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2
A systematic decision-making approach for the assessment of hybrid renewable 3
energy applications with techno-economic optimization: Application to 4
the Rajamangala University of Technology Srivijaya (Trang campus), 5
Southern Thailand 6
Weerasak Chaichan
1*, Jompob Waewsak
2, and Yves Gagnon
37
1
Faculty of Engineering, Thaksin University (Phatthalung Campus), 8
Phatthalung, 93210, Thailand 9
2
Research Center in Energy and Environment, Faculty of Science, 10
Thaksin University (Phatthalung Campus), Phatthalung, 93210, Thailand 11
3
Université de Moncton, Edmundston, New Brunswick, Canada 12
* Corresponding author, Email address: [email protected] 13
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Abstract 15
This paper proposes a systematic decision-making approach for the assessment of 16
hybrid renewable energy applications with techno-economic optimization, with 17
application to the Rajamangala University of Technology Srivijaya (Trang Campus) in 18
southern Thailand. Using the Hybrid Optimization Model for Electric Renewable 19
(HOMER) Pro simulation tool, the techno-economic aspects of a grid-connected 20
combined solar photovoltaics (PV), wind turbine generator (WTG) and energy storage 21
(Li-Ion battery), are optimized in order to minimize the net present cost (NPC) and the 22
levelized cost of energy (LCOE). The model inputs are NASA-Surface Meteorology and 23
Solar Energy (SSE) and Modern-Era Retrospective analysis for Research and 24
Manuscript
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Applications (MERRA) databases, and monthly power loads of the campus analyzed. The 25
results are compared and optimized amongst four combinations. It is found that the 26
optimal hybrid renewable energy power system offers the lowest NPC and the lowest 27
LCOE to satisfy the full load of the campus.
28 29
Keywords: Hybrid renewable energy, Optimization, Net present cost, Levelized cost of 30
energy, Energy storage 31
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1. Introduction 33
In the last few years, as the use of fossil fueled is being questioned as a viable 34
long term source of energy, renewable energy has come to play an important role in 35
electricity portfolios (Hernandez, Alonso, Alvarez, & Carrillo, 2014; Singh, Singh, &
36
Kaushik, 2016). In particular, solar and wind resources are generating electricity for large 37
scale facilities and in rural remote areas (Zhou, Lou, Li, Lu, & Yang, 2010). Besides the 38
large scale applications, solar power generation and energy storage systems have proven 39
to be attractive energy sources not only for remote areas (Agarwal, Kumar, & Varun, 40
2013), but also for commercial buildings (Shaahid & Elhadidy, 2004; Kazem, Khatib, &
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Sopian, 2013). However, the disadvantage of solar and wind energy is their stochastic 42
nature, thus affecting their reliability as an energy source for electricity generation.
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Hybridization of both wind and solar energy was used to solve the problems 44
mentioned above, followed by increases in the complexity of systems (Yang, Zhou, Lu, 45
& Fang, 2008). In many countries, many rural areas are far away from transmission lines 46
(Borhanazad, Mekhilef, Ganapathy, Modiri-Delshad, & Mirtaheri, 2014; Mbaka, Mucho, 47
& Godpromesse, 2010). However, these areas can have important renewable energy 48
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potentials, such as solar and wind energy, and hybrid technology development can be the 49
most appropriate approach. In the literature, hybrid solar/wind energy is often used in 50
remote areas (Borhanazad et al., 2014; Yong-jian, Da-wei, Hong-xun, & Ya-feng, 2009).
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It was found that a hybrid power generation system between solar and wind energy can 52
reduce greenhouse gas (GHG) emissions and diesel consumption by up to 50%
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(Blechinger et al., 2016).
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The economic and environmental aspects of a stand alone renewable power 55
generation system were evaluated in Yemen (Ajlan, Tan, & Abdilahi, 2017). The 56
optimization results based on the levelized cost of energy (LCOE) and using HOMER 57
found that a hybrid solar/wind energy successfully reduced GHG emission by 30%. For 58
its part, a study on the economic feasibility of LCOE by using HOMER to simulate an 59
off-grid area in China showed that hybrid renewable energy systems can reduce GHG 60
emissions by 40 to 70%, in comparison to conventional electricity generation (Ye et al., 61
2017).
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Hybrid energy systems analysis is quite complex. Therefore, it is necessary to 63
design software tools. Previously, the nineteen software tools were compared (Sinha, &
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Chandel, 2014). HOMER was found to be the most widely used tools due to it has the 65
maximum combination of renewable energy systems. In addition, HOMER performs the 66
optimization and sensitivity analysis. Thus, the evaluation of many possible system 67
configurations can easier and faster.
68
In 2019, Thailand’s electricity generation portfolio was comprised of natural gas 69
(59.5%), coal and lignite (16.5%), fuel oil and diesel (0.1%), and renewable energy 70
(23.9%) (Department of Alternative Energy Development and Efficiency, 2020). From 71
the national policy, the Thai government is encouraging large investments in clean energy 72
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through the PDP 2018 policy, where investments in renewable energy are planned, 73
focusing on solar and wind power (Ministry of Energy (Thailand), 2018).
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Aligned with the Thai government’s objective of reducing the usage of fossil fuel 75
for electricity generation, this paper proposes a systematic decision-making approach for 76
the assessment of hybrid renewable energy applications with a techno-economic 77
optimization using the HOMER Pro computer simulation toolkit, with application to the 78
Rajamangala University of Technology Srivijaya (Trang Campus) in southern Thailand.
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Hybrid renewable energy-based power applications, consisting of combined solar 80
photovoltaics (PV), wind turbine generator (WTG), and energy storage (Li-Ion battery), 81
are analyzed for better system reliability than without energy storage (Agarwal et al., 82
2013), as well as a grid connection for power backup. The results can provide a systematic 83
decision-making approach for renewable energy investments for the campus.
84 85
2. Methodology 86
In order to assess the most suitable hybrid power generation technology for the 87
location analyzed, this paper applied the HOMER Pro computer simulation toolkit as a 88
research tool for the analysis, planning and design optimization, as shown in Figure 1.
89
The input parameters of potential optimal system configurations with an objective 90
function are related to technical aspects and cost characteristics (e.g., solar PV, wind 91
turbine, power converter, battery, grid connection, and economics). The optimization 92
constraints are load demand, resources, technical aspects, reliability, and GHG emissions.
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The output results of the optimal hybrid renewable energy configurations are optimized 94
for equipment sizes, costs data, energy generation and consumption, and total GHG 95
emissions.
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2.1. Mathematical modelling and cost specifications of hybrid renewable energy systems 97
2.1.1. Mathematical modeling of the photovoltaics 98
photovoltaics (PV) converts solar irradiance into electricity. In this simulation, the 99
CanadianSolar MaxPower CS6X-325P PV panel model is used with a rated capacity of 100
325 W and an efficiency of 16.94%. The lifetime is assumed as the same as the project 101
lifetime (25 years). The operating temperature is 45°C, the derating factor is set as 102
12%/year, the capital and replacement costs are 700 $/kW and the O&M costs per year 103
are 14 $/kW (Duman & Güler, 2018). The power of the PV output in time ( P
PV( ) t ) is 104
calculated using Eq. (1) (Bagheri, Shirzadi, Bazdar, & Kennedy, 2018; Mills & Al-Hallaj, 105
2004), 106
( ) =
T1 25
PV PV ,r PV P cell
T
G t
P t P . f . . C T t
G STC (1)
107
where P
PV ,ris the capacity of the PV array under standard test conditions in kW, f
PVis 108
the derating factor, G and
TG STC are the actual conditions and standard conditions of
T109
solar radiation, respectively, on the PV panel in kW/m
2, and C is the temperature
P110
coefficient power (%/°C).
111
For its part, the real-time temperatures of the PV panels ( T
cell( ) t ) were expressed 112
by Eq. (2) (Akhtari & Baneshi, 2019), 113
( ) =
T1
ccell cell ,NOTC ,NOTC
T ,NOTC
G t
T t T t T T . .
G . (2)
114
where T is the ambient air temperature in °C, T
cell ,NOTCand T
,NOTCare the nominal 115
operating temperature of the PV and the temperature of the ambient air, respectively, in 116
°C,
cis the PV array efficiency (%) of the electrical conversion, while and are the 117
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PV solar absorbance (%) and solar transmittance, respectively, of the shield above the 118
surface of the PV array (%).
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2.1.2. Mathematical modeling of the WTG 120
For the simulation, the 3-blade 10 kW Eocycle E010 WTG model is used and 121
connected to the hybrid renewable power system at the AC bus. The lifetime of the WTG 122
is 20 years, the hub height is 16 m, the cut-in and cut-out wind speed is 2.75 m/s and 123
20 m/s respectively, the capital and replacement costs are 20,000 $ (Abo-elyousr &
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Elnozahy, 2018), and the costs of O&M is 150 $/year (Dawoud et al., 2015). The wind 125
speeds at the hub height ( v ) are obtained using Eq. (3),
w126
0
0
ln ( ) =
ln
hub
w anem
anem
H v t v t . H
H H 127 (3)
where v
anemis the speeds at the height of the anemometer in m/s, H
huband H
anemare 128
the heights of the hub and the anemometer, respectively, in m, and H is the surface
0129
roughness length in m.
130
The output power from the WTG under standard conditions of temperature and 131
pressure at each wind speed is calculated by determining the wind speed at the hub high 132
using Eq. (4) (Dahiru & Tan, 2020), 133
3 3
3 3
0
( ) =
0
w i
w i
w,r i w r
r i
w,STP
w,r r w o
w o
v t v v t v
P . v v t v
v v
P t
P v v t v
v t v 134 (4)
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where P
w,ris the wind turbine generator power output at rated wind speed, v is the rated
r135
wind speed, v and
iv are the cut-in and cut-out wind speeds, respectively, of the WTG.
o136
The output value to actual conditions is calculated by the actual air density value 137
using Eq. (5) (Bagheri, Delbari, Pakzadmanesh, & Kennedy, 2019; HOMER, 2016), 138
( ) =
w w,STP
o
P t P t . t (5)
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where and
oare the real air density in kg/m
3and the air density at standard pressure 140
and temperature (1.225 kg/m
3), respectively.
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2.1.3. Mathematical modeling of the power converter 142
DC/AC and AC/DC power converters are required for the hybrid system, which 143
is integrating DC power sources, such as PV, while the demand is an AC load. In this 144
study, a power converter model System Converter is used. The lifetime of the power 145
converter is 15 years, the capital and replacement costs of the power converter are 146
750 $/kW, while the costs of O&M are 15 $/kW/year (Duman & Güler, 2018), with 147
an efficiency of 95%.
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2.1.4. Mathematical modeling of the Li-Ion battery 149
The battery is the energy storage in the hybrid renewable energy power system 150
that provides a more reliable power source, while being able to cover sudden increases in 151
the load demand. Therefore, it contributes in improving the system stability and 152
reliability. The Trojan SSIG 06 255 model is used in this study. The lifetime cycle of the 153
battery is 914.3 kWh, the capital and replacement costs of the battery are 167 $/unit, the 154
O&M costs are 8 $/unit/year, a nominal capacity of 1.52 kWh, a nominal voltage of 6 V, 155
a roundtrip efficiency of 80%, and maximum charge and discharge currents of 45 A and 156
300 A, respectively. The minimum state of charge (SOC) is set to 30 % (Duman & Güler, 157
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2018). The storage capacity of the considered batteries is given by Eq. (6) (Baek et al., 158
2016), 159
wh
=
L Conv BATC E . AD . . .DOD (6)
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where E is the total load in kWh/day,
LAD is the daily autonomy, DOD is the depth of 161
discharge of the battery, while
Convand
BATare the converter and battery efficiencies, 162
respectively.
163
2.1.5. Economic model 164
As mentioned above, in this study, the total net present cost (NPC) and the 165
levelized cost of energy (LCOE) are used as the main indicators to compare the economic 166
feasibility of the various scenarios studied. The objective function is to minimize the total 167
NPC of the hybrid renewable energy system, which can be calculated using Eq. (7) 168
(HOMER, 2016; Acuña et al., 2018), 169
=
proj
Objective function min NPC min TAC
DF i,n (7)
170
where TAC is the total annualized cost ($/year), DF i,n
projis the discount factor based 171
on the interest rate.
172
The LCOE is calculated to compare the different hybridization scenarios. The 173
LCOE is the cost per unit of useful electrical energy produced by the hybrid renewable 174
energy system, and it is expressed as in Eq. (8) (HOMER, 2016), 175
load
LCOE TAC
E (8)
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where E
loadis the total electrical load (kWh/year) of the hybrid renewable energy system.
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The renewable fraction (RF) is the fraction of the energy delivered to the total load 178
from renewable power sources, which is calculated by determining the wind speed at the 179
hub height using Eq. (9) (HOMER, 2016), 180
= 1-
g ,non ren loadRF E / E (9)
181
where, E
g ,non renis the nonrenewable electrical production (kWh/year).
182
2.2 Mathematical modelling of the air pollution 183
Power generation from fossil fuels affects air pollution by releasing carbon 184
dioxide (CO
2) to the atmosphere. Therefore, in this study, the total CO
2emissions are 185
calculated from the hybrid renewable energy system. It is related to the rate of fuel 186
consumption (Halabi & Mekhilef, 2018) as given in Eq. (10):
187
3 667
comp f fuel f c
