power dispatch of hybrid photovoltaic-genset system using...
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POWER DISPATCH OF HYBRID PHOTOVOLTAIC-GENSET SYSTEM USING
HEURISTIC BIO-INSPIRED APPROACH
CHUA HOCK GUAN
A thesis submitted in
fulfillment of the requirements for the award of the
Degree of Master in Electrical Engineering
Faculty of Electrical and Electronic Engineering
Universiti Tun Hussein Onn Malaysia
SEPTEMBER 2013
v
ABSTRACT
Malaysia is advancing into solar energy as a replacement of alternative source of an
electrical energy production to meet the escalation load demand due to the strategic
equatorial region. Hybrid PV-Genset system are developed to generate power as it
can be fitted into its local geography and other condition according to specification.
However, this does not guarantee that the hybrid system generates power optimally
because weather conditions (solar insolation, temperature and others) changes
periodically and influences the power generation and dispatch. Therefore, under
these circumstances the hybrid system does not operate optimally and lead to over
stress the system components resulting a higher maintenance cost due to frequent
wear and tear. Particle Swarm Optimisation (PSO) is implemented into two
non-linear optimisation areas such as the PV power tracker system and the power
dispatch controller. The required bio-inspired algorithm searches the best potential
optimal state for the maximum PV power generation as well as determining the
suitable power dispatch arrangement of the small scale hybrid PV-Genset system.
Fuzzy Logic Controller (FLC) and logic gate are used as a battery monitor for the
power dispatch controller depending on the capability and availability. The hybrid
PV-Genset system modelling was simulated using two types of tropical weather
conditions (sunny and rainy). As an outcome, the obtained simulation results and
series of analysis are conducted using MATLAB/SIMULINK environment. Through
the analysis and results, the contribution of each hybrid system component operates
at the optimum level and hybrid power is dispatch to the load based on the hybrid
PV-Genset system capability.
vi
ABSTRAK
Disebabkan oleh rantau strategik khatulistiwa, Malaysia meningkatkan penghasilan
tenaga solar sebagai pengganti sumber alternatif pengeluaran tenaga elektrik untuk
memenuhi permintaan beban elektrik. Sistem hibrid PV-Genset dicipta dan dipasang
mengikut spesifikasi yang sesuai dengan geografi tempatan serta keadaan lain untuk
penjanaan kuasa elektrik. Walau bagaimanapun, ini tidak menjamin bahawa penjaaan
kuasa elektrik oleh sistem hibird beroperasi pada tahap yang optimum kerana
perubahan cuaca (radiasi solar, suhu dan lain-lain) mempergaruhi penjanaan dan
penghantaran kuasa. Jikalau operasi ini berterusan, sistem hibrid tidak akan
beroperasi secara optimum dan ini menyebabkan tekanan ke atas komponen sistem
dan mengakibatkan kos penyelenggaraan yang tinggi kerana kerosakan yang kerap.
Particle Swarm Optimisation (PSO) digunakan di dalam dua bahagian
pengoptimuman bukan linear seperti sistem pengesanan kuasa PV dan pengawal
penghantaraan janakuasa. Algortima bio-inpirasi akan mencari keadaan optimum
untuk meningkatkan penjanaan kuasa sistem PV serta menentukan susunan sistem
hibrid yang berskala kecil bagi penghantaraan kuasa. Pengawal Logik Kabur (FLC)
dan get logik digunakan sebagai pengawal batteri untuk pengawal penghantaran
kuasa bergantung kepada keupayaan dan kemampuan batteri. Simulasi permodelan
sistem hibrid PV-Genset mengunakan dua jenis keadaan cuaca tropika (cerah dan
hujan). Keputusan simulasi yang diperolehi melalui penggunaan perisian
MATLAB/SIMULINK. Melalui analsis dan keputusan, sumbangan setiap komponen
serta penghantraan kuasa hibrid beroperasi pada tahap yang optimum bergantung
pada permintaan beban berdasarkan kepada kemampuan sistem hibrid PV-Genset.
vii
CONTENTS
THESIS STATUS APPROVAL
EXAMINERS DECALRATION
TITLE i
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
CONTENTS vii
LIST OF TABLES xiii
LIST OF FIGURES xv
LIST OF SYMBOLS AND ABBREVIATIONS xxi
LIST OF APPENDICES xxiv
CHAPTER 1 INTRODUCTION 1
1.1 Project background 1
1.2 Problem statement 4
1.3 Research objectives 5
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1.4 Research scopes 6
1.5 Thesis outline 7
1.6 Research contribution 8
1.7 Summary 9
CHAPTER 2 SOLAR INSOLATION FORECASTING USING
ARTIFICIAL NEURAL NETWORK (ANN) 10
2.1 Introduction 10
2.2 Solar insolation forecasting 11
2.3 Artificial neural network (ANN) in solar insolation
forecasting 12
2.4 Previous research and works for solar insolation
forecasting using ANN 14
2.5 Summary 19
CHAPTER 3 HYBRID PV-GENSET SYSTEM AND POWER
DISPATCH STRATEGY 20
3.1 Introduction 20
3.2 Hybrid PV-Genset system 21
3.2.1 Hybrid PV-Genset system components 22
3.2.1.1 Photovoltaic array (PVA) system
development and arrangement 22
3.2.1.2 Battery storage system development
and arrangement 27
3.2.1.3 Generator set system development
and arrangement 29
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3.2.2 Connection topologies of hybrid PV-Genset
system 31
3.2.2.1 Series hybrid PV-Genset system 31
3.2.2.2 Switched hybrid PV-Genset system 32
3.2.2.3 Parallel hybrid PV-Genset system 33
3.3 Dispatch power strategy of hybrid PV-Genset system
to the load 36
3.3.1 Classical operation based on switching rule
technique 36
3.3.2 Dispatch power strategies using
Bio-Inspired/AI algorithm technique 41
3.4 Summary 49
CHAPTER 4 ARTIFICIAL NEURAL NETWORK (ANN) AND
PARTICLE SWARM OPTIMISATION (PSO)
BASED OPTIMAL POWER TRACKER 51
4.1 Introduction 51
4.2 Project overview 52
4.3 Experimental set up and data collection 52
4.4 Artificial Neural Network (ANN) design structure
in solar insolation forecasting 55
4.4.1 Procedure of Multi-Layer Perceptron
network design 56
4.4.2 The learning process 58
4.4.3 The generalisation check 60
4.4.4 Network response performance analysis 60
x
4.4.5 Simulation results for solar insolation forecast 62
4.5 PSO Algorithm for optimal power tracking 64
4.5.1 Overview of particle swarm optimisation (PSO) 64
4.5.1.1 Concept of PSO technique 65
4.5.2 Implementation of PSO algorithm for
optimal power tracker 68
4.5.2.1 PSO algorithm for optimal
power tracker 69
4.5.3 Simulation results for optimal power tracker
using PSO algorithm 72
4.6 Summary 75
CHAPTER 5 MODELLING OF HYBRID PV-GENSET SYSTEM AND
OPTIMAL POWER DISPATCH CONTROLLER USING
FLC AND PSO 76
5.1 Introduction 76
5.2 Photovoltaic (PV) module 78
5.3 PV Generator and PSO-MPPT model 85
5.4 Buck-Boost converter 87
5.5 Lead-Acid battery storage system 89
5.6 Solar charging controller and shunt protection 93
5.7 Inverter 96
5.8 Diesel Generator 98
5.8.1 Diesel generator characteristics 99
5.9 FLC-PSO based power dispatch controller for
hybrid PV-Genset system 100
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5.9.1 FLC for storage battery monitor 101
5.9.1.1 Input and output parameters of
FLC model 102
5.9.1.2 Rule base of the FLC model 103
5.9.1.3 FLC based battery monitor block 104
5.9.2 Power dispatch strategy 106
5.9.3 Implementation of PSO algorithm for
power dispatch 109
5.9.3.1 PSO algorithm for power dispatch 110
5.9.3.2 Objective function calculation
method for the PSO algorithm 113
5.9.3.3 Simulation results for power
dispatch using PSO 113
5.9.4 FLC-PSO Based power dispatch
controller block 116
5.10 Load profile 117
5.11 Summary 120
CHAPTER 6 SIMULATION RESULTS AND DISCUSSION 121
6.1 Introduction 121
6.2 Simulation results of the hybrid PV-Genset system 124
6.3 Analysis of the hybrid PV-Genset system behaviour 135
6.4 Discussion of the hybrid PV-Genset system using
the applied bio-heuristic approach 142
6.4.1 The advantage and the operation of the
applied bio-heuristic approach into the
hybrid PV-Genset system 142
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6.4.2 Generated power of the hybrid
PV-Genset system during sunny weather 144
6.4.3 Generated power of the hybrid
PV-Genset system during rainy weather 146
6.6 Summary 148
CHAPTER 7 CONCLUSION AND RECOMMENDATION FOR
FUTURE WORKS 150
7.1 Conclusion 150
7.2 Recommendation for future works 154
REFERENCES 156
APPENDIX A 163
APPENDIX B 164
APPENDIX C 171
APPENDIX D 172
APPENDIX E 173
APPENDIX F 174
APPENDIX G 175
APPENDIX H 179
APPENDIX I 180
APPENDIX J 183
VITA 184
xiii
LIST OF TABLES
2.1 Summarised comparison of the standard and momentum
back-propagation 16
2.2 Summarised network topology for solar insolation forecasting
application 18
3.1 Advantages and disadvantages of system configuration 35
3.2 Different cases for hybrid PV-Genset system operation using
classical technique 41
3.3 Different cases for operation control of the PVDB hybrid
system 46
3.4 Different cases for dispatch power strategies using
Bio-Inspired/AI technique 48
4.1 Number of neurons corresponding to the MLP network
performance on July 11th
2011 (Sunny day) and July 15th
2011
(Rainy day) 62
4.2 Simulation selection parameters for power tracker 72
4.3 Comparison between the actual and PSO based optimal power tracker 74
5.1 Band Gap Energy for different type of PV material 80
5.2 Comparison of parameters of the MATLAB model and
SUNSET AS 80M data sheet values at standard condition 82
xiv
5.3 Relationship between open-circuit battery voltage and state of
charge 90
5.4 Truth table for switch A in charge regulation 94
5.5 Truth table for switch B in charge regulation 94
5.6 Measured values for inverter efficiency 96
5.7 Rule base of the FLC model 104
5.8 Truth table for point Z for FLC controller 105
5.9 Power dispatch conditions for operation control of the hybrid
PV-Genset system 106
5.10 Simulation selection parameters for power dispatch 114
5.11 Actual generated power 114
5.12 Simulation results for cost optimisation using PSO 114
5.13 Daily load demand 119
5.14 Total energy use allocation in a day 119
6.1 Optimum PV generated power at different solar insolation and
temperature 140
6.2 Variation of initial and final objective function with increase
solar insolation 140
6.3 PSO parameters used in optimal power tracker 140
6.4 Hourly average electric production percentage during sunny
day 145
6.5 Hourly average electric production percentage during rainy
day 148
xv
LIST OF FIGURES
1.1 Overview of main component in PV-Genset hybrid system
using bio-inspired approach for power dispatching 4
2.1 Operation of ANN architecture 12
2.2 Scheme of a neural network of the multilayer perceptron type
(Model M1) 17
3.1 Illustration of the p-n junction in the solar cell of the PV system 23
3.2 The Photovoltaic hierarchy from cell to array 24
3.3 Solar Modules in array consist of Mp parallel branches each
with Ms Modules in series 25
3.4 Series-parallel configuration for PV generator 25
3.5 Series hybrid PV-Genset system configuration 32
3.6 Switched hybrid PV-Genset system configuration 33
3.7 Parallel hybrid PV-Genset system configuration 34
3.8 Block diagram of a hybrid system controller 39
3.9 Rule based optimal power dispatching for stand-alone PV
system 44
3.10 Main and sub-algorithm 45
3.11 Approaches and techniques used in this hybrid system 49
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4.1 Block diagram for optimal power tracking 52
4.2 The tilted position of the solar insolation logger during logging 53
4.3 12 hours daily average insolation 53
4.4 Average 12 hours solar insolation per hour 54
4.5 Measured solar insolation at sunny day (July 11th
2011) and
rainy day (July 15th
2011) 54
4.6 Network architecture of MLP in solar insolation forecast 56
4.7 Activation function used in MLP network 57
4.8 MLP network design and architecture 58
4.9 The mean squared error training process on July 11th
2011
(Sunny day) 59
4.10 The mean squared error training process on July 15th
2011
(Rainy day) 59
4.11 Corelation coeffcient of the MLP network on July 11th
2011
(Sunny day) 61
4.12 Corelation coeffcient of the MLP network on July 15th
2011
(Rainy day) 61
4.13 Solar insolation measured and prediction on July 11th
2011
(Sunny day) 63
4.14 Solar Insolation measured and prediction on July 15th
2011
(Rainy day) 63
4.15 Birds food searching with PSO 65
4.16 Concept of modification of a searching point by PSO 67
4.17 Flowchart of PSO algorithm 68
4:18 PSO block diagram 69
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4.19 Flowchart of PSO algorithm for Optimal Power Tracking 71
4.20 The evolution of the objective function and the global best
position of 1/P Pv optimal 73
4.21 The evolution of the objective function and the global best
position of VMPP 74
5.1 Proposed parallel hybrid PV-Genset system 77
5.2 Equivalent circuit of the solar cell 78
5.3 Module current and power of the PV modelling at standard
condition 82
5.4 PV Module current with different insolation at T = 25°C 83
5.5 PV Module power with different insolation at T = 25°C 83
5.6 PV module current with different temperature at G=1000W/m2 84
5.7 PV module power with different temperature at G=1000W/m2 84
5.8 Measured average temperature (Sunny Day) 85
5.9 Measured average temperature (Rainy day) 85
5.10 Meteorological data in SIMULINK look-up table block 86
5.11 Input and output parameters of the PV generator and
PSO-MPPT block 87
5.12 Input and output parameter of the Buck-Boost converter block 89
5.13 Battery equivalent circuit model 90
5.14 Modelling of Lead-Acid battery using SIMULINK block
diagram 93
5.15 Block diagram of solar charger controller 94
5.16 Input and output parameters of the solar charging controller
block 95
xviii
5.17 12V/400W Inverter efficiency curve 97
5.18 Inverter block using „Look-up‟ table in SIMULINK 97
5.19 Required diesel generator power 98
5.20 Fuel consumption versus output power for 1kW diesel
generator 100
5.21 Input parameters (SOC and battery voltage) for the FLC model 102
5.22 Output parameter for the FLC model 103
5.23 Control surface of the FLC based on the battery operating
process 104
5.24 FLC for battery power dispatch based on the storage battery
capability 105
5.25 Strategy and operation of the hybrid PV-Genset controller 108
5.26 Optimal power dispatch strategy for hybrid PV-Genset system 109
5.27 Block diagram of power dispatch using PSO 110
5.28 Flowchart or PSO algorithm for power dispatch 112
5.29 Cost optimisation at various load demand 115
5.30 Power dispatch controller using PSO algorithm and FLC 117
5.31 Summation of load profile using step up function blocks 118
5.32 Load profile for both sunny and rainy weather 118
6.1 Hybrid PV-Genset system 123
6.2 Photovoltaic voltage, VPV and maximum power point voltage,
VMPP under sunny weather 124
6.3 Variation of input and output current from the DC converter at
sunny day 125
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6.4 Variation of input and output power from the DC converter at
sunny day 126
6.5 Working condition of the storage battery during sunny day 127
6.6 Variation of state of charge during charge and discharge at
sunny day 127
6.7 Variation of storage battery during charging and discharging at
sunny day 128
6.8 Generated diesel power at sunny day 129
6.9 Load demand and generated power 129
6.10 Photovoltaic voltage, VPV and maximum power point voltage,
VMPP under rainy weather 130
6.11 Variation of input and output current from the DC converter
during rainy day 131
6.12 Variation of input and output power from the DC converter
during rainy day 131
6.13 Working condition of the storage battery during rainy day 132
6.14 Variation of state of charge during charge and discharge at
rainy day 133
6.15 Variation of storage battery during charging and discharging at
rainy day 133
6.16 Generated diesel power at rainy day 134
6.17 Load demand and generated power at rainy day 134
6.18 Hybrid system generated power during sunny day 136
6.19 Diesel generator fuel consumption cost during sunny day 137
6.20 Hybrid system generated power during rainy weather 138
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6.21 Diesel generator fuel consumption cost during rainy weather 138
6.22 Optimal PV power output for different solar insolation at
T = 25°C 139
6.23 PV generated power for different temperature at
G = 1000 kW/m2 139
6.24 Contribution of PV generator under tropical weather condition 141
6.25 Hybrid system generated power during sunny weather using
FLC and switch on-off controller 143
6.26 Generated power of each hybrid system components during
sunny day 144
6.27 Average electric production and stored during sunny day 145
6.28 Power generated of the hybrid PV-Genset system during
sunny day 146
6.29 Generated power of each hybrid system components during
rainy day 147
6.30 Average electric production and stored during rainy day 147
6.31 Power generated of the hybrid PV-Genset system during
rainy day 148
xxi
LIST OF SYMBOLS AND ABBREVIATIONS
w Weight (unitless)
b Bias (unitless)
m2 Meter square
t Time (min or s)
h Hour
°C Celsius
R2 Coefficient of determination
Ah Ampere Hour
Wh Watt Hour
η Discharging Rate
P (+) Positive
N (-) Negative
G Irradiance/Insolation (kw/m2)
I Current (A)
J Joule
P Power (W)
PPV Optimal Optimal Photovoltaic Power (W)
Ppv Photovoltaic Power (W)
PLEF Load Power (W)
PB Battery Power (W)
PD Diesel Generator Power (W)
xxii
Pt Total Power Generated (W)
T Temperature (°C or K)
V Voltage (V)
W Watt
RM Ringgit Malaysia
GMEAS Measured Irradiance/Insolation
GEST Estimated Irradiance/Insolation
VMPP Voltage at Maximum Power Point
PMPP Maximum Power Point
IMPP Current Maximum Power Point
VOC Open Circuit Voltage
IPV Photovoltaic Current
AC Alternating Current
AI Artificial Intelligence
ANN Artificial Neural Network
DC Direct Current
DFC Dynamic Filter/Capacitor
DG Diesel Generator
DP Dynamic Programming
DSM Demand Side Management
F Fuel Consumption
FIS Fuzzy Inference System
FLC Fuzzy Logic Controller
GA Genetic Algorithm
Genset Generator set
GPF Green Power Filter
GUI Graphical User Interface
Ms Module in Series
Mp Module in Parallel
xxiii
MAPE Mean Absolute Percentage Error
MAE Mean Absolute Error
MLP Multilayer Perceptron
MOPSO Multi Objective Particle Swarm Optimisation
MPT Maximum Power Tracking
MPPT Maximum Power Point Tracker
NMSE Normalised Mean Square Error
PI Proportional Integral
PSO Particle Swarm Optimisation
PV Photovoltaic
PVA Photovoltaic Array
PVDB Photovoltaic-Diesel-Battery
RBF Radial Basis Function
RMSE Root Mean Square Error
SDL Solar Data Logger
SFC Specific Fuel Consumption
SOC State of Charge
UMPSO Uniform Design and Inertia Mutation Particle Swarm Optimisation
UTHM Universiti Tun Hussein Onn Malaysia
UPS Uninterruptable Power Supply
VSI Voltage Source Inverter
xxiv
LIST OF APPENDICES
APPENDIX TITLE PAGE
A MATLAB code for solar insolation forecast using
Artificial Neural Network (ANN) 163
B Basic Particle Swarm Optimisation (PSO) algorithm
procedure 164
C MATLAB code for optimal power tracker using Particle
Swarm Optimisation (PSO) 171
D MATLAB code for modelling SUNSET AS 80M PV
module 172
E SUNSET AS 80M solar module data sheet 173
F MATLAB code for PSO-MPPT algorithm for 4 PV arrays 174
G PSO algorithm objective function calculation cost per
electricity for hybrid system 175
H MATLAB code for power dispatch using Particle Swarm
Optimisation (PSO) 179
I Results for evolution of objective function for various
weather condition 180
J MEAN WELL TS-200/400 Inverter specification 183
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CHAPTER 1
INTRODUCTION
Hybrid PV-Genset system is one of the most common integrated power generation
system used in most countries. Furthermore, it is known for its reliable
off-grid/stand-alone power supply. This chapter provides the general information of
this research with brief explanation in each section. The respective sections are
divided as project background, problem statement as well as the objective and scope
of this research.
1.1 Project background
Interests on conserving and minimising the impact on the environmental issues are
2
increasing as ideas on developing green technology for power generation application
have attracted the attentions of many countries. Solar photovoltaic (PV) system
generates pollution-free electricity and its solar energy is available at no cost, as
compared to the conventional generation system that using fossil fuel. The advantage
of solar energy is sustainable and eco-friendly. However, harnessing its energy leads
to a major drawback due to the inconsistency as it fluctuates in nature. Climatic
condition and fluctuation of solar insolation and temperature are the major setbacks for
PV generator system. Therefore, integration with a diesel generator is required to curb
the issue of inconsistency power supply from the PV generator. Nonetheless, it is not
only to obtain a reliable and stable power supply but also to meet the high load demand
at various operating conditions [1].
Power capacity of a diesel generator is available in wide range, from one
kilowatt to several hundred kilowatts. Furthermore, it is widely use in either small or
medium range size of power generation as an electrical energy production scheme [2].
Diesel generator is meant to operate during high load demand period simultaneously
charging the storage battery to reach the highest generation efficiency and to reduce
the fuel consumption [3].
Typically, hybrid PV-Genset system is often known as an alternative off-grid
power supply, conventional generation system and islanded power supply. In order to
design this hybrid system scheme, factors like weather condition, operation and
maintenance has to be considered. Hence, the design and control operation of the
system is constructed in such a way to adapt with the large number of non-linear
variable components [4]. Most of the proposed empirical operation rules in the hybrid
system can be said reasonable, however they are not suitable for optimal operation
rules in regard to generator fuel consumption [3]. This is because the control operation
rule does not determine the optimal operation of the diesel generator during low load
demand.
Hybrid PV-Genset power generation performance is dependent on factors like
cell temperature, fault level of PV array, output power, insolation level, operation and
3
fuel consumption of diesel generator. Solar insolation level defines as the solar
radiation energy received on the given surface area and recorded during a given time.
In PV system, a constant cell temperature with a higher insolation level results higher
generation on power and voltage. If cell temperature rises, this lowers the open circuit
voltage. The power output increases with a steep gradient which is almost proportional
to the solar insolation at a constant cell temperature, however the power output
decreases when the cell temperature increases. Other adverse performance of the PV
arrays could be affected by aging of the system, dust and mechanical damage [5]. As
for diesel generators, it is recommended to operate at a higher load demand for better
efficiency performance and lower fuel consumption as to prevent internal glazing of
the generator combustion chamber. In addition, the results of the frequent stop and
start of the diesel generator leads to high maintenance cost due to wear and tear of the
diesel generator engine.
Likewise, a method is required to optimise the power generation of the PV
system under these intermittent weather and condition. As solar insolation varies with
time, such as year, month and hour, the insolation level needs to be forecasted in order
to make full use of the solar energy. A power tracker approach is required in the PV
system as it can track the potential optimal power at any operating condition.
Moreover, a power management with a proper dispatch strategy is essential to dispatch
the power of a hybrid system to its load. Therefore, this research presents a method on
optimal power tracking and power dispatch strategy for the hybrid PV-Genset system
using bio-inspired approach. The general overview of this research is illustrated in a
block diagram shown as in Figure 1.1.
4
PV Array
Generator Set
Optimal Power Tracker
Power Dispatch Strategy
Storage Battery
Load
Hybrid power dispatch using
Bio-Inspired Approach
Figure 1.1: Overview of main component in hybrid PV-Genset system using
bio-inspired approach for power dispatching
1.2 Problem statement
The integrated scheme of the hybrid PV-Genset system utilises more than one power
sources as its reliability is depends on the diesel generator as a compensator to the
inconsistent PV array (PVA) [2]. Thus being well known of its advantages, it is
important to operate the hybrid PV-Genset system with optimal power at unforeseen
operating condition with extended operational life, less cost and power efficient [1-4]
by using an effective optimal power tracker method along with a proper dispatch
control algorithm.
The power generated of the PVA panels can be optimised using a bio-inspired
optimal power tracker. This approach improves the efficiency of the PV panel power
generation at the inconsistent solar insolation and temperature condition. However, the
optimal power tracker need to be comprehended the behavioural pattern of the solar
energy as to increase the solar insolation level at specific location and throughout the
year [5]. The behavioural pattern of the solar insolation can be familiarised by utilising
the forecasting method which can provides a set of prediction data for the possible
forthcoming solar insolation.
5
Power dispatch strategy refers to a control system with a management
approach of supplying consistent power depending on the system components ability
[6]. It can be improved by using a suitable control algorithm and power dispatch
strategy depending on the area of deployment and requirement by managing the
distribution of the generated power supply to the load depending on the hybrid systems
power generation capability and load demand. Although control algorithms for the
power dispatch are offered either in empirical operation rules or classical optimisation
methods [3-4], yet it is not so simple and straightforward to attain [4] especially
regarding to fuel consumption of the diesel generator [3].Without a proper power cost
optimisation method and a dispatch strategy, the power generated from the hybrid
PV-Genset system is not fully utilise and consequently leads to battery wear
[6] and the hybrid system lifecycle will be shortened [7-8]. Furthermore, not many
hybrid PV-Genset systems highlight the problem of optimal power tracker in
conjunction with a power dispatch strategy capability under various and unpredicted
operating situations. However, most researchers emphasise more on the optimum
operation strategies and the optimum sizing of the components [9].
Therefore, the importance of this research is to curb the problem with an
efficient simulation of a hybrid PV-Genset system with an optimal power tracker and a
practical power dispatch controller in conjunction with the proposed dispatch strategy,
control algorithm, method and approach. It will optimise the generated power from the
renewable energy while hybrid power is dispatch to the load based on the hybrid
system component capability.
1.3 Research objectives
The major objective of this research is to propose an integrated scheme for power
utilisation and dispatching of the hybrid PV-Genset system with load. Its measurable
6
objectives are as follows:
(1) To forecast the solar insolation using artificial intelligence (AI).
(2) To develop a control strategy for the hybrid PV-Genset system along the
storage battery that will manage, dispatch and supply the hybrid power to the
load.
(3) To analyse the operation and the performance of the proposed hybrid
PV-Genset system.
1.4 Research scopes
It is too vast for any single research work under a given time frame to cover all the
topics related to the optimal power dispatch of the hybrid PV-Genset system with
random load. Therefore, this project will focus on certain parameters of the system.
(1) The data collection of the solar insolation, temperature and other data will be
carried out at UTHM electrical power laboratory for a period of 12 hours, i.e
from 7 am to 7 pm.
(2) The control module design will based on the inter-operation of hybrid
PV-Genset system components capability and ability while effciently dispatch
the generated hybrid power to the load demand.
(3) The analysis of the small scale hybrid PV-Genset system is conduct under
sunny and weather condition.
7
1.5 Thesis outline
This subsection provides a general overview of the thesis compile chapters as it is
elaborated in stages corresponding to the research work that has been done. The
thesis is divided into seven chapters.
Chapter 1 highlights the introduction and importance of this research as a
start. Along with the provided information, the collective facts for the problem
statement are discussed, followed by the research objective, scopes, thesis outline
and summary.
Chapter 2 elaborates the theory, basic concept, implementation and previous
works of the solar insolation forecasting using Artificial Neural Network (ANN)
approach. The gather information helps to understand and identify the suitable ANN
network selection for solar insolation forecasting.
Chapter 3 discusses about the theory, application and previous works done by
the other researches related to the hybrid PV-Genset system. The background studies
helps to elaborate and understand the possible improvement of the hybrid system in
detail.
Chapter 4 explains the implementation of the optimal power tracker using
ANN and Particle Swarm Optimisation (PSO). The approach begins with the
explanation of the artificial neural network used as solar insolation forecast. Then,
the forecasted data is implemented to search the optimal photovoltaic power using
particle swarm optimisation algorithm. MATLAB is used as a simulation tool in both
approaches.
Chapter 5 describes the modelling of the hybrid PV-Genset system using
optimal power dispatch controller using Fuzzy Logic Controller (FLC) and PSO. The
hybrid system modelling is developed for offline simulation using the approaches
given in Chapter 4. Each system component and the dispatch strategy are given in
details. MATLAB-SIMULINK is used as a simulation tool to verify the hybrid
8
PV-Genset system modelling.
Chapter 6 expounds the simulation results, analysis and discussion of the
hybrid PV-Genset system. This chapter discusses the characteristic behaviour on how
the hybrid system adapts with the various operating conditions. Each hybrid system
component is analysed and reviewed using a define parameters.
Chapter 7 presents the research conclusion and recommendation for future
works. This chapter concludes the research work along with some future
recommendations for further improvement.
1.6 Research contribution
This research has proposed and developed a bio-heuristic based power optimisation
approach for hybrid PV-Genset system under intermittent weather and various
operating condition. The bio-heuristic approach optimises the power generation of
the PV system corresponding to the weather condition and optimally dispatches the
hybrid power to load by managing the optimal configuration of the hybrid power
system. The proposed approach has the following features:
(a) New methods
The Artificial Neural Network (ANN) based solar insolation forecast and Particle
Swarm Optimisation (PSO) based optimal power tracker and optimal power dispatch
are the newly methods that has been used and considered in the hybrid PV-Genset
system development.
9
(b) Non-complex algorithm for non-linear power optimisation application
The optimisation algorithm that have been applied (PSO) are simple,
non-complicated and easy to compute for the non-linear power optimisation
problems based on the objective function selection.
(c) Simulation for system testing
The system modelling provides a practical simulation hybrid PV-Genset system
testing for any related studies on power optimisation and hybrid power generation
performance.
1.7 Summary
The synopsis of this chapter emphasises on the introduction for the whole research
done. The research background is elaborated by highlighting the advantages and
drawbacks of the hybrid PV-Genset system. It is followed by the explanation on the
potential obstacles occurred during the operation of the hybrid PV-Genset system.
The objective and scopes are asserted to specify the goals and the limitations of the
research work. Thesis outline explains the general overview of each chapter for the
ease of the reader to aid the clarity of the research work. Finally, the research
contribution expounds the benefit of this proposed methodology and approach to the
existing application.
26
CHAPTER 2
SOLAR INSOLATION FORECASTING USING ARTIFICIAL NEURAL
NETWORK (ANN)
2.1 Introduction
This chapter provides detail information of the previous research and works
regarding to solar insolation forecasting using Artificial Neural Network (ANN). The
previous research and work will be reviewed, summarised and justified. The outcome
of this chapter will specify the suitable ANN topology for the solar insolation forecast
used in this research. The subsequent subsection in this chapter highlights the
importance, the basic concept of ANN and the current approach for solar insolation
forecasting using ANN.
11
2.2 Solar insolation forecasting
Malaysia is one of the tropical countries in South East Asia and is blessed with
abundance of sunlight. The country experienced only two monsoon seasons and
approximate 6 hours of sunshine per day due to its strategically situated on the
equatorial region [10]. The average scale of solar insolation receives in Malaysia is
ranging between 4.21 kWh/m2 to 5.56 kWh/m
2 [10]. As for the ambient temperature,
it remains almost uniformly through the year as it average scale ranging between
26 °C to 36 °C [10]. Therefore hybrid PV-Genset system is a viable alternative in
Malaysia for electrical production scheme with comprehensive study and research on
climate conditions despites on the system cost.
Solar energy is utilised as electrical energy conversion production scheme for
power generation purposes. This solar energy is also known as solar insolation as it is
measured through the suns energy that shines on the surface of the earth during a
particular time. Solar insolation data is important as it illustrates the pattern of the
variation level and designates a potential location with the highest solar insolation
[11]. Unfortunately, this data are not always available due to cost and difficulty
constraints in measurement because there are existences of only few meteorology
stations especially in remote areas. Hence, there is an alternative method on
generating this data [12-14]. Due to the solar insolation pattern changes periodically
and demand growth on solar energy, modelling and forecasting is essential. Likewise,
solar insolation forecasting maximises the usage of solar energy [5] as it improve the
operation control of solar energy harvesting and energy optimisation [12] in PV
system.
Artificial Intelligence (AI) is a biological technique that is adopted from
living creatures and has been applied in many areas in stand-alone and hybrid PV
systems. AI such as ANN is one of the most common successful methods applied in
modelling and forecasting of solar insolation data as to curb the non-linear
12
meteorological condition [14]. Hence, ANN is effortless applied by most researchers
on this application in order to study the changes of solar insolation throughout a
potential location on solar energy. The following subsection explains further on the
forecasting application using ANN in corresponding to the selected previous works of
few researchers at different areas.
2.3 Artificial neural network (ANN) in solar insolation forecasting
ANN mimics the human brain biological nervous system [15]. There are five
elements in an artificial neuron i.e. inputs, weights, summing function, activation
function and output [15]. Each function of the ANN element imitates the four
important biological operations of the human brain (synapses, dendrites, cell body
and axon). These operations are receiving input signal from other neurons, combine
them, execute the information process and produce an output result [12] as shown in
Figure 2.1.
Figure 2.1: Operation of ANN architecture
ANN is classified by it network structure, training or learning algorithm and
activation function. The structure comprises of an input layer, a few hidden layers
∑ I
Axon
hillock
Activation
function
Axon brings
neuron output Y
Summation of
inputs (∑wiXi)
Neurons
inputs Xi
Synaptic
weights
X1
X2
Xp
w1
w2
wp
13
and an output layer [16]. There are various types of ANN network structure available
and Multilayer Perceptron (MLP) is one of them. MLP is used in many solar
insolation forecast [5] as it is the cheapest method that uses a certain input
parameters such as latitude, longitude, altitude and sunshine duration [11], sunshine
ratio [12], mean daily solar irradiance, mean daily air temperature, day and month
[13], sunshine accuracy and mean average temperature [11]. MLP is preferable in
most solar insolation modelling and forecasting [13] due to its inherent capability of
logical input-output mapping and its learning ability to perform a specific task.
Besides MLP topology, Radial Basis Function (RBF) is another well-known topology
which offers a better result than MLP restrictedly if there are more input parameters
[11-12].
MLP network has different connection styles and learning algorithms as it is
adapted to its structure and convergence time. There are two types of learning
algorithm, supervised and unsupervised. With supervised learning, weights in the
network are adjusted to produce the desired output. On the contrary, unsupervised
learning does not depend on the target data to achieve the desired output as the aim is
to find the core structure of the data [11]. One of the common supervised learning
algorithms used is back-propagation algorithm [17]. Back-propagation algorithm is
applied in the network design as it is to train the network subset data. Likewise, most
insolation level forecast [11 - 17] uses back-propagation algorithm.
The activation function refers to the output relation of the network to the
input based on the input activity level. Sigmoid function is widely used as activation
function due to its non-linearity function whose output lies in between 0 and 1 [18].
It is used in between each hidden layers in the network structure. As the activation
function is applied in the network, a new summary output neuron is obtained [15].
14
2.4 Previous research and works for solar insolation forecasting using ANN
Several studies for the solar insolation forecast using various types of
topology have ensued. Table 2.1 shows the summary of the various ANN topology
used from the ensued research.
M.Mohandes et. al. (2000) [11], compared the performance network of the
monthly mean daily solar radiation falling on the horizontal surface using RBF
network, MLP network and classical regression model. The input network parameters
comprised of 41 different locations in Saudi Arabia. The results indicated that the
RBF network produced better results than the MLP network as the average Mean of
the Absolute Percentage Error (MAPE) for RBF is 10.1 while MLP is 12.6 [11].
Atsu S.S. Dorlo et. al. (2002) [12], initially estimate the clearness index and
compared the solar radiation estimation performance of the eight different stations in
Oman based on the long-term data by using MLP and RBF networks. Their results
have concluded that both network performed well based on Root Mean Square Error
(RMSE) as RBF error ranges from 0.83 MJ/m2/day to 10.08 MJ/m
2/day, while MLP
error ranges from 1.01 MJ/m2/day to 9.41 MJ/m
2/day. Consequently, they concluded
that the best MLP network which uses 3 hidden layers has the minimal mean and
standard deviation of the root mean square errors. However, RBF network was
selected due to less computation time compared to MLP network [12]. The network
proposed in [11-12] provides a simple and practical approach for solar insolation
forecast using sets of long-term data.
Adel Mellit and Alessandro Massi Pavan, (2010) [13], implemented and
tested using K-fold cross validation method in 24 solar irradiance forecast as to
improve the MLP network generalisation capability in four different experiments
under sunny and rainy weather. The K-fold cross validation method splits the data
into K number of subsets during each activity in training, validation and testing. In
each subsets of the cross validation step for each activity, a single subset is remained
15
as the test set while the rest as training sets. The cross validation process reiterates
the K number of time and subsets. As their results, the coefficient of determination,
(R2), for sunny weather ranges from 0.95 to 0.99 while cloudy weather ranges from
0.92 to 0.97. In addition, the effectiveness of the forecast network is compared with
the grid connected photovoltaic plants which give a good correlation of
determination result of 0.90. Thus, the result shows that the Mean Absolute Error
(MAE) is less than 5% and the correlation coefficient ranges from 90% to 92% [13].
The approach used in [13] proposed a good network generalisation indication for
each execute activity by portioning the sets of data in each K number of subsets and
times where the training set is validated with a test set. Nevertheless, it is not vital as
the network generalisation capability can be improved using various numerical tool
such as Levenberg-Marquardt and Bayesian regularisation which is defined in the
MATLAB neural network toolbox.
Melit, Shaari, H.Mekki and N.Khorissi, (2008) [14], introduced a low cost
real time forecasting application using a reconfigurable Field Programming Gate
Array (FPGA) hardware (Xilinx and VirtexII) and Hardware Description Language
(VHDL) to forecast the daily solar radiation based on ANN architecture. The
performance of each written program environment using VHDL and MATLAB for
the back-propagation MLP network forecaster simulation is compared to determine
the results accuracy. In MATLAB simulation, the best architecture obtained results is
98% and the coefficient of determination comprised of a single layer with 9 neurons.
Whereas using VHDL environment, the data is coded in 18 bits fixed point and
simulated in (MODELSIM) which gives an acceptable result. As a conclusion, they
remark a good agreement between the data simulated from MATLAB and VHDL
simulation environment [14]. This approach introduces a great potential for practical
real time solar radiation forecast hardware development using FPGA at a minimum
cost.
Özgür, Humar, Ali and Muammer, (2010) [15], proposed a standard
back-propagation and back-propagation with momentum training algorithm in daily
16
solar radiation prediction in Turkey. The network topology consists of 5 inputs, a
hidden layer with 10 neurons and an output. Therefore number of iteration and
coefficient of determination, R2, results from both standard and momentum
back-propagation is 15000, 0.9870 and 7500, 0.9821 respectively. The result shows
that the momentum back-propagation computes faster and better correlation than the
standard back-propagation. In contrast, the MAE and RMSE result of a standard
back-propagation outpaced the momentum back-propagation by 1.02% and 0.8%
difference. Therefore, they concluded that the standard back-propagation gives a
better result with slow computational time than the momentum back-propagation as
the Mean Relative Error (MRE) concluded with 8.96% and 10.12% each [15]. Table
2.1 shows the summarise comparison of the model. It is noted that the proposed
classical back-propagation method with or without momentum offers the
fundamental background of ANN and the ability to be utilised in the solar radiation
prediction application. Although the prediction results are convincing, yet this
approach can be improved using a different network topology and numerical tools.
Table 2.1: Summarised comparison of the standard and momentum back-propagation
Statistical measurement Standard
Back-Propagation
Momentum with
Back-Propagation
No. of iteration 15000 7500
R2
0.987 0.9821
MRE 8.96% 10.12%
∆ MAE 1.02%
∆ RMSE 0.8%
Mónica Bocco, Gustavo Ovando and Silvina Sayago, (2006) [16], developed
a MLP back-propagation type neural network to estimate the solar radiation at
Córdoba, Argentina. Three layers network and five neurons in the hidden layer is use
to simulate 8 types of forecast result models, M1-M8. Figure 2.2 shows the neural
network structure of the multilayer perceptron type of model M1. The estimation
result indicates the root mean square error ranging between 3.15 MJ/m2d to 3.88
17
MJ/m2d
1. The 14 hours cloudiness records of the M6-M8 models result a pertinent
error in estimation in each model.
Figure 2.2: Scheme of a neural network of the multilayer perceptron type (Model M1)
On contrary, the temperature and precipitation information of the M3 and
M4 model result a greater error in the estimation and the difference are irrelevant to
each model. M1 and M2 obtained the best estimation results as the model present a
better adjustment. In the M1 model observation result, the underestimation
percentage reaches 15% when the daily radiation exceeds 25 MJ m-2
d-1
through an
analysis dispersion diagram of the observed and estimated value as it shows a
seasonal behaviour pattern correctly to those of the observed ones [16].
Table 2.2 shows the summarised topology used for the discussed solar
insolation forecast.
E1 O1
E2 O2
E3 O3
E4 O4
E5 O5
S
W1
W5
W5,5
W1,1
18
Table 2.2: Summarised network topology for solar insolation forecasting application
Author(s) Year Network topology and field
Mohandes, Balghonaim,
Kassas, Rehman &
O.Halawani [11]
2000
Comparison of RBF and MLP network for
monthly mean daily solar radiation estimation
in Saudi Arabia
Atsu, Joseph & Ali [12] 2002
Comparison of RBF and MLP network for
monthly solar radiation estimation of 8
different locations in Oman
Mellit & Alessandro [13] 2010 MLP network with cross-validation method
for 12 hours solar irradiance forecasting
Mellit, Shaari, Mekki &
Khorissi [14] 2008
Application of MLP network in FPGA
hardware for real time solar radiation
prediction
Solmaz, Humar, Ali &
Muammer Özgören,[15] 2010
Application of classical network topology
such as standard back-propagation and
back-propagation with momentum for daily
solar radiation prediction in Turkey.
Mόnica, Gustavo &
Silvina [16] 2006
MLP feed forward network for daily solar
radiation estimation in Argentina
From the collected information and discussion, the methods from [11 and 12] are
adopted for this forecasting method. It is decided that the best network topology use
for solar insolation forecast for this research is feed forward multilayer perceptron
with a back-propagation training algorithm. This approach shows a potential and
viability for evaluating solar energy possibilities. However, due to constraints in
obtaining proper sets of long term data, this approach provides a non-complex
forecasting application which requires non-compulsory selective range of input
parameters as it is suitable for the objectives requirements. Besides that, the proposed
topology has the capability to learn the complex input-output relationship which
could be challenging for other model with conventional algorithmic approach.
19
2.5 Summary
Within this chapter, the importance, the basic concept of ANN and the existing
approach for the solar insolation forecasting using ANN are discussed. The chapter
begins with the significance of solar insolation forecasting using ANN as the
enhancement for solar energy utilisation. This approach offers a simpler and viable
forecasting method compare to other conventional computation method especially
for non-linear meteorological condition. Besides that, each function in the ANN
network component is elaborated in term of its task. Investigation on the preceding
solar insolation forecast approach are ensued and justified to its methodology, ANN
topology and application. Through the justification of each ensued preceding
approach, MLP network with back propagation technique is selected for solar
insolation forecast application. The develop design of the MLP network will be
discuss further in Chapter 4.
26
CHAPTER 3
HYBRID PV-GENSET SYSTEM AND POWER DISPATCH STRATEGY
3.1 Introduction
This chapter delivers substantial materials on the existing development and previous
research of the hybrid PV-Genset system. Within this chapter, there are two main
sections comprise of the hybrid system construction and the power dispatch strategy.
Both sections will describe, justify and summarise of its existing operation and
previous development of this system. The outcome of these sections emphasises on
the explanation of each system component, the various connection topologies and the
power dispatch strategy of the hybrid system.
21
3.2 Hybrid PV-Genset system
The hybrid PV-Genset system satisfies the electricity demands to the consumers
while conserving the nature, the environment and the system itself. In general, hybrid
PV-Genset system is the integrated PV system with another form of fossil fuel driven
power generation system as back-up supplementary power generation, such as diesel
generator. As to ensure a consistent supply of electrical power at all time,
rechargeable storage batteries are also integrated into the hybrid system [19].
Hybrid PV-Genset system is usually ideal for remote applications such as
communication stations, military installations and rural areas. However, the
operation of the hybrid PV-Genset system must be able to adapt in its local
geography and other specifics to supply the electricity demand needs [19].
A hybrid PV-Genset system can be identified either stand-alone or
grid-connected system. In remote and rural areas, stand-alone hybrid system would
assist in expanding the electricity access in the isolated region. Therefore,
stand-alone PV hybrid system is most often found on island. As for grid connected
system it is connected to a large independent grid typically for public electricity grid
and feed the power back into the main grid. This is also known as decentralised
electricity generation [20].
Likewise in many cases of hybrid PV-Genset system, it offers a clean and
more cost-effective efficient power than sole diesel generator system. Ultimately, it
serves to fully utilise the available solar energy for electrical power generation and to
reduce fuel consumption of the diesel generator. As a result, renewable energy is a
preferable solution for either grid-connected or stand-alone system [20]. Thus, the
following subsection aids to elaborate the further understanding and the previous
works of the components, connection topologies and power dispatch strategy used in
the hybrid PV-Genset system.
22
3.2.1 Hybrid PV-Genset system components
Hybird PV-Genset system consists of photovoltaic array system (PVA), battery storage
system and generator set. These are the main components that provide electrical power
to the load. In order to operate efficiently, all of these components must withstand
under all conditions. Power condition unit such as switching power converters and
inverters are also important as it is used to accommodate the variable nature of power
output and prevent malfunction of the hybrid system. These components will be
explained further in Chapter 5. Hence in this subsection, the theory of various system
development and arrangement for different approaches along with other researches
previous work is explained.
3.2.1.1 Photovoltaic array (PVA) system development and arrangement
Solar cells are the fundamental element of the photovoltaic system (PV) which
converts sunlight or solar energy directly to electricity. A solar cell consists of a
positive and negative (P-N) junction formed in a semiconductor material [21]. They
are much similar to any solid-state electronic devices such as diode, transistors and
integrated circuits. Generally, solar cells are manufactured into PV modules for
practical operations [22]. Various semiconductor materials give a different efficiency
and cost. Thus, silicon is one the most common semiconductor material used in solar
cells. There are several types of solar cells made from silicon such as
mono-crystalline silicon (c-Si), poly-crystalline silicon (poly-Si) and
amorphous-silicon [21].
Solar cells comprise of P-N junction that are fabricated into a thin wafer or
23
layer of silicon material. During the dark, the current-voltage (I-V) output
characteristics of a solar cell has an exponential characteristics same as diode [21].
When the solar cells is exposed to solar energy, it creates photons with energy
greater than the semiconductor band gap energy causing an electron-hole pair where
the electrons are knocked loose from the atoms in material. These electrons are
carried away under the influence of the internal electric fields of the P-N junction
and create current proportional to the incident radiation. During the short circuited
solar cell, the current flows in the external circuit while as for open circuited solar
cell, the current is shunted internally by the intrinsic P-N junction diode. Therefore,
these diode characteristics set the open circuit voltage characteristics of the cell.
During electron-hole pairs are formed, the electron charges reach to the
neighbouring junction, causing the electric field in the depletion region push the
holes into the positive side (p-side) and push the electrons into the negative side
(n-side) as shown in Figure 3.1. Each of the p-side and n-side accumulates holes and
electrons creating a voltage in between the load and conducts current. If the electrical
contacts are attached to top and bottom of the cell, the electrons drift out from the
n-side into the connecting wire, through the load and back to the p-side as shown in
Figure 3.1 [22]. Since wire cannot conduct holes, the electrons move around the
circuit. Therefore, the positive current flows in the opposite direction of the electrons
where the current goes from the p-side to the load and back into the n-side. In
general, the conventional current flows in the opposite direction from the electrons.
Figure 3.1: Illustration of the p-n junction in the solar cell of the PV system
Sunlight
Front Electrode (N-)
Anti-reflection
Coating
Current
N-type
Silicon (P+) P-type
Silicon (B-)
Back Electrode (P+)
24
In electrical energy production schemes, solar cells are mass-produced into
photovoltaic modules which are integrated into DC power-producing unit
configuration. Typically, many PV systems development uses solar panels, where it
is the physical connection of modules with supports to form arrays [22]. Figure 3.2
shows that PV system has various categories, starting from cells to arrays.
Figure 3.2: The Photovoltaic hierarchy from cell to array
Figure 3.3 illustrates how the PV modules are connected in arrays with Mp
(Module in parallel) parallel branches each with Ms (Module in series) [23]. The
arrangement of PV module portrays a vital task in terms of efficiency. Figure 3.4
shows three different types of arrangement donating various efficiencies. The A
arrangement gives efficiency up to 97.2%, while B arrangement at 96.8% and C at
96.2% [23]. In this research, the A arrangement is use. Besides the PVA arrangement
Cell
Module
Panel/String
s
Array
156
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