optimal load shedding for microgrids with unlimited...
TRANSCRIPT
OPTIMAL LOAD SHEDDING FOR MICROGRIDS WITH
UNLIMITED DGs
NIK MOHD. ZAITUL AKMAL BIN MUSTAPHA
A project report submitted in
fulfillment of the requirement for the award of the
Degree of Master of Electrical
Faculty of Electrical and Electronics Engineering
Universiti Tun Hussein Onn Malaysia
DECEMBER 2013
v
ABSTRACT
Recent years, increasing trends on electrical supply demand, make us to search for
the new alternative in supplying the electrical power. A study in micro grid system
with embedded Distribution Generations (DGs) to the system is rapidly increasing.
Micro grid system basically is design either operate in islanding mode or
interconnect with the main grid system. In any condition, the system must have
reliable power supply and operating at low transmission power loss. During the
emergency state such as outages of power due to electrical or mechanical faults in
the system, it is important for the system to shed any load in order to maintain the
system stability and security. In order to reduce the transmission loss, it is very
important to calculate best size of the DGs as well as to find the best positions in
locating the DG itself.. Analytical Hierarchy Process (AHP) has been applied to find
and calculate the load shedding priorities based on decision alternatives which have
been made. The main objective of this project is to optimize the load shedding in the
micro grid system with unlimited DG’s by applied optimization technique
Gravitational Search Algorithm (GSA). The technique is used to optimize the
placement and sizing of DGs, as well as to optimal the load shedding. Several load
shedding schemes have been proposed and studied in this project such as load
shedding with fixed priority index, without priority index and with dynamic priority
index. The proposed technique was tested on the IEEE 69 Test Bus Distribution
system.
vi
ABSTRAK
Semenjak kebelakangan ini, peningkatan kadar permintaan bekalan elektrik,
memaksa kita untuk mencari alternatif baru dalam membekalkan kuasa elektrik.
Kajian terhadap penggunaan sistem grid mikro dengan aplikasi Generator
Pengagihan Tertanam (DG) untuk sistem ini semakin mendapat tempat dan kian
meningkat. Sistem grid mikro sama ada beroperasi dalam mod bersambung atau
berpisah daripada system grid utama adalah bertujuan untuk membekal atau menjana
bekalan kuasa elektrik yang berkekalan dan mengurangkan kebocoran atau
kehilangan bekalan semasa proses penghantaran kepada beban bekalan. Semasa
keadaan kecemasan seperti gangguan kuasa disebabkan kerosakan elektrik atau
mekanikal dalam sistem itu, ia adalah amat penting untuk sistem menumpahkan atau
mengurangkan, apa-apa beban untuk mengekalkan kestabilan sistem dan
keselamatan. Kajian terperinci dan menyeluruh bagi saiz dan lokasi DG yang sesuai
amat perlu untuk mengurangkan kehilangan penghantaran dalam sistem grid
mikro.Mengoptimum beban yang perlu ditumpahkan adalah amat penting untuk
memastikan kestabilan dan keselamatan system.Proses Hierarki Analisis (AHP) telah
diguna pakai untuk mencari dan mengira indek keutamaan bagi setiap
beban.berdasarkan alternatif keputusan yang telah diambil. Objektif utama projek ini
adalah untuk mengoptimumkan beban yang perlu ditumpahkan dalam sistem grid
mikro dengan aplikasi DG tanpa had. Teknik yang telah digunakan adalah Algoritma
Pencarian Graviti (GSA).Teknik ini digunakan untuk mengoptimumkan penempatan
dan saiz beberapa DGdalam sistem grid mikro, dan juga untuk mengoptimumkan
beban yang perlu ditumpahkan. Tiga teknik skim menumpahkan beban telah dikaji
dan dibincangkan dalam kajian ini. Teknik yang dicadangkan telah diuji pada Sistem
Ujian Bus Pengagihan IEEE 69.
vii
CONTENTS
TITLE i
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xi
LIST OF SYMBOLS AND ABBREVIATIONS xii
LIST OF APPENDICES xv
CHAPTER 1 1
1.1 Project background 1
1.2 Problem statement 3
1.3 Project objective 4
1.4 Project scope 4
1.5 Organization of the thesis 5
1.6 Summary 5
CHAPTER 2 6
2.1 Micro grid (MG) system 6
2.2 Distribution Generation (DG) 7
2.2.1 Reason for DG 8
2.2.2 DG technologies 9
2.3 Load shedding concept 11
2.4 Solution method : Gravitational Search Algorithm (GSA) technique 13
2.4.1 Initialization 14
2.4.2 Fitness evaluation of the all agents 14
viii
2.4.3 Calculate the Gravitational Constant 15
2.4.4 Update Gravity and Inertia Masses 15
2.4.5 Calculate the Total Force 16
2.4.6 Calculate Acceleration and Velocity 16
2.4.7 Position update 17
2.4.8 Repetition 17
2.5 Summary 19
CHAPTER 3 20
3.1 Project methodology 20
3.2 Problem formulation to minimize the total load shedding. 20
3.2.1 Equality constraints 21
3.2.2 Inequality constraints 21
3.3 Characteristic and model of DG 22
3.4 Modeling of DGs in Load Flow studies 23
3.4.1. DG modeled as PQ Node 23
3.4.2. DG modeled as PV type 25
3.5 Analytic Hierarchy Process 25
3.6 Newton –Raphson method 26
3.7 Power Flow solution 28
3.7.1 Power Flow equation 29
3.7.2 Line flows and losses 31
3.8 Simulation flow charts 32
3.8.1 Simulation flow chart to optimal placement and size 33
3.8.2 Simulation flow chart to optimal load shedding problem. 34
3.9 Summary 35
CHAPTER 4 36
4.1 Optimal DGs placement and sizing 36
4.2 Optimal load shedding scheme 41
4.2.1 The load with fixed weighting index or priority index 42
4.2.2 The load without weighting index or priority index 47
4.2.3 The load with dynamic weighting index or priority index 51
CHAPTER 5 54
5.1 Conclusion 54
ix
5.2 Recommendation 55
REFERENCES 56
APPENDIX 60
VITA 134
x
LIST OF TABLES
Table 3.1: Preference value 26
Table 4.1: Criteria comparison table 42
Table 4.2: Simulation result for Method 4.2.1 45
Table 4.3: Simulation result for Method 4.2.2 47
Table 4.4: Simulation result for Method 4.2.3 51
xi
LIST OF FIGURES
Figure 2.1: Basic micro grid architecture 7
Figure 2.2: Typical power system with multiple DGs 12
Figure 2.3: GSA Algorithm 18
Figure 3.1: A typical bus of the power system 30
Figure 3.2: Flow chart to optimize placement and size 33
Figure 3.3: Flow chart to optimize load shedding problem 34
Figure 4.1: The IEEE 69 Test Bus Distribution System 36
Figure 4.2: Result of convergence 38
Figure 4.3: Bus Transmission Loss in MW 39
Figure 4.4: Bus Transmission Loss in Mvar 39
Figure 4.5: Power at Bus in MVA 40
Figure 4.6: Comparison performance with & without DGs 40
Figure 4.7: Priority Index by criterion 44
Figure 4.8: Simulation result for Method 4.2.1 46
Figure 4.9: Simulation result for Method 4.2.2 50
Figure 4.10: Simulation result for Method 4.2.3 53
xii
LIST OF SYMBOLS AND ABBREVIATIONS
- Acceleration
aij - Lower diagonal matric
aji Upper diagonal matric
best(t) - fitness of all agents of the best ( minimum) .
F - Objective Function
- Total Force acting on ith agent
- Fitness on the jth agent at t time
G (t) - Gravitational Constant
Iij - The line current Iij, measured atbusi
Iji - The line current Iij, measured at bus j
Kbest - The set of initial K agent with the best fitness value and the largest
- Mass of ith agent at t time
Mi(t) - Forces acting on ith masses
Mj(t) - Forces acting on jth masses
N - The number of buses in the system,
NOx - Nitrogen Oxide
- The active power
- The active powers supplied to the load
xiii
-The active load powers,
-The active power injections at bus
-The active power generations at bus i
-The load at bus-i with DG unit is to be modified
-The reactive power
-The reactive power injections at bus
-The reactive powers supplied to the load
-The reactive load powers,
-The reactive power generations at bus i
-Random number between the intervals [0,1]
Sij -The complex powers from i bus
Sji -The complex powers from j bus
-The power loss in
t -Current epoch
-Velocityof ith agent at t time in ith
worst(t)- -Fitness of all agents of the and worst (maximum).
-Position of the ith mass in dth dimension
-The next position of ith agent in dth
-Weighting factor which are problem dependent
-Weighting factor which are problem dependent
� -A small constant
-Maximum Eigen value
CI -Consistency Index
CR -Consistency Ratio
xiv
DG -Distributed Generation
GSA -Gravitational Search Algorithm
KCL -Kirchhoff Current Law
LAN -Local Area Network
MG -Micro Grid
p.u -Per units
RES -Renewable Energy Resources
UTHM -Universiti Tun Hussein Onn Malaysia
xv
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Bus Data 60
B Line Data 64
C Bus Data Without DGs 68
D Line Data Without DGs 72
E Bus Data With DGs 83
F Line Data With DGs 87
G Load With Priority Index 98
H Load shed with priority index 102
I MATLAB program to optimize DGs placement 105
and size
J MATLAB program to optimize load shedding 122
problem
CHAPTER 1
INTRODUCTION
1.1 Project background
Using devices with modern technologies is almost inevitable in every aspect of
human life. Most activities in daily life cannot be executed smoothly in the event of
power failure. This phenomenon proves that the existence and stability of electricity
is important, since it has significant impact on human convenience and comfort in
performing daily tasks. The industrial sector also requires electricity to produce the
output that is efficient, fast and effective. Machines in this sector typically require
high-powered electricity.
There is an arising concern in the issues of growth in load demand in
developing and developed countries, which has significant impacts to the power
system planners and operators. The growth of load demand is estimated to be in
increasing trend and the trend will continue to expand in near future. A prolonged
electrical power supply interruption would result in serious financial implication to
the customers. However, in emergency situation, the system itself requires making
load shedding decisions based on its security aspects such as voltage, current, power
and frequency constrains to maintain its stability and reliability.
Nowadays, many countries are working on micro-grid system (MG)
technology which is based on distributed generation (DG). The MG is a small-scale
complex energy network system, embedded by many small-scale generations such as
wind turbines, micro turbines, photo voltaic generation system, bio-gas generation
system, electronic equipment, loads and ancillary facilities etc [1]. MG is designed to
function independently as an islanded system in the non-electrified remote areas, and
2
the MG can operate in a grid connected mode by connecting the MG to a low voltage
utility grid
DG is normally defined as a small generation unit (<10 MW) installed in
distribution system [2]. As today and in the future Distributed generation will be
played an important role in emerging electric power system. Distributed generation
technologies can be categorized as non-renewable DG and renewable energy. In
distribution system, DG units are installed close to the load side, hence the energy
losses in transmission lines and distribution network is reduced. In another factor,
applying DG technologies in many cases provide flexibility and mobility due to their
small size, short construction time compared to traditional power plants. In fact, the
penetration of DG in the power system will change the structure of the grid as well as
operation and planning for traditional power systems. It also will increasethe
complexity of controlling, protecting and maintaining of distributed systems [1].
Comprehensive planning on installation of distributed generation is needed
and requires the definition of several factors such as the best technology to be used,
the number and capacity of units, the best location, the type of network connection
and etc. The impact of DG in system operating characteristic, such as electric losses,
voltage profile, stability and reliability needs to be appropriately evaluated [3]. Due
to this characteristic constrains, the most important factor to evaluate is DG
allocation and sizing. Allocation of DG at the wrong places or non-optimal places
can incurred system losses, increase in cost and the worst case having an effect
opposite to the desired.
Normally when there is emergency state in a distributed generation system,
operators will shed most of the loads except the priority loads, as the last resorts to
maintain system integrity. The nearby generator has to keep supplying the electricity
to the priority loads. However this method cannot utilize the maximum capacity of
the distributed generators. We may face problem in defining the nearby generation
when there are multiple distributed generations.
For this purpose a study is done in order to optimize the load shedding in the
micro grids with unlimited number of distributed generators (limit to 2DGs).The load
shedding problem will be formulated as an optimization problemand combined with
load flow studies to optimizing the DG installation places and sizing as to minimize
loss. Then the results will be discussed base on the formulated problem.
3
1.2 Problem statement
Nowadays, the power electricity demand is growing fast and it must keep supplying
the electricity even though in crisis or emergency state. One of the alternative
solutions is applying micro grid system with embedded distributed generation to the
current system or totally islanded from the main grid system. Increasing price of
crude oil also give a challenge to power engineers to work and generates the
electricity from renewable energy resources such as photovoltaic arrays, wind
turbines, geothermal energy and small hydro. Applying the distributed generation
might benefit to both parties either provider or user. For utility, distributed
generation can help to maintain system stability, provide the spinning reserve and
reduce the transmission and distribution cost [4]. On top of that, end user might have
flexibility and improve quality of electricity supply. Electricity generating from the
renewable resources also can reduce the emission from traditional power plants.
Energy services industry in Malaysia today is monopoly by Tenaga Nasional
Berhad (TNB). It was different compared to developed countries such as United
State of America, United Kingdom and in European countries where by the energy
services is provided and shared by many companies. It will be acompetition between
the companies involved in the energy supply, indirectly encourage the industry to
develop and provide the best service to the customers. It is hope that the competition
also would reduce the power generation cost. However, there will be electricity price
fluctuation in new environment. In order to avoid the effect of price fluctuation and
transmission and distribution cost, installing the distributed generation is better for
end customers.
More distributed generation in the power systems will change the structure of
the distribution system and will introduce the new problem [4]. One of the problems
is the optimal load shedding. There are some researchers have studied in this matter,
researcher [5]had studied on the power system in a large pump station (simulated by
computer program). The studies show the load shedding scheme can maintain the
power supply to important load during disturbance. In [6] they did studied for captive
power plants. The main duty of load shedding is to maintain the power balance after
islanding. A few more results are presented by other researchers. In [7], the
4
respective researchers have applied and control load shedding schemes with Local
Area Network ( LAN ). Outcomes of the study, the control the load shedding based
on the priorities list. In [4], an optimal load shedding strategy for power system with
multiple DGs is presented and in this paper discretization and mathematical
programming has been introduced. In [3], a Genetic Algorithm is us for Distributed
Generation allocation to reduce losses and improve voltage profile. In [8], a Genetic
algorithm is applied to optimal placement of multi Distributed Generation in
distribution system with considering DG bus available limits.
Hence a new approach is proposed to optimize the load shedding problem as
to reduce load shedding cost in micro grid considering unlimited DGs units by using
Gravitational Search Algorithm ( GSA).
1.3 Project objective
The main objectives are:
• To find the best size and placement of DGs in micro grids in order to
reduce the system loss.
• To optimize the load shedding in microgrids in order to optimizeload
shedding amount to be shed.
1.4 Project scope
There are 3 scopes of this project. They are:
• This study has focus on reducing system loss due to allocation of DGs
and sizing using GSA technique.
• To formulate and simulate the load shedding problem (optimize the
amount of the load to be shed) based on steady state model.
• To obtain the result 2 by using GSA technique.
5
1.5 Organization of the thesis
The progress report is orderly into 5 chapters. The content of each chapter explained
briefly below.
Chapter 1:presents the background, objective and the scope of the project. The
chapter also summarizes the content of the thesis.
Chapter2: discusses about the theory of the project along with the literature review.
Chapter 3: gives a detail discussion on the design of the project and the
methodology used to construct the project.
Chapter 4: present and discusses details finding and results of the project. Also do
analyzed the findings data and make a brief comments about the results.
Chapter 5: give a summary about the findings of the project; make a conclusion and
suggestion towards achievement of the project.
1.6 Summary
This chapter of this thesis discusses about the introduction for the whole project.
Firstly, the principle and concept of the microgrids, distributed generation and load
shedding are introduced. Next, the problem statement is discussed. Then, the next
part is about the objectives and scopes of the project. Lastly, the thesis outline is
discussed which will give an overview for the reader about the thesis.
CHAPTER 2
LITERATURE REVIEW
2.1 Micro grid (MG) system
Recently, there has been an increasing interest in the application of DG to enhance
power supply reliability, especially in the event of main power failure. The recent
concept is to group a cluster of loads and paralleled DG systems within a certain
local area, which is referred to as a MG. The IEEE 1547.4 working group is
dedicated to providing general guidelines for design and operation of such an
islanded power system [9].
The MG appears as a single consumer by the national supply system. It can
be quickly switched on and off from the large network. If utility company offers
cheap electricity, the owner of micro grid may purchase electricity from them.
However when the power offered is expensive or fails completely, the consumer can
use the MG separately.
Basic MG architecture is shown in Figure 2.1. This consists of a group of
radial feeders, which could be part of a distribution system or a building’s electrical
system. There is single point of connection to the utility called point of common
coupling. Some feeders, (Feeders A-C) have sensitive loads, which require local
generation. The non-critical load feeders do not have any local generation. In our
example, this is Feeder D Feeders A-C can island from the grid using the static
switch which can separate in less than a cycle. In this example there are four micro
sources such as fuel cells, micro turbine, gas turbine and electronic storage at nodes
8, 11, 16 and 22, which control the operation using only local voltages and currents
measurements.
7
When there is a problem with the utility supply the static switch will open,
isolating the sensitive loads from the power grid. Feeder D loads ride through the
event. It is assumed that there is sufficient generation to meet the loads demand.
When the micro grid is grid-connected power from the local generation can be
directed to Feeder D [10].
2.2 Distribution Generation (DG)
Distributed generation (DG) is related with the use of small generating units installed
at strategic points of the electric power system or location of load center .DG can be
used in isolated way, supplying the consumer’s local demand, or integrated into grid
supplying energy to the remainder of the electric power system. DG technologies can
run on renewable energy resources, fossil fuels or waste heat. Equipment ranges in
size from less than a kilowatt(kW) to tens of megawatts(MW). DG can meet all or
part of a customer’s power needs. If connected to a distribution or transmission
system, power can be sold to the utility or third party.
Figure 0.1: Basic micro grid architecture
8
DG and renewable energy resources(RES) have attracted a lot of attention
worldwide. Both are considered to be important in improving the security of energy
supplies by decreasing the dependency on imported fossil fuels and in reducing the
emission of Green House gases (CHGs). The viability of DG and RES depends
largely on regulation and stimulation measures which are a matter of political
decisions.
2.2.1 Reason for DG
DG can be applied in many ways and some examples are listed below:
• It may be more economic than running a power line to remote locations.
• It provides primary power, with the utility providing backup and
supplemental power.
• It can provide backup power during utility system outages, for facilities
requiring uninterrupted service.
• For cogeneration, where waste heat can be used for heating, cooling or steam.
Traditional uses include large industrial facilities with high steam and power
demands, such as universities and hospitals.
• It can provide higher power quality for electronic equipment.
• For reactive supply and voltage control of generation by injecting and
absorbing reactive power to control grid voltage.
• For network stability in using fast-response equipment to maintain a secure
transmission system.
• For system black-start to start generation and restore a portion of the utility
system without outside support after a system collapse.
DG can provide benefit for consumers as well as for utilities. Some example are
listed below :
9
• Transmission costs are reduced because the generators are closer to the load
and smaller plants reduce construction time and investment cost.
• Technologies such as micro turbines, fuel cells and photovoltaic can serve in
several capacities including backup or emergency power, peak shaving or
base load power.
• Given the uncertainties of power utility restricting and volatility of natural
gas prices, power from a DG unit may be less expensive than conventional
electric plant. The enhanced efficiency of combined heat and power (CHP)
also contributes to cost savings.
• DG is less capital intensive and can be up and running in a fraction of the
time necessary for the construction of large central generating stations.
• Certain type of DG, such as those run on renewable resources or clearer
energy systems, can dramatically reduce emissions as compared with
conventional centralized large power plants.
• DG is well suited to providing the ancillary services necessaryfor the stability
of the electrical system.
• DG is most economical in application where it covers the base load electricity
and uses utility electricity to cover peak consumption and the load during DG
equipment outages, such as a standby service.
• DG may allow customers to sell excess power or ancillary services to power
markets, thus increasing numbers of suppliers selling energy and increasing
competition and reducing market power.
•
2.2.2 DG technologies
DG technologies include engines, small wind turbines, fuel cells and photovoltaic
systems. Despite their small size, DG technologies are having a stronger impact in
electricity markets.
10
There is no single DG technology can accurately represent the full range of
capabilities and applications or the scope benefits and costs associated with DG.
Some of these technologies have been use form many years, especially reciprocating
engines and gas turbines. Others, such as fuels cells and micro turbines, are relative
new developments. Several DG technologies are now commercially available, and
some are expected to be introduced or substantially improved within next few years.
i. Reciprocating engines. Diesel and gas reciprocating engines are well-
established commercial DG technologies. Industrial-sized diesel engines can
achieve fuel efficiencies in excess of 40% and relatively low cost per
kilowatt. While nearly half of the capacity was ordered for standby use, the
demand for units for continuous or peak use has also been increasing.
ii. Gas turbines. Originally developed for jet engines, gas turbines are now
widely used in the power industry. Small industry gas turbines of 1-20 MW
are commonly used in combined heat and power applications. They are
particularly useful when higher temperature steam is required than can be
produced by a reciprocating engine. The maintenance cost is slightly lower
than for reciprocating engines, but so is the electrical conversion efficiency.
Gas turbines can be noisy. Emissions are somewhat lower than for engines,
and cost-effective NOx emission control technology is commercially
available.
iii. Micro turbines. Micro turbines extend gas turbines technology to units of
small size. The technology was originally developed for transportation
application, but is now finding place in power generation. One of the most
striking technical characteristic of micro turbines is its extremely high
rotational speed. The turbines rotates up to 120,000 r/min and the generator
up to 40,000 r/min. Individual units range from 30 to 200kW but can be
combined into systems of multiple units. Low combustion temperatures can
assure very low NOx emission levels. These turbines make much less noise
than an engine of comparable size. Natural gas is expected to be the most
common fuel but flare gas, landfill gas or biogas can also be used. The main
11
disadvantages of micro turbines are their short track record and high costs
compared with gas engines.
iv. Fuel cells. Fuel cells are compact, quite power generators that use hydrogen
and oxygen to make electricity. The transportation sector is the major
potential market for fuel cells, and car manufacturers are making substantial
investments in research and development. Power generation, however, is seen
as a market in which fuel cells could be commercialized much more quickly.
Fuel cells can convert fuels to electricity at very high efficiencies (35-60 %),
compared with conventional technologies. As there is no combustion, other
noxious emissions are low. Fuel cells can operate with very high reliability
and so could supplement or replace grid-based electricity. Only one fuel cells
technology for power plants., as a phosphoric acid fuel cell plant (PAFC), is
currently commercial available. Three other types of fuel cells, namely
molten carbonate (MCFC), proton exchange membrane (PEMFC) and solid
oxide (SOFC), are the focus of intensive research and development.
v. Photovoltaic systems. Photovoltaic systems are a capital-intense, renewable
technology with very low operating costs. They generate no heat and are
inherently small scale. These characteristic suggest that photovoltaic systems
are best suited to household or small commercial applications, where power
prices on the grid are highest. Operating costs are very low, as there are no
fuelling costs.
vi. Wind. Wind generation is rapidly gaining a share in electricity supply
worldwide. Wind power is sometimes considered to be DG, because the size
and location of some wind make it suitable for connection at distribution
voltages.
2.3 Load shedding concept
When a sudden loss of generation is occurred, there will be a mismatch between
energy supplied and energy demanded which will lead to system frequency drop. If
governor action cannot activate the available spinning reserve from generating units
12
quickly enough to restore the system to its normal operating frequency, the system
will collapse, which will lead to a large scale loss of load. So, a suitable corrective
control action should be applied to restore the system to a new steady-state operating
condition.
Load shedding is an effective corrective control action in which a part of the
system loads are is connected according to certain priority in order to steer the power
system from the existing potential dangers with the least probability of disconnecting
the importance loads. Load shedding is considered as the last resort tool for use in
that extreme situation and usually the less preferred action to be adopted, but in this
kind of problem it is vital to prevent the system from collapsing[11].Figure 2.2 is
typical power system with multiple DGs. DGs may located around the distribution
system that is connected to a grid system.
When there is a major disturbance, the power system DGs may enter the
emergency state. Load shedding is one of the emergency control actions. It is needed
in three cases:
First, when there is a disturbance in the grid system, the system operator may
request the distribution utility or industrial customer to shed load to maintain the
Figure 0.2: Typical power system with multiple DGs
13
system integrity. Sometimes, the distributed generation system has agreement with
the grid system to curtail power in emergency state.
Second, when there is a major disturbance in distributing generation system
itself, for example, one or more distributed generation is lost, the load shedding is
needed to ensure the power consumption within certain limit.
Third, when there is a major disturbance in the interconnection, the power
systems with distributed generation may be disconnected from the grid system [4].
To maintain the power balance and system stability, the load shedding may
be conducted in the power system with distributed generation. All the three cases can
be divided into two categories. In the first category, the distributed generation system
is still connected to the grid system. Usually, the frequency in such system is
maintained by the grid system. In the second category, the distributed generation
system is separated from the grid system. It becomes islanding system. The dynamic
model is needed in analyzing such islanding system [4].
Basically, there are two objectives for load shedding. The first one is to
maintain the power supply to the important loads, secondly is to minimize the shed
load of load shedding.
2.4 Solution method : Gravitational Search Algorithm (GSA)
technique
Gravitational Search Algorithm[12] is a population based search algorithm based on
the law of gravity and mass interaction. The algorithm considers agents as objects
consisting of different masses. The entire agents move due to the gravitational
attraction force acting between them and the progress of the algorithm directs the
movements of all agents globally towards the agents with heavier masses. Each agent
in GSA is specified by four parameters. Positioning of the mass in dth dimension,
Inertia mass, active gravitational mass and passive gravitational mass. The positions
of the mass of an agent at specified dimensions represent a solution of the problem
and the inertia mass of an agent reflect its resistance to make its movement slow.
Both the gravitational mass and the inertial mass, which control the velocity
of an agent in specified dimension, are computed by fitness evolution of the problem.
The positions of the agents in specified dimensions (solutions) are updatedina
14
iteration and the best fitness along with its corresponding agent is recorded. The
termination condition of the algorithm is defined by a fixed amount of iterations,
reaching which the algorithm automatically terminates. After termination of the
algorithm, the recorded best fitness at final iteration becomes the global fitness for a
particular problem and the positions of the mass at specified dimensions of the
corresponding agent becomes the global solution of that problem. The algorithm can
be summarized as below:
2.4.1 Initialization
If one assumes that there is a system with N (dimension of the search space) mass,
the mass of the ith position is explained as follows. At first, the position of the mass
is fixed randomly.
(2.1)
Where :
2.4.2 Fitness evaluation of the all agents
For all agents, the best and worst fitness which is calculated at each epoch is
described as follows.
(t) (2.2)
(t) (2.3)
Where :
(t) = Fitness on the jth agent at t time.
15
and = fitness of all agents of the best ( minimum) and worst
(maximum).
2.4.3 Calculate the Gravitational Constant
The Gravitational Constant (G (t)) at t time is calculated as follows :
(2.4)
Where :
G0 = Initial value of the gravitational constant is chosen at random.
� = Constant
t = current epoch
T= total iteration of number
2.4.4 Update Gravity and Inertia Masses
The gravity and inertia masses are updated as follows
(2.5)
Where :
(2.6)
Where :
16
2.4.5 Calculate the Total Force
The Total Force acting on ith agent (t) is calculated as follows
(2 7)
Where :
Kbest = the set of initial K agent with the best fitness value and the largest
mass.
Forces acting on ith masses (Mi(t)) of the jth mass (Mj(t)) at a certain
t time is described by the theory of gravity as follows;
(2.8)
Where :
� = a small constant.
2.4.6 Calculate Acceleration and Velocity
The Acceleration and Velocity of ith agent at t time in ith
dimension is calculated through the law of gravity and the laws of motion as follows.
(2.9)
(2.10)
17
Where :
Randi= Random number between the intervals [0.1]
2.4.7 Position update
The next position of ith agent in dth dimension is updated as follows.
(2.10)
2.4.8 Repetition
The steps from (2.4.2) to (2.4.7) are repeated until the iterations reach the criterion.
At the end of the iteration, the algorithm returns the value associated with the
position of the agent on a particular dimension. This value is the global solution of
optimization problems as well. Procedures for implementing the GSA method to the
problem of voltage control are shown below:
(i) Determining the parameters of GSA.
(ii) Initializing a population with random positions.
(iii) Evaluating the fitness function.
(iv) Updating the gravity constant (G).
(v) Calculating the inertial mass (M) for each agent.
(vi) Calculating the acceleration (a).
(vii) Updating the velocity (v).
(viii) Updating the position of agent.
18
(ix) Repeating again starting from step c to h and stop until the maximum number of
iterations has been met.
The flowchart of the GSA algorithm, as shown in Figure 2.3.
Figure 0.3: GSA Algorithm
19
2.5 Summary
This chapter has discussed about the literature review for this project. The short
review on MG system definition and operation is discussed. The explanation on
distributed generation are also reviewed. In order to relate it with the title of this
project, the definition of load shedding is also discussed. Lastly the overview and
algorithm of Gravitational Search Algorithm (GSA) is presented.
CHAPTER 3
METHODOLOGY
3.1 Project methodology
This research is adopting methods approach involving studies on optimizing load
shedding with unlimited distribution generation in micro grid system. This study has
formulated the load shedding problem in MG system as a optimization problem. Its
objective function is to optimize of load shedding after disturbance with utilizing the
generators to supply the electricity to the important loads. In order to optimize the
load shedding, system loss must be reduce. To achieve that, power flow studies such
as optimizing the location and size of distribution generation was carried out. Then
the load shedding scheme have to take place in order to maintained the system
stability and security.
3.2 Problem formulation to minimize the total load shedding.
Objective function can be formulated as below :
] (3.1)
Where :
N is the number of buses in the system,
and are the active and reactive load powers,
and are the active and reactive powers supplied to the load,
and are weighing factors which are problem dependent.
21
3.2.1 Equality constraints
The active and reactive powers can written as :
(3.2)
(3.3)
Where :
and are the active and reactive power generations at bus i.
and are the active and reactive power injections at bus i.
The load flow equation are stated as :
(3.4)
(3.5)
Where :
V’s and �’s are system buses voltage magnitudes and phase angles.
3.2.2 Inequality constraints
(3.6)
i = 1,…, N (3.7)
(3.8)
(3.9)
Where :
22
is a maximum voltage phase angle difference between buses i and j. This
represent line loading limits. Substituting from equation (3.2) and (3.3) into (3.1) ,
the objective function F can be rewritten as :
(3.10)
3.3 Characteristic and model of DG
For steady state model (DGs is still connected to the grid system), there are three
kinds of distributed generation. The first kind of distributed generations may include
gas turbine, combustion engines and hydro generation. Those generations are similar
to the central generation. They can be dispatched in emergency state. They have two
constraints for this kind distributed generation. That is the output and the ramp rate.
It must be pointed out that minimum output of some generation is an important
constraint because of the cogeneration. They must generate certain power to ensure
the heat supply. The ramp rate exists because the generator needs certain time to
increase its output. These constraints can be written as ;
(3.11)
(3.12)
Where is the increasing output from moment t-1 to moment t.
The second kind of distributed generations include some renewable energy,
such as wind turbine, photovoltaic array. These generations cannot be dispatched in
emergency state. Their outputs are mainly decided by the input energy, such wind or
solar thermal. Some type of generation output may be slightly affected by the system
frequency and voltage. It may be written as ;
(3.13)
The third kind of distributed generation is storage equipment. Some storage
equipment can be dispatched in the emergency state. However it has different
23
characteristic. Usually, the storage equipment has certain energy E. If it releases
power fast, the energy would be consumed quickly. This constraint can be written as;
(3.14)
Where ;
Pgt is the release power at time t.
Another constraint is method to handle reactive power for these generations. Some
renewable energy such as wind turbine does not generate reactive power. They may
obtain reactive power from the system by fixed amount of capacitor, or by controlled
capacitor with fixed power factor.
(3.15)
3.4 Modeling of DGs in Load Flow studies
In system with DGs, the generation of Photo voltaic systems, Fuel cells, Micro
turbines and some wind turbine units are injected into the power grid via power
electronic interfaces. In such cases, the model of a DG unit in load flows depends on
the control method which is used in the converter control circuit. The DGs which
have control over the voltage by regulating excitation voltage (Synchronous
generator DGs) or the control circuit of the converter used to control ‘P’ and ‘V’
independently, then the DG unit may be model as PV type.
Other DGs like induction generator based units or converter used to control P
and Q independently, then the DG shall be modeled as PQ type. Using these models
for DGs, Current injection based load flow method is employed for distribution
system studies.
3.4.1. DG modeled as PQ Node
A DG unit can be modeled as three different ways in PQ node mode as below:
24
3.4.1.1. DG as a ‘Negative PQ Load’ Model of PQ Mode
In this case the DG is simply modeled as a constant active (P) and reactive (Q) power
generating source. The specified values of this DG model are real (PDG) and reactive
(QDG) power output of the DG. It may me noted that Fuel cell type DGs can be
modeled as negative PQ load model. The load at bus-i with DG unit is to be modified
(3.16)
(3.17)
3.4.1.2. DG as a ‘Constant Power Factor’ Model of PQ Mode
The DG is commonly modeled as constant power factor model. Controllable DG’s
such as synchronous generator based DG’s and power electronic based units are
preferably modeled as constant power factor model. For example, the output power
can be adjusted by controlling the exciting current and trigger angles for synchronous
generator based DG’s and power electronic based DG’s, respectively. For this model,
the specified values are the real power and power factor of the DG. The reactive
power of the DG can be calculated by (3.15) and then the equivalent current injection
can be obtained by (3.16);
(3.18)
(3.19)
3.4.1.3. DG as ‘Variable Reactive Power’ Model of PQ Mode
DGs employing Induction Generators as the power conversion devices will act
mostly like variable Reactive Power generators. By using the Induction Generator
based Wind Turbine as an example; the real power output can be calculated by Wind
Turbine power curve. Then, its reactive power output can be formulated as a function
comprising the real power output, bus voltage, generator impedance and so on.
Reactive power consumed by a Wind Turbine can be represented as a
function of its Real Power, can be written as;
56
REFERENCES
[1] Sedighizadeth and R. Bayat, "Using genetic algorithm for distributed generation
allocation to reduce losses and improve voltage profile," in Universities Power
Engineering Conference, UPEC 2007 42nd International, 2007.
[2] C. Yammani, S. Mahaswarapu and s. Matam, "Optimal Placement of Multi DGs
in Distribution system with Considering the DG Bus Available Limits," in
Energy and Power, 2012.
[3] A. Malekpour, A. Seifi, M. Hesamzadeh and N. Hosseinzadeh, "An optimal
load shedding approach for distribution networks with DGs capacity defiency
modelling of bulked power supply," 2008.
[4] C. Wu, F. Wen and Y. Lou, "Electric Utility Deregulation and Restructing and
Power Technologies," in DRPT 2008 Third International Conference , 2008.
[5] S. Shilling, "Electrical transient stability and underfrequency load shedding
analysis for a large pump station," Industry Applications, IEEE Transactions ,
pp. 194-201, 1997.
[6] K. Rajamani and U. Hambarde, "Islanding and load shedding schemes for
captive power plants," Power Engineering Society 1999 Winter Meeting, IEEE ,
pp. 805- 809 , 1999.
[7] Q. Bin, L. Yilu, E. Kiat Chan and L. Lawrence, "LAN-Based Control for Load
Shedding," IEEE Computer Application in Power, pp. 38-43, 2001.
[8] Ding Xu and Adly Girgis, "Optimal Load Shedding Strategy in Power Systems
with Distributed Generation," IEEE Winter Meeting, Power Engineering
57
Society, vol. 2, pp. 788-792, 2001.
[9] P. Du and J. Nelson, "Two-step solution to optimal load shedding in micro
grid," in Power Systems Conference and Exposition, PSCE '09,, 2009.
[10] R. Lasseter and P. Paigi, "Microgrid : A conceptual solution," in Power
Electronics Specialists Conference, IEEE 35th Annual.
[11] A. Adel, E. E. Abou, Z. E.-D. A. and R. Sh., "Optimal Load Shedding in Power
Systems," in The eleventh International Middle East Power systems
Conference,MEPCON'2006, 2006.
[12] E. Rashedi, H. Nezamabadi-pour and S. Saryazdi, "GSA : A Gravitational
Search Algorithm," ELSEVIER, no. 179, pp. 2232-2248, 2009.
[13] A. Ishizaka and A. Labib, "Analytic Hierarchy Process and Expert
Choice:Benefits and Limitations," 2009.
[14] R. Malekpour, A. R. Seifi and M. R. Hesamzadeh, "Considering Dispersed
Generation in Optimal Load Shedding for Distribution Networks," in 14th
Iranian Conference on Electrical Engineering ICEE2006, 2006.
[15] T. Ackermann, G. Andersson and L. Soder, "Distributed Generation: A
Definition," Electric Power Systems Research, vol. 57, pp. 195-204, 2001.
[16] A. Malekpour and S. A. R, "An Optimal Load Shedding Approach for
Distribution Networks with DGs Considering Capacity Modelling of Bulked
Power Supply".
[17] A. L.D, S. Pushpendra and LS Titare, "Differential Evolution Applied for
Anticipatory Load Shedding with Voltage Stability Considerations," 2012.
[18] TKA Rahman, SRA Rahim and I. Musirin, "Optimal Allocation and Sizing of
Embedded Generators," in National Power and Energy Conference (PECon)
2004 Proceeding, Kuala Lumpur, 2004.
58
[19] Hadi Saadat, Power System Analysis, McGraw Hill, 2004.
[20] P. Wang and R. Billinton, "Optimum load shedding technique to reduce the total
customer interruption cost in a distribution system," IEEE Proc. Generation
Transmission Distribution , vol. 147, no. 1, pp. 51-56, Jan 2000.
[21] W.P. Luan, M.R. Irving and J.S. Daniel, "Genetic algorithm for supply
restoration and optimal load shedding in power system distribution networks,"
IEE Proc- Gener.Transm.Distrib, vol. 149, March 2002.
[22] Kwang Y. Lee and Mohamed A. El-Sharkawi, Modern Heuristic Optimization
Techniques, Theory And Applications To Power System, IEEE Press Editorial
Board, 2008.
[23] Y Halevi and D. Kottick, "Optimization of Load Shedding System," IEEE
Transactions in Energy Conversion, vol. 8, pp. 207-213, 1993.
[24] L.P. Hajdu, J. Peschon, W.F.Tinney and D.S. Piercy, "Optimum load shedding
policy for power systems," IEEE Transactions on Power Apparatus and
Systems, vol. 87, no. 3, pp. 784-795, 1968.
[25] K.A. Palaniswanny and J. Sharma, "Optimum load shedding taking into account
of voltage and frequency characteristics of loads," IEEE Transactions on Power
Appratus and Systems, vol. 104, no. 6, pp. 1342-1348, 1985.
[26] Ding Xu and Adly A Girgis, "Optimal Load Sheding Using Dynamic Model," in
IASTED Conference Power and Energy Systems(PES), Marbella, Spain, 2000.
[27] John Doughlas, "Power Delivery in the 21st Century," EPRI Journal, Summer
1999.
[28] N.D.R Sarma, S. Ghosh, K.S. Prakasa Rao and M. Srivinas, "Real Time Service
Restoration in Distribution Networks - a practical approach," IEEE PWRD, pp.
2064 - 2070, 1994.
[29] K. Aoki, N. Nara, M. Itoh, T. Satoh and H Kuwabara, "A new algorithm for
59
service restoration in distribution systems," IEEE PWRD, vol. 4, no. 3, pp.
1832-1839, 1989.
[30] P. Daly and J. Morrison, "Understanding the Potential Benefits of Distributed
Generation on Power Delivery Systems," in IEEE Power Engineering Society
Summer Meeting.
[31] Kevin Warwick, Arthur Ekwue and Raj Aggarwal, "Artificial Intellignet
techniques in Power Systems," The Institution of Electrical Engineers London,
1997.
[32] L. Lei Lai and T. Fun Chan, Distributed Generation, England: John Wiley &
Sons, Ltd, 2007.
[33] G. H.H and K. B.C, "Application of Analytic Hierarchy Process (AHP) in Load
Shedding Scheme for Electrical Power System".
[34] B. F. Rad and M. Abedi, "An optimal load shedding scheme during contingency
situation using Meta-Heuristics Algorithms with application of AHP method,"
in Amirkabir University of Tehcnology, Tehran, Iran.
[35] R. Suresh, C. Kumar, S. Sakthivel and S. Jaisiva, "Application of Gravitational
Search Algorithm for Real Power Loss and Voltage Deviation Optimization,"
International Journal of Engineering Science and innivative
Technology(IJESIT), pp. Volume 2, Issue 1, January 2013.
[36] K. Mistry, V. Bhavsar and R. Roy, "GSA based Optimal Capacity and Location
Determination of Distributed Generation in Radial Distribution system for Loss
Minimization".
[37] T. Rahman, S. Rahim and I. Musirin, "Optimal Allocation and Sizing of
Embedded Generators," in IEEE, 2004.
[38] F. Shokooh, J. Dai, S. Shokooh and J. Tastet, "An Intelligent Load Shedding
(ILS) System Application in a Large Industrial Facility," in IAS/IEEE, 2005.