15

CHAPTER 2

MODERN TRENDS IN POWER SYSTEM

RELIABILITY ANALYSIS

2.1 INTRODUCTION

Electrical power systems are very complex and highly integrated.

Failure in any part of the system can cause interruptions of supply to end

users. Power system reliability is increasingly a concern to the power

industry and society at large. At present, power system operations are to be

handled in a heterogeneous environment. Generally, reliability analysis is

being carried out during planning stage of power system operations. In order

to maintain the operational state of the power systems at the required levels

and subsequently to meet the load demand satisfactorily, the power system

operations such as state estimation, reliability analysis etc., are to be carried

out at frequent intervals. Perhaps, the above operations have to be invoked

dynamically whenever the power system resumes its operation back after had

experienced sudden failure or outage. Reliability analysis has to be carried

out at regular intervals during operating period of power systems in order to

monitor the customer requirement satisfaction at desired levels. The reliability

evaluation system should be dynamically adaptable to the current operating

conditions of the power systems.

Due to the increase in demand of power, the size of the power

system grows exponentially large. Private power industries have joined with

public sectors to cope up with the power demand. Power system operations

16

are becoming complex and the data required for analysis have been stored in

different formats and are distributed in a heterogeneous environment. The

power systems basic operations such as load flow, stability analysis,

reliability analysis etc., are being carried out using different power system

applications executing in heterogeneous platforms. Integration of responses

due to various power system operations is a major task and power system

applications should be interoperable even though every system has its own

way of representation and implemented using different paradigms. Integrity

can be achieved by modifying the legacy power system applications into

interoperable services either by converting the applications developed using

different paradigms into a single paradigm or an efficient solution has to be

found out to make the existing applications interoperable without modifying

the existing system. An effective way is needed to convert the power system

applications as services and publish the services over the Web and make them

easily discoverable. A service oriented open system made up of variety of

power system services operating on dissimilar platforms has be to developed

so that more extensive data and applications can be shared easily and flexibly.

The primary function of a power system is to supply its customers

with electrical energy as economically as possible with acceptable reliability

and quality. Power system reliability is defined as the ability of the system to

satisfy the customer demand. Demands for electric power with high reliability

and quality have increased tremendously in the past few decades due to the

digital revolution. It is expected that the requirements for high quality,

reliable power supply will continue to increase in the immediate future.

Customers such as commercial, industrial and residential users expect a

highly reliable supply with relatively low rates. The electric power industry

throughout the world is undergoing considerable changes in information

systems, and Web enabled service oriented architectural models are emerging

17

to support the integration of different power system applications. The

evolving changes in power system planning and operation needs require a

distributed control center that is decentralized, integrated, exible, and open.

As the markets expand and the power grid becomes more

congested, operational reliability is becoming more crucial. Maintaining

system reliability requires more robust data acquisition, better analysis and

faster coordinated controls. A distributed system is essential for meeting the

stringent timing and reliability requirements. To summarize, in a competitive

environment, economic decisions are made by market participants

individually and system wide reliability is achieved through coordination

among parties belonging to different companies, thus the paradigm has shifted

from centralized to decentralized decision making. This requires data and

application software in control centers to be decentralized and distributed.

The practice of interconnecting individual power systems into large

grids has resulted in economies in capital and operating expenses as well as

improved reliability (Ewart and Kirchmayer 1971). The full exploitation of

these benefits presents an increasingly complex problem to the power system

operator; consequently, the electric utility industry is devoting greater effort

to the application of automation technology to the solution of system op-

erating problems. The relevant developments in automation technology are

associated with analog and digital computers, data collection and supervisory

control equipment, communication devices, and displays. The computers are

being applied on a time-shared basis as a valuable tool in solving the

problems of power system planning, operations control, and operations

accounting, all of which involve both economic and reliability considerations.

18

2.2 POWER SYSTEM RELIABILITY CONCEPTS

Electricity is a basic commodity that drives the economic

productivity and prosperity of a society. Modern electrical power systems

have the responsibility of providing a reliable and economic supply of

electrical energy to their customers. The economic and social effects of loss

of electric service can have significant impacts on both the utility supplying

electric energy and the end users of the service. Maintaining a reliable power

supply is therefore a very important issue in power system design and

operation. Reliability of a power system is generally designated as a measure

of the ability of the system to provide customers with adequate supply. It is

one of the primary performance criteria of power systems. Major outages can

have a significant economic impact on end users as well as power utilities.

Power system has been significantly affected by a wide range of outage

events caused by incorrect planning, operational error, equipment failures,

environmental conditions, adverse weather effects, and load conditions.

Large-scale blackouts are emphasizing the importance of reliability issues.

Reliability is one of the major factors for planning, design, operation, and

maintenance of electric power systems. Failures in any part of the system can

cause interruptions of supply to end users.

Due to the complexity of modeling and computation, it is a difficult

task to analyze the entire grid configuration. Traditionally, functional zones

are used to divide an overall power system into appropriate subsystems or

functional areas that can be analyzed separately (Billinton and Allan 1996).

These functional areas are generation, transmission and distribution. The

function of the generation system is to make sure that enough capacity is

available to meet the load / demand at any time. Transmission and distribution

systems need to be reliable in making sure the electricity generated can be

19

delivered to the consumers. System planners have been assigned the role of

planning for forecasting the load into the future and plant capacity addition to

meet the load and provide a level of reliability in case some of the plants are

out on maintenance or breakdown.

Reliability of the generation system is divided into adequacy and

security. System adequacy relates to the existence of sufficient generators

within the system to satisfy the consumer load demand or system operational

constraints. System adequacy is associated with static conditions of the

system and does not include system disturbances. System security on the

other hand relates to the ability of the system to respond to disturbances

arising within the system. Therefore system security is associated with the

response of the system to whatever perturbation it is subjected to. In this

study, the reliability evaluations are focused on the generation system

adequacy without taking into account of system security.

According to Endrenyi (1978), if an improvement in system

reliability is required, it can be achieved by using either better components or

a system design incorporating more redundancy. Redundancy in generation

system means the installation of more generating capacity than normally

required which will incur more cost for the added reliability since the

additional capacity added will only be needed at the times of emergency. In a

generation system study, the total system generation is examined to determine

its adequacy to meet the total system load requirement. This activity is usually

termed as “generating system adequacy assessment”. The transmission system

is ignored in generating system adequacy assessment and is treated as a load

point. The main idea of the generating system adequacy assessment is to

estimate the generating capacity required to meet the system demand and to

have excess capacity to cater for planned and forced outage events.

20

System adequacy involves the existence of sufficient facilities in

the system to satisfy the customer demand. These facilities include the

generating capacity required to generate enough energy and the transmission

and distribution elements needed to transfer the generated energy to the

customer load points. Adequacy involves static system conditions rather than

system disturbances and is affected by many factors such as the installed

capacity, unit sizes, unit availabilities, maintenance requirements, and

interconnections and so on. System security, however, concerns the ability of

the system to respond to disturbances. Power systems have to maintain certain

levels of static and spinning reserves in order to achieve a required level of

adequacy and security.

It becomes traditional (Billinton and Satish Jonnavithula 1996)

within the field of power system reliability evaluation to divide the power

system into three functional zones: generation, transmission, and distribution.

The three functional zones can be combined to form hierarchical levels. It has

been convenient to do so because utilities have traditionally been divided into

these functional zones for purposes of organization, planning, operations and /

or analysis. Although deregulation has effected the disaggregation of the

traditional utility into many separated organizations, most of these

organizations still have these corresponding internal divisions or are solely

dedicated to the oversight of one of these functional zones. The main purpose

of the functional zone division, in terms of reliability evaluation, is to provide

a succinct means for identifying the part of the power system being analyzed.

Figure 2.1 shows an organization of the functional zones in hierarchical

levels, where the levels increase according to analysis complexity.

In a hierarchical level I (HL-I) study, the total system generation

including interconnected assistance is examined to determine its adequacy to

meet the total system load demand. Reliability assessment at HL-I is normally

21

defined as generating capacity adequacy evaluation. The transmission

network and the distribution facilities are not included in an assessment at the

HL-I level. Adequacy evaluation at hierarchical level II (HL-II) includes both

the generation and transmission in an assessment of the integrated ability of

the composite system to deliver energy to the bulk supply points. This

analysis is usually termed as composite system reliability evaluation (or bulk

power system reliability evaluation).

Figure 2.1 Hierarchical Levels of Power System Functional Zones

Adequacy assessment at hierarchical level III (HL-III) includes all

of the three functional zones and is not easily conducted in a practical system

due to the computational complexity and scale of the problem. These analyses

are usually performed only in the distribution functional zone.

Functional

Zone I

(Generation)

Functional

Zone III

(Distribution)

Functional

Zone II

(Transmission)

Hierarchical level I

(HL-I)

Hierarchical level II

(HL-II)

Hierarchical level III

(HL-III)

22

2.3 GENERATING CAPACITY ADEQUACY EVALUATION

Hierarchical level I (HL-I) corresponds to analysis of the generation

function only. This was the earliest power system reliability problem

addressed, with the first work during the year 1933-1934, with a later seminal

contribution in 1947. The so-called “Calabrese” method forms the basis of the

loss of load approach which is still the most widely used probabilistic

technique in the reliability evaluation of generating capacity. In HL-I

evaluation, the reliability of the transmission is ignored, and the only concern

is in estimating the necessary generating capacity to satisfy the system

demand and to perform corrective and preventive maintenance on the

generating units. Adequacy evaluation at HL-I involves determination of the

total system generation required to satisfy the total load requirement. In the

study of adequacy evaluation, the reliability of the transmission and

distribution zones and their ability to move the generated energy to the

customer load points are not included. The basic model at HL-I is shown in

Figure 2.2.

Figure 2.2 Basic Model

The basic modelling approach for the generating system adequacy

assessment consists of three parts as shown in Figure 2.3.

Total System

Generation

Total System

LoadG

23

Figure 2.3 Conceptual Model in Adequacy Assessment at HL-I

The generation and load models are convolved to form an

appropriate risk model where the element of interest is the risk of generation

capacity less than the load. In short, adequacy evaluation of generation

systems consists of three general steps:

Create a generation capacity model based on the operating

characteristics of the generating units

Build an appropriate load model

Combine the generation capacity model with load model to

obtain a risk model

The risk indices obtained are overall system adequacy indices and

do not include transmission constraints and transmission reliabilities. A wide

range of methods has been developed to perform generating capacity

reliability evaluation. These techniques can be categorized into two types,

analytical methods and simulation methods. Analytical methods represent the

system by mathematical models and evaluate the reliability indices using

direct numerical solutions. Simulation methods estimate the reliability indices

by simulating the actual process and random behaviour of the system, treating

the problem as a series of experiments. The most widely used analytical

Generation

Model

Load

Model

Risk

Model

24

technique in HL-I evaluation is the loss of load approach. This process has

been extended to include the loss of energy.

Both analytical and simulation methods have their own merits and

demerits. Analytical techniques can provide the expected index values in a

relative short computation time. Assumptions are sometimes needed to

simplify the problem, particularly when the system and the operating

procedures are complex. Simulation methods generally require longer

computing times and more computational resources, but theoretically, can

include all aspects and contingencies in the power system. There is increasing

interest in modeling the system behaviour more comprehensively and in

producing a more informative set of reliability indices. The development of

increased computing power has made the use of simulation methods a

practical and viable tool for large system reliability assessment.

The reliability indices obtained indicate the ability of the generating

facilities to meet the system demand. In the analytical method, the generating

system model used for generation capacity adequacy assessment is a Capacity

Outage Probability Table (COPT) which can be created using a recursive

technique and for the load model, the daily peak load or hourly load for a

period of one year is normally used to form the Load Probability Table (LPT).

According to Wang and McDonald (1994) the process of evaluation of power

system reliability starts by creating a mathematical model of a system or a

subsystem and then proceeding with a numerical solution, summarized in the

following general steps:

Define the boundary of the system and list all the components

included

Provide reliability data such as failure rate, repair rate, repair

time, scheduled maintenance time, etc., for every component

25

Establish reliability model for every component

Define the mode of system failure, or define the criterion for

normal and faulty systems

Establish a mathematical model for the system reliability and

its basic assumptions

Select an algorithm to calculate the system reliability indices

Generating system reliability evaluation is an important aspect for

future system capacity expansion. It provides a measure of reliability or

adequacy to make sure that the total generation system capacity is sufficient

to provide adequate electricity when it is needed. The estimation of reliability

indices is essential at the time of planning and expansion. Generally various

number of basic reliability indices such as Loss of Load Probability (LOLP),

Loss of Load Expectation (LOLE), Loss of Energy Probability (LOEP), and

Loss of Energy Expectation (LOEE) are used to assess generating capacity

adequacy. The power system reliability analysis needs huge volume of failure

data, which are heterogeneous in nature. A convenient data representation

model is required to enhance the interoperability between heterogeneous

applications.

2.4 CURRENT TRENDS IN POWER SYSTEM RELIABILITY

ESTIMATION

The physical structure of the power system network changes

dynamically in the deregulation environment and its size is keeping on

increasing to meet the power demand. Consequently, the power system

operations are becoming extremely complex. As the electricity industry

moves towards restructuring, it seems clear that system reliability will

continue to be an extremely important area of research. Application of

Information and Communication technologies has become inevitable in the

26

analysis of modern power systems regarding its planning, operations, control

and maintenance. Entities with a system wide perspective, such as a power

pool or other control area or even generating companies with large generator

portfolios will most likely perform various types of reliability analyses to

determine whether there is an adequate electricity supply. Many electricity

production simulation models have been adapted to the new market paradigm.

The development of power systems has been rapidly changed at the same

time, the range of power of computers has increased by orders of magnitude,

while their costs per unit of throughput and size have markedly decreased.

Both analytical and Monte Carlo simulation models are being

effectively utilized in the estimation of power system performance indices.

Traditional software design based on a procedural view, written in

FORTRAN and C computer languages, is strongly linked to the power system

industry, mainly due to its mathematical vocation. These languages have been

supporting power system studies for a long time. In general, FORTRAN is the

most widely used programming language to support, for instance, reliability

studies in a variety of power utilities and research academic environments.

This happens mainly due to its suitable performance when running with

mathematical approaches, as well as their wide mathematical libraries. One of

the objectives of the current research is to focus on new ways to build remote

services for power system reliability evaluation in order to emphasize the

natural transition from traditional tools to a modern computing environment.

This environment will be adequate to handle the size of the modern power

systems and its complex operations.

Edmund Handschin et al (1998) had proposed an object oriented

simulation model for the purpose of determining a dynamic plan for a

transmission system in a deregulated open access environment. They have

developed a global object oriented system for power system representation

27

and for solving transmission planning problems. Over the last two decades,

the object-oriented programming and the object-oriented design have

improved the software development for power system analysis. Fangxing Li

and Broadwater (2004) have presented an architectural design for a

framework which aims to summarize the commonalities in software design

for power distribution systems. They have discussed the benefits of the

framework with respect to cost, reusability, maintenance and language

independent interfaces. The architectural framework developed by them

supports common distribution system algorithms such as load flows, short

circuits and reliability assessment. The application developers who use this

framework can extend its functionality to support optimal reconfiguration,

risk analysis etc. The framework also provides an environment to model

distributed computing to allow power system applications running at different

machines in client-server mode.

The framework consists of four layers: component layer, iterator layer,

algorithm layer and distributed algorithm layer. The functions of the

component layer are classified into different groups that include directly

connected components, load flow related parameters and reliability related

parameters. The iterator layer presents an abstraction of topological

management to traverse power distribution systems according to prescribed

traversal rules. The algorithm layer models the fundamental algorithms like

load flow, short circuit and reliability algorithm. The distributed algorithm

layer provides an abstraction of distributed computations. Since the

architecture of this framework is layer-driven with strict top-down

dependencies, the software coupling is reduced and reusability and

extensibility are gained much and improved.

Ramachandran and Sankaranarayanan (1993) have developed a

recursive algorithm to extract minimal-cuts from fault tree representation of

28

the power system networks and hence obtained the system failure probability.

A non-linear tree structure is used to construct fault trees that represent

system conditions symbolically and include all the basic fault events that may

occur or expected to occur in the system. Due to complexity in the operation

and maintenance of the power system, even a power system network of small

size has large number of basic fault events. The way in which the fault tree is

represented has a great influence in the amount of storage requirements and

the computation time. An object oriented model had been developed to

represent the fault tree using non-linear tree structure and the behaviour for

minimal cuts extraction was encapsulated. This approach was tested with a

power system of 3 buses and 5 lines, a power supply system and with a

sample transmission system.

Ozdemir (2010) demonstrated the reliability estimation of a sample

transmission network shown in Figure 2.4 using minimal-cuts.

Figure 2.4 Sample Power Transmission System

The power transmission capacities of the three transmission lines

H1, H2 and H3 are 8 MW where a 10 MW load is consumed by two generators

G1 and G2. The reliability values of the components are given as follows.

L110 MW

T1

10 MW

H2

8 MWG1

T2

10 MW10 MWH3

8 MW

H1

8 MW

G2

10 MW

1 2

3

29

RG1 = 0.98, RT1 = 0.99

RG2 = 0.97, RT2 = 0.98

RH1=RH2= RH3 = 0.98

The equivalents of G1-T1 and G2-T2 are represented by A and B

respectively.

RA = RG1. RT1 = 0.9702

RB = RG2. RT2 = 0.9506

QA = 1 - RA = 0.0298

QB = 1 - RB = 0.0494

where, QA and QB are the failure probabilities. Since a minimal path should

provide a power of 10 MW, the components A, B and H1 comprise 2/3 of the

system which is in series with H2 and H3 as shown in Figure 2.5.

Figure 2.5 Series-Parallel Representation of Sample Power

Transmission Network

The extracted minimal paths are,

{ A H1 H2 H3, B H1 H2 H3 and A B H2 H3 }

A

B

H1

H2 H3 OutIn

30

and the derived minimal-cuts are,

{ H2, H3, A B, A H1 and B H1 }

Using these minimal-cuts, the failure probability of the system is

estimated as follows:

5

S i

i 1

Q Q C

)CQ(C)(CQQ jiiS

(by omitting the third and higher order terms)

Since QH1, QH2 and QH3 are equal,

Qs QH + QH + QAQB + QAQH + QBQH - Q2

H

QS 0.043056

and hence the reliability of the sample transmission network Rs is estimated as

follows:

RS = 1- QS = 0.956944

The above power transmission network has been represented as a

fault tree with all possible fault events and minimal cuts are extracted using

the recursive algorithm proposed by the authors, Ramachandran and

Sankaranarayanan (1993). The probability of occurrence of the fault event

represented as the root element of the fault tree is estimated. A logical

database is created to access the intermediate event by pre-order traversal and

by using the exact location of the event in the fault tree, the probability of

occurrence of this event is estimated by using Boolean operations AND / OR,

which are associated to constitute that fault event.

31

Brown (2002) have developed a Web based model for distribution

system planning. They have described the implementation of a Web based

distribution system analysis tool, which is named as “Internet based Planning

and Analysis of Distribution Systems – iPAD” tool. This tool has a user

interface, an analysis engine for each and every power system operation and

data storage. Initially the tool was used to analyze radial power flow, radial

feeder system reliability assessment and network security assessment. Web

based feeder planning strategy proposed by them has the potential to provide

better feeder planning at a lower cost. It has the potential to be just as full

featured as locally installed software, but is not subject to the disadvantages

of expensive maintenance and version control. Application service providers

have the ability to provide mass customization so that the tool matches each

planning department’s unique needs and processes, and can offer their

services through off balance sheet lease agreements.

Yeddanapudi et al (2005) have developed a predictive reliability

assessment tool for distribution systems. The predictive reliability analysis

has been carried out on an actual distribution system and the difference in

indices computed using historical and predictive methods is illustrated.

Initially, historical reliability indices have been estimated for a 66 feeder

distribution system by considering outages caused by overhead or

underground line failures, outages due to transmission failures, outages

caused by failures of components like switches, fuses, substation breakers

etc., and events caused by public interference. In the proposed predictive

method of estimation of reliability indices of distribution system, the failure

rates and average repair times of each component are considered. In this

predictive analysis, each contingency is simulated; its effect on each of the

components in the system has been determined and weighted by the

probability of the contingency. The predictive algorithm has been iterated by

32

adjusting the input outage data in order to obtain reliability indices that

matched with those obtained from historical analysis.

One of the major benefits of power distribution system fault

forecasting is the use of such information to assess the reliability of the

system. Adediji and Joseph (2009) have discussed the need for fast and

accurate techniques to estimate the reliability of power distribution systems.

The duration of interruption in power distribution system is comparatively

high and comes at random. A software model has been developed based on

Box-Jenkins time series mathematical techniques for fault predictions and

reliability calculations of distribution systems. The system analysis,

forecasting and reliability estimation modules have been built into the

software model named as “Reliability Calculation Analysis Software”. The

software model was used to simulate faults for another ten years from which

the overall reliability of the system was determined. The adequacy of the

chosen mathematical model for fault prediction was determined by comparing

the computed chi-square with standard statistical values. The developed

software program is flexible and can be upgraded as data varies.

Da Rosa et al (2010) applied intelligent agent techniques with

sequential Monte Carlo simulation for power system reliability assessment.

The techniques used in power system reliability assessment can be divided

into two categories: analytical and simulation. Analytical approaches adopt

state space estimation, but simulation can either adopt state space

representation or chronological representation. The reliability assessment of

large power systems by chronological Monte Carlo simulation is very

expensive both in time and computational requirements. An intelligent

distributed environment has been created and sequential and non-sequential

Monte Carlo simulation approaches have been used for estimating power

system reliability indices by adopting intelligent agent features. Multiple

33

agents have been deployed such as Sequence Producer Agent, State Evaluator

Agent and Index Calculator Agent in order to handle the state selection, state

evaluation and index calculation stages of Monte Carlo simulation while

estimating reliability indices. Briefly, a multi-agent system is proposed to

apply with sequential Monte Carlo simulation for estimating reliability

indices. The proposed methodology is applied to the IEEE-RTS system and

the obtained results are compared with analytical and chronological Monte

Carlo simulation techniques to verify the performance achieved by the

sequential Monte Carlo simulation with intelligent agents.

The power system industry has been utilizing advancement in

communication and information technology over the years in order to

improve efficiency, reliability, security and quality of service. Increasing

complexity of power grids, growing demand and requirement for greater grid

reliability and security are the factors that concern about the need for

advanced communication technologies for proper co-ordination and

appropriate control actions in power system operations. Application of

modern technologies for monitoring and control of power system operations

intelligently leads to the concept of ‘smart grid’. Khosrow Moslehi and Ranjit

Kumar (2010) have analysed the Smart Grid with reliability perspective. Grid

reliability challenges have been analyzed in detail and the Smart Grid

resource types, namely renewable resources, demand response, electric

storage and transportation are critically reviewed. Reliability has always been

in the forefront of power grid design and operation due to the cost of outages

to customers.

Wind and Solar are the most rapidly expanding renewable

resources. The unpredictability of wind energy resources is indicated by their

low capacity factors, which are much lower than conventional generators.

This creates challenging problem in the control and reliability of the power

34

grid. The wind power forecasting errors also present scheduling problems.

Solar is the most abundant source of energy. The variability of solar energy

resources is very much impacted by climate and sunlight availability. Large

scale solar resources face various transmission limitations. Demand response

allows consumer load reduction in response to emergency conditions on the

electric grid. Such condition is more prevalent during peak load or congested

operation. Demand response does not substantially change the total energy

consumption since a large fraction of the energy saved during the load

curtailment period is consumed at a more appropriate time.

Load rejection as an emergency resource to protect the grid from

disruption is well understood and is implemented to operate either by system

operator command or through under-frequency and under-voltage relays. In a

Smart Grid, the load rejection schemes can be enhanced to act more

intelligently and based on customer participation. As such, demand response

schemes could improve reliability.

Storage resources tend to make the net demand profile flatter and

hence, are expected to improve reliability. Various storage technologies are

emerging to store the wind and solar energies. From reliability point of view,

electric transportation has features similar to both demand response resources

and storage resources. Plug-in electric vehicles continue to become more

popular as environmental concerns increase. The authors have developed an

architectural framework to facilitate the design, development and grid-wide

integration of various components as well as the emergence of standards and

protocols needed for the Smart Grid.

In spite of tremendous research works were reported in the

literature regarding Web enabled solutions for power system analysis,

comparatively less attention has been given to the reliability analysis. Due to

complex nature of power system operations and deregulation polices, it is of

35

greater importance as requirements are emerging to focus research on service

automation, service launching and service chaining for effective control of

power system operations and maintenance. Distributed service models have

to be developed that will support interoperability between services on

different platforms in a reliable, scalable and adaptable manner.

2.5 XML ANNOTATIONS FOR POWER SYSTEM

RELIABILITY DATA REPRESENTATION

In power systems, data exchange plays a vital role for variety of

applications, such as real-time monitoring, maintenance planning,

transmission and distribution planning, and operation standardization of

power systems. Power system industries are now increasingly becoming

privatized and hence the system data is becoming increasingly distributed,

with more constrained and complex operational and control requirements.

Since deregulation, there has been an increasing need for power companies to

exchange data on a regular basis. Both operational and equipment data of the

power system need to be exchanged for proper operation of the power system.

Power system operations are becoming complex and the data required for

analysis have been stored in different formats and are distributed in a

heterogeneous environment.

An innovative and comprehensive strategy is proposed for power

system reliability data representation, which is capable of accessing power

system data from any data source and converts them into a common format

using pre-defined XML document template. At present, different power

utilities use different power system planning and reliability estimation

applications that store data in IEEE format and in their own proprietary data

formats, which makes the sharing of power system data as a difficult process.

An XML annotation scheme is adopted for power system reliability

data generation service. The XMLised representation of power system data

36

<generator_data>

<general>

<gen>Total Number of Generator Groups</gen>

<tsc capacity=”MW”>Total System Capacity</tsc>

<id>Generation System Reliability Data </id>

</general>

<generatorgroup>

<groupid>Generator Group Id </groupid>

<unit_id>Id Number of Unit</unit_id>

<unit_type>Type of the Unit</unit_type>

<nu>Number of Units</nu>

<unitsize type="MW">Capacity of the Unit</unitsize>

<for>Forced Outage Rate</for>

<mttf type="hour">Mean Time To Failure</mttf>

<mttr type="hour">Mean Time To Repair</mttr>

<sm type="weeksperyear">Scheduled Maintenance</sm>

</generatorgroup >

</generator_data>

offers reliable data exchange between legacy power system applications. By

making every power system application conforms to this standard and sending

data between applications in a common format, the complexities found in

interchange of data are reduced.

In Power System Reliability Analysis, based on the characteristics

of the generating units such as thermal, hydro etc., the system has been

divided into number of groups. The generator and load data are required for

evaluating the Generation System Reliability (GSR). The generator data

stores the value of number of units, unit size, forced outage rate, mean time to

failure and mean time to repair. The generator data required for GSR analysis

is represented using XML as follows:

37

<load_data>

<general>

<mls type="MW"> Maximum Load in System </mls>

<id>Weekly peak load in Percent of Annual Peak</id>

</general>

<peakload>

<week_id>Week Id Number </week_id>

<perc>percentage of peak Load</perc>

<peak type="MW">Actual Peak Load</peak>

</peakload>

</load_data>

The load data stores the value of weekly peak load in percent of

annual peak. The load data required for GSR analysis is represented as

follows:

The XMLised representation of power system data offers reliable

data exchange between legacy power system applications. The power system

reliability estimation data can be represented comprehensively and at the

same time conformed to the W3C standards of XML representation using

XML annotations. The power system reliability data pertaining to the IEEE

Reliability Test System (IEEE – RTS) are represented using XML annotations

and the corresponding XML document and its XML schema are generated.

The IEEE – RTS is a 24 Bus system with 10 generator buses, 17 load buses,

33 transmission lines, 5 transformers and 32 generating units. The system

peak load is 2850 MW and the total generation is 3405 MW. The single line

diagram for the IEEE – RTS 24 Bus system is shown in Figure 2.6.

38

Figure 2.6 Single Line Diagram of IEEE – RTS 24 Bus System

The generator reliability data for the IEEE – RTS system with nine

groups and total capacity of 3405 MW is given in Table 2.1. The weekly

peak load in percent of annual peak is given in Table 2.2. The maximum load

in the system is 2850 MW.

39

Table 2.1 Generator Reliability Data for IEEE Reliability Test System

IndexUnit

Group

Unit

Type

No.

of

Units

Unit

Size, C

(MW)

Forced

Outage

Rate

MTTF

(m = 1/ )

(Hour)

MTTR

(r = 1/ )

(Hour)

Scheduled

Maintenance

(Weeks/year)

1 U12Oil

/Steam5 12 0.02 2940 60 2

2 U20 Oil 4 20 0.10 450 50 2

3 U50 Hydro 6 50 0.01 1980 20 2

4 U76Coal

/Steam4 76 0.02 1960 40 3

5 U100Oil /

Steam3 100 0.04 1200 50 3

6 U155Coal /

Steam4 155 0.04 960 40 4

7 U197Oil /

Steam3 197 0.05 950 50 4

8 U350Coal /

Steam1 350 0.08 1150 100 5

9 U400 Nuclear 2 400 0.12 1100 150 6

Table 2.2 Weekly Peak Load in percent of Annual Peak

WeekPeak

Load

(%)

Peak

Load

(MW)

WeekPeak

Load

(%)

Peak

Load

(MW)

WeekPeak

Load

(%)

Peak

Load

(MW)

WeekPeak

Load

(%)

Peak

Load

(MW)

1 86.2 2456.7 14 75.0 2137.5 27 75.5 2151.8 40 72.4 2063.4

2 90.0 2565.0 15 72.1 2054.9 28 81.6 2325.6 41 74.3 2117.5

3 87.8 2502.3 16 80.0 2280.0 29 80.1 2282.8 42 74.4 2120.4

4 83.4 2376.9 17 75.4 2148.9 30 88.0 2508.0 43 80.0 2280.0

5 88.0 2508.0 18 83.7 2385.5 31 72.2 2057.7 44 88.1 2510.8

6 84.1 2396.9 19 87.0 2497.5 32 77.6 2216.6 45 88.5 2522.2

7 83.2 2371.2 20 88.0 2508.0 33 80.0 2280.0 46 90.9 2590.6

8 80.6 2297.1 21 85.6 2439.6 34 72.9 2077.6 47 94.0 2679.0

9 74.0 2109.0 22 81.1 2311.4 35 72.6 2069.1 48 89.0 2536.5

10 73.7 2100.5 23 90.0 2565.0 36 70.5 2009.2 49 94.2 2684.7

11 71.5 2037.8 24 88.7 2528.0 37 78.0 2223.0 50 97.0 2764.5

12 72.7 2072.0 25 89.6 2553.6 38 69.5 1980.7 51 100.0 2850.0

13 70.4 2006.4 26 86.1 2453.9 39 72.4 2063.4 52 95.2 2713.2

40

The @XmlRootElement annotation is used with a class or enum

type to map that class or enum type to an XML element. When a class is

annotated with @XmlRootElement annotation, then its values are represented

as XML elements in an XML document. The @XmlRootElement annotation

is used to define an XML element name for the XML schema type of the

corresponding class. The @XmlRootElement annotation for power system

reliability data representation is given as follows:

@XmlRootElement(name=”reliability”,

namespace=”http://www.reliablity.com/ieee-rts”)

public class PSReliabilityData

{

int groupId;

String unitId;

String unitType;

int numofUnits;

int unitSize;

double forcedOutageRate;

int mttf;

int mttr;

int scheduledMaintenance;

public PSReliablityData(int gID, String uID, String uType, int nou, double

outageRate, int mttf, int mttr, int sMaintenance) { …….. }

}

The pre-defined Marshaller class in the javax.xml.bind package is

responsible for governing the process of serializing java content trees to XML

data. In order to associate the Marshaller reference to the PSReliabilityData

class, an XML Binding Context reference has to be created, which will load

the type ‘PSReliabilityData.class’ to the memory and then instantiate the

41

same to obtain the context reference. The code segment to produce the XML

document to represent power system reliability data is as follows:

JAXBContext context =

JAXBContext.newInstance(PSReliabilityData.class);

Marshaller marshaller = context.createMarshaller();

marshaller.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, true);

OutputStream os=new FileOutputStream("ieee-rts.xml");

marshaller.marshal(new PSReliabilityData(1, “U12”, “Steam”, 5, 12, 0.02,

2940, 60, 2), os);

The marshaller reference serializes the power system reliability

data into an XML document with pre-defined formatting scheme as

represented in the setProperty() method and the XML output, which is stored

in ‘ieee-rts.xml’ file, is given below:

<?xml version="1.0" encoding="UTF-8"?>

<ns2:reliability xmlns:ns2="http://www.reliability.com/ieee-rts">

<groupId>1</groupId>

<unitId>U12</unitId>

<unitType>OilorSteam</unitType>

<numofUnits>5</numofUnits>

<unitSize>12</unitSize>

<forcedOutageRate>0.02</forcedOutageRate>

<mttf>2940</mttf>

<mttr>60</mttr>

<scheduledMaintenance>2</scheduledMaintenance>

</ns2:reliability>

The ‘schemagen’ utility generates the necessary XML schema

document as per the PSReliabilityData class defined. It generates two schema

files, one is based on the element values given within the @XmlRootElement

42

annotation and the other one is based on the class which is to be mapped. The

XML schema for the class to be mapped is to be obtained using the schema

definition file ‘schema2.xsd’ as given in the attribute ‘schemaLocation’ of the

localpart <xs:import>. The generated XML schema files are given as follows:

<!--schema1.xsd-->

<?xml version="1.0" encoding="UTF-8"?>

<xs:schema version="1.0"

targetNamespace="http://www.reliability.com/ieee-rts"

xmlns:xs="http://www.w3.org/2001/XMLSchema">

<xs:import schemaLocation="schema2.xsd"/>

<xs:element name="reliability" type="psReliabilityData"/>

</xs:schema>

<!--schema2.xsd-->

<?xml version="1.0" encoding="UTF-8"?>

<xs:schema version="1.0"

xmlns:xs="http://www.w3.org/2001/XMLSchema">

<xs:complexType name="psReliabilityData">

<xs:sequence>

<xs:element name="groupId" type="xs:int"/>

<xs:element name="unitId" type="xs:string"/>

<xs:element name="unitType" type="xs:string"/>

<xs:element name="numofUnits" type="xs:int"/>

<xs:element name="unitSize" type="xs:int"/>

<xs:element name="forcedOutageRate" type="xs:double"/>

<xs:element name="mttf" type="xs:int"/>

<xs:element name="mttr" type="xs:int"/>

<xs:element name="scheduledMaintenance" type="xs:int"/>

</xs:sequence>

</xs:complexType>

</xs:schema>

43

This annotation model for representing power system data is

efficient only to small systems or test cases. For large power systems where

data has been stored in text files or in relational databases and are represented

in their own proprietary formats and are scattered geographically apart, a Web

service model is efficient that will generate XML data dynamically as per the

service requirements and as per the XML schema defined.

2.6 WEB SERVICE BASED POWER SYSTEM RELIABILITY

DATA GENERATION MODEL

Internet provides a heterogeneous environment for Web

applications development. Number of power system applications, which are

hosted in the Web is countless and it is a growing phenomenon. Many power

utilities on the Web provide the various applications that include power

system planning, on-line operations monitoring, state estimation, stability

analysis, smart metering, energy management, data acquisition and

information sharing. These applications have been developed in different

platforms using different programming and scripting paradigms. The data

needed for those applications are in different formats and are scattered

desperately. Interoperability becomes a major issue in most of the Web

enabled power system applications.

Most of the power system applications store their planning,

operational and maintenance data in relational databases. The major problem

with most of the relational databases is their incompatible formats while

accessing by various power system applications of heterogeneous nature. In

order to enhance the interoperability and for flexible data sharing between

power system applications in a distributed environment, it becomes very

essential to convert the power system data stored in the relational databases

into XML documents. An efficient, reliable and secure method is required for

44

transforming the data and for transferring the same in a distributed

environment, especially when the size of data is large as in case of real time

large power systems. Since in the deregulated environment, the various

power utilities are interconnecting their individual power systems in order to

meet the demand and to meet the quality requirements, there is a real problem

of communication for exchanging the data among various applications. The

conversion of power system data into text based XML document is preferred

in SOAP messages, both in request and response payloads.

The proposed Web Service model shown in Figure 2.7 has the

capability to dynamically generate the power system reliability data in XML,

fetching the required data from the database. The proposed XMLised power

system data representation model significantly reduces the engineering efforts

required to integrate its data in the Web services environment. This ensures

interoperability between various power system applications in a heterogeneous

environment. The major steps involved in the Web service based power

system reliability data generation model in JAX-WS environment are data

representation, defining endpoint interface, its implementation, service

description, service deploying in virtual server, creation of client side and

server side artifacts and invoking. The proposed model is tested with the

Roy Billinton Test System (RBTS) with 6 Buses whose single line diagram is

shown in Figure 2.8. The generator reliability data for the test system is given

in Table 2.3 and the load data is given in Table 2.4.

45

Figure 2.7 XMLised Power System Reliability Data Generation Model

Figure 2.8 Single Line Diagram of the RBTS

Power system

Clients

JAX-WS

Web

Service

Web servlet

(Middleware

Component)

Power System

Reliability

Database

Invoke

ServiceInvokes

Request for Data

Response as Raw

Data

Raw

Reliability

Data

XMLised

Reliability Data

2

20 MW

1x40 MW

4x20 MW

2x 5 MW

Bus 3

Bus 5

Bus 4

2x40 MW

1x20 MW

1x10 MW

40 MW

20 MW

85 MW

20 MW

3

67

8

4

5

1

9

Bus2

Bus1

Bus 6

46

Table 2.3 Generator Reliability Data - RBTS

Unit

No

Bus

No

Unit

Type

Unit

Size, C

(MW)

Failure

Rate

(Occ/yr)

Repair

Time

(Hours)

Failure

Probability

1 1 Oil / Steam 40 6.0 45 0.030

2 1 Oil 40 6.0 45 0.030

3 1 Hydro 10 4.0 45 0.020

4 1 Coal / Steam 20 5.0 45 0.025

5 2 Oil / Steam 5 2.0 45 0.010

6 2 Coal / Steam 5 2.0 45 0.010

7 2 Oil / Steam 40 3.0 60 0.020

8 2 Coal / Steam 20 2.4 55 0.015

9 2 Coal / Steam 20 2.4 55 0.015

10 2 Coal / Steam 20 2.4 55 0.015

11 2 Coal / Steam 20 2.4 55 0.015

Table 2.4 Weekly Peak Load in percent of Annual Peak - RBTS

Week

Peak

Load

(%)

Week

Peak

Load

(%)

Week

Peak

Load

(%)

Week

Peak

Load

(%)

1 86.2 14 75.0 27 75.5 40 72.4

2 90.0 15 72.1 28 81.6 41 74.3

3 87.8 16 80.0 29 80.1 42 74.4

4 83.4 17 75.4 30 88.0 43 80.0

5 88.0 18 83.7 31 72.2 44 88.1

6 84.1 19 87.0 32 77.6 45 88.5

7 83.2 20 88.0 33 80.0 46 90.9

8 80.6 21 85.6 34 72.9 47 94.0

9 74.0 22 81.1 35 72.6 48 89.0

10 73.7 23 90.0 36 70.5 49 94.2

11 71.5 24 88.7 37 78.0 50 97.0

12 72.7 25 89.6 38 69.5 51 100.0

13 70.4 26 86.1 39 72.4 52 95.2

47

2.6.1 XMLised Power System Reliability Data Generation Service

Interface

The service interface XMLisedPSDataInt provides the contract

between the power system client and XMLised Data Generation service

provider. This contract allows the exchange of information between the client

and the service provider. The service endpoint interface to generate data for

reliability analysis is as given below:

The annotation @WebService is used to identify the

“XMLisePSDataInt” as service endpoint interface and the annotation

@WebMethod is used to expose the method “generateXMLData()” as a Web

service in the JAX-WS environment. The power system clients need not be

aware of any underlying technology or programming paradigm which the

service is using. The service interface encapsulates all aspects of the network

protocol used for communication between clients and service provider.

Decoupling the service interface code from the service

implementation code enables the system to deploy two code bases on separate

tiers, potentially increasing the deployment flexibility. The power system

clients communicate with the data generation service endpoints for invoking

the services. The input required for the data conversion model is the service

package power;

import javax.jws.*;

@WebService

public interface XMLisePSDataInt {

@WebMethod

public void generateXMLData(String xmlfile);

}

48

endpoint implementation, which has been published with a virtual server and

provides an URL to the appropriate Web Service Descriptor file through

which the client can access the XMLised power system reliability data

generation service. In case of JAX-WS based applications, the Web service

implementation class itself implicitly defines a service endpoint interface. If

there is an explicit service end point interface, then it must be notified using

an element ‘endpointInterface’ associated with the @WebService annotation

while implementing the same.

The JAX-WS runtime provides an Annotation Processing Tool

(APT), which has a list of annotations to be used with the endpoint

implementation to generate the endpoint interface and other components and

also provides ‘wsgen’ and ‘wsimport’ commands to generate portable server

side and client side artifacts in order to enable the client to communicate with

the Web service. This implementation class contains the required method

generateXMLData(), which converts the RBTS data stored in the relational

database into XML documents.

2.6.2 XMLised Power System Reliability Data Generation Service

Implementation

The Web service implementation class (XMLisePSDataImpl)

defines the methods that a client can invoke for accessing the service and

encapsulates all aspects of the network protocol used for communication

between the clients and the service provider. The implementation class,

XMLisePSDataImpl, is annotated as a Web service endpoint using the

@WebService annotation and it defines a method named

generateXMLData(), annotated with the @WebMethod annotation, which

exposes the method to Web service clients.

49

The implementation of the XMLisePSDataInt defines the service

‘generateXMLData()’ within XMLisePSDataImpl class. The service utilizes

a middleware component to communicate to the database server to obtain the

required generation system reliability data and load data and converts the data

into an XML document. The generateXMLData() method writes the

XMLised RBTS data to a disk file whose filename is given as the parameter.

An URL connection is established within the service to access the middleware

component. The middleware component named as “webservlet” is deployed

in the Tomcat Web server. The code segment for the implementation of the

reliability data generation service for the RBTS system is given as follows:

@WebService(endpointInterface="powersystem.XMLisePSDataInt")

public class XMLisePSDataImpl implements XMLisePSDataInt

{

URL u;

URLConnection uc;

BufferedReader br;

String data = null;

@WebMethod(operationName = "generateXMLData")

public void generateXMLData(String xmlfile)

{

u=new URL ("http://localhost:8080/power/webservlet");

uc=u.openConnection();

File f = new File(xmlfile);

FileOutputStream fos = new FileOutputStream(f);

PrintStream ps = new PrintStream(fos);

ps.println("<?xml version=\"1.0\" encoding=\"UTF-8\"?>");

ps.println("<rbts>");

InputStream is=uc.getInputStream();

InputStreamReader isr=new InputStreamReader(is);

br=new BufferedReader(isr);

data=br.readLine();

50

import power.XMLisePSDataImpl;

public class WSPublisher

{

public static void main(String[] args)

{

Endpoint.publish("http://localhost:5000/WS/power",

new XMLisePSDataImpl());

}

}

while(!data.equals("end"))

{

ps.println(data);

data = br.readLine();

}

ps.println("</rbts>");

ps.close();

}

}

The JAX-WS engine provides a tool named as ‘wsgen’ to generate

server side artifacts. The portable artifacts, which are generated using

‘wsgen’ utility are actually Java Bean classes and are named as

GenerateXMLData and GenerateXMLDataResponse respectively. These files

are responsible for marshaling / unmarshaling of method invocations and

responses. Marshaling and unmarshaling are the transformation from XML to

Java objects and vice versa. These bean components are used to marshalling

and unmarshaling the SOAP request and response messages between the

client and the service provider.

2.6.3 Service Deployment on a Virtual Server

At the time of deployment, the servlet container maps the

implementation class to WSDL using the annotations specified. The service

is to be deployed onto a virtual server as follows:

51

When the above application is executed, it creates an inherent

virtual HTTP server, which provides an environment for deploying

XMLisePSDataImpl service. It also generates a Web service descriptor file

using WSDL through which the clients can access the power system

reliability data generation service. The virtual server configuration defines

the service endpoint address, using which the reliability data generation

service deployment descriptor is created.

After successful deployment, the service description can be

accessed from the URL, “http://localhost:5000/WS/power?WSDL”. The

power system clients can use this endpoint address for accessing the XMLised

power system reliability data generation services. Thus, when the client gives

a request for a particular power system reliability data to the remote

generateXMLData() method using the GenerateXMLData bean, the

reliability data stored in the database has been converted into an XML

document using the ‘XMLisePSDataImpl’ service and the response is

accessed through the GenerateXMLDataResponse bean. The JAX-WS

runtime maps the Java types to standard XML types and forms a SOAP

message that encapsulates the method call and parameters, then passes the

SOAP message through the SOAP handlers and then to the server-side service

port. The JAX-WS enables exchange of SOAP requests and responses

through an API that hides the details about the SOAP message to the power

system clients.

In the proposed JAX-WS model, the WSDL is used as the metadata

language for describing the power system data generation services. The Web

service descriptor represents information about the interface and semantics of

how to invoke a service. It contains the information about the data type,

binding (XMLisePSDataIntBinding) and address information for invoking the

52

services from the service provider. The power system reliability data

generation service descriptor is defined as follows:

<definitions name="PowerSystemDataService"

targetNamespace="urn:power">

<types>

<schema targetNamespace="urn:power" xmlns:tns="urn:power">

<complexType name="double">

</complexType>

</schema>

</types>

<message name="XMLisePSDataInt_generateXMLData">

<part name="String_1" type="xsd:string" />

</message>

<message name="XMLisePSDataInt_generateXMLDataResponse" />

<portType name="XMLisePSDataInt">

<operation name="generateXMLData" parameterOrder="String1">

<input message="tns:XMLisePSDataInt_generateXMLData" />

<output

message="tns:XMLisePSDataInt_generateXMLDataResponse"/>

</operation>

</portType>

<binding name="XMLisePSDataIntBinding"

type="tns:XMLisePSDataInt">

<soap:binding

transport="http://schemas.xmlsoap.org/soap/http"

style="rpc"/>

</binding>

<service name="PowerSystemDataService">

<port name="XMLisePSDataIntPort"

binding="tns:XMLisePSDataIntBinding">

<soap:address location=”http://localhost:5000/WS/power?WSDL”>

</port>

</service>

</definitions>

53

The service descriptor document consists of several key structural

elements for describing power system data generation service. The

<definitions> element defines the name of the service as

‘PowerSystemDataService’ and declares the namespace as ‘power’. The

<types> element defines the schema and data types that would be used to

describe the data. The <message> element represents the name of the method

to be invoked and the response type. The <portType> element provides the

abstract definition of the operation (XMLisePSDataInt) of the service, request

and response messages. The <binding> element specifies a concrete protocol

(SOAP) used for representing messages. The <service> element represents the

port name (XMLisePSDataIntPort) and location of the services.

Using the ‘wsimport’ utility provided by the JAX-WS environment,

the client side artifacts especially the stub and other bean classes are

generated. The ‘wsimport’ uses the endpoint reference i.e.,

“http://localhost:5000/WS/power?WSDL” as its input to generate the

necessary artifacts. The client side stub is created using the name of the

implementation class i.e., ‘XMLisePSDataImplService.class’ and using the

reference to an instance of this class, the client is invoking the

generateXMLData() method that is deployed in the remote server. The output

of the method is written to the disk file, ‘rbts.xml’, which represents the

reliability data in XML format required for analyzing the RBTS system. The

code segment of the client application is as follows:

54

import power.*;

public class XMLisePSDataClient

{

public static void main(String [] args)

{

XMLisePSDataImplService service=new XMLisePSDataImplService();

XMLisePSDataInt client=service.getXMLisePSDataImplPort();

client.generateXMLData("rbts.xml");

}

}

The proposed XMLised power system reliability data generation

service is successfully deployed that acquires the required reliability data

dynamically from a database server and creates necessary XML documents.

The XMLisePSDataImpl service endpoint and the port in which the service is

available are known to all the clients, regardless of the language or platform

used. When the power system client invokes the remote generateXMLData()

method on the port, The client uses the generated XMLisePSDataImplService

class, which identifies the URI of the data generation service that is extracted

from the WSDL and retrieves a proxy to the XMLisePSDataImplService

class. The proxy which also known as a port is obtained by invoking the

method getXMLisePSDataImplPort() on the service. The port implements the

XMLisePSDataInt endpoint interface, which is defined by the service. The

power system client finally invokes the port’s generateXMLData() method by

passing the file name to which the output has to be written.

The generated XMLised output as stored in the ‘rbts.xml’ file for the

Unit No. 1 of the RBTS system as given in the Table 2.1 is shown below:

55

<?xml version="1.0" encoding="UTF-8"?>

<rbts>

<unitno>1</unitno>

<busno>1</busno>

<unittype>Oil</unittype>

<unitsize>40</unitsize>

<failurerate>6.0</failurerate>

<repairtime>45</repairtime>

<failureprob>0.03</failureprob>

</rbts>

Any power system client can use the endpoint address for accessing

the data generation services. The proposed power system reliability data

generation service acquires the required power system data dynamically from

a database server and creates necessary XML documents those can be used as

inputs for doing the operations such as reliability prediction and adequacy

assessment. The data generation service endpoint is available to any type of

client, regardless of the language or platform used.

2.7 CONCLUSION

The concepts of power system reliability and the current trends in

reliability analysis especially of generation and distributed systems are

reviewed in this chapter. An innovative and comprehensive solution is

obtained using XML annotations, which represents the power system

reliability data in XML form that confines to XML standards and

specifications. An annotated Web service model for the representation of

power system data in XML format has been implemented using JAX-WS

environment to enhance the interoperability between power system clients

and reliability estimation service providers. The XML data generation model

has been tested with IEEE-RTS and RBTS systems.