the TOF
analysis can be easily and effectively used by market/system
operators to determine feasible transactions, and also by
market participants to make profitable market decisions. New
functions, such as transaction security cost analysis to allow
for transaction adjustment, will be added in the future using a
similar modularized design.
VII. REFERENCES
[1]
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[3]
[4]
[5]
[6]
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April 1998.
Claudio A. Cañizares (S’86, M’92, SM’00) received in April 1984 the
Electrical Engineer Diploma from the Escuela Politécnica Nacional (EPN),
Quito-Ecuador, where he held different teaching and administrative positions
from 1983 to 1993. His MS (1988) and PhD (1991) degrees in Electrical
Engineering are from the University of Wisconsin-Madison.
Dr. Cañizares is currently an Associate Professor at the University of
Waterloo, E&CE Department, and his research activities concentrate mostly
in studying stability, modeling and computational issues in ac/dc/FACTS
systems.
Ajit Singh completed his B.Sc. degree in Electronics and Communication
Engineering (1979) at BIT, India, and M.Sc. (1986) and Ph.D. (1991) degrees
in Computing Science at University of Alberta, Canada. From 1980 to 1983,
he worked at the R & D department of Operations Research Group, the
representative company for Sperry Univac Computers in India. From 1990 to
1992, he was involved with the design of telecommunication systems at BellNorthern Research, Ottawa. He is currently an Associate Professor at
Department of Electrical and Computer Engineering, University of Waterloo.
Dr. Singh has published several research papers in the areas of software
engineering, network computing, database systems, and artificial intelligence.