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This research describes the design of a rooftop photovoltaic system to meet the partial energy needs of an apartment building in Newfoundland. A residential apartment building consisting of 54 apartments at fresh water road, St. John's is selected for this work whose average energy demand is 54KW (building is heated using oil). A grid-tied solar photovoltaic system without any battery backup has been deigned for this site because local grid is available and some net-metering laws are in place. The system has been designed by keeping in mind that the produced energy must not be greater than the average energy consumption of the building. Detailed system design, steady state modeling, dynamic modeling, control system design, analysis and required protection system are explained in this paper. Some cost analysis and installation details are also included in the paper.
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Design and analysis of a rooftop PV system for an
apartment building in Newfoundland
Asif ur Rehman
Department of Electrical and
Computer Engineering
Memorial University of
Newfoundland
St. John’s Canada
aurehman@mun.ca
Hashem Elsaraf
Department of Electrical and
Computer Engineering
Memorial University of
Newfoundland
St. John’s Canada
hasmaelsaraf@mun.ca
Atefeh Zare
Department of Electrical and
Computer Engineering
Memorial University of
Newfoundland
St. John’s Canada
azare@mun.ca
Tariq Iqbal
Department of Electrical and
Computer Engineering
Memorial University of
Newfoundland
St. John’s Canada
tariq@mun.ca
AbstractThis research describes the design of a rooftop
photovoltaic system to meet the partial energy needs of an
apartment building in Newfoundland. A residential apartment
building consisting of 54 apartments at fresh water road, St.
John’s is selected for this work whose average energy demand is
54KW (building is heated using oil). A grid-tied solar photovoltaic
system without any battery backup has been deigned for this site
because local grid is available and some net-metering laws are in
place. The system has been designed by keeping in mind that the
produced energy must not be greater than the average energy
consumption of the building. Detailed system design, steady state
modeling, dynamic modeling, control system design, analysis and
required protection system are explained in this paper. Some cost
analysis and installation details are also included in the paper.
Keywords Solar photovoltaic power system, Grid-tied
photovoltaic system, Renewable energy system
I. INTRODUCTION AND LITERATURE REVIEW
Global energy demand is increasing gradually due to the
growing population. This increasing demand of energy is
leading towards environmental hazards because major portion
of energy is generally produced by fossil fuel. Renewable
energy resources can provide carbon free energy, but they can’t
be used to fulfil the requirements of energy sector due to their
extraordinary initial cost and energy production limitations [1].
Energy can easily be converted from one form to another and
most common energy carrier is electricity. As per International
Energy Agency (IEA) total energy consumption of the world
will increase 1.3% each year to 2040 with 2.3% growth in 2018.
Fossil fuel has been a major source of energy, but it is available
in a limited quantity. We have used 60% of fossil fuel storage
since last 200 years so relying on this kind of technology is not
a good choice [2]. Presently, Power is generated in a centralized
facility and distributed to the distant areas through transmission
lines. Adopting renewable energy resources is the best solution
for all energy demand and climate change problems but
implementing these technologies in existing centralized power
system is very costly. Distributed power system and micro
renewable energy units can make it easy to adopt these
renewable technologies at relatively lower cost [3].
Grid-tied solar photovoltaic system is emerging as one of the
leading renewable energy technology. Many researchers
working in this field e.g [4] evaluates a 100KW grid-connected
solar photovoltaic system for Nagar Nigam Kota Rajasthan.
This study also discusses the guidelines and technical
specification of various components of grid-connected PV
system installed at the same site. It analyzes the feasibility of
that system resulting 167,822 KWH production annually by
using PVsyst software. In [5] the author attempts to highlight the
steady state integration impacts of solar photovoltaic (PV)
generation to existing transmission and distribution grids. That
study shows a model of existing transmission and distribution
grid in the Northern Cape region of South Africa, known as the
solar corridor using Matlab Software. The study concluded that
the amount of PV generation integrated to the grid has a limit if
the steady state stability of the existing grid is to be maintained.
The study [6] presents a robust control strategy for a solar
photovoltaic (PV)-based distributed generation system (DGS)
with seamless transition capabilities from islanded to the grid-
connected mode and vice versa. The proposed DGS consists of
a solar PV array, a dc-dc boost converter, voltage source
converter (VSC), and local nonlinear loads. In grid-connected
mode, VSC regulates the dc-link voltage and the boost converter
operates the solar PV array at the maximum power point. Test
results of that study demonstrate the system capabilities under
the abnormal grid and unbalanced nonlinear load conditions.
A combined grid-connection and power-factor-correction
technique for a photovoltaic (PV) system is proposed in [7]. The
author used maximum power point tracking dc/dc converter
served as a charger for the battery bank. That study applied a
bidirectional inverter as a generator during sunlight, supplying
power to the load. The inverter is also used as a charger to
maintain the voltage level of the batteries in the absence of
sunlight. the experiments performed by author on a 1-kW PV
system show satisfactory results of the power management and
the unity power factor at the utility side. Our research is about
the designing of a grid-tied solar photovoltaic system to meet he
energy needs of a residential building. The 285-Freshwater road
apartment’s building has been selected for the designing of solar
photovoltaic power system. As per the electric bill of a particular
apartment, the average 3-bedroom apartment in that building is
consuming 872 KWh monthly in winter. Detailed system
design, steady state modeling, dynamic modeling, control
system analysis and protection designing of this system is being
carried out in this research.
II. SYSTEM DESIGN
The basic site parameters that are required for the designing
a solar photovoltaic system are solar irradiance of that area,
average temperature of the location, available space on roof top,
local grid specifications and energy consumption of the
building. This specific location has solar irradiance of 3.15
KWh/m2/day and average temperature of 5.56C. This data has
been calculated with the help of a software named HOMER by
putting the location of this building. Total Available area on the
roof of this building is 763 m2 which can be used for the
installation of solar panels. The electric bill of an apartment
shows 872 KWh monthly consumption of an apartment in
winter. There are total 54 apartments in this building so the
average power consumption is calculated below.
Total hours in a month = 24×30 = 720 hours
Avg. hourly demand of 1 apartment = 872/720 = 1.2 KWh
There are also some double bedroom apartments in the
building so we can take this value as a round value of 1 KWh
per apartment.
Avg. hourly Electrical demand of the building = 1x54 = 54 KWh
The building space and water are heated by oil. The basic
components of a grid-tied solar power units are Solar panel,
Power Inverter, PV Combining box, PV roof-top mounting
frames, connection cables, lightening protection equipment,
grounding pit and Metering and protection panel. If there are
20% system losses then required solar panels are
Solar Panel required for system = 54/0.8 = 67.5 KW
The calculation shows that any value near to 67.5KW is good
for solar panel sizing to get required power after compensating
losses. Total 228 solar panels (390W each) are used in this
project making 12 strings of 19 panels each with a total
66.1KWp DC capacity of this system. There are also some
losses due to stationary solar panels but we are not considering
those losing for this calculation. The second most important part
of this system is PV inverter. Grid-tied solar inverter is always
selected as per the capacity of solar panels and voltage
specifications of local utility grid. SMA Sunny Tripower
CORE1 62-US Inverter is used in design because output
efficiency of this specific inverter is maximum in Helioscope.
This inverter provides 62KW at 208VAC, 60Hz so it is
compatible with local utility grid. Selected inverter has 6 MPPT
trackers with maximum current input capacity of 20A each and
every tracker has 2 DC input strings. The voltage range of MPPT
tracker is 550VDC-800VDC [8].
Total MPPT trackers of the inverter = 6
PV string input for each MPPT tracker = 2
Total string of inverter = 12
Total number of solar panels = 228
Solar panels for each string = 228/12 = 19
Open circuit voltage for each panel = 39.5VDC [9]
Total VOC of each string = 39.5 x 19 = 750.5VDC
There are total 12 input strings in this inverter and 19 solar
panels in series will be connected to each string making
750.5VDC each. Other calculations of wire size, PV combining
box size and steady state model of the system is carried out in
HelioScope software.”.
III. STEADY STATE MODELING OF THE SYSTEM
Helioscope is an online Solar PV designing and analysis tool
which provides steady state modeling data of the designed
system. System is designed after selecting freshwater apartment
building in Helioscope. The user just need to select desired PV
inverter, PV panels, location of the building, placement of solar
panels, and allowable wiring losses of the system in the software
then this will automatically calculate the required wires,
protection and control equipment for the system. Fig. 1 shows
the string connections and placement of solar panels on roof of
the selected building. Space between each row of solar panel is
selected 1.3 meter to avoid shading on any solar panel due to the
other row. The tilt angle of solar panels is set to 32 degrees
which is 15 degrees less than latitude to get the more output in
summer.
Fig. 1. Solar panel placement and connections
Fig. 2 shows the expected monthly production graph of the
system by considering solar irradiance and temperature factors
for selected location. This graphs clearly shows that the
maximum production is in summer (June, July) and minimum
production is in winter (Nov, Dec). This graph shows that
designed system can produce 77,432 KWh per year at selected
location.
Fig. 2. Monthly energy production of the system
Fig. 3 shows all the losses in the system including inverter
losses, wiring losses, shading losses, mismatch losses, solar
irradiance losses, soiling losses and reflection losses. There are
total 12.5% losses and the system are operating at 87.5%
performance ratio. There are no temperature losses in the system
because average temperature of the site is always within the
ambient temperature of the solar panels.
Fig. 3. System Losses
IV. DYNAMIC MODELING AND ANALYSIS
Dynamic modelling is conducted to study the effects of the
proposed system on the grid. Matlab software is used for
dynamic modeling of the designed system. The default solar
irradiance model of Matlab is attached to the PV block through
a rate limiter which converts the sudden change in irradiance
from 250 to 750 W/m2 into a slower buildup with a slope. This
setup is however unrepresentative of real irradiance which
varies every hour and does not follow a stable value throughout
time. Therefore, hourly irradiance for St. johns is obtained from
HOMER. The values are saved in an excel file. There is a total
of 8760 irradiance data points each representing 1 hour in a year.
The data is plotted in MATLAB as a variable named
“Irradiance”. Fig 3 shows the irradiance plot for the entire year.
This is accomplished by running the simulation for 87.6 seconds
and inputting the irradiance output through an integrator
(accumulator) block to obtain the energy density which in this
case is 1.15 *106 Wh/m2. By dividing (1000 * 365) using the
gain block, the average daily energy density is obtained as 3.15
kWh/m2-day which is the exact same value given by HOMER.
Fig. 4. Hourly irradiance plot
In the original Simulink file, the temperature block is a
constant block set at 25 degrees Celsius. Similarly, this approach
is unrepresentative of real temperature variation. Gaining the
real-time temperature data is accomplished by obtaining hourly
temperatures in St. john’s and compiling them into one variable
with 8760 data points (saved as temp). The panel used in this
project is not saved as an option in MATLAB so it is plotted
manually from the datasheet. A few values are changed in the
default inverter model to simulate the current project. The
nominal power of the inverter is set to 75000 VA. The DC
voltage is set 750 volts. The upper and lower output limits of the
inverter are set to 800 and 550 respectively as per the datasheet
of the inverter [11]. Perturb and Observe technique is used to
achieve MPPT in this system. Fig. 5 shows the dynamic
simulation design of the designed system in Simulink. The
numbers in yellow highlight show the results of 0.24 seconds
simulation time (first day of January).
Fig. 5. Simulated system in Simulink
Voltage sensors attached to an RMS block and a display at
multiple points show that the RMS voltage at the load is 119.9
volts while the line to line RMS voltage of the grid 239.9 volts.
The THD levels before and after the filter are shown in fig. 6.
Before the filter the maximum THD level was 1.85 however
after the filter this number dropped to zero.
Fig. 6. THD before filter (red) and after filter (green)
Fig. 7 shows Irradiance, mean DC voltage and mean DC
power of the PV module. It can be observed from fig. 7 that Dc
voltage and power output of the system is totally dependent on
solar irradiance. The power output of the designed system does
not change immediately after change in solar radiations. The
simulation results show that total irradiance for that day is 0.594
kWh/m2-day which is much less than January’s average of 1.28
kWh/m2-day. The energy of the PV panels is 39 kWh/day,
While the energy density is 0.104 kWh/m2-day making the
efficiency of the panel 17.5%. Fig. 8 shows the voltage and
current outputs of the installed inverter. The output voltage of
inverter is always stable but the value of current is changing as
per the availability of sunlight and electrical demand of the
building.
Fig. 7. Irradiance (top), Vdc mean (middle), Pdc mean (bottom)
Fig. 8. Voltage (top) and current (bottom) at the grid
V. CONTROL SYSTEM DSIGN AND ANALYSIS
A controller is designed to control the inverter based on
maximum power point tracking (MPPT) algorithms and pulse
width modulation (PWM). Normally, MPPT controller is used
to control DC-DC converters, in this design it is utilized to
generate modulating signal [12][13].
A. Inverter Control
The inverter, coupled to the PV source, converts DC power
from the PV to AC power. Inverter switches are controlled using
PWM. In order to generate an activation signal, the comparator
receives a modulating signal and carrier signal. The carrier
signal is a high-frequency triangular wave. The controlled
modulating signal is generated in order to achieve the desired
inverter output. Control stage obtain a sinusoidal modulating
signal with a frequency equal to the desired output frequency of
the inverter. Every time the carrier signal crosses the modulating
signal, the activation signal is toggled to the switches [14]. A
control system consists of four control loops that generate the
modulation signal: a phase lock loop (PLL), a maximum power
point (MPP) controller, a voltage controller, and a current
controller. Fig. 9 shows the desired circuit diagram of the
system.
Grid
Vs
LCL Filter
a
b
a'
b'
PV
Ipv Vpv
Vref
MPPT
Iref
i
DC Voltage
Controller
Current
Controller
PLL
Vs i
IPLL VPLL
PWM
Vdc
Ө
Vdc
Iref
dPWM
Fig. 9. Block diagram of the inverter control system
B. Incremental conductance algorithm for MPPT controller
Incremental conductance (IC) is another conventional two
sensors algorithm. The algorithm is illustrated in Fig. 10. The
basics for this algorithm are that (∂I/∂V) at MPP is zero, positive
when the voltage is below Vmpp, and negative when the voltage
is higher than Vmpp. Current and voltage are sampled in order
to calculate the value of (∂I/∂V). The MPPT regulates the PWM
control signal of the DC-AC inverter until the condition: (∂I/∂V)
+ (I/V) = 0 is satisfied [15].
Fig. 10. Flowchart of incremental conductance algorithm
C. Simulation results
Different solar irradiance values are entered into the solar
panel at 25 Degree Celsius to evaluate the designed system. The
most important dynamic simulation results are shown in fig 11
and fig. 12. Fig. 11 shows the relation between solar radiation
and power output of solar panels. Lower portion of Fig.11 shows
the comparison between actual output of solar panels and power
output of inverter. Fig.12 shows the voltage and current of local
grid after connecting the designed system to it.
Fig. 11. Solar irradiance & PV output.
Fig. 12. Grid voltage and current.
VI. COST ANALYSIS
System advisory model (SAM) software is used for cost
analysis of this system. Major equipment used in this system are
solar panels, grid-tied inverter and solar panel mounting frames.
The estimated prices of these items are mentioned in table 01. In
this table the balance of the system equipment means PV
combiner boxes, DC & AC breakers, solar PV cables, inverter
cables, grounding pits, surge arresters, fuse boxes and other
minor material required for installation. The prices of balance of
the system equipment, labor and installation overheads are
estimated by SAM software.
TABLE I. TABLE TYPE STYLES
Item
Price C$
Qty
Solar Panel
261 [18]
228
Inverter
6875 [19]
01
Mounting Frames
68 [20]
228
Balance of system equipment
23,823
01
Installation labor
26,470
01
Installation margin
66,174
01
A. Simulation results of cost analysis
SAM result shows that the total cost of this system is 211,283
CAD which seems to be a bit higher than the average solar PV
installation prices worldwide. SAM calculated the prices for
balance of the system equipment, installation labor and
installation margins by default. 52% of direct cost is considered
under sales tax and the sales tax rate is considered 5% which is
default by the software as per local region. The mortgage
setting for final cost analysis of the system is at 100 percent
debt fraction with loan terms of 25 years. Fig 13 shows the
summary of results calculated by SAM. The results in SAM
shows that this system will produce 78,789 KWh per year with
a performance ratio of 87 percent which is almost near to the
results calculated in Helioscope. The capacity factor of this
system is 13.6 percent and all investment will be recovered in
24 years in form of electric bill savings with the installation of
this system.
Fig. 13. Result summary of SAM
Figure 14 shows the demand and supply graph of the system
which shows that this system can provide a minor fraction of
power to the building and almost 8000 CAD per month can be
saved in electricity bill
Fig. 14. Demand and supply graph of the system
VII. CONCLUSION
The designed system will generate 77,432 KWH per year at
installation cost of 3.19 CAD per watt. The DC rated power of
the system is 66,120 watts so the total initial cost of the system
is approximately 211,000 CAD. As per the local electricity rates,
the pay-back period of this project is 24 years and the capacity
factor of designed system in 13.8 %. The results of the dynamic
simulation show that the PV efficiency is 17.5% with low
harmonics injected into the grid (after the filter) and rms voltage
are grid compatible at 119.9 VAC phase to neutral and 239.9
VAC phase to phase. Two models were proposed for irradiance
and temperature simulation and compared against hourly data in
which the results showed more than 90% similarity. The system
used P&O and IC algorithms for MPPT controller in inverter.
Protection devices are selected by considering Photovoltaic
protection standards. This system will reduce a significant
amount of electricity from the local grid, which eventually
contributes towards the environmental improvement.
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The development of Maximum Power Point Tracking (MPPT) techniques is continuing in order to increase the generated energy from photovoltaic (PV) generators. A variety of MPPT techniques have been proposed and classified based on three main categories: offline, online and hybrid techniques. This paper presents a review of the most popular techniques for offline and online tracking of the Maximum Power Point (MPP), which are the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Hill Climbing (HC) techniques, respectively. This is in addition to a review for all hybrid techniques reported in the literature demonstrating their main merits and shortcomings. Moreover, the present paper combines the ANFIS and HC as a hybrid technique for the first time. The proposed technique involves the features of the ANFIS and HC techniques and mitigates their shortcomings in order to increase the generated PV electrical energy. The proposed technique is a combination of two stages to assess the duty ratio (control signal) being applied to a boost converter for MPP tracking. The first stage includes a set point calculation loop to estimate the duty ratio. The second stage involves a fine tuning loop to determine the exact duty ratio corresponding to the MPP. This achieves maximum power transfer to the load even under nonuniform climatic conditions using a relatively simple control system. The proposed technique has been simulated in MATLAB/SIMULINK environment and compared with some other MPPT techniques (the Constant Voltage (CV), ANFIS, HC, Incremental Conductance (IncCond) techniques) for steady state and rapidly changing climatic conditions (Ropp and sine radiation tests) as well as load variations. The results reveal that the proposed hybrid MPPT technique outperforms other MPPT techniques in term of performances indicators, which include the tracking speed, tracking accuracy and energy gain factor.
Conference Paper
This paper presents a new centralized adaptive under frequency load shedding method. Occasionally, after initial frequency drop following severe disturbances, although the system frequency returns to its permissible value, however, the system might become unstable due to voltage problems. In this regard, the paper proposes a new centralized adaptive load shedding method to enhance the voltage stability margin (VSM) during under frequency conditions. Selection of loads to be shed in the proposed method depends on the loads' power factor, generators reactive power output as well as the location of the disturbance. The proposed method is implemented on the dynamic simulated model of the modified IEEE 30-Bus test system and is compared with the conventional approach to confirm the applicability and effectiveness.
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In this paper, a control strategy is proposed to achieve decentralized power management of a PV/battery hybrid unit in a droop controlled islanded microgrid. In contrast to the common approach of controlling the PV unit as a current source, in the proposed strategy, the PV unit is controlled as a voltage source that follows a multi-segment adaptive power/frequency characteristic curve. The proposed power/frequency characteristics, of the hybrid unit and of the whole microgrid, adapt autonomously to the microgrid operating conditions so that the hybrid unit may supply the maximum PV power, match the load, and/or charge the battery, while maintaining the power balance in the microgrid and respecting the battery state of charge (SOC) limits. These features are achieved without relying on a central management system and communications, as most of the existing algorithms do. The control strategy is implemented using multi-loop controllers, which provide smooth and autonomous transitions between the operating scenarios. Small-signal stability of the proposed control loops is investigated and the system performance is experimentally validated on a 3.5 KVA microgrid.
Conference Paper
The World is gradually shifting focus toward its renewable energy resource. Driven by an increasing demand for electricity and widening gap between demand and supply. World has target 800 GW of Renewable power by 2035, Without including Small Hydro power plant and has been enduring a pressing need to reduce its high and increasing greenhouse gas emission. Renewable Energy in the world day by day increases its installation capacity. Renewable electric energy demand has grown by an average 30% per annum over the past 20 years against a backdrop of rapidly declining costs and prices. By early 2010, policy targets for renewable energy at the national level existed in at least 85 countries worldwide, including all 27 European Union member states .Many national targets are for shares of electricity production, typically 5-30 percent, but range from 2 percent to 90 percent. By early 2010, more than two-thirds of the 85 countries with existing national targets were aiming for 2020 or beyond in some manner. Developing countries can virtually stabilize their C02 emissions by 2025 and reduce afterwards, whilst at the same time increasing energy consumption due to economic growth. OECD countries will be able to reduce their emissions by up to 90% by 2050.
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A combined grid-connection/power-factor-correction technique for a photovoltaic (PV) system is proposed in this letter. A maximum power point tracking dc/dc converter served as a charger for the battery bank. A bidirectional inverter is applied as a generator/discharger during daytime, supplying power to the load. The inverter can also be used as a charger to maintain the minimum required voltage level of the batteries when the PV power is insufficient. Experiments on a 1-kW PV system show satisfactory results of the power management and the unity power factor at the utility side.