Load Balancing for Distributed and Integrated Power Systems Using Grid Computing

8/3/2019 Load Balancing for Distributed and Integrated Power Systems Using Grid Computing

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Load Balancing for Distributed and Integrated Power Systems using Grid computing

CHAPTER 1

INTRODUCTION

Grid computing is a software technology, which uses the spare computing resources. This

grid computing is now seen as a powerful and important tool for the power system applications.

These days the power system operation and control involves large data-intensive, time-intensive

applications. Therefore it can get a better solution by using this new technology. Also the power

system is moving towards the renewable energy sources. In this case it helps them in providing

cheap and efficient electrical energy supply.

The demand for conservation of the current conventional energy has been increased due

to the growing concern on the deteriorating condition of our environment. The control and

operations of modern power systems are becoming data-intensive, information-intensive,

communication-intensive and computation-intensive due to much of the reliance of these power

systems on computerized communications and control.

Therefore, since Grid Computing is considered as an inexpensive means for “super-

computing” for dealing with heterogeneous and distributed computing. Grid Computing is the best

option in providing the necessary storage media for the huge data, intensive computational

processes and also the required computing resources. Thus, grid computing can be utilized to

fulfill the requirement for efficient load balancing.

1.1 Background Theory

In a distributed and integrated power systems, it is vital to ensure that each power source

(generator, wind turbine, etc) is working within its allowed parameters. These parameters are

normally based on the current power load that are sometimes have been forecasted within

regular intervals (weekly, monthly or yearly). Anyway, these non real-time forecasts have their

drawbacks and may not be supplying correct information when any of these events occur:

1) Sudden failure of any of the power sources.

2) Unexpected increase or decrease of power demand within a short timeframe.

During the occurrence of any of these events, power load balancing within these power

sources are required within immediate timeframe in order to ensure that there will be no power

interruptions.

Department of Computer Science and Engineering 1




1.1.1 Load Balancing

In computer network, load balancing is a technique to distribute workload evenly

across two or more computers, network links, CPUs, hard drives, or other resources, in order to

get optimal resource utilization, maximize throughput, minimize response time, and avoid

overload. For parallel applications, load balancing attempts to distribute the computation load

across multiple processors or machines as evenly as possible with objective to improve

performance. Generally, a load balancing scheme consists of 3 phases: information collection,

decision making and data migration. During the information collection phase, the information of

workload distribution and the state of computing environment is gathered. The decision making

phase focuses on calculating an optimal data distribution, while the data migration phase

transfers the excess amount of workload from overloaded processors to under loaded ones.

In power applications, the transmission system provides for base load and peak load

capability, with safety and fault tolerance margins. The peak load times vary by region largely due

to the industry mix. In very hot and very cold climates home air conditioning and heating loads

have an effect on the overall load. They are typically highest in the late afternoon in the hottest

part of the year and in mid-mornings and mid-evenings in the coldest part of the year. This makes

the power requirements vary by the season and the time of day. Distribution system designs

always take the base load and the peak load into consideration.

The transmission system usually does not have a large buffering capability to match the

loads with the generation. Thus generation has to be kept matched to the load, to prevent

overloading failures of the generation equipment.

Multiple sources and loads can be connected to the transmission system and they must

be controlled to provide orderly transfer of power. In centralized power generation, only local

control of generation is necessary, and it involves synchronization of the generation units, to

prevent large transients and overload conditions. In distributed power generation the generators

are geographically distributed and the process to bring them online and offline must be carefully

controlled.





CHAPTER 2

CONCEPT AND DESIGN

Grid computing is the combination of computer resources from multiple administrative

domains applied to a common task, usually to a scientific, technical or business problem that

requires a great number of computer processing cycles or the need to process large amounts of

data.

One of the main strategies of grid computing is using software to divide the pieces of a

program among several computers, sometimes up to many thousands. Grid computing is

distributed, large-scale cluster computing, as well as a form of network-distributed parallel

processing

2.1 PROPOSED MODEL FOR INTEGRATING GRID COMPUTING WITH POWER

APPLICATIONS

In this section we focus on utilizing Grid Computing for real-time power load balancing

operations that are based on the model for integrating Grid Computing with power applications as

illustrated in Figure 1.





From Figure 1, further descriptions of the model are as follows:

a) The Grid Computing model contains 2 main servers (server01 and server02) and 8 computing

resources (client01 – client08). Server01 stores the list for all power system applications and

server02 acts as a resource broker.

b) The Grid Computing is integrated with the power systems applications via an application

interface.

c) The power applications interact with server01 to obtain the current list of all power applications

connected to the Grid Computing and therefore, these power applications will interact with each

other for real-time data transfer and load-balancing operations as depicted with the white arrows.

d) Server01 interacts with server02 to find available Grid Computing resources meeting certain

specifications in order to perform real-time load forecasting, as depicted with the black arrows.

2.1.1 Power World Simulator

Power World Simulator “simulation software” has been used for simulating the power

applications in this model. Power World Simulator is an interactive graphical-based power

application for simulating high voltage power systems that runs only on MS Windows-based OS.

It is a power simulation package designed from ground up to user-friendly and highly interactive.

This simulator has the power for engineering analysis and its interactive and graphical that can be

used to explain the power system operation. It is a standalone package for power flow. This

simulator allows the users to visualize the system through the use of full colour animated one line

diagram with full zooming facility. Thus, this simulator provides a convenient medium for

simulating the evolution of power system over time.

2.1.2 Globus Toolkit version 4.0.2 (GT4)

For the Grid Computing middleware, Globus Toolkit version 4.0.2 (GT4) is selected since

a) It is an open source software for development issue.

b) It is considered the “de facto standard” of Grid Computing.

Grid Resource Management involves coordination of a number of components, including

resource registries, factories, discovery, monitoring allocation and data access. The globus toolkit

includes a set of components to help users have a standard set of interfaces for the coordination

of the above activities. The Globus Toolkit provides a number of components for doing data

management. The data management services provide standard means for helping to manage the

grid computing environment. The GridFTP is a standard extension to normal FTP(File Transfer





protocol) that works with grid computing data requirements. This is a high performance, secure,

reliable, data transfer protocol that is optimized for high bandwidth across wide area networks.

This is a standard that provides security, parallel transfer capabilities, and channel reusability and

efficient transfer of bulk data. The Globus Toolkit provides the most commonly used

implementation of this protocol.

2.2 TOOLS

The following section describes the main tools used in the development of integrating Grid

Computing with the power applications.

2.2.1. Wine

Wine (the short form for Wine Is Not an Emulator) allows MS Windows applications to be

executed on Linux OS. Using Wine, Power World Simulator software has been successfully

installed and executed on Linux machines.

2.2.2. Java Robot Class

Since Power World Simulator is a graphical-based simulation, it is not possible to save the

simulation data relating to the generators’ performances on a continuous basis. Therefore the

Java Robot Class has been used to control, save the simulation data and also to load new

simulation data. This class is used to generate native system input events for the purposes of test

automation, self-running demos, and other applications where control of the mouse and keyboard

is needed. In order to achieve this purpose, we have to:

a) Capture the image screen for each mouse movement in order to save the simulations data and

loading new data.

b) Identify the exact x and y coordinates of where the mouse needs to be moved and clicked.

Using this class to generate input events differs from posting events to the AWT event

queue or AWT components in that the events are generated in the platform's native input queue.

For example, Robot.mouseMove will actually move the mouse cursor instead of just generating

mouse move events. Basically, a Robot object makes it possible for our program to temporarily

take over control of the mouse and the keyboard.





2.2.3. Karajan Workflow

Karajan is a workflow language and workflow engine. Karajan is a Grid parallel task

management language and an execution engine. It aims to provide the scientific community with

an easy-to-use tool to define and manage complex jobs on computational Grids, while keeping

scalability and offering some advanced features, such as failure handling, check pointing,

dynamic execution, and distributed execution. Karajan also provides a web-service interface,

which can be used to remotely control and monitor the execution. An instance of the Karajan

service can be used to execute multiple specifications at one time. The Karajan specifications are

written in an XML based language. XML has the advantage of a very strict and well defined

structure.

Through Karajan, grid users can easily define set of complex tasks by creating the

appropriate XML file. Karajan Workflow has a number of libraries and one of these libraries is the

System Library. The System Library contains the elements <parallelFor> and <parallel> that allow

parallel execution of tasks as illustrated in Figure 2.

Thus, by using Karajan Workflow, we can submit jobs and transfer files in parallel.





CHAPTER 3

ANALYSIS AND APPLICATIONS

3.1 DEMONSTRATION OF REAL-TIME LOAD BALANCING USING POWER

APPLICATION GRID INTERFACE (PAGI)

To demonstrate the functionalities of the interface between the Power World Simulator

and the Grid Computing, a Java-based GUI program called ‘Power Application Grid Interface’ or

PAGI is developed as illustrated in Figure 3.

From Figure 3, PAGI is divided into two parts:

a) Set appropriate settings for running the Power World Simulator.

b) Displaying the data received from other power applications (simulators).

During the execution of PAGI, the simulations data (for e.g. generators’ loads) are saved

to a fixed filename ‘data.aux’.

3.1.1 Real-time data transfer PAGI performs real-time data transfers by using the Globus GridFTP function to all other

simulators based on the list obtained from server01. This list is continuously obtained from

server01 in order to ensure that the simulation data are transferred to all power applications

connected to the Grid Computing. PAGI also displays the data received from all other simulators

in order to view the current load usage at other simulators (or power plants’ sites).





3.1.2 Real-time Load balancing

For the mechanism of real-time load balancing operations, PAGI was developed based on

the following process flow as shown in Figure 4.





Real-time load balancing operations are basically executed by means of sending a

specific command which is known as the ‘change command’ from one simulator to the other

simulator(s). Here, we substituted the term ‘simulators’ with ‘sites’ that has the meaning of where

the power plants are actually located.

From Figure 4, PAGI also allow the user to specify the settings for the load balancing

operations:

a) Minimum load percentage before a ‘change command’ is sent to other sites in order to balance

the load balance operations.

b) Set the specific sites that shall receive the ‘change command’ or let PAGI decide which sites

automatically receive ‘change command’.

To calculate how much load is required to be added at sites receiving the ‘change

command’, the following formula is used:

Where,

Pa: Additional power load

Pc: Current power load

Pm: Maximum power load allowable as set by the user

Ef: Expansion factor

Pg: The maximum power of the generator at the origin site

The expansion factor, Ef , is included in order to increase the additional power load to a

certain factor. This will ensure that the power generated at the recipient sites is slightly more than

the actual power need.

3.1.3 Load Forecasting

There are important differences in load between weekdays and weekends. The load on

different weekdays also can behave differently. For example, Mondays and Fridays being

adjacent to weekends, may have structurally different loads than Tuesday through Thursday. This

is particularly true during the summer time. Weather conditions influence the load. In fact,

forecasted weather parameters are the most important factors in load forecasts. Various weather

variables are considered for load forecasting. Temperature and humidity are the most commonly

used load predictors.





Since all power application simulations are submitting their data to server01, a mechanism

that utilizes Grid Computing resources has been developed in order to perform load forecast

calculations for each simulation. The pseudo code for this mechanism is as follows:

Using the resource broker function at server02, find available Grid Computing resources.

For each simulation data, the following tasks are executed in parallel

1. Split the data based on the number of available resources.

2. Submit the split data to each resource.

3. Submit jobs in parallel to perform load forecast on all available resources.

4. Return the forecast value to relevant simulation.

5. Submit a job the relevant simulation to analyze the forecast value and take appropriate action if

required.

End For

The ‘For...Loop’ stated above is executed by using Karajan Workflow that allows parallel jobs

submissions and data transfers. For task no. 5, the power application simulations shall send the

‘change command’ to other simulation(s) if the forecast value reaches or exceeds the working

limit of the power systems. This particular task is included in order to create decentralized

distributed power systems, moving away from a traditional centralized control management for

power systems.

3.2 GRID COMPUTING’S PRIORITY AND ADVANTAGES IN POWER UTILITIESCOMPANIES

In order to find the level of priority of grid computing technology to the power utilities

companies, institutions and academies, first it should identify what will be the benefits of this

technology to them. These benefits are as follows:

a) Real-time data transmission among all power plants which dissolves the function of a

centralized control centre.

b) Real-time load balancing process among all power plants to ensure that the power load are

evenly distributed. In crucial situations such as power outages at one of the power plants, real-

time load balancing could be performed to minimize the damage caused by the power outage.

c) Shorter time required for load forecast calculation which allows the power plant to react faster

for the near future changes in power load demands.





CHAPTER 4

COMPARISON AND EVALUATION

4.1 RESULTS AND ANALYSIS

In analyzing the performance for real-time data transfer, the time taken for submitting

simulations data to a different number of sites by using parallel mode (based on Karajan

Workflow) is compared with the sequential mode. The results of the comparisons are as shown in

Figure 5.

From Figure 5, it is clear that the time taken for submitting data in sequential mode takes

longer time as compared to the parallel mode. Also, in the parallel mode it could be recognized

that time curve doesn’t show smooth proportional flew, the possible reasons for that are:

a) The start-up time required during the execution of the workflow.

b) Full bandwidth utilization for each data transfer.

For the real-time load balancing operations, it has been successfully tested on sending

the ‘change command’ to other simulators (sites) receiving the ‘change command’. These

recipient sites will immediately load the new simulation data received from the sender that will

increase the power load at those locations. Also, utilizing all the available Grid Computing





resources was successfully done, in order to perform load forecasting operations. The forecast

values were then returned to each relevant power systems where, if these values exceed a

specific limit, decentralized load balancing operations are then executed. The load forecasting

performance on different number of resources calculations are analyzed and the results are as

illustrated in Figure 6.

Figure6 illustrates that the time taken for load forecasting reduces quite significantly when

more Grid Computing resources are selected. Thus, by having the required resources, hourly

load forecasting can be calculated where the forecast value shall be return to each power system

for decentralized load balancing operations.





4.2 TRADITIONAL AND GRID METHODOLOGY

As workloads fluctuate during the course of a month, week, or even through a single day,

the grid computing infrastructure analyzes the demand for resources in real time and adjusts the

supply accordingly.

Implementing the specific task within both technologies traditional and grid was achieved

to compare and analyze new technology benefits and differences, traditional method by using

normal unique machine, then job was submitted and time is measured. The same task was

submitted into grid system successfully. Time comparison shows that grid system consumed

much less time with high efficiency and high accuracy. From figure 7, it is clear that traditional

method consumed 260 minutes (blue cylinder) while grid computational process needed 75

minutes to achieve same job (red cylinder).





CONCLUSIONS

The model of integrating the Grid Computing with distributed power systems has been

managed to build and operate the target to perform real-time load balancing operations. Using

Java Robot Class as means to run interactive graphical-based power systems simulation Power

World Simulator also achieved and managed to save the simulation data which are then

transferred to other simulations in real-time by using the Globus GridFTP function. Real-time load

balancing operations are implemented by sending a ‘change command’ from one power system

to the other power system(s) once the load exceeds the specific limit. By utilizing the available

Grid Computing resources, load forecasts are calculated where the forecast values are then

returned to the relevant power systems in order to take appropriate actions if required. The

application is split into various sub tasks and are submitted to grid, each node performs their work

and send back the result to the job submitting machine. As the jobs are processed in parallel, the

results are obtained in less time.

The process of integrating Grid Computing into the power industry can be concluded as

economically and technically feasible. It means that power utilities companies should not have

any problems in financial factors and in meeting the technical requirements.





REFERENCES

1. R. Al-Khannak, B. Bitzer, Load Balancing for Distributed and Integrated power systems using

grid computing, IEEE 2007.

2. Michael Hategan, Gregor von Laszewski, Java CoG Kit Karajan/Gridant Workflow Guide,

http://www.cogkit.org/release/4_0_a1/manual/workflow.pdf.

3. Joshy Joseph, Craig Fellenstein, Grid Computing, Pearson, 2007, page-239.

4. Yawei Li and Zhiling Lan,A survey of load balancing in Grid computing, Springer Berlin /

Heidelberg,2005,pages:280-285






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Load Balancing for Distributed and Integrated Power Systems Using Grid Computing