i

ENG470 Engineering Honours Thesis

Wind-Solar Energy Integration including Battery Storage at Murdoch University Project Report

Yuvraj Singh

Unit coordinator: Dr. Gareth Lee

Supervisor: Dr. Ali Arefi, Mr. Craig Carter

ii

Declaration

I declare that this thesis is solely my own research and does not contain any content from

any previous studies conducted on the similar topic.

Signed:

Yuvraj Singh

iii

Abstract

This report demonstrates the process involved to identify the most economic renewable

energy generation system for Murdoch University (MU). The most economic renewable

energy generation system that would help in reducing the annual electricity consumption off

the grid for MU. The analysis involved PV System, Wind System, Wind-PV System and Wind-

PV System including the battery storage system. The analysis commenced with the

assessment of the availability of solar and wind resource at the MU. Western Australia gets a

significant amount of solar irradiance throughout the year and it was available to download

from the website of Bureau of Meteorology. The wind speed data were obtained from the

weather station located Murdoch University, which helped to determine the strength and

intensity of wind speed. For the purpose of the analysis, solar irradiance data and wind speed

data for the year 2015 was used for the specific reason, explained further in the report.

Murdoch University electricity consumption in the year 2015 was 22.29 GWh with the

maximum load of 5.78 MW. Mr Andrew Hanning, Energy Manager at MU helped to obtain

the load consumption data of MU. Then analysis followed by the identification of the energy

production potential of solar and wind using the photovoltaics and wind turbines. Microsoft

Excel and Homer, a computer software model helped to calculate the energy production of

photovoltaics and wind turbines. A 2.0 MW PV system was used for the analysis, as the study

conducted by the previous student concluded it to be the maximum size that could be

installed on the rooftop of MU. Wind system included two Enercon E-53 wind turbines each

with 800kW rated capacity. The selection of the wind turbine for the purpose of the analysis

was based on its maximum power output corresponding to the wind speed at MU. Enercon

E-53 was tallest among other wind turbines analyzed to identify to the most suitable wind

turbine for the proposed location at MU. Further, the analysis included the assessment of

reduction in the electricity consumption from the grid, of the MU for the year 2015, by

integrating different renewable energy generation system in the distributed network of MU.

From different combinations of renewable energy systems used for the analysis, the

combination of Wind-PV system produced the significant amount of reduction in the annual

energy consumption from the grid. The annual electricity consumption of MU reduced from

22.29 GWh to 13.31 GWh. Analysis also included the assessment of reduction in the capacity

and network demand charges affected by the decline in the annual electricity consumption

of MU from the grid. The Wind-PV system produced annual savings of $743,144 on the cost

of electricity consumed from the grid by MU and annual savings of $555,242 on the network

demand and capacity charges.

iv

Acknowledgments

I would like to thank my supervisors, Mr. Craig Carter and Dr. Ali Arefi for their continuous

support throughout my project.

I would also like to thank my wife and my parents for supporting me throughout my journey

to become an electrical engineer.

v

Table of Contents 1 Introduction ........................................................................................................................ 1

1.1 Aim .............................................................................................................................. 2

1.2 Murdoch University ..................................................................................................... 3

1.3 Previous Studies .......................................................................................................... 5

1.4 Software Used ............................................................................................................. 6

2 Resource Analysis ............................................................................................................... 7

2.1 Load Analysis ............................................................................................................... 7

2.2 Solar Analysis ............................................................................................................... 8

2.3 Wind Analysis .............................................................................................................. 9

2.4 Site Analysis ............................................................................................................... 12

3 Energy Production ............................................................................................................ 16

3.1 Wind Energy Production ........................................................................................... 16

3.2 Solar Energy Production ............................................................................................ 20

4 Economic Analysis ............................................................................................................ 22

4.1 Grid Only.................................................................................................................... 22

4.2 PV System Analysis .................................................................................................... 23

4.3 Wind System Analysis................................................................................................ 25

4.4 Wind and PV system.................................................................................................. 27

4.5 Wind-PV-Battery Storage System Analysis ............................................................... 28

4.6 Discussion .................................................................................................................. 30

5 Conclusion & Future Studies ............................................................................................ 31

5.1 Conclusion ................................................................................................................. 31

5.2 Future Studies ........................................................................................................... 32

6 References ........................................................................................................................ 33

Appendix 1 ............................................................................................................................... 36

Appendix 2 ............................................................................................................................... 37

Appendix 3 ............................................................................................................................... 46

vi

List of Tables

Table 1: Height above sea level ............................................................................................... 10

Table 2: Annual mean wind speed recorded from anemometer at Murdoch University. ...... 10

Table 3: Long-Term mean wind speed estimated at Jandakot Airport ................................... 11

Table 4: Rated output and hub height of wind turbines used for the analysis ....................... 14

Table 5: Terrain Description (A.L. Rogers, 2009) ..................................................................... 15

Table 6: Energy production calculated for different wind turbines using Power Curve

Polynomial. .............................................................................................................................. 17

Table 7: Total energy production for different wind turbines estimated in Homer ............... 18

Table 8: General specifications of Enercon E-53 ..................................................................... 19

Table 9: Average commercial solar system prices per watt. (Limited, 2017).......................... 21

Table 10: Cost of electricity bought from grid. ........................................................................ 22

Table 11: Annual charges that incorporates grid cost. ............................................................ 23

Table 12: New cost of buying electricity from the grid. .......................................................... 23

Table 13: Cost Summary of the PV system .............................................................................. 23

Table 14: Energy production of the PV-Grid System ............................................................... 24

Table 15: Calculate new capacity and network demand charges ........................................... 25

Table 16: Cost summary of the grid-connected wind system. ................................................ 25

Table 17: Total energy production of the Wind system .......................................................... 26

Table 18: Calculated new capacity and network demand charges. ........................................ 26

Table 19: Cost Summary of the grid-connected Wind and PV system .................................... 27

Table 20: New calculated annual capacity and network charges ............................................ 27

Table 21 General properties of Gildemeister Vanadium Flow Battery.: ................................. 28

Table 22: Cost summary of the Wind-PV-Battery Storage System.......................................... 29

Table 23: New capacity and demand charges ......................................................................... 29

Table 24: Economic analysis of different renewable energy generation systems. ................. 31

vii

List of Figures

Figure 1: Decline in commercial solar prices from year 2014-2017 (Limited, 2017) ................ 2

Figure 2: Murdoch University south street campus .................................................................. 3

Figure 3: Electricity bill breakdown of MU ................................................................................ 4

Figure 4: Electricity bill breakdown (Energy I. , 2015) ............................................................... 4

Figure 5: Annual energy consumption of MU ............................................................................ 7

Figure 6: Comparison between the Off peak and Peak load of MU .......................................... 8

Figure 7: Monthly mean solar irradiance fallen at MU in year 2015......................................... 9

Figure 8: Annual wind speed recorded from anemometer at Murdoch University................ 11

Figure 9: Inter-Annual wind speed variations recorded at Jandakot Airport .......................... 12

Figure 10: Murdoch University south street campus .............................................................. 12

Figure 11: General rule of minimum tower height. (Geoff Stapleton, 2013) .......................... 13

Figure 12: Power curve polynomial. ........................................................................................ 16

Figure 13: Power curve of the wind turbine (Homer Energy, n.d.) ......................................... 17

Figure 14: Capacity factor calculated in Homer ....................................................................... 19

Figure 15: Power curve of Enercon E53 wind turbine. ............................................................ 20

Figure 16: Comparison between the existing grid energy consumption and new grid

consumption ............................................................................................................................ 24

Figure 17: Comparison between the existing grid energy consumption and new grid

consumption ............................................................................................................................ 27

Figure 18: Comparison between the existing grid energy consumption and new grid

consumption ............................................................................................................................ 28

Figure 20: PV system analysed in Homer................................................................................. 37

Figure 21: Cost summary of the PV system calculated in Homer. ........................................... 38

Figure 22: Payback period ........................................................................................................ 38

Figure 23: Electricity Production of PV system. ....................................................................... 39

Figure 24: Cost summary of Wind system. .............................................................................. 40

Figure 25: Payback Period of Wind System. ............................................................................ 40

Figure 26: Energy production of wind system. ........................................................................ 41

Figure 27: Wind-PV system analysed in Homer ....................................................................... 41

Figure 28: Cost summary of Wind-PV system ......................................................................... 42

Figure 29: Payback period ........................................................................................................ 42

Figure 30: Energy production of Wind-PV system ................................................................... 43

Figure 31: Wind-PV-2.48MWh Battery storage system........................................................... 43

Figure 32: Cost summary of Wind-Pv-2.48MWh battery storage system. .............................. 44

Figure 33: Payback period. ....................................................................................................... 44

Figure 34: Energy production of Wind-PV-2.48MWh battery storage system. ...................... 45

viii

Acronyms

MU Murdoch University

PV Photovoltaics

NPC Net Present Cost

DC Direct Current

AC Alternating Current

AEMO Australian Energy Market Operator

MS Microsoft

RET Renewable Energy Target

CMD Contracted Maximum Demand

REGS Renewable Energy Generation Systems

BOM Bureau of Meteorology

1

1 Introduction

Renewable energy is produced using natural resources that constantly replenish and never run out. There is a number of natural resources such as solar energy, Wind energy, Hydropower, Biogas, Ocean energy etc. used to generate electricity. Generating electricity from renewable resources is increasing significantly, which is initiated by different reasons. Carbon emission is one of the reasons contributing towards the growth of electricity generation from renewable resources. The current source of generating electricity in Australia is primarily coal, which emits a large amount of carbon into the atmosphere. The carbon emission level of Australia in 2005 was 605 Mt CO2-e which decreased by 9.1 percent for year to March by 550Mt CO2-e but the quarterly results between 1990 and 2016 shows that emissions from electricity have had the largest growth, dumping 59.5 megatons into the atmosphere, an increase of 49.2 percent (Department of Environment and Energy, 2017).

Australian Government has imposed climate change policies such as The Clean Energy Act legislated in 2011, which established long-term goals to reduce the emission to 80 percent below 2000 levels by 2050 (Climate Change Authority, n.d.). Australian Government has also introduced Renewable Energy Target (RET) in the electricity sector in 2001 to mitigate the emission of carbon into the atmosphere. The RET target was split into two schemes, the Large-scale Renewable Energy Target (LRET) that supports large-scale projects and the Small-scale Renewable Energy Scheme (SRES) that supports the installation of small-scale systems. This scheme creates a financial incentive for the establishment and the growth of the renewable energy power stations. The primary objective of the RET is to source two percent of Australia’s electricity generation from renewable sources (Austrlalian Government, Clean Energy Regulator, n.d.). A number of business, schools, and organizations have taken the advantage of RET scheme and have or are installing the solar PV systems utilizing their roof space as Australia gets a significant amount of solar energy throughout the year, which has been very advantageous. University of Queensland, St Lucia campus has installed a rooftop (2.3 MW) PV system, one of the largest integrated installation in Australia (University of Queensland, 2017). Charles Sturt University in New South Wales, Wagga Wagga campus installed 1.77 MW rooftop PV system incorporating 6000 PV panels, roughly the equivalent of powering 400 typical Australian Households (Charles Sturt University, 2017). Murdoch University has also taken initiative to take the advantage of RET Scheme to generate electricity onsite from renewable sources. Generating electricity on University campus from renewable sources would offer a reduction in the electricity consumption off the grid that could benefit the savings on the cost of electricity bought from the grid and at the same time would offer the reduction in the carbon emissions. However, there is few number of integrated renewable energy systems that include small-scale wind turbines. Piney Lakes, City of Melville, Perth, Western Australia house the 12kW grid connected integrated system including 5kW wind turbine and 7kW PV system. The system also incorporates a battery storage system to store energy from the renewable resources for use later (City of Melville, 2017). Moreover, the cost of commercial solar prices has reduced over the years in Australia (Australian Energy Resource Assesment, 2009). Energy storage is a rapidly developing sector; battery storage offers the storage of excess electricity generated by renewable resources for use later. Vanadium flow batteries have grown in demand over the years in Australia and offer significant benefits. Vanadium Redox Flow Batteries employ vanadium ions in different oxidation states to store chemical potential energy (Australian Vanadium Limited , 2017). The Vanadium batteries, also

2

referred to as cell cubes come in different sizes. A 100kWh cell cube containerized Vanadium flow battery is deployed in Busselton, Western Australia. The cell cube, installed along with 15kW PV system and provides the benefit of storing the excess energy generated by PV system ( VSun Energy, 2017). Figure 1 shows the decline in the commercial solar prices in Australia from 2014-2017. As a result, generating electricity from renewable energy has increased in Australia over the years which has made Renewable energy sector more competitive and has made available, better and cheaper technologies in the market to generate electricity from the renewable sources.

Figure 1: Decline in commercial solar prices from the year 2014-2017 (Limited, 2017)

1.1 Aim The primary aim of the project is to identify an optimum onsite renewable energy power

generation system for Murdoch University (MU) that can operate in conjunction with the grid.

The specific objectives involved in the project are listed below:

1. The analysis includes analyzing the availability of solar and wind sources at MU.

2. Estimating the energy production of different wind turbines at MU.

3. Analysing the total energy production of the different renewable energy generation

system such as PV, Wind, Wind-PV and Wind-PV-Battery systems.

4. Estimating the reduction in the total energy consumption of MU from the grid that

renewable energy generation systems can offer.

5. Conducting the cost-benefit analysis to identify the most economic renewable energy

generation system.

Calculate the reduction in the capacity and network demand charges with the

inclusion of the renewable energy generation system into the distributed network of

MU.

3

1.2 Murdoch University

Murdoch University South Street Campus is located in the Perth suburb of Murdoch, Western

Australia, 15km south of the Perth and 5km east of Fremantle.

Figure 2: Murdoch University south street campus

Murdoch University has high electricity demand with the maximum peak load of 5.78 MW

satisfied primarily by the electricity supplied by the grid. Due to high-electricity consumption,

the University pays high electricity bills. In addition to the cost of buying electricity from the

grid, University’s electricity bill includes a significant amount of capacity and network demand

charges. Figure 3 shows the primary components of electricity bill of MU. Capacity charges

contribute 28 percent of the total electricity bill, which is charged by the Australian Energy

Market Operator (AMEO) based on the maximum annual peak load of the MU. AEMO makes

sure that the required capacity is available throughout the year to serve the annual load

demand of MU. Whereas the network demand charges are, the transportation charges

charged to MU by the Western Power based on the Contracted Maximum Demand (CMD)

under the particular tariff. It contributes 19 percent of the total electricity bill of the MU. The

CMD is expressed in Kilo Volt Amperes (KVA), which comprise of three different charges; fixed

demand charges, variable demand charges and a variable demand length charge calculated

by multiplying the demand length price by the electrical distance to the zone substation by

the CMD. The zone substation is the nearest substation from where the electricity is

transported to serve the electricity consumption of MU.

4

Figure 3: Electricity bill breakdown of MU

According to the infinite energy, Australia’s solar power systems installers; the network

demand and capacity charges are the dominant components of the electricity bill, which can

be seen in Figure 4 indicating the percentages of different charges that make up the electricity

bill. It shows that charges are higher to transport the electricity via Western Power's

network of poles and wires (38.4%) than it costs to produce the electricity at the power

generation station (33.3%) (Energy I. , 2015). However, the exact contribution of all the

relevant charges would depend upon the individual client’s electricity consumption.

Figure 4: Electricity bill breakdown (Energy I. , 2015)

Capacity Charge

28%

Network Charge

19%

Energy Charge53%

ELECTRCITY BILL BREAKDOWN

Peak Demand capacity

22%

Energy33%

Renewable Energy Target Costs

4%

Ancillary Services2%

Market Fees1%

Network Charges38%

ELECTRCITY BILL BREAKDOWN

5

1.3 Previous Studies Identifying an optimum renewable generation system for a particular geographical location

depends on various aspects such as availability of renewable sources, reliable technology, and

the existing load demand. Research to obtain an optimum renewable energy system is

undergoing at different places for different reasons. Several previous studies were analyzed

to get an idea of the process involved to identify an optimum renewable energy systems as

follows:

The study conducted by Sami Alhusayni on the Cost-Benefit Analysis of PV and Storage System

with the perspective to install at Murdoch University (MU) provides detail insight of the

process used to analyze the optimum PV system. The analysis primarily performed in HOMER,

to analyze the economic benefit of the PV system. The analysis involved, identification of an

optimum size of the PV system for MU utilizing its rooftop. The analysis concludes that it

would be cost-effective to install 2MW PV system facing North, on the rooftop of the

university campus. Installing the 2MW PV system would offer a significant reduction in the

annual electricity consumption from the grid. However, the analysis does not include a

reduction in the reserve capacity and network demand charges. Potential of installing the

battery storage at the campus disregarded in the analysis due to high prices of the battery

storage systems and as the proposed PV system just have the ability to offset the peak load

demand.

Another research paper presented the strategy used to analyze the optimization of a power

system consisting of wind and solar systems including the battery storage for optimal

matching of supply and demand at a particular location. The study purposed the methodology

to determine the optimal power flow from a battery storage system, the optimal combination

of wind and solar systems for the selected battery storage system and finding the optimal

capacity of battery storage system. The study also demonstrates the benefits of using battery

storage systems in conjunction with the wind and solar systems. The solar photovoltaic

energy system cannot provide reliable power during non-sunny days. Similarly, wind system

cannot satisfy constant load demands due to significant fluctuations in the magnitude of wind

speeds from hour to hour throughout the year. Hence, the output power of wind turbines

fluctuates making the wind power non-dispatchable. Furthermore, they can cause frequency

deviations and power outage particularly when wind power penetration is significant, i.e.,

when there is a large amount of wind power into the distributed network. Therefore, energy

storage systems will be required for each of these systems in order to satisfy the power

demands (Muhammad Khalid, 2015).

6

1.4 Software Used HOMER is an Optimization Model used for simulation purposes of different Renewable Energy Generation Systems (REGS). HOMER models a renewable energy power system’s physical behavior and its life cycle cost, the total cost of installing and operating the system over its lifespan. HOMER allows the comparison between different design options such as Grid only, PV, Wind, Wind-PV systems including the battery storage system. HOMER performs three principal tasks: simulation, optimization, and sensitivity analysis. In the simulation process, HOMER models the performance of a different renewable energy generation systems configuration, every thirty minutes of the year to determine its technical feasibility and life-cycle cost. However, it can be used to compute the performance of the REGS for the different time intervals as well. In the optimization process, HOMER simulates many different system configurations in search of the one that satisfies the technical constraints at the lowest life-cycle cost. In the sensitivity analysis process, HOMER performs multiple optimizations under a range of input assumptions to gauge the effects of uncertainty or changes in the model inputs. Optimization determines the optimal value of the variables over which the system designer has control such as the mix of components that make up the system and the size or quantity of each. Sensitivity analysis helps assess the effects of uncertainty or changes in the variables over which the designer has no control, such as the average wind speed or the future fuel price.

7

2 Resource Analysis

2.1 Load Analysis Two different meters HVMSR1 and HVMSR2 record the total electricity consumption of MU

for each 30-minute time interval. Instead of analyzing the load data from two meters

separately, the combined load data of both meters was used for the analysis. The load data

for the year 2015 used for the analysis was provided by Mr. Andrew Hanning, the energy

manager at MU. Load data for the year 2015 was preferred over the load data for the year

2016 as one of the transformers in the distributed network of MU malfunctioned in 2016 for

some reason, which affected the overall consumption of electricity from the grid. Murdoch

University consumed 22.29 GWh energy from the grid in the year 2015. Figure 5 shows the

annual energy consumption of MU from the grid recorded every 30-minute time interval. It

shows the high-energy consumption during the Feb-March.

Figure 5: Annual energy consumption of MU

Figure 6 shows the comparison between off-peak and peak time load of MU recorded every

30-minute time interval. The peak time of the MU is between 8 am to 10 pm, off-peak, time

is between 10 pm to 8 am from Monday-Friday, and it is off-peak time on Saturday-Sunday.

The maximum peak load of University in recorded in the year 2016 was 5.78 MW with the

base load of 2.5 MW. The maximum load was during Feb-March as it is a time when the

semester starts and surprisingly it was during the off-peak time although the maximum load

consumption is typical during general working hours. University had high electricity

consumption during March, which declined significantly in the following month than

remained consistent throughout the remaining year, shown in figure 6. The University had

44% load factor for the year 2015, the high load factor shows the steady consumption of the

University. Full calculations are shown in Appendix 1.

0

0.5

1

1.5

2

2.5

3

3.5

An

nu

al E

ner

gy C

on

sum

pti

on

(M

Wh

)

Time of the year

Annual Energy Consumption of MU

8

𝐿𝑜𝑎𝑑 𝐹𝑎𝑐𝑡𝑜𝑟 =𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛

𝑃𝑒𝑎𝑘 𝐿𝑜𝑎𝑑 × 𝐷𝑎𝑦𝑠 × 24

Figure 6: Comparison between the Off-peak and Peak load of MU

2.2 Solar Analysis Electricity generation from Photovoltaics (PV) depends upon the total solar energy falling on

the earth’s surface as they convert the solar energy into useful electrical energy. The solar

energy is variable in nature as the amount of solar energy falling on earth surface varies

seasonally and on a daily basis. The average amount of annual solar irradiance that falls in

Perth is around 2000kWh/m2/year (Alhusayni, 2017). As the load data of MU for a year, 2015

was used for the analysis that is why the solar irradiance data of the year 2015 of MU was

taken into consideration for the analysis. Figure 7 shows the annual solar energy fallen at MU

during the year 2015 Monthly mean solar irradiance for the year 2015 fallen on MU is

available to download from Bureau of Metrology (BOM) website (Meteorology, 2018). Solar

energy had high mean value during the summer (September – March) but started to decline

as the weather started to get cooler, between (April-August).

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

30

min

ute

Lo

ad C

on

sum

pti

on

(M

W)

Peak Time Load Vs Off Peak Time LoadPeak Time Load

Off Peak Time LoadPeak Load 5.78MWBase Load 2.5MW

9

Figure 7: Monthly mean solar irradiance fallen at MU in the year 2015

2.3 Wind Analysis In general, the wind is a motion of the air and the Sun is the primary source of the energy

contained in the wind. The airflows from high to low-pressure areas due to variations in the

atmospheric pressure caused by the uneven heating of the earth by the solar radiations. In

Australia, high and low-pressure systems pass from west to east over the continent. In the

warmer months (November – April), low-pressure systems predominate over the continent

and bring cyclones to the topics and southeasterly trade winds and sea breezes to the higher

altitudes. In the cooler months (May – October) high-pressure are predominant and these

bring south-easterly trade winds to the tropics and strong westerly winds to the southern

parts of the continent (Berril, 2004)

Wind resource analysis for this study commenced with an assessment of the availability of

wind speed at the MU. Wind speed data recorded at the weather station located at MU

helped to determine the variability and intensity of the wind speeds at the site. The weather

station on the MU campus records different weather elements and associated parameters

such as wind speed, wind direction, rainfall, solar radiation etc. (Weather Station, 2017). Wind

speed data for the year 2015 recorded at 10-minute intervals was obtained from MU weather

station. MU is 22.260 m above sea level whereas the anemometer used to record the wind

speed data is installed on a hill at 29.952 m above sea level. The height of the anemometer

mast is 10m from its reference height. The proposed location for installing wind turbines is at

the same elevation as MU, which is 22.260 m. Therefore, the effective height of the

anemometer is 17.692 as it includes the additional 7.692m of elevation of the 10m

anemometer mast provided by the hill. Table 1 shows the difference between height above

sea level for proposed site at MU and base of the anemometer mast.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sola

r Ir

rad

iacn

e (k

Wh

/m2

/day

Year 2015

Monthly Average Daily Solar Irradiance at Murdoch University

10

Height Above Sea Level (m)

Proposed site of wind turbines at MU 22.26

Base anemometer mast 29.952

Difference 7.692 Table 1: Height above sea level

Annual mean wind speed for the year 2015 recorded from the anemometer at MU was

calculated using the following formula; series of N wind speed observations,𝑈𝑖, each averaged

over the time interval ∆𝑡. Wind speed is measured in m/s.

𝑈 =

1

𝑁∑ 𝑈𝑖

𝑁

𝑖=1

(1)

𝑈 = the long-term mean horizontal wind speed (m/s)

𝑁 = series of wind speed observations

𝑈𝑖 = horizontal wind speed averaged over the time interval

From the data, the calculated annual mean wind speed is 5.36 m/s at the MU campus shown

in Table 2.

Months Monthly mean wind speed (m/s)

Jan 6.10

Feb 5.60

Mar 5.64

Apr 5.80

May 4.65

Jun 5.00

Jul 4.52

Aug 5.16

Sep 5.23

Oct 5.01

Nov 5.63

Dec 6.02

Annual Mean Wind Speed 5.36 Table 2: Annual mean wind speed recorded from anemometer at Murdoch University.

Figure 8 shows the variations in the monthly mean wind speed data of the year 2015 recorded

from the anemometer at MU, showing the variable nature of the wind energy. It shows that

wind speed recorded between Nov-April was high as compared to other months as it started

to decline in the following months with the wind speed recorded between the May-July being

the lowest.

11

Figure 8: Annual wind speed recorded from anemometer at Murdoch University.

Wind speed data recorded at the nearest Meteorological Bureau weather station located at

Jandakot Airport only 8kms from the MU was also obtained for correlation with the wind

speed data recorded at MU weather station. The long-term mean wind speed at the Jandakot

location calculated using the formula 1 for wind speed data of past 9 years is 4.24m/s, shown

in table 3. The wind speed for Jandakot airport obtained from BOM was recorded at three-

hour intervals. The mean annual wind speed for 2014 and 2015 matches the 9 years mean

wind speed (4.24m/s) recorded at Jandakot Airport that is why wind speed data of the year

2015 recorded at MU weather station used for the analysis as it represents the average windy

year. Statistically, one-year data is generally sufficient to predict the long-term seasonal

mean wind speed. (A.L. Rogers, 2009).

Year Mean Wind Speed (m/s)

2008 4.19 2009 4.33 2010 3.96 2011 4.39 2012 4.21 2013 4.15 2014 4.24 2015 4.24 2016 4.43

Long-Term Mean Wind Speed 4.24 Table 3: Long-Term mean wind speed estimated at Jandakot Airport

Figure 9 shows the inter-annual variations over the time scales greater than one year of wind

data recorded at Jandakot Airport, Weather station.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Win

d S

pee

d (

m/s

)

Year 2015

Monthly mean wind speed (m/s)

12

Figure 9: Inter-Annual wind speed variations recorded at Jandakot Airport

2.4 Site Analysis In general, the elevated site or open lands where winds are unimpeded by trees and buildings

is preferred as this is where wind turbines generate more energy but the proposed location

to install wind turbines at MU campus is shown in figure 10 highlighted as a red circle has

relatively rough terrain. The proposed location has a large number of Pinus Pinaster and

Eucalyptus marginata. The average height of the Pinus Pinaster and Eucalyptus marginate

trees is 20m but the height of mature Pinus Pinaster and Eucalyptus marginate can be up to

20-35 m tall.

Figure 10: Murdoch University south street campus

3.70

3.80

3.90

4.00

4.10

4.20

4.30

4.40

4.50

2008 2009 2010 2011 2012 2013 2014 2015 2016

Mea

n W

ind

Sp

eed

(m

/s)

Years

13

Turbulence due to rough surfaces around or isolated obstacles such as trees or buildings can

slow down the wind speed. The contour Maps for MU is provided in Appendix 3. Surface

roughness interferes with the smooth flow of the air slowing the wind speed close to the

ground. The influence of this effect decreases with the increasing height, producing the

vertical wind speed profile. The increase in wind speed with height defines the phenomenon

of wind shear. So it is required to install the wind turbines at the purposed location at

sufficient height to access the stronger and less turbulent airflow above the canopy. A general

rule for minimum tower height is that the bottom of the turbine rotor, or blades, should be

at least 10m above the tallest obstruction within 150m or the nearby prevalent tree height.

For trees, this means the mature tree height over the 20– 30-year life of the turbine, not the

current tree height (Geoff Stapleton, 2013).

Effectively, this means the minimum tower height is:

(Height of tallest obstacle within 150m) + (10m buffer) + (length of blade of selected wind system)

Figure 11: General rule of minimum tower height. (Geoff Stapleton, 2013)

10m

14

As explained in wind source analysis, wind speed increase with the increase in height so the

wind speed at a hub height of the wind turbines was calculated to determine wind energy

production of each wind turbine. Table 4 shows the rated output and standard hub height of

the different wind turbines used for analysis.

Wind Turbines Rated Output (kW) Hub Height (m)

EO25 25 30

Norvento nED100 100 36

Vergnet 32-m 275 32

Vergnet 55-m 275 55

Wind Flow 45 500 38

EW DW 54 900 50 Enercon E53 800 73

Vestas V82 1,650 70 Table 4: Rated output and hub height of wind turbines used for the analysis

There are two different mathematical models or laws generally used to predict the variation

in the wind speed with elevation above the ground, Logarithmic Law, and Power Law. For

purpose of this report, Logarithmic Law (Log Law) is used to calculate mean wind speed over

the MU at different hub heights of the wind turbines, as follows:

𝑈ℎ𝑢𝑏 = 𝑈𝑎𝑛𝑒𝑚 ∗ln (

𝑍ℎ𝑢𝑏

𝑍𝑜)

ln (𝑍𝑎𝑛𝑒𝑚

𝑍𝑜)

(2)

𝑈ℎ𝑢𝑏 = the wind speed at hub height of the wind turbine (m/s)

𝑈𝑎𝑛𝑒𝑚 = the wind speed at anemometer height (m/s)

𝑍ℎ𝑢𝑏 = the hub height of the wind turbine (m)

𝑍𝑎𝑛𝑒𝑚= anemometer height (m)

𝑍𝑜 = the surface roughness length (m)

ln (… ) = the natural logarithm

The proposed location to install wind turbines as mentioned in the site analysis is surrounded

by a large number of trees so a surface roughness length of Z0=250 mm was taken into

consideration. The approximate surface roughness lengths for various terrain types are

shown in table 5.

15

Terrain Description Zo(mm)

Very Smooth, ice or mud 0.01

Calm open Sea 0.2

Blown Sea 0.5

Snow Surface 3

Lawn Grass 8

Rough pasture 10

Fallow field 30

Crops 50

Few Trees 100

Many Trees, hedges, few buildings 250

Forest and woodlands 500

Suburbs 1500

Centers of cities with tall buildings 3000 Table 5: Terrain Description (A.L. Rogers, 2009)

In comparison to the Log law, the power law represents a simple model for the vertical wind

speed profile.

𝑈ℎ𝑢𝑏

𝑈𝑎𝑛𝑒𝑚= (

𝑍ℎ𝑢𝑏

𝑍𝑎𝑛𝑒𝑚)

𝛼

(3)

𝑈ℎ𝑢𝑏 = the wind speed at hub height of the wind turbine (m/s)

𝑈𝑎𝑛𝑒𝑚 = the wind speed at anemometer height (m/s)

𝑍ℎ𝑢𝑏 = the hub height of the wind turbine (m) = the range given in table 4

𝑍𝑎𝑛𝑒𝑚= anemometer height (m) = 17.692 m

𝛼 = the power law exponent

The value of the power law exponent depends upon the parameter such as elevation, time of

the day, season, and nature of the terrain, wind speed, temperature and various thermal and

mechanical mixing parameters. So due to the complexity of the power law to determine the

exact power law exponent, the Logarithmic law was used to determine the wind speed at

different hub heights.

16

3 Energy Production

3.1 Wind Energy Production The kinetic energy of the wind determines the power and the energy in the wind. The power

in the wind largely depends on the cube of the wind speed (Berril, 2004). The specific wind

power defined as follows:

𝑃0 = 0.5 × 𝜌 × 𝑣3

𝑃0 = the specific wind power (W/m2)

𝜌 = the density of air (kg/m3). The density of air is around 1.225kg/m3 at sea level

𝑣 = the velocity of the air (m/s)

For a given area, such as the swept area by a wind turbine, the power in the wind is simply:

𝑃 = 𝑃𝑜 × 𝐴

Where

P = the power in the wind, (W)

𝑃0 = specific wind power, (W/m2)

𝐴 = the capture area, (m2)

Energy production of the different wind turbines shown in Table 4 was analyzed to identify

the most suitable wind turbine corresponding to the wind speed at MU. The Power Curve

Polynomial spreadsheet was used to calculate the energy production of all the different wind

turbines. It was used in one of the renewable energy units during the study course to calculate

the energy production of a particular unit. Figure 11 shows the Power Curve Polynomial fit of

the particular wind turbine. However, each wind turbine used for the analysis had different

power curve based on its rated capacity.

Figure 12: Power curve polynomial.

y = -1.2332x6 + 94.622x5 - 2743.9x4 + 37981x3 -261006x2 + 903584x - 1E+06

R² = 1

0

100000

200000

300000

400000

500000

600000

700000

800000

0 5 10 15 20

Win

d T

urb

ine P

ow

er

Ou

tpu

t (k

W)

Wind Speed (m/s)

Power Curve (3 to 14m/sec)

Series1

17

The energy production of different wind turbines was calculated using the Power Curve Polynomial spreadsheet is shown in table 6:

Wind Turbines Rated Output (kW) Mean Output (kW) Energy Production (kWh)

EO25 25.0 7.9 69,389

Norvento nED100 100.0 38.9 340,913

Vergnet 32-m 275.0 67.5 590,924

Vergnet 55-m 275.0 86.9 761,348

Wind Flow 45 500.0 138.7 1,215,021

EW DW 54 900.0 281.6 2,466,456

Enercon E53 800.0 321.8 2,819,372

Vestas V82 1,650.0 630.4 5,522,679 Table 6: Energy production calculated for different wind turbines using Power Curve Polynomial.

At the same time, Homer a computer model was used to calculating the energy production

of each wind turbine listed in table 4. Homer refers to the wind turbine's power curve to

calculate the expected power output from the wind turbine at that wind speed under

standard conditions of temperature and pressure. In figure 12, the red dotted line indicates

the hub-height wind speed, and the blue dotted line indicates the wind turbine power output.

If the wind speed at the turbine hub height is not within the range defined in the power curve,

the turbine will produce no power. This follows the assumption that wind turbines produce

no power at wind speeds below the minimum cut-off or above the maximum cut out wind

speeds (Homer Energy, n.d.).

Figure 13: Power curve of the wind turbine (Homer Energy, n.d.)

Later Homer applies the density correction formula to estimate the wind turbine, power

output. Power curves typically specify wind turbine performance under conditions of

standard temperature and pressure (STP). To adjust to actual conditions, HOMER multiplies

the power value predicted by the power curve by the air density ratio, according to equation

4.

18

𝑃𝑊𝑇𝐺 = (𝜌

𝜌𝑜) × 𝑃𝑊𝑇𝐺,𝑆𝑇𝑃 (4)

𝑃𝑊𝑇𝐺 = the wind turbine power output (kW)

𝑃𝑊𝑇𝐺,𝑆𝑇𝑃 = the wind turbine power output at standard temperature and pressure (kW)

𝜌 = the actual air density (kg/m3)

𝜌𝑜 = the air density at standard temperature and pressure (1.225kg/m3)

Table 7 shows the total energy production calculated in Homer, it is less than the total energy

production calculated using Power Curve Polynomial by 1%. The difference is due to density

correction method followed by the Homer to calculate the wind energy production.

Wind Turbines Rated Output (kW) Mean Output (kW) Energy Production (kWh)

EO25 25.0 7.8 68,078.0

Norvento nED100 100.0 38.5 337,169.0

Vergnet 32-m 275.0 66.3 580,576.0

Vergnet 55-m 275.0 86.1 754,291.0

Wind Flow 45 500.0 137.0 1,198,052.0

EW DW 54 900.0 279.0 2,441,500.0

Enercon E-53 800.0 318.0 2,785,680.0

Vestas V82 1,650.0 619.0 5,423,698.0 Table 7: Total energy production for different wind turbines estimated in Homer

The wind energy production calculated in Homer used for the analysis purposes as it is based

on the actual air density, standard temperature, and pressure. Wind turbine with the highest

capacity factor had an advantage over the other wind turbines as it produced highest average

output against the wind speed at the MU. Few wind turbines produced high capacity factors

such as Norvento nED100, Enercon E-53, and Vestas V82. Norvento nED100 is only 100kW

wind turbine and more than 10 wind turbines are required to match the energy production

of other wind turbines with high rated output and it would require more land for the

installation so that is why Norvento nED100 excluded from the consideration. Vestas V82 did

not produce required capacity factor in comparison to its rated output so Vestas V82 was not

taken into consideration. Enercon E-53 had the advantage over the other wind turbines

because of its highest capacity factor, as it would be required in low number to produce the

energy production. Any structure above 30 meters or more above the ground within 30 km

of the aerodrome must be notified to Royal Australian Air Force (RAAF) as they maintain all

the database of tall structures in the country. Enercon E-53 being a 73 m tall it is also required

to notify Civil Aviation Security Authorities (CASA) as it is likely to create an obstacle for

aviation services at Jandakot Airport.

19

The following formula used to calculate the capacity factor of different wind turbines.

𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 =

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑜𝑤𝑒𝑟 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑤𝑖𝑛𝑑 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠

𝑅𝑎𝑡𝑒𝑑 𝑃𝑜𝑤𝑒𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑤𝑖𝑛𝑑 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 (5)

Figure 14 shows the comparison between capacity factors of different wind turbines

calculated in Homer.

Figure 14: Capacity factor calculated in Homer

Enercon E53 with the rated capacity of 800kW had the highest capacity factor among the

other wind turbines with respect to the energy production, corresponding to the annual

wind speed at the site. The following table shows the general specifications of the Enercon

E53 including its calculated annual energy production as well its capacity factor.

General Specifications

Hub Height (m) 73

Rated Output (kW) 800

Number of Blades 3

Rotor Diameter (m) 53

Swept Area m2 2,198

Total Energy Production (kWh/yr) 2,785,680

Capacity Factor (%) 40% Table 8: General specifications of Enercon E-53

31

%

38

.5%

24

%

31

%

27

% 31

.00

% 39

.8%

37

.5%

1 2 3 4 5 6 7 8

PER

CEN

TAG

E O

F C

AP

AC

ITY

FAC

TOR

WIND TURBINES

CAPACITY FACTOR

Capacity Factor Homer

20

Figure 15: Power curve of Enercon E53 wind turbine.

The wind turbine produced highest capacity factor than other wind turbines. The cut in wind

speed of the turbine is 3m/s, the speed at which the turbine starts to rotate and generate

power. The amount of electrical power generated by wind turbines depends upon the wind

speed at a particular site as the wind speed increases the level of electrical power generated

by wind turbine start to rise as well. The following figure shows the Power Curve of the

Enercon E-53 indicating its electrical power output at different wind speeds. The wind turbine

produces its maximum rated output power at the wind speed of 14m/s also referred as rated

output wind speed. As the wind speed increases above its rated output wind speed, it

enforces pressure on the wind turbine blades. At a wind speed of 28-34m/s, a cutout wind

speed, the braking system becomes active to prevent any damages to the wind turbine (Wind

Power Program, n.d.). Enercon E-53 has upwind rotor with active pitch control. The gearless

drive concept combined with an efficiently streamlined rotor blade design, E-53 offers better

performance and reliability. The innovative aerodynamic design of ENERCON rotor blades

promises extraction of maximum power from the wind. The rotor blades have low noise

emissions and minimal structural loads that ensure an optimal yield in the widest variety of

weather conditions (Enercon, 2018).

3.2 Solar Energy Production Homer a computer software model helped to calculate the energy produced by the 2 MW PV

system over the year 2015. The previous student conducted a study to find an optimum PV

system and concluded that 2MW PV is the maximum size of PV system that can be installed

on the rooftop of MU so that is why 2MW PV system was taken into consideration. Total

energy calculated for 2MW PV system in Homer was 3.34 GWh.

The selection of the solar panels for the purpose of this project solely based on the market

price of solar panels not on the type of technology, as there are different types of solar panels

-

100

200

300

400

500

600

700

800

900

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Pow

er O

utp

ut

(kW

)

Wind Speed (m/s)

Enercon E53 Power Curve

21

available in the market using different technologies. The price of the solar panels declined

over the past years due to growth in the technology and the growing demand of generating

power at households or at a commercial level. In 2016, 68 new commercial solar projects

commissioned in Australia adding capacity of 23MW to the existing total capacity. Table 9

shows average commercial solar system prices per watt for different cities in Australia. All

prices in the tables below include incentives available through the federal Renewable Energy

Target as well as GST, but do not incorporate installation cost, meter installation fees or

additional costs such as ground-mounting, grid protection or grid connection studies (Limited,

2017). For the purpose of this project as MU based in Perth, the average price $1.17 of solar

panels used for the analysis.

Average 10kW 30kW 50kW 100kW

Adelaide, SA $1.22 $1.34 $1.22 $1.18 $1.13

Brisbane, QLD $1.24 $1.33 $1.23 $1.23 $1.16

Canberra, ACT $1.16 $1.35 $1.22 $1.08 $0.99

Hobart, TAS $1.25 $1.44 $1.25 $1.20 $1.12

Melbourne, VIC $1.28 $1.44 $1.28 $1.25 $1.17

Sydney, NSW $1.16 $1.24 $1.18 $1.13 $1.09

Perth, WA $1.17 $1.24 $1.07 $1.25 $1.14

Average $1.22 $1.34 $1.21 $1.19 $1.12 Table 9: Average commercial solar system prices per watt. (Limited, 2017)

22

4 Economic Analysis An economic analysis conducted to identify an optimum onsite renewable power generation

system for the lifetime of 20 years. Apart from generating power onsite from PV, the analysis

include the contribution of wind turbines and the battery storage. Analysis conducted with

the combination of Homer and excel spreadsheets helped to determine the reduction in the

electrical consumption from the grid with the integration of renewable resources in the

distributed network. Reduction in the electrical consumption from the grid decreased the

maximum demand of the load offering the savings in the network demand and capacity

charges. Different scenarios were analyzed for the purpose of the analysis. The disadvantage

of using Homer is the option to add network demand and capacity charges into the grid cost

is not available. So the capacity and network demand charges were added to the cost of

buying electricity for each scenario using excel spreadsheets.

4.1 Grid Only Homer-calculated the total cost of electricity bought from the grid based on the amount of

total energy purchased from the grid, 22.28 GWh, during the year 2015. Utility company

charges the University for the use of energy based on the time of the use. Homer calculated

the cost of electricity purchased from the grid based on the peak and off-peak rates specified

for the different time of the day. The report does not include the documentation of the rates

charged by the utility company to MU due to confidentiality. Total annual cost of energy

consumed by MU Homer is $1.785 million. The NPC calculated by Homer is the present value

of all the costs the system incurs over its lifetime, minus the present value of all the revenue

it earns over its lifetime. The replacement and capital costs of the system are zero as there is

no initial investment required. Table 10 shows the total cost of buying electricity for 20 years

which fell under the operations and maintenance cost (O&M).The total cost of buying

electricity from the grid calculated by Homer does not include any additional charges such as

network demand and capacity charges as Homer does not have any input to include these

charges.

Component Capital ($ ) Replacement ($) O&M ($) Total ($)

Grid $0 $0 $35,060,349 $35,060,349

System $0 $0 $35,060,349 $35,060,349 Table 10: Cost of electricity bought from the grid.

The network demand and capacity charges cost around $1 million per annum to MU; table 11

shows the description of all the associated existing charges, charged along the cost of buying

electricity from the grid including the total amount of charges for 20 years lifetime.

Type of Charge Value

Existing Capacity Charge ($/kVA) 927,987

Existing Network Charge (time of use) ($/kVA) 613,102

Supply Charge 7,469

AEMO Fees and LFAS Charges 99,686

Total 1,648,245

GST 115,377

Grand Total 1,763,622

23

Grand Total for 20 years 35,272,433 Table 11: Annual charges that incorporate grid cost.

After the inclusion of all the charges into the grid cost calculated by Homer the cost

increased to $70.3 million, shown in table 12.

Component Capital ($ ) Replacement ($) O&M ($) Total ($)

Grid $0 $0 $70,332,782 $70,332,782

System $0 $0 $70,332,782 $70,332,782 Table 12: The new cost of buying electricity from the grid.

4.2 PV System Analysis The previous study conducted by Sami Alhusayni on the “Cost-Benefit Analysis of PV and

Storage Installation at Murdoch University” concluded that it would be cost-effective to install

(2MW) rooftop PV system facing north at the University campus (Alhusayni, 2017). However,

economic analysis did not include the calculation of new network demand and capacity

charges. The same system was used for this analysis to estimate the savings on new network

demand and capacity charges. Homer simulation is shown in Appendix 2.

Component Capital ($ ) Replacement ($) O&M ($) Salvage ($) Total ($)

Grid $100,000 $0 $14,765,128 $0 $14,865,128

Inverter $277,778 $128,665 $1,432 $0 $407,874

PV System $2,340,000 $0 $202,254 ($85,819) $2,456,435

System $2,717,778 $128,665 $14,968,814 ($85,819) $17,729,437 Table 13: Cost Summary of the PV system

The initial cost of the PV system is $2.7 million calculated in Homer, shown in table 13. Homer

a computer software model calculated NPC of the system based on the costs associated with

each component that contributed towards the initial capital cost and the total NPC of the

system. There is an additional cost added to the grid NPC by the Homer, a capital cost, used

as grid interconnection charge, a one-time fee charged by the utility for allowing a power system

to connect to the grid. HOMER does not apply this fee to grid-only systems, but rather to grid-

connected systems that include some other generation source (Energy H. , Grid Inrerconnection

charge, n.d.). Homer also calculated the salvage value of the PV system ($85,819), a value that

is remaining in a PV system at the end of the project lifetime. The project lifetime is 20 years

whereas the PV system has 25 years of a lifetime so Homer deducted that salvage value from

the total NPC of the PV system. HOMER assumes linear depreciation of components, meaning

that the salvage value of a component is directly proportional to its remaining life. It also

assumes that the salvage value depends on the replacement cost rather than the initial capital

cost. (Energy H. , Homer Pro 3.10- Salvage Value, n.d.). With the inclusion of PV, system into

the distributed network the peak load reduced from 5.78MW to 4.79MW. Figure 15 shows

the comparison between the existing and new load.

24

Figure 16: Comparison between the existing grid energy consumption and new grid consumption

The electricity consumption from the grid reduced to 19 GWh from 22.29 GWh as PV system

generated 3,341,202 kWh energy over the 20 years lifetime. Table 14 shows the energy

production estimated by Homer for the PV system. The payback time for the PV system

calculated in Homer is 11.48 years. Homer calculated payback time by calculating the period

in which the cumulative cash flow difference between the grid only system and the PV system

switched from negative to positive. The payback is an indication of how long it would take to

recover the difference in investment costs between the current system and the base case

system (Energy H. , Homer Pro Version 3.7, User Manual, Year 2016).

Production kWh/Yr %

AC Primary Load 22,280,768 100

PV System 3,341,202 14.9

Grid Purchases 19,018,685 85.1

Total 22,359,887 100 Table 14: Energy production of the PV-Grid System

Table 15 shows the calculated new capacity and network demand charges. It also includes additional charges such as supply charge, AEMOO feed and LMFAS charges including the GST on the total price. The PV system produced annual savings of $278,671 on network demand and capacity charges, the PV system produced annual savings of $276,504 on the energy purchased from the grid by MU. The PV system produced the total annual savings of $555,176.

-1000

0

1000

2000

3000

4000

5000

6000

7000En

ergy

Co

nsu

mp

ti (

kWh

)

Time of the Year

Existing grid cosumption Vs New grid consumption

Measured Load

New Load

25

Type of Charge Value

New Capacity Charge ($/kVA) 780,312

New Network Charge (time of use) ($/kVA) 500,337

Supply Charge 7,469

AEMO Fees and LFAS Charges 99,686

Total 1,387,804

GST 97,146

Grand Total 1,484,950

Grand Total for 20 years 29,699,001 Table 15: Calculate new capacity and network demand charges

As mentioned above the Homer does not have the option to input capacity and network

demand charges into the grid cost so the new payback period of the PV system inclusive of

network demand and capacity charges was calculated using the excel spreadsheet. After

including all the capacity and demand charges into to the cost of the electricity purchased

from the grid for PV and Grid Only system. The payback period changed significantly from

11.47 years to 4.89 years. Payback period calculated by using equation 6.

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑠𝑡

𝐶𝑢𝑚𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 (6)

4.3 Wind System Analysis

Two Enercon E-53 wind turbines with the total capacity of 1.6 MW taken into the

consideration for the analysis. The initial capital cost estimated in Homer is $3.3 million. The

total NPC $17.03 million estimated by the Homer for the grid-connected wind system for 20

years lifetime of the project shown in table 16. In comparison to the PV system, the NPC of

the grid-connected wind system is low as the Inverter used in the grid-connected PV system

excluded in this scenario as wind turbines produce AC power and directly connects to the AC

bus. However, the initial cost of setting up the wind system is higher than PV system because

of the high capital cost of the wind turbines. The capital cost of the wind turbines does not

include any installation and cabling cost. The calculated cost of wind turbines is the

multiplication of the rated capacity of the wind turbines and the price $2/watt, the price of

wind turbines calculated using equation 7. The operations and maintenance cost of the wind

turbines considered is 2% of the total cost of the wind turbine.

𝑊𝑖𝑛𝑑 𝑇𝑢𝑟𝑏𝑖𝑛𝑒 𝑃𝑟𝑖𝑐𝑒 = 𝑊𝑖𝑛𝑑 𝑇𝑢𝑟𝑏𝑖𝑛𝑒 𝑅𝑎𝑡𝑒𝑑 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑋 $2/𝑊𝑎𝑡𝑡 (7)

Components Capital ($) Replacement($) O&M($) Total ($)

Enercon E-53 [800kW] $3,200,000 $0 $628,361 $3,828,361

Grid $100,000 $0 $13,100,209 $13,200,209

System $3,300,000 $0 $13,728,571 $17,028,571 Table 16: Cost summary of the grid-connected wind system.

Table 18 shows the total energy production of the wind system, In comparison to the PV

system; wind turbines produced more energy over the 20 years lifetime reducing the total

electricity consumption from the grid.

26

Production kWh/Yr %

Enercon E-53 [800kW] 5,594,568 25.1

Grid Purchases 16,691,625 74.9

Total 22,286,194 100 Table 17: Total energy production of the Wind system

Payback time of the wind system calculated in Homer is 8.52 years. After the inclusion of the

capacity and network demand charges into the cost of electricity purchased from the grid the

payback period of the wind system reduced to 4.26 years. Calculated new capacity and

network demand charges based on the new energy consumption shown in table 20.

Types of Charges Value

New Capacity Charge ($/kVA) 691,253

New Network Charge (time of use) ($/kVA) 539,436

Supply Charge 7,469

AMEO Fees and LFAS Charges 99,686

Total 1,337,844

GST 93,649

Grand Total 1,431,493

Grand Total for 20 years 28,629,870 Table 18: Calculated new capacity and network demand charges.

Although the wind system made a significant reduction in the electricity consumption from

the grid the maximum load demand did not change much. Some days of the year with no wind

or very low wind, wind turbines produced zero energy production thereby load demand

during certain days of the year remained high, that explains the intermittency of the wind.

The wind system reduced the overall capacity charges due to reduction in the total energy

consumption from the grid as during the certain times of the year; the wind turbines produced

excess electricity. The Wind system produced savings of $332,158 on network demand and

capacity charges and produced savings of $442,956 on the cost of electricity bought from the

grid. The annual net savings produced by the Wind system is $775,124. The wind system

produced 681kWh excess electricity annually. The penetration of excess power produced by

the wind in the distributed network can cause the power outages. Having a battery storage

system in conjunction with the wind system would store all the excess energy generated by

the wind system.

27

Figure 17: Comparison between the existing grid energy consumption and new grid consumption

4.4 Wind and PV system The analysis includes the combination of two Enercon E53 wind turbines 1.6MW and 2MW

PV system. The following table shows all the associated costs of each component contributing

towards total NPC of the system estimated by Homer. An initial investment of the Wind and

PV system requires $6.6 million as it includes the cost of wind turbines and PV panels.

Component Capital ($ ) Replacement ($) O&M ($) Salvage ($) Total ($)

Enercon E-53 [800kW] $3,200,000 $0 $628,361 $0 $3,828,361

Grid $100,000 $0 $10,576,721 $0 $10,676,721

Inverter $260,417 $120,623 $1,342 $0 $382,382

PV system $2,340,000 $0 $202,254 ($85,819) $3,156,435

System $6,600,417 $120,623 $11,408,678 ($85,819) $18,043,899 Table 19: Cost Summary of the grid-connected Wind and PV system

Homer calculated 9.8 years as the payback time of the Wind and PV system whereas the new

payback period calculated in excel inclusive of network demand and capacity charges were

5.28 years.

Types of charges Value

New Capacity Charge ($/kVA) $ 581,719

New Network Charge (time of use) ($/kVA) $ 440,451

Supply Charge $ 7,469

AMEO Fees and LFAS Charges $ 99,686

Total $ 1,129,326

GST $ 79,053

Grand Total $ 1,208,379 Table 20: New calculated annual capacity and network charges

The combination of Wind and PV system resulted in significant reduction in the capacity and

network demand charges. The system produced the annual savings of $695,345 on the cost

of electricity bought from the grid. The system also produced annual savings of 555,242 on

1500200025003000350040004500500055006000

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Ener

gy C

on

sum

pti

on

(kW

h)

Time of the year

Existing energy consumption Vs NEW energy cosnumption

2015 MeasuredLoad (kW)

New Load Profile (kW)

28

network demand and capacity charges. Figure 21 shows the changes in the existing grid load,

the peak load demand changed from 5.78 MW to 4.22 MW. During certain times of the year,

the system generated excess electricity, a total of 256.95MWh/yr. The excess electricity can

be exported back to the grid or a battery storage could be used to store the excess electricity

for further load reduction that would even save on capacity and network demand charges.

Figure 18: Comparison between the existing grid energy consumption and new grid consumption

4.5 Wind-PV-Battery Storage System Analysis The Gildemeister Vanadium redox flow battery used for the analysis has a nominal capacity

of 2.48 MWh and can potentially supply the maximum power of 300 kW continuously for 8

hours or all at once in one hour. The purpose of using the battery storage system is to store

the excess electricity generated by the renewables resources onsite that would benefit by

shaving off the peak load demand to some extent and would save on excessive network

demand and capacity charges. The general properties of the battery storage system used for

analysis, shown in table 21.

General Properties

Nominal capacity (V) 700

Nominal Capacity (kWh) 2480

Nominal Capacity (Ah) 3540

Round Trip Efficiency (%) 70

Maximum Charge Current (A) 291

Maximum Discharge Current (A) 441 Table 21 General properties of Gildemeister Vanadium Flow Battery.:

-2000

-1000

0

1000

2000

3000

4000

5000

6000

7000

Time of the year

Ener

gy C

on

sum

pti

on

(kW

h)

Existing grid consconsumption Vs New grid consumption

2015 Measured Load New Load

29

Table 24 shows the cost summary of Wind-PV-Battery Storage system that does not include

capacity and network demand charges added to the cost of electricity bought from the grid.

Employing a battery storage into the Wind-PV system increased the initial cost of the Wind-

PV system to $7.19 million.

Component Capital ($ ) Replacement ($) O&M ($) Salvage ($) Total ($)

Enercon E-53 [800kW] $3,200,000 $0 $628,361 $0 $3,828,361

Gildemeister Battery Storage $1,736,000 $0 $170,724 $0 $1,906,443

Grid $100,000 $0 $10,565,152 $0 $10,665,152

Inverter $260,416 $120,623 $1,253 $0 $382,382

PV North $2,340,000 $0 $202,254 ($85,819) $2,456,435

System $7,636,416 $120,623 $13,101,835 ($85,819) $20,773,056 Table 22: Cost summary of the Wind-PV-Battery Storage System

The calculated capital cost of the vanadium flow batteries, based on their current price in the

market, which is around $700/kWh. Formula 8 used to calculate the total price of the battery

shown below; full calculation is shown in Appendix 1

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝐵𝑆𝑆 = 𝑁𝑀 𝑜𝑓 𝐵𝑆𝑆 × 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦($ 𝑘𝑊ℎ)⁄ (8)

𝐵𝑆𝑆 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝑆𝑦𝑠𝑡𝑒𝑚

The Wind-PV system including the 2.48 MWh battery storage system was analyzed in Homer

and MS Excel. The system modeled in MS excel is based on the assumptions, it was assumed

that the battery storage system would be used only once in a day. As mentioned above, the

battery storage has the capacity to deliver 300kW continuously for 8hours or all at once in 1

hour. So from every day’s load of the Wind-PV system, 300kW subtracted to obtain the new

load of the system. Deducting 300kW from the load profile of wind-PV system did not produce

significant changes in the load demand. After adding the battery storage into Wind-PV

system, new capacity and demand charges calculated, shown in table 23.

Types of charges Value

New Capacity Charge ($/kVA) 580,469

New Network Charge (time of use) ($/kVA) 417,084

Supply Charge 7,469

AEMO Fees and LFAS Charges 99,686

Total 1,104,708

GST 77,330

Grand Total 1,182,038

Table 23: New capacity and demand charges

30

The Wind-PV system generated 279 MWh excess energy over the period of one year at the

average of 704kWh per day that assumed of being used to charge the battery storage

system every day. The formula used to calculate energy generated per day by Wind-PV

system as follows; full calculation shown in Appendix 1.

𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦

=𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑊𝑖𝑛𝑑/𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 𝑜𝑣𝑒𝑟 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝑎𝑦𝑠 𝑖𝑛 𝑎 𝑌𝑒𝑎𝑟

(9)

𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑜𝑓𝑓 𝑡ℎ𝑒 𝑔𝑟𝑖𝑑 = 𝑁𝐶 − 𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦 (10)

𝑁𝐶 − 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝑆𝑦𝑠𝑡𝑒𝑚

However, the nominal capacity of the storage system is 2480kWh and the energy generated

by the Wind-PV system was 704kWh so another assumption made, to charge the remaining

capacity of the battery storage system from the energy bought straight from the grid during

the off-peak time. The cost of charging the battery storage off grid calculated was $117 per

day accumulated to $42751 per year. Formula 11 used to calculate the cost to charge the

battery. The system produced annual savings of $695,779 on the cost of electricity bought

from the grid but the cost to charge the battery storage system was deducted from the

savings on the cost of electricity bought from the grid, which reduced to $653,027. The system

also produced the savings of $586,329 on capacity and network demand charges. The system

produced annual net savings of $1.25 million. Payback time calculated in Homer is 17.51 years

whereas payback time after including all the charges reduced to 6.51 years.

𝐶𝑜𝑠𝑡 𝑡𝑜 𝑐ℎ𝑎𝑟𝑔𝑒 𝑡ℎ𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦= 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑜𝑓𝑓 𝑔𝑟𝑖𝑑 × 𝑂𝑓𝑓 𝑃𝑒𝑎𝑘 𝑇𝑖𝑚𝑒 𝑅𝑎𝑡𝑒𝑠

(11)

In Homer, Cycle charging method used to charge the battery storage system. Cycle charging

uses the renewable resources to meet the load and any surplus energy charges the battery.

The simulation performed in Homer for Wind-PV system including the 2.48MWh battery

storage system shown in Appendix 2.

4.6 Discussion To satisfy the objective of this project different renewable resources were analyzed to identify

an optimum grid connected onsite renewable generation system for MU. The analysis of PV

system, Wind System, and Wind- PV system conducted to identify their electricity generation

capacity in order to reduce the electricity consumption of MU from the grid. The analysis of

each system also included the reduction in the capacity and network demand charges due to

a decrease in the total electricity consumption from the grid. Table 24 shows the comparison

between the economics of different renewable generation systems. The Wind-PV system with

the 2.5MWh battery storage system produced more savings on the network demand and

capacity charges. The Wind-PV-Battery Storage system has high initial cost the system as the

31

cost of the battery storage system adds the significant amount of value to the total cost of

the system. The algorithm used to control the battery system for this scenario was assumptive

but a smart battery management system in place would store the electricity and discharge

the battery storage as required. However, the battery storage system would require more

charging and discharge in order to reduce the maximum electricity consumption from the

grid, which would affect the lifetime of battery storage system. The (2MW) PV system has

lowest initial capital cost among other systems and offers a decent amount of reduction in

the network demand and capacity charges with the payback time of only 4.9 years. The Wind-

PV system offered the maximum reduction in the annual savings on all the charges, having a

battery storage system with smart battery management system can offer more savings.

System Capacity

(MW) Initial

Investment Total 20 years

Savings

Net 20 years Savings

Payback Period (Years)

PV 2 $2,717,778 11,103,526 8,385,748 4.90

Wind 1.6 $3,300,000 15,502,494 12,202,494 4.26

Wind-PV 3.6 $5,900,417 25,011,763 19,111,346 4.72

Wind-PV-2.5MWh Battery 3.6 $7,619,056 25,547,267 17,928,211 6.74 Table 24: Economic analysis of different renewable energy generation systems.

5 Conclusion & Future Studies

5.1 Conclusion Generating electricity from renewable energy generation systems (REGS) on campus would

provide savings on the cost of electricity bought from the grid and at the same time would

provide savings on the network demand and capacity charges. In order to achieve that

different REGS such as PV system, Wind system and the combination of PV and Wind and

Battery Storage systems were anal. It was required to obtain an optimum system that could

offer a significant amount of benefits of generating electricity on campus. A maximum of 2

MW PV system can be installed on the rooftop of MU and it would not produce sufficient

energy to meet the peak load demand of the MU. In order to meet the maximum load

demand, it is required to have an alternative form of renewable energy system for generating

electricity on campus. It was identified that having wind turbines along with solar panels

would offer a significant amount of reduction in the total savings on the cost of electricity

bought from the grid and savings on the network demand and capacity charges. Generating

electricity from wind turbines on campus required analysis of the wind speed at the campus.

Annual mean wind speed calculated at MU was 5.36m/s. further, the estimation of wind

energy production by different wind turbines was analyzed. The selection of the wind turbines

was based on the capacity of the wind turbines. Enercon E-53 wind turbine was chosen for

the analysis of this study. A combination of wind and solar panels offered a significant amount

of savings in the capacity and network demand charges and the cost of electricity bought from

the grid. The benefit of having a battery storage system was analyzed with the combination

of Wind-PV system. However, having a battery storage in conjunction with Wind-PV system

increased the payback of time of the system as the battery storage system adds the value to

the initial cost of the system.

32

5.2 Future Studies The system requires a study on the advanced battery model that can be used to control the

flow of energy from the battery storage to meet the required load demand. Two Enercon E-

53 wind turbines were analyzed in this study to identify the amount of reduction in the

existing electricity consumption of MU that it could offer. Further studies may include

assessment of more than two wind turbines at the campus as it may offer high amount

reduction in the existing electricity consumption of MU. However, Enercon E-53 wind turbines

are 73 m tall so it is required to contact CASA to discuss MU’s plan of installing wind turbines

at the University campus. So further studies may include the assessment of wind turbines

other than Enercon E-53 as the wind turbine height may cause disruptions in the project plans.

The system requires bigger battery size that could offer a constant supply of maximum power

for long hours during a day to meet the maximum electricity demand.

33

6 References Clean Energy Australia. (2016). Clean Energy Australia Report 2016. ACT: Complete Colour

Printing.

Murdoch University. (2017). Perth Campus. Retrieved from www.Murdoch.edu.au:

http://www.murdoch.edu.au/life-at-murdoch/perth-campus

Solar Choice. (2017). Solar Choice. Retrieved from www.Solarchoice.net.au:

https://www.solarchoice.net.au/blog/battery-storage-throughput-not-battery-cycle-

life

VSun Energy. (2017). VRB Installation Case Studies. Retrieved from

www.vsunenergy.com.au: https://www.vsunenergy.com.au/case-studies/busselton-

western-australia/

A.L. Rogers, J. M. (2009). Wind Energy Explained. West Sussex: John Wiley & Sons Ltd.

Alhusayni, S. (2017). Cost Benefit Analysis of PV and Storage Installtion at Murdoch

University. Perth.

Australian Energy Resource Assesment. (2009). Solar Energy. Australian Energy Resource.

Australian Government, Department of Foreign Affairs and Trade. (2017-2018). Climate

Change. Retrieved from WWW.dfat.gov.au: http://dfat.gov.au/international-

relations/themes/climate-change/Pages/australias-climate-action.aspx

Australian Vanadium Limited . (2017). Vanadium batteries. Retrieved from

www.australianvanadium.com.au: http://australianvanadium.com.au/vanadium-

batteries/

Austrlalian Government, Clean Energy Regulator. (n.d.). Renewable Energy Target. Retrieved

January 22, 2017, from http://www.cleanenergyregulator.gov.au/RET/About-the-

Renewable-Energy-Target/History-of-the-scheme

Berril, T. (2004). Wind Energy Conversion Systems-Resource Book. Brisbane: Renewable

Energy Centre.

Charles Sturt University. (2017). Wagga Wagga Solar PV Installation. Retrieved from

www.csu.edu.au:

https://www.csu.edu.au/division/facilitiesm/projects/details/wagga/ww-solar-pv-

photovoltaic-installation

City of Melville. (2017). Power . Retrieved from www.melvillecity.com.au:

http://www.melvillecity.com.au/environment-and-waste/piney-

lakes/about/building-technology/power

34

Clean Energy Council Australia. (2015). Clean Energy Austrlaia Report. ACT: Complete Colour

Printing.

Climate Change Authority. (n.d.). Australia's policies on climate change. Retrieved January

22, 2017, from http://climatechangeauthority.gov.au/reviews/targets-and-progress-

review/part-b

Department of Environment and Energy. (2017). Quarterly Update of Australia’s National

Greenhouse Gas Inventory. Canberra: Commonwealth of Austrlaia.

Enercon, E. F. (2018, January 27). Enercon E-53. Retrieved from www.enercon.de:

https://www.enercon.de/en/products/ep-1/e-53/

Energy, H. (n.d.). Grid Inrerconnection charge. Retrieved from www.homerenergy.com:

https://www.homerenergy.com/support/docs/3.10/grid_interconnection_charge.ht

ml

Energy, H. (n.d.). Homer Pro 3.10- Salvage Value. Retrieved from www.homerenergy.com:

https://www.homerenergy.com/support/docs/3.10/salvage_value.html

Energy, H. (Year 2016). Homer Pro Version 3.7, User Manual. Boulder: Homer Energy .

Energy, I. (2015, February 12). WA Electrcity Tariifs Explained. Retrieved from

www.infiniteenergy.com.au: https://www.infiniteenergy.com.au/electricity-tariff-

makeup-perth-wa

Geoff Stapleton, G. M. (2013). Australia's Guide to Environmentally Sustianable Homes.

Retrieved from www.yourhome.gov.au: http://www.yourhome.gov.au/energy/wind-

systems

Hayden, E. (2013). Introduction to Microgrids. Virginia: Securicon.

Homer Energy. (n.d.). Calculating Hub Height Wind Speed. Retrieved from

www.homerenergy.com:

https://www.homerenergy.com/support/docs/3.10/how_homer_calculates_wind_t

urbine_power_output.html

Limited, S. C. (2017). Current solar power system prices: Residential and Commercial.

Retrieved from www.solarchoice.net.au: https://www.solarchoice.net.au/blog/solar-

power-system-prices

Mark Robinson, Y. S. (2016). Energy Audit of Building 340 Murdoch University. Perth.

Meteorology, B. o. (2018, Jan 27). Daily Global Solar Exposure. Retrieved from

http://www.bom.gov.au:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=193&p_displ

ay_type=dailyDataFile&p_startYear=&p_c=&p_stn_num=009187

Muhammad Khalid, A. V. (2015). Optimization of a Power System Consisting of Wind and

Solar Power Plants and Battery Energy Storage for Optimal Matching of Supply and

Demand. IEEE Conference on Control Applications (CCA), 739-743.

35

Statics, A. B. (2017). Consumer Prices Index, Australia. Retrieved from Australian Bureau of

Statics:

http://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/6401.0Feature%20A

rticle2Mar%202017?opendocument&tabname=Summary&prodno=6401.0&issue=M

ar%202017&num=&view=

Storage, G. E. (n.d.). Optimal use of microgrids. Gildemeister Energy Solutions, 3.

University of Queensland. (2017). St Lucia. Retrieved from www.uq.edu.au:

https://www.uq.edu.au/solarenergy/pv-array/st-lucia

Weather Station, M. U. (2017). About Murdoch's Met Station. Retrieved from

wwwmet.murdoch.edu.au: http://wwwmet.murdoch.edu.au/about

Wind Power Program. (n.d.). Wind turbine power ouput variation with steady wind speed.

Retrieved from www.wind-power-program.com: http://www.wind-power-

program.com/turbine_characteristics.htm

36

Appendix 1 Load Factor:

𝐿𝑜𝑎𝑑 𝐹𝑎𝑐𝑡𝑜𝑟 =𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛

𝑃𝑒𝑎𝑘 𝐿𝑜𝑎𝑑 × 𝐷𝑎𝑦𝑠 × 24

𝐿𝑜𝑎𝑑 𝐹𝑎𝑐𝑡𝑜𝑟 =22291355𝑘𝑊ℎ

5780𝑘𝑊 × 365 × 24= 44%

Total Energy generated by Wind-PV system over the period of one day:

𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦 =𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑊𝑖𝑛𝑑/𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 𝑜𝑣𝑒𝑟 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝑎𝑦𝑠 𝑖𝑛 𝑎 𝑌𝑒𝑎𝑟

𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦 =256952𝑘𝑊ℎ

365= 704𝑘𝑊ℎ

Energy required charging the battery:

𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑜𝑓𝑓 𝑡ℎ𝑒 𝑔𝑟𝑖𝑑 = 𝑁𝐶 − 𝐸𝑛𝑒𝑟𝑔𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦

𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑜𝑓𝑓 𝑡ℎ𝑒 𝑔𝑟𝑖𝑑 = 2480𝑘𝑊ℎ − 704𝑘𝑊ℎ = 1776𝑘𝑊ℎ

𝑁𝐶- 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝑆𝑦𝑠𝑡𝑒𝑚

Cost to charge the battery off the grid:

𝐶𝑜𝑠𝑡 𝑡𝑜 𝑐ℎ𝑎𝑟𝑔𝑒 𝑡ℎ𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 = 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑜𝑓𝑓 𝑔𝑟𝑖𝑑 × 𝑂𝑓𝑓 𝑃𝑒𝑎𝑘 𝑇𝑖𝑚𝑒 𝑅𝑎𝑡𝑒𝑠

Total cost of 300kW BSS:

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝐵𝑆𝑆 = 𝑁𝑀 𝑜𝑓 𝐵𝑆𝑆 × 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦($ 𝑘𝑊ℎ)⁄

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝐵𝑆𝑆 = 2480 × 700 = $1,736,000

𝐵𝑆𝑆 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑜𝑟𝑎𝑔𝑒 𝑆𝑦𝑠𝑡𝑒𝑚

𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑃𝑟𝑖𝑐𝑒 = $700/𝑘𝑊ℎ

Total cost of 1MW BSS:

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝐵𝑆𝑆 = 𝑁𝑀 𝑜𝑓 𝐵𝑆𝑆 × 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦($ 𝑘𝑊ℎ)⁄

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝐵𝑆𝑆 = 5000 × 700 = $3,500,000

Payback period of PV system:

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑠𝑡

𝐶𝑢𝑚𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =$2.7𝑚𝑖𝑖𝑙𝑖𝑜𝑛

$555,176= 4.89 𝑦𝑒𝑎𝑟𝑠

37

Payback period of Wind system:

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =$3.3𝑚𝑖𝑖𝑙𝑖𝑜𝑛

$442,996= 4.26 𝑦𝑒𝑎𝑟𝑠

Payback period of Wind PV system:

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =$6.6𝑚𝑖𝑖𝑙𝑖𝑜𝑛

$695,345= 5.28 𝑦𝑒𝑎𝑟𝑠

Payback period of Wind PV-300kWh battery storage system:

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 =$8.9𝑚𝑖𝑖𝑙𝑖𝑜𝑛

$695,779= 6.51 𝑦𝑒𝑎𝑟𝑠

Appendix 2 PV System

Figure 19: PV system analyzed in Homer

38

Figure 20: Cost summary of the PV system calculated in Homer.

Figure 21: Payback period

39

Figure 22: Electricity Production of PV system.

Wind system

40

Figure 23: Cost summary of Wind system.

Figure 24: Payback Period of Wind System.

41

Figure 25: Energy production of the wind system.

Wind-PV system

Figure 26: Wind-PV system analyzed in Homer

42

Figure 27: Cost summary of Wind-PV system

Figure 28: Payback period

43

Figure 29: Energy production of Wind-PV system

Wind-PV-2.48MWh battery storage system

Figure 30: Wind-PV-2.48MWh Battery storage system

44

Figure 31: Cost summary of Wind-Pv-2.48MWh battery storage system.

Figure 32: Payback period.

45

Figure 33: Energy production of Wind-PV-2.48MWh battery storage system.

46

Appendix 3

MU Biodiversity Management Plan

16

2

12

7

15

17

8

17

4

6

51

11

19

20

23

9

22

1310

18

3

25

14

24

26

21

1

3

42

8

5

16

13

7

15

6

9

10

1711

14

12

ErMp

Ppcc

Ppcc

Ppcc

EmBaAfXpSl

Pl

Ppcc ErMpAf

Ppcc

ErNfBl

Ppcc

CcPg

Ppcc

CcPg

EmBaAfXp

Ppcc

MpAfLsp

PpLl

Ppcc

CcEmXp

CcBa

PpLl

CcEmXp

Xp

Pl

Cc

Ppcc

CcEmXp

ErMpAc

CcPg

Ppcc

ErMpAc

PpCcCp

ErMpAc

CcBa

Tt

EmBaAfXp

Pl

LaGr

CcBa

Pp

EmBaAfXpSl

ErNfBl

CcPg

EmBaAfXp

ErMpAc

Cc

EmBaAfXpSl

EgPl

Cc

PpCcCp

EmBaAfXp

CcEmXp

ErMpAc

Afpg

389000 389500 390000 39050064

5050

064

5100

064

5150

0

GDA 1994 MGA Zone 50

MURDOCH UNIVERSITYBIODIVERSITY MANAGEMENT PLAN

VEGETATION COMMUNITIES

1:6,500SC AL E @ A3

A U T H O R : R D C H E C K E D : J N D AT E : J U N 2 0 1 2 P R O J E C T N O : 2 7 5 3 - 11

0 100 200 300 400 500 m

CLIENT: MURDOCH UNIVERSITY

LegendStudy areaCurrent ReservesPotential Reserves

Vegetation Communitites

ErMpAcEucalytpus rudis subsp. rudis and Melaleuca preissianaOpen Woodland over Adenanthos cygnorum, Xanthorrhoeapreissii, Chamelaucium uncinatum and weeds

Afpg Allocasuarina fraseriana open woodland over planted gardensCc Corymbia calophylla open woodlandCcBa

CcEmXp Corymbia calophylla and Eucalyptus marginata overXanthorrhoea preissii open shrubland

CcPg Corymbia calophylla open woodland over planted gardens

EmBaAfXpEucalyptus marginata, Banksia littoralis and Allocasuarinafraseriana woodland over Xanthorrhoea preissii shrubland and weeds

EmBaAfXpSlEucalyptus marginata, Banksia littoralis and Allocasuarinafraseriana woodland over Xanthorrhoea preissiiandStrilingia latifolia shrubland

ErMp Eucalyptus rudis and Melaleuca preissiana woodland overTypha sp. and Scirpus sp. sedgeland

ErMpAf Eucalyptus rudis and Melaleuca preissiana over planted gardens

ErNfBl Eucalyptus rudis and Banksia littoralis open woodland overNuytsia floribunda open shrubland

LaGr

MpAfLsp Melaleuca preissiana open woodland over Astartea aff. fasicularisshrubland over Lepidosperma sp. sedgeland

Pp Pinus pinaster woodland

PpCcCp Pinus pinaster and Corymbia calophylla woodland with scatteredCallitris preissii

PpLl Pinus pinaster woodland over Leptospermum laevigatum open scrub

Ppcc Pinus pinaster woodland with scattered Corymbia calophyllaTt Leptospermum laevigatum closed scrubXp Xylomelum occidentale scattered treesEgPl Eucalyptus gomphocephala open woodland over planted gardensPl Planted

MAP 10Aerial imagery supplied by NearMap (2012)