electrical power generation analytics; a case study … · 2019-06-08 · formed in 1954 as a...

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UNIVERSITY OF NAIROBI

COLLEGE OF ARCHITECTURE AND ENGINEERING

DEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEERING

PROJECT 032: ELECTRICAL POWER GENERATION ANALYTICS; A CASE STUDY

OF THE KENYA NATIONAL POWER GRID.

SUBMITTED BY:

NAME: OUKO JOHN ROBERT

REG. NO: F17/1457/2011

SUPERVISOR: PROF. JACKSON M. MBUTHIA

EXAMINER: MR.S. L. OGABA

Project report submitted in partial fulfillment of the requirement for the award of the degree of

Bachelor of Science in ELECTRICAL AND ELECTRONIC ENGINEERING of the University

of Nairobi.

Date of submission: 16th May, 2016

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DECLARATION OF ORIGINALITY

1) I understand what plagiarism is and I am aware of the university policy in this regard.

2) I declare that this final year project report is my original work and has not been submitted

elsewhere for examination, award of a degree or publication. Where other people’s work or my

own work has been used, this has properly been acknowledged and referenced in accordance

with the University of Nairobi’s requirements.

3) I have not sought or used the services of any professional agencies to produce this work

4) I have not allowed, and shall not allow anyone to copy my work with the intention of passing

it off as his/her own work.

5) I understand that any false claim in respect of this work shall result in disciplinary action, in

accordance with University anti-plagiarism policy.

Signature:

……………………………………………………………………………………

Date:

……………………………………………………………………………………

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DEDICATION

I dedicate this project to my adorable, loving, zealous mentor, my father, my mum, and my

friends – Peter, Rajab, Martin and Onesmus.

Thank you for your unwavering love and support.

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ACKNOWLEDGEMENT

I would like to acknowledge the department of Electrical and Information Engineering for

entrusting me with this project. I thank my supervisor, Prof. Mbuthia for guiding me throughout

this endeavour. His insightful guidance cannot go unmentioned.

I would also like to thank my family for their hard work and dedication in ensuring that I have

the chance to pursue this degree.

I would also like to thank my friends and fellow classmates who believed in me and encouraged

me to always push on.

Last but not least, I would like to thank God for the gift of life, health and all the blessings that

have enabled me to come this far and to finish this project.

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ABSTRACT

This project describes a solution to Economic Load Dispatch (ELD) problem as a power generation

analytics. By economic dispatch means, to find the generation of different units in a plant so that the total

fuel cost is minimum and at the same time the total demand and losses at any instant must be met by the

total generation.

The MATLAB program was developed to solve Economic Load Dispatch through Particle swarm

Optimization. The results were able to be displayed on an interface.

Alongside the problem of ELD, a database was created in PostgreSQL. A web interface was implemented

to enable a user input data into the database.

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TABLE OF CONTENTS

ABSTRACT……………………………………………………………………………………….……5

1.0 INTRODUCTION………………………………………………………………………….……....8

1.1 Problem Statement…………………………………………………………………………9

1.2 Project Objectives………………………………………………………………………….9

1.2.1 Specific Objectives………………………………………………………………………9

1.3 Project Justification………………………………………………………………………...9

1.4 Project Scope………………………………………………………………………………..10

1.5 Project Organization………………….……………………………………………………10

2.0 LITERATURE REVIEW………………………………………………………………………….11

2.1 Current System of Power Generation in Kenya…………………………………………..11

2.2 Existing Business Process…………………………………………………………………13

2.3 Load Curve………………………………………………………………………………...15

2.4 Base Load and Peak Load………………………………………………………………….15

2.5 Analytics……………………………………………………………………………………16

2.6 Geographic Information System…………………………………………………………….16

2.7 GIS and Power System………………………………………………………………………17

2.8 Interface Development……………………………………………………………………….19

2.9 Data Collection……………………………………………………………………………….20

2.10 Database Development……………………………………………………………………20

2.11 Conclusion………………………………………………………………………………….21

3.0 METHODOLOGY……………………………………………………………………………………22

3.1 Database Development………………………………………………………………………22

3.2 Development of Web Interface………………………………………………………………24

3.3 A Solution to Economic Load Dispatch Problem……………………………………………24

3.3.1 Algorithm…………………………………………………………………………………..25

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4.0 RESULTS AND ANALYSIS…………………………………………………………………….28

5.0 DISCUSSION…………………………………………………………………………………….35

6.0 CONCLUSION……………………………………………………………………………………35

7.0 RECOMMENDATION……………………………………………………………………………35

8.0 REFERENCE………………………………………………………………………………………36

9.0 APPENDIX…………………………………………………………………………………………37

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1. INTRODUCTION

In Kenya, the energy sector is dominated by imported petroleum used mainly in the modern sector and

wood fuel which is largely used by rural communities. In terms of energy supply, wood fuel provides

about 68% of the total energy requirements, petroleum energy 20%, electricity 9% and other sources

account for 3%. Commercial energy consumption is also dominated by petroleum (70%), followed by

electricity and coal accounting for the remaining 30%.

Electricity in Kenya is produced from hydro, thermal, wind and geothermal sources. The total installed

capacity is 2,294.82 MW. The overall demand for electricity has continued to increase as large

commercial and industrial consumers continue to be the main users of electrical energy. The key players

in the power sector are Kenya Electricity Generation Company (KenGen) and Independent Power

Producers (IPP). These companies generate electrical energy. The Ministry of Energy (MoE) formulates

policy on the energy sector, in addition to administering the Rural Electrification Scheme. The generating

companies sell the power in bulk to Kenya Electricity Transmission Company (KETRACO) for

transmission. Energy Regulatory Commission (ERC) reviews electricity tariffs and enforces safety and

environmental regulations in the power sector as well as safeguarding the interests of consumers. The

Rural Electrification Authority (REA) implements rural electrification projects of behalf of the

Government of Kenya. Kenya Power & Lighting Company (KPLC) owns all distribution assets and retail

to costumers. [6]

The Structure of Power Systems Generating stations, transmission lines and the distribution systems are

the main components of an electric power system. Generating stations and a distribution station are

connected through transmission lines, which also connect one power system (grid) to another. A

distribution system connects all the loads in a particular area to the transmission lines. For economical

and technological reasons, individual power systems are organized in the form of electrically connected

areas or regional grids. In practice, however, power station location will depend upon many factors—

technical, economical and environmental. Bulk power can be transmitted to fairly long distances over

transmission lines of 400 KV and above.

Balancing the need to improve system reliability and reduce operation costs is the greatest challenge for

today’s utility decision makers, a challenge that is successfully met with the display of real-time data on a

web interface. This can be used for planning and monitoring power generation resources. This is useful

for determining optimum generation potential, formulating what-if scenarios, and managing facility assets

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of the Kenya power generation companies. This will also be used to analyze the market potential of the

power utilities.

A power system has several power plants. Each power plant has several generating units. At any

point of time, the total load in the system is met by the generating units in different power plants.

Economic dispatch determines the power output of each power plant and power output of each

generating unit within a power plant which will minimize the overall cost of fuel needed to serve

the system load.

1.1 Problem Statement

In practical power systems which are capable of feeding a bounded range of electrical load demand,

optimizing the operation costs of the generation units is very important from an economic perspective.

Hence, usually Economic Dispatch (ED) techniques are used to determine a condition with the lowest

possible generation costs. The load demand, transmission power losses and generation cost coefficients

are the parameters that must be taken into account for any ED technique. The modern generation units

present non-linear behaviours with multiple local optima in their input-output characteristics. Therefore,

the economic dispatch problem formulation shall be discontinuous, multi model and extremely nonlinear.

1.2 Project Objective

The main objective of this project is to create a web interface and database that allows a user to build a

dynamic display of the Kenya power generation analytics.

Specific Objectives

To study the current system of power generation in Kenya.

To create a database in PostgreSQL and a web interface that allows a user to input data into the

database.

To perform and display power generation analytics – Economic Load Dispatch.

1.3 Project Justification

As earlier explained in the introduction, increased demand for power and major developments in

technology require power to run hence the need to streamline and optimize workflow.

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A solution to Economic Load Dispatch was carried out as an analytic of Electric Power Generation.

MATLAB is being used as an implementation tool in that it allows implementation of algorithms, and

creation of user interfaces where a user could be able to visualize the variations in generator loadings with

respect to power demand. Furthermore, MATLAB offers the platform to study dynamic systems in real-

time.

1.4 Project Scope

The project will entail the following:

Acquisition and manipulation of data on electric generation in Kenya.

Development of web interface using PHP for the input of data to a database.

Database development i.e. PostgreSQL

To carry out Economic Load Dispatch in MATLAB and display the results dynamically.

To draw results that will describe the fulfillment of the main objective.

1.5 Project Organization

The report will be organized into the following chapters:

Chapter 2 will give the literature review.

Chapter 3 will discuss the project methodology.

Chapter 4 will discuss the results of the project.

Chapter 5 will address some recommendations and come up with a conclusion.

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2. LITERATURE REVIEW

Electricity generation is the process of generating electrical power from other sources of primary energy.

The fundamental principles of electricity generation were discovered in the 1820s by a British scientist

Michael Faraday. Basically, electricity is generated by the movement of a loop of wire between the poles

of a magnet. In the electric utilities, generation is the first process in the delivery of electricity to

consumers. Electricity is most often generated at a power station by electromechanical generators,

primarily driven by heat engines, kinetic energy of flowing water and wind and geothermal power.

Giant wheels called turbines are used to spin the magnets inside the generator. It takes a lot of energy to

spin the turbine and different kinds of power plants get that energy from different sources. In a

hydroelectric station, falling water is used to spin the turbine. In nuclear stations and in thermal

generating stations powered by fossil fuels, steam is used. A wind turbine uses the force of moving air.

2.1 Current System of Power Generation in Kenya

The history of Kenya’s power sector can be traced back to 1922 when the East African Power & Lighting

Company (EAP&L) was established through a merger of two companies: The Mombasa Electric Power &

Lighting Company (1908) and Nairobi and Power syndicate. The Kenya Power Company (KPC) was

formed in 1954 as a subsidiary of the EAP&L largely confined to Kenya, and was later changed to Kenya

Power and Lighting Company Limited (KPLC) in 1983. This was 100% state owned.

The power sector in Kenya has been undergoing restructuring and reforms since the mid-1990s. Kenyan

government officially liberalized power generation as part of the power sector reforms in 1996.The Kenya

Electricity Generation Company (KenGen), which remained entirely state owned, became responsible for

the generation assets.

Kenya’s current effective installed electricity capacity is 2,294 MW [ 1]. Electricity supply is

predominantly sourced from hydro and fossil fuel (thermal) sources. Just until recently the country lacked

significant domestic reserves of fossil fuels. The country has over the years had to import substantial

amount of crude oil and natural gas. The discovery of oil reserve in Kenya might change the energy

situation in the country. Connectivity to the national grid in Kenya currently stands at 28%.

The sources of electricity in the country together with their corresponding effective capacity can be tabled

as shown in the table below.

Table 1: Sources of electricity with their corresponding effective capacities

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Source Capacity (MW) Capacity (%)

Hydro 827.02 36%

Fossil fuels 811.3 35%

Geothermal 593 26%

Bagasse cogeneration 38 2%

Wind 25.5 1%

Total 2,294.82 100%

This can be viewed as follows

Figure 1

Considering the current and the projected consumption, it is immediately recognizable that there’s need

for expansion of the power generation capacity of the country. Resources to be considered in the system

expansion plan include geothermal, hydro, wind, coal, oil-fired and nuclear power plants. Geothermal

resources remain the choice for the future generating capacity in Kenya.

Power generation in Kenya can be broadly divided into two:

KenGen is the main player in the electricity, with a current installed capacity of 1,176 MW. It’s

owned 70% by the government of Kenya and 30% by private shareholders.

IPPs who are private investors in the power sector involved in generation either on a large scale

or for the development of renewable energy under the Feed-in-Tariff policy. Current players

comprise; Iberafrica, OrPower, Tsavo, Mumias, Imenti and Rabai power plants.

Capacity (%)

Hydro

Fossil fuels

Geothermal

Bagasse cogeneration

Wind

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2.2 Existing Business Process

A business process is a set of activities that will accomplish a specific organizational goal. It may be

visualized as a flowchart of a sequence of activities with interleaving decision points. A business process

begins with a mission objective and ends with achievement of the business objective. Business processes

are designed to add value for the customer and should not include unnecessary activities. The outcome of

a well-designed business process is increased effectiveness and efficiency.

The modern power system around the world has grown in complexity of interconnection and power

demand. The focus has shifted towards enhanced performance, increased customer focus, low cost,

reliable and clean power. In this changed perspective, scarcity of energy resources, increasing power

generation cost, environmental concern necessitates optimal economic dispatch.

Unit Commitment (UC) and Economic load dispatch (ELD) are significant research applications in power

systems that optimize the total production cost of the predicted load demand. The UC problem determines

a turn-on and turn-off schedule for a given combination of generating units, thus satisfying a set of

dynamic operational constraints. ELD optimizes the operation cost for all scheduled generating units with

respect to the load demands of customers.

Besides achieving minimum total production cost, a generation schedule needs to satisfy a number of

operating constraints. These constraints reduce freedom in the choice of stating up and shutting down of

generating units. These constraints are usually the status restriction of individual generating units,

minimum up time, minimum down time, capacity limits, generation limit for the first and the last hour,

limited ramp rate, spinning reserve constraints, etc.

To get different output power, we need to vary the fuel input measured in $/Hr. knowing the cost of fuel,

input to the generating unit can be expressed as $/Hr. Let Ci $/Hr be the input cost to generate power of Pi

MW in unit i. For each generating unit there shall be a minimum and maximum power generated as Pimin

and Pimax.

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Figure 2: Input-Output curve

If the input-output curve of unit I is quadratic, then

$/Hr

In reality power stations neither are at equal distances from load nor have similar fuel cost functions.

Hence for providing cheaper power, load has to be distributed among various power stations in a way

which results in lowest cost for generation. Practical economic dispatch (ED) problems have highly non-

linear objective function with rigid equality and inequality constraints.

Figure 3: Economic dispatch process

Survey of earlier work:

Load Demand Generating

Units

System

Coefficients

Incremental

Fuel Cost

Load

Distribution

between

Units

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Iba, N. Norman and H proposed the classical Differential Evolution for solving ELD problems with

specialized constraint handling mechanisms. Khamsawang et. Al, proposed the original Differential

Evolution for ELD with regenerated population technique and tuning of parameters.

Mariani et. Al., proposed a hybrid technique that combined the differential evolution algorithm with the

generator of chaos sequences and sequential quadratic programming technique. Aniruddha et. Al., offered

a hybrid combination of differential evolution with biogeography based optimization to accelerate the

convergence speed and to improve the quality of the ELD solutions. During 2007, R.Balamurugan et Al.

presented a Self-Adaptive Differential Evolution Based Power Economic Dispatch of Generators with

Valve-Point Effects and Multiple Fuel Options. Ali Keles in, has reported the results of experiments

performed on a series of the UC test data using the binary differential evolution approach combined with

a simple local search mechanism. S. Patra et Al. developed a differential evolution approach for solving

the UC problem using binary and integer code. It was observed that both the techniques converged

towards the same optimal solution with different number of generations.

In all the literatures listed, either the Unit Commitment or the Economic Load Dispatch problem is solved

individually. Solving UC-ELD problems using hybrid techniques generates a complete solution for the

real time power system thereby justifying the advantages of the proposed techniques.

2.3 Load Curve

This is the curve showing the variation of load on the power utility with respect to time. The load on a

power station is never constant; it varies from time to time. These load variations during the whole day

(i.e., 24 hours) are recorded half-hourly or hourly and are plotted against time on the graph. The curve

thus obtained is known as daily load curve as it shows the variations of load with respect to time during

the day. This curve indicates at a glance the general character of the load that is being imposed on the

utility generation assets. These curves are useful in the selection of generator units for supplying

electricity and also in preparing the operation schedule of generation assets.

2.4 Base Load and Peak Load

The changing load on the power station makes its load curve of variable nature. It is clear that load on the

power station varies from time to time. However, a close look at the load curve reveals that load on the

power station can be considered in two parts, namely; Base load and Peak load

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Base Load - The unvarying load which occurs almost the whole day on the station is known as Base load.

As base load on the station is almost of constant nature, therefore, it can be suitably supplied without

facing the problems of variable load.

Peak Load - The various peak demands of load over and above the base load of the station is known as

peak load. These peak demands of the station generally form a small part of the total load and may occur

throughout the day.

2.5 Analytics

Typically, analytics is the statistical methodology to answer a business question. Analytics can be

descriptive, predictive or prescriptive. Analytics is about more than building and running models; it is a

closed loop process of data exploration and discovery, model creation and validation, then getting the

results to the right people at the right time and learning from the results to further refine the process.

Increasingly, with near-real-time data on the smart grid, analytics is being applied to determine the best-

case scenario and answer situational questions. To answer these questions, power utilities such as

generation require more data and proven models that are available for decision support, returning results

quickly and reliably.

Most power plant organizations operate in a data-rich, information-poor environment. Without analytics,

utilities will underperform when trying to identify new revenue opportunities or minimize bad debt,

optimize integration of renewables or understand their customers. The concept of analytics is so crucial

for power generating organizations in the emerging energy landscape. It addresses key areas of the

utilities business by creating an efficient business processes. These areas include customers, risk

management, operations and data.

2.6 Geographic Information Systems (GIS)

This is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial

or geographical data. GIS applications are tools that allow users to create interactive queries, analyze

spatial information, edit data in maps, and present the results of all these operations. GIS can relate

unrelated information by using location as the key index variable. GIS data represents real objects such as

roads, trees and even electrical power networks. GIS packages are increasingly including analytical tools

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as standard built-in facilities creating a new dimension to business intelligence which, when openly

delivered via intranet, democratizes access to geographic and social network data.

Geometric networks are linear networks of objects that can be used to represent interconnected features,

and to perform special spatial analysis on them. A geometric network is composed of edges, which are

connected at junction points, similar to graphs in mathematics and computer science. Just like graphs,

networks can have weight and flow assigned to its edges, which can be used to represent various

interconnected features more accurately. Geometric networks are often used to model road networks and

public utility networks, such as electric, gas, and water networks.

2.5 GIS and the Power System

GIS can be a valuable information technology in the electric utility industry and it has many applications

in the power sector. Many articles have been published describing how power plants can improve their

efficiency and reliability by adopting a power generation network model based on GIS application.

Bodies managing power plants ensure the reliability of electrical power generation making it critical to

take advantage of decision support tools, such as GIS, to optimize and streamline current and planned

business enterprise practices. Analysts use GIS to create powerful visualizations for planning, markets,

and operations with focus on supporting planning activities. GIS is also used for planning of generation

expansion to ensure future electric reliability and to accommodate the connection to the grid of new

electric generation. PJM Interconnection operates the world’s largest competitive wholesale electricity

market and the largest centrally dispatched territory in North America. PJM uses locational marginal

pricing (LMP) to establish a unique price for each node or location on the transmission system. If there is

no congestion in the transmission system, the LMP level is the same throughout the transmission grid.

Planning engineers are also working toward a GIS interface with a load flow program, the Power System

State Estimation, which creates better visualizations of resultant contingencies on the system caused by

new generation, new transmission, generation retirements, and other changes. Integral GIS is working

with PJM to build the foundation for an enterprise GIS on a platform of ESRI’s ArcGIS and ArcSDE

software and Microsoft® SQL Server 2000.

By providing a geographically oriented view of electric generation structures, devices and network, GIS

helps the power plant utility managers visualize, analyze and understand their facilities. Public Service

Company of New Mexico (PNM) is the largest provider of electricity and gas in the state of New Mexico.

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PNM and POWER Engineers, Inc., developed and implemented eTAMIS, a software application built on

the ArcGIS platform, which supports high-voltage transmission line facilities management. This

application, which can be connected to the network in the office or disconnected in the field, includes

real-time routing and tracking, online analysis of structure information, a Fault Location tool, integration

of several layers of base information, an inspection and maintenance module, and report functionality.

According to Bohlen C. & Lewis L.Y. (2009), this study focuses on the effect that removal of

hydropower dams has on property values in the state of Maine. The goal of the study is to be able to apply

the analysis not only to other potential dam removals, but to also be used when siting new dam

construction. A method known as hedonic property value analysis was used to estimate the role that

specific features of a property play in its value. GIS was used to locate properties and determine their

proximity to both the river and the dams and to determine geographic and economic characteristics for

properties based on their surroundings and census data. The results are important to consider not only

when planning a dam removal, but also when planning dam construction. This study is important to the

role of GIS in the field of renewable energy because it shows how GIS can be used not only in the early

stages of a renewable energy project, but also in the end of life and decommissioning of a renewable

project. This study also shows how useful GIS can be in a predominantly economic analysis.

Defne, Z., Haas, K.A., & Fritz, H.M. (2011) discusses the use of Geographic Information Systems as a

decision support tool for the assessment and selection of sites for tidal stream power projects. Due to the

location of tidal stream energy resources, many factors specific to each site, including potential energy

resource, physical and environmental aspects, as well as social and economic effects must be considered.

Data from many different sources, pertaining to many different factors, were entered into a

geodatabase. Analysis was performed to determine which areas met the different criteria, and which sites

best met all criteria. After analysis, maps were created to display areas that were favorable for tidal

energy projects. This study was important not only because of the contribution GIS made in this specific

tidal energy project, but also because it demonstrated that the multi-criteria assessment method previously

used for wind energy projects could easily be adapted for other renewable energy projects.

According to Defne, Z., Haas, K.A., Fritz, H.M., Jiang, L., French, S.P., Shi, X., Smith, B.T., Neary,

V.S., & Stewart, K.M. (2012), this article discusses the creation of a public national geodatabase that

facilitates the sharing of tidal stream resource data for the entire United States coast. The author discusses

the acquisition of tidal resource data by both collection and modeling, as well as the validation of said

data. The collected data is stored in a geodatabase on an ArcGIS server. This has been made available to

the public through an interactive map interface on the web. Users have the ability to locate specific

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regions and download the data. Making this data easily available to the general public will allow

preliminary tidal resource studies to be performed more quickly and easily than ever before, which will

certainly serve to advance the field and the market.

Dominguez, J., & Amador, J. (2007) examines the general role that Geographic Information Systems

play in the field of renewable energy resources. The authors focus on three major categories of

application: GIS as a decision support system, distributed generation of electricity, and decentralized

generation of electricity. In the role of decision support system, GIS was used to determine both the

economic and environmental impacts of a project. Economic analysis focused largely on concepts of

supply and demand. GIS was used to determine the availability of a resource in a region and if there was

enough demand to make it economically viable in that same region. When considering distributed

generation of electricity, GIS is used primarily to determine the location of the resources and their

proximity to potential points of connection to the energy grid system. This type of analysis is most

commonly performed for wind resources because of the effects of topography on the resource. It can also

be used for biomass resources in order to examine the location of the resource, location of generation

sites, and the necessary transport of biomass in between. The final application discussed was that of

decentralized generation of electricity. The projects studied focused primarily on rural areas of Europe as

well as developing nations. The projects themselves focused mainly on market issues of supply and

demand. The authors conclude by highlighting the benefits of the various applications of GIS, but also

point out the uncertainty in many of the studies due largely to economic factors. This study is important

because of the emphasis it places on markets and economics. These factors are key to any energy project,

and must be taken into consideration for any potential projects to be implemented successfully.

2.8 Interface Development

This is the design of user interfaces for machines and software with the focus on maximizing the user

experience. The goal an interface design is to make the user’s interaction as simple and efficient as

possible in terms of accomplishing user goals. It focuses on anticipating what users might need to do and

ensuring that the interface has elements that are easy to access, understand, and use to facilitate those

actions.

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2.9 Data Collection

Data is the key to knowing the energy usage and capabilities within a given power system. In this study,

landbase and static data were used. Administrative boundaries will be of prime importance to delimit the

area under study. Spatial data for generating stations can be obtained from websites of the organizations

governing the power plants. Non-spatial data will also be used for this case study. These include the

attributes of the generators and prime movers. The data collected will be categorized under power

network specialists as follows:

Power Network Planner - This is the person who has the responsibility of preparing network

plans with prime consideration on capacity, scalability and expandability. He/she ensures timely

implementation of network plans regarding the expansion and improvement of existing core

network facility.

Power Network operator - This is the person entitled with the daily operation of the generators.

Power Network Dispatch Specialist - This is the network operator entitled with the responsibility

of deciding what kind of generators to be used at any given time for power dispatch.

Power Network System Study Specialist - This is the person entitled with the responsibility of

consuming the data provided to generate load flows. He/she looks at the kind of data that helps in

load flow. This information regarding load flows is provided to other network operators for

efficient electrical power generation.

Power Network Fault Analyst - This is the operator entitled with the responsibility of identifying

faults on generators and provides information on how to isolate the associated faults.

Power Network analytics Specialist - This is the network operator who is provided with

information on what generators produces power and is also concerned with power analytics and

customers.

2.10 Database Development

A database is an organized collection of data. It is a collection of data schemas, table fields, queries,

reports, views and other objects. Databases are used to support internal operations of organizations and to

underpin online interactions with customers and suppliers. Again, databases are used to hold

administrative information and more specialized data such as engineering data or economic models.

A database management system is a computer software application that interacts with the user, other

applications and the database itself to capture and analyze data. Well-known DBMS include MySQL,

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PostgreSQL, Microsoft SQL Server and Oracle. Map database can be used in electrical power generation

to perform visual navigation duties such as finding the location of various power plants and the relevant

data associated with those power plants.

Conclusion

The main objective of this project is to create a web interface and database that allows a user to build a

dynamic display of the Kenya power generation analytics. For this case study, all the data used are

derived from the websites of the governing bodies of these power stations. A database will be developed

in PostgreSQL. This database includes the electrical and operational characteristics of such generating

plants and the Units’ power production recordings.

An Economic Load dispatch is being described and its solution using Particle Swarm Optimization (PSO)

is being presented. This also means that it is desirable to find the optimal generating unit commitment

(UC) in the power system for the next H hours. This will be carried out in MATLAB to identify the

loading of each generating unit together with the cost of fuel of each. The results of the combination of

generating units will be visualized a word interface.

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3.0 METHODOLOGY

In this research methodology, a brief description of the methods and procedure for the case study is given.

The case study was carried out for three power generating stations, that is, Kindaruma, Kamburu and

Gitaru.

3.1 Database Development

The data are based on relational database model. The database in consideration will be PostgreSQL. Once

the layers are established, the non-spatial data are then added as attributes to the digitized features. The

attributes’ tables are linked to the spatial themes containing geographic information. The database created

therefore shall include location and descriptive information for all the different components of the

generation system.

Definition of terms:

Data Schema. In database, data schema refers to the structure that represents the logical view of the

entire database. It defines how the data is organized and how the relations among them are associated.

Table Fields. A table field consists of columns and rows. In relational database, a table is set of data

elements using a model of vertical columns and horizontal rows, the cell being the unit where a row and a

column intersect.

Data Formats. In database, data format refers to the way in which the data is stored in the program or

file.

Having defined the terms above, the data schema/table fields/data formats shall be generated for the GIS.

This can be illustrated in the table 1 below;

Static Table

gen_manfcr gen_serial_no gen_id gen_owner lat long phase

Associate Electric

Industries

11-1626-8118-007 G1-40365 KenGen -0.806 37.811 Three

Associate Electric Industries 11-1626-8129-003 G2-40364 KenGen -0.808 37.8104 Three

Associate Electric Industries 11-1626-8116-009 G3-40363 KenGen -0.807 37.814 Three

Rode Koncar 11-1731-4221-007 G4-40373 KenGen -0.80927 37.68671 Three



Asea Brown Boveri 11-1828-6671-003 G7-40366 KenGen -0.79665 37.7497 Three



Page | 23

comm_date area_name capacity(MW) kv_rating synch_speed(rpm) Frequency(Hz) pf

1968 Kindaruma 200 132 1800 50 0.9

1968 Kindaruma 80 132 1800 50 0.9

1968 Kindaruma 50 132 1800 50 0.9

1974 Kamburu 35 132 1800 50 0.9

1974 Kamburu 30 132 1800 50 0.9

1974 Kamburu 40 132 1800 50 0.9

1999 Gitaru 81.5 132 1800 50 0.9

1978 Gitaru 72.5 132 1800 50 0.9

1978 Gitaru 72.5 132 1800 50 0.9

load_factor(

%)

pu_imped turbine_typ

e

turbine_manfcr rated_turb_speed(rp

m)

rated_head(m)

43.2 0.61 Kaplan

(Hydro)

BOVING 214.3 32

43.2 0.61 Kaplan

(Hydro)

BOVING 214.3 32

43.2 0.62 Kaplan

(Hydro)

GEHydro 214.3 32

41.36 0.56 Francis

(Hydro)

Litostroj 273 71

41.36 0.58 Francis

(Hydro)

Litostroj 273 71

41.36 0.56 Francis

(Hydro)

Litostroj 273 71

42.8 0.61 Francis

(Hydro)

GEHydro 300 136

42.5 0.63 Francis

(Hydro)

GEHydro 300 136

42.7 0.61 Francis

(Hydro)

GEHydro 300 136

Dynamic Table

Page | 24

Generator_ID Lat Long Pmin (MW) Pmax (MW) Efficiency (%)

G1-40365 -0.806 37.811 50 200 87

G2-40364 -0.808 37.8104 20 80 88

G3-40363 -0.807 37.814 15 50 87

G4-40373 -0.80927 37.68671 10 35 87

G5-40374 -0.80773 37.68624 10 30 88

G6-40375 -0.80669 37.68547 12 40 88

G7-40366 -0.79665 37.7497 21 81.5 87

G8-40367 -0.7967 37.7494 18 72.5 88

G9-40368 -0.7969 37.7487 17 72.5 88

3.2 Development of web interface to input data into a database

Having collected the data on the nine generating units, there arises the need to store them in a database.

This was only possible by creating a web interface which allows a user to key in the generator

characteristics as well as the attributes of the prime mover.

PHP codes were written in Notepad++ development kit and then connected to PostgreSQL database

through a PHP code containing credentials of the database. This was then run in Google Chrome.

XAMPP was set up to make it extremely easy for creation of a local web server.

3.3 A solution to Economic Load Dispatch Problem

This is the operation of generation facilities to produce energy at the lowest cost to reliably serve

consumers, recognizing any operation limits of generation and transmission facilities.

Production cost = Min (Fuel cost + Start-up cost + Shut- down cost)

This is subject to the following constraints;

System Power balance or load constraint.

Spinning reserve

Unit constraints or local constraints

Ramp up constraint

Ramp down constraint

Unit initial status

Units on fixed generation

Page | 25

Algorithm

No

Yes

No Yes

Figure 4

For this optimization, only six generating units will be required.

Table 2: A 6-Unit System Data

Unit Pmax Pmin a b c 1 200 50 0.00375 2.00 240

2 80 20 0.01750 1.75 200

3 50 15 0.06250 1.00 220

4 35 10 0.00834 3.25 200

5 30 10 0.02500 3.00 220

6 40 12 0.02500 3.00 190

Initialize Particles

Calculate fitness values for

each particle

Use each particle’s velocity value to

update its data values

Assign best particle’s pBest value to

gBest

End

Calculate velocity for each particle

Keep previous pBest

Assign current fitness as

new pBest

Is current fitness value

better than pBest

If iteration

completed

Page | 26

The search procedures of the proposed method were as shown below;

Step 1: Specify the upper and lower bound generation power of each unit and calculate Pmax and Pmin.

Initialize randomly the individuals of the population according to the limit of each unit including

individual dimensions, searching points and velocities.

Step 2: To each individual Pg of the population, employ the B-coefficient loss formula to calculate the

transmission loss PL.

Step 3: Calculate the evaluation value of each individual Pgi in the population.

Step 4: Compare each individual’s evaluation value with its pBest. The best evaluation value among the

pbests denoted as gbest.

Step 5: Modify the member velocity v of each individual Pgi according to (5Vid(t+1)

= ω.Vi(t)

+ C1*rand

()*(pbestid-Pgid(t)

) + C2*rand()*(gbestd-Pgid(t)

)

Step 6: If Vid(t+1)

>Vdmax

, Vid(t+1)

= Vdmax

If Vid

(t+1)>Vd

min, Vid

(t+1)= Vd

min

Step 7: Modify the member position of each individual Pgi according to Pgid(t+1)

=Pgid(t)

+ Vid(t+1)

Pgid(t+1)

must satisfy the constraints, namely the prohibited operating zones and ramp rate limits. If Pgid(t+1)

violates the constraints, the Pgid(t+1)

must be modified toward the near margin of the feasible solution.

Step 8: If the evaluation value of each individual is better than the previous Pbest, the current value is set

to be Pbest. If the best Pbest is better than gbest, the value is set to be best.

Step 9: If the number of iterations reaches the maximum, the go to step 10. Otherwise, go to step 2.

Step 10: The individual that generates the latest gbest is the optimal generation powerof each unit with

the minimum total generation cost.

Pseudo code

For each particle

{

Initialize particle

}

Do until maximum iterations or minimum error criteria

}

For each particle

Page | 27

{

Calculate data fitness value

If the fitness value is better than pbest

{

Set pbest = current fitness value

}

If pbest is better than gbest

{

Set gbest = pbest

}

}

For each particle

{

Calculate particle velocity

Use gbest and velocity to update particle data

}

Page | 28

4. RESULTS AND ANALYSIS

The current system of power generation in Kenya was investigated.

The table below illustrates the sources of electricity with their respective effective capacities.


Hydro 827.02 36%

Fossil fuels 811.3 35%

Geothermal 593 26%

Bagasse cogeneration 38 2%

Wind 25.5 1%

Total 2,294.82 100%

Power generation in Kenya can be broadly divided into two:

KenGen is the main player in the electricity, with a current installed capacity of 1,176 MW. It’s

owned 70% by the government of Kenya and 30% by private shareholders.

IPPs who are private investors in the power sector involved in generation either on a large scale

or for the development of renewable energy under the Feed-in-Tariff policy. Current players

comprise; Iberafrica, OrPower, Tsavo, Mumias, Imenti and Rabai power plants.

Future sources of electricity

Year Demand Capacity

2013 1191 1600

2016 2500 2295

2030 1500 19200

Projected capacity – 2031


Geothermal 5530 26%

Nuclear 4000 19%

Coal 2720 13%

GT-NG 2340 11%

MSD 1955 9%

Imports 2000 9%

Wind 2036 9%

Hydro 1039 5%

Page | 29

Total 21620

Figure 5: Web Interface Layout

The above shows the web interface that was created to enable a user to input generator attributes to be

stored in the database.

The database created is shown in figure 6 below. This was created in PostgreSQL

Page | 30

A solution to Economic Load Dispatch with Particle Swarm Optimization generated the following results;

Table 3: Results for Power Demand = 150MW

Generating Units Power Generated (MW) Fuel Cost ($/Hr)

1 50 349.375

2 20.682 243.679

3 18.7344 261.865

4 20.6629 273.59

5 18.785 285.177

6 21.6881 266.824

Total power generated = 150.552 MW

Total fuel cost = 1680.51 $/Hr

Optimal lambda = 8308.87



1 50 349.375

2 33.1508 277.246

3 29.9616 310.844

4 33.1075 322.935

1 46%

2 18%

3 12%

4 8%

5 7%

6 9%

Power Generated (MW)

Page | 31

5 30 332.5

6 34.6895 324.152






1 55.277 362.012

2 66.3357 393.095

3 50 437.304

4 35 330.66

5 30 332.5

6 40 350




1 46%

2 18%

3 12%

4 8%

5 7%

6 9%


Page | 32



1 97.8508 471.607

2 80 452

3 50 437.304

4 35 330.66

5 30 332.5

6 40 350




1 46%

2 18% 3

12%

4 8%

5 7%

6 9%


1 46%

2 18%

3 12%

4 8%

5 7%

6 9%


Page | 33



1 154.753 639.314

2 80 452

3 50 437.304

4 35 330.66

5 30 332.5

6 40 350



Optimal lambda = 76669



1 196.559 778.002

2 80 452

3 50 437.304

4 35 330.66

5 30 332.5

6 40 350




1 46%

2 18%

3 12%

4 8%

5 7%

6 9%


Page | 34

Display of the Power Generation Analytics

The analytics considered was Economic Load Dispatch. The results of the MATLAB simulation were

displayed in a word interface.

1 46%

2 18%

3 12%

4 8%

5 7%

6 9%


Page | 35

5. DISCUSSION

Kenya’s current effective installed electricity capacity is 2294MW. Electricity supply is predominantly

sourced from hydro and fossil fuel sources. Connectivity to the national grid in Kenya currently stands at

28%.

It is recognized that economic load dispatch of power system results in a great saving for electric utilities.

Economic Load Dispatch is the problem of determining how much each unit should generate to meet the

load demand at minimum cost. The formulation of economic load dispatch has been discussed and the

solution is obtained by particle swarm optimization. The effectiveness of this algorithm has been tested on

systems and analyses the behaviour of demand and the cost of fuel. It is found that the result obtained for

the economic load dispatch is minimum.

The results were displayed on an interface. This was dynamic in the sense that it provided real time data

according to the user’s choice.

6. CONCLUSION

A database was created in PostgreSQL. This was used to hold information regarding generator attributes

with their geo-locations. A web interface was also developed to allow a user input data into the database.

As a power generation analytic, Economic Load Dispatch has a significant influence on economic

operation of power systems. Optimal dispatching saves huge amount of costs to electric utilities. The

results of this optimization were able to be displayed on an interface according to a user’s choice of load

demand.

RECOMMENDATION

This approach of displaying the analytics on a word interface does not guarantee better solution to the

project. Therefore, an improvement can be made to display the analytics on a web page for better

optimization.

Page | 36

REFERENCE

[1] Kenya Energy Situation – energypedia.info

[2] Energy Regulatory Commission

[3] PSO Technique for solving Economic Dispatch problem considering the generator constraints. Dr.

L.V. Narashimba Rao.

[4] Analysis of Economic Load Dispatch & Unit Commitment using Dynamic Programming

[5] Unit Commitment and Economic Load Dispatch using self-adaptive Differential Evolution. Surekha

P, N. Archana.

[6] A GIS based decision model for determining the best path for connection to a power distribution

network; A case study of Kenya Power and Lighting Company

[7] EEE-III Electric Power Generation [10EE36]

[8] Examining the economic impacts of hydropower dams on property values using GIS. Bohlen C. &

Lewis L.Y. (2009)

[7] Amador, J. (2000). Analysis of the technical parameters in the application of GIS in the regional

integration of the renewable energy for decentralized production of electricity.

[8] Amador, J. & Dominguez, J. (2005). Application of geographical information systems to rural

electrification with renewable energy sources. Renewable Energy, 30(12), 1897-1912.

[9] Defne, Z. Haas, K.A and Fritz H.M (2011). GIS based multi-criteria assessment of tidal stream power

potential: A case study for Georgia, renewable and sustainable energy review, 15, 2310-2320.

[10] A. Stanimirović, D. Stojanović, L. Stoimenov, S. Đorđević–Kajan, M. Kostić, A. Krstić,

"Geographic Information System for Support of Control and Management of Electric Power Supply

Network", Proceedings of IX Triennial International Conference on Systems, Automatic Control and

Measurements SAUM.

Page | 37

APPENDIX

1.0 CODE FOR CREATION OF WEB INTERFACE

Page | 38

2.0 CODE FOR ECONOMIC DISPATCH USING PSO

Page | 39

Page | 40

Page | 41

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