Distributed Renewable Energy Storage in the
National Electricity Market:
Options for Commercial Energy Users and
Implications for Utilities
Gillian Hector
Dissertation submitted to the
School of Engineering and Information Technology
Murdoch University
for the degree of
Master of Science in Renewable Energy
2015
Declaration
I declare that this dissertation is my own account of my research, except where
acknowledged and referenced, and contains as its main content work which has not
previously been submitted for a degree at any tertiary education institution.
Gillian Hector
October 2015
i
Abstract
Given its ability to enable firm supply, electrical energy storage is increasingly viewed
as a solution to the intermittency of renewables. While many studies have focussed on
the benefits and implications of energy storage for utilities and residential energy users,
options for commercial energy users within Australia's National Electricity Market
(NEM) have been largely ignored. This dissertation provides a techno-economic
comparison of the available energy storage technologies and summarises the literature
to determine which are the most appropriate and cost effective. Different technologies
provide different advantages and no single technology may be able to meet all of the
requirements of commercial end users. While lithium ion batteries are expected to
dominate the NEM as costs decline, their dominance may be challenged in future by
hybrid aqueous batteries which provide environmental advantages and are relatively low
maintenance. The increased deployment of renewable energy storage technologies
requires utilities to adapt their business models. Given the advanced deployment rates of
renewable energy storage in the German market, a case study comparison of German
utilities Rheinsch-Westfalisches Electrizitatswek (RWE) and E.ON versus NEM
utilities AGL Energy and AusNet Services is performed. The comparison finds that the
NEM utilities are better placed to adapt to the future challenges of an increasingly
decentralised energy market, but further policy support is required to accelerate this
transition. Policy options are formulated from the perspective of innovation systems
theory and systems thinking. This approach not only addresses the regulatory and
economic barriers that need to be overcome for the widespread deployment of
renewable energy storage, but also ensures that utilities have the economic incentives
and policy certainty required to support this aim.
ii
Contents
Abstract ...............................................................................................................................i
List of Figures ...................................................................................................................iv
List of Tables ..................................................................................................................... v
List of Abbreviations ........................................................................................................vi
Acknowledgements ....................................................................................................... viii
1. Introduction ................................................................................................................ 1
1.1. Background ......................................................................................................... 1
1.2. The Case for Storage ........................................................................................... 2
1.3. Scope ................................................................................................................... 4
1.4. Objectives ........................................................................................................... 7
1.5. Dissertation Structure ......................................................................................... 8
2. Methods and Analytical Framework .......................................................................... 9
2.1. Literature Review ............................................................................................... 9
2.2. Selected Case Studies ....................................................................................... 14
2.3. Analysis Methods ............................................................................................. 15
2.4. Limitations ........................................................................................................ 16
3. Electrical Energy Storage Technologies .................................................................. 18
3.1. Applications for Commercial Users................................................................. 19
3.2. Storage Categories ............................................................................................ 23
3.3. Battery Energy Storage ..................................................................................... 23
3.4. Electromagnetic Energy Storage....................................................................... 38
3.5. Techno-Economic Comparative Assessment ................................................... 41
3.6. Cost Comparison ............................................................................................... 46
3.7. Future Outlook .................................................................................................. 49
4. The National Electricity Market ............................................................................... 53
4.1. Market Overview .............................................................................................. 53
4.2. Key Market Participants ................................................................................... 56
4.3. State of the Market ............................................................................................ 57
5. Implications for Utilities .......................................................................................... 62
5.1. Implications of Increased Renewable Energy Storage ..................................... 62
5.2. Business Model Comparison ............................................................................ 68
5.3. Discussion ......................................................................................................... 80
iii
6. Policy and Regulatory Options ................................................................................ 92
6.1. Regulatory and Policy Context ......................................................................... 92
6.2. Overcoming Cultural Resistance ...................................................................... 93
6.3. Policy Certainty ................................................................................................ 98
6.4. Technical and Regulatory Certainty ............................................................... 102
6.5. Improving Economic Outcomes ..................................................................... 104
7. Conclusion and Recommendations ........................................................................ 108
7.1. Conclusions ..................................................................................................... 108
7.2. Recommendations ........................................................................................... 110
Appendix A: Demand Management Calculations ......................................................... 111
Appendix B: Retail Contract Savings ............................................................................ 112
References ..................................................................................................................... 113
iv
List of Figures
Figure 1: Annual NEM electricity demand vs solar generation ....................................... 2
Figure 2: Example of EES operation and application ...................................................... 3
Figure 3: System drivers for energy storage ..................................................................... 5
Figure 4: Map of the National Electricity Market ............................................................ 6
Figure 5: Technology adoption and market diffusion .................................................... 15
Figure 6: Example of demand management using energy storage ................................. 21
Figure 7: Energy supply shift incorporating storage ..................................................... 22
Figure 8: Energy storage categories ............................................................................... 23
Figure 9: Schematic of battery operation ....................................................................... 24
Figure 10: Example of battery charge discharge behaviour ........................................... 25
Figure 11: Li-ion schematic view showing battery function .......................................... 28
Figure 12: Transgrid's iDemand energy system configuration ....................................... 29
Figure 13: NaS schematic view ...................................................................................... 30
Figure 14: Schematic view of a redox flow battery being discharged. .......................... 32
Figure 15: Zinc-air battery operation ............................................................................. 34
Figure 16: Internal features of the Aquion AHI battery ................................................. 37
Figure 17: Schematic view of SMES system ................................................................. 39
Figure 18: Comparison of rated power, energy and discharge duration ........................ 41
Figure 19: Total capital cost of selected storage technologies ($/kWh),........................ 46
Figure 20: Life cycle costs of large-scale energy storage ($/kW-year) ......................... 47
Figure 21: Levelised cost of energy ($/kWh) ................................................................. 48
Figure 22: Levelised cost of storage ($/kWh) ................................................................ 49
Figure 23: Projected battery cell costs for bulk energy storage ..................................... 50
Figure 24: Australian installed battery energy storage ................................................... 52
Figure 25: Electricity supply chain ................................................................................. 53
Figure 26: NEM generation by fuel type ........................................................................ 54
Figure 27: NEM quarterly volume weighted average quarterly spot prices................... 58
Figure 28: NSW electricity futures ................................................................................. 59
Figure 29: Qld electricity futures ................................................................................... 59
Figure 30: SA electricity futures .................................................................................... 60
Figure 31: Vic electricity futures .................................................................................... 60
Figure 32: NEM forecast surplus generating capacity (MW) ........................................ 61
Figure 33: Residential and commercial solar PV installations (MW) ............................ 64
Figure 34: Declining energy consumption and rising peak demand .............................. 67
Figure 35: Solar PV generation versus AusNet Services' network load profile ............. 67
Figure 36: RWE's energy supply chain .......................................................................... 69
Figure 37: Owners of generation in the NEM. ............................................................... 73
Figure 38: RWE German generation by fuel type .......................................................... 75
Figure 39: RWE Innogy venture capital portfolio .......................................................... 75
Figure 40: AusNet Services infrastructure assets ........................................................... 76
Figure 41: AGL's new business revenue model ............................................................. 77
Figure 42: E.ON customer centric model ....................................................................... 78
v
Figure 43: Customer satisfaction score .......................................................................... 82
Figure 44: High spot prices in SA .................................................................................. 85
Figure 45: AusNet Services projected revenue .............................................................. 88
Figure 46: AusNet Services' flattening expenditure ....................................................... 88
Figure 47: E.ON stock price history ............................................................................... 89
Figure 48: NEM governance and regulatory structure. .................................................. 92
Figure 49: Technology's place in the social system ....................................................... 94
Figure 50: Moore's technology adoption life cycle ........................................................ 95
List of Tables
Table 1: Publications used in literature review of EES technologies ............................. 10
Table 2: Required storage characteristics for commercial applications ......................... 22
Table 3: Comparison of EES Technical Characteristics ................................................ 43
Table 4: Comparison of EES technical characteristics and applications ....................... 44
Table 5: Key NEM Statistics for 2013-14 ...................................................................... 54
Table 6: Australia's fossil fuel resources ........................................................................ 55
Table 7: PV generation as a proportion of NEM consumption ...................................... 63
Table 8: AEMO NEM Forecast Energy Storage Capacity (MWh) ................................ 63
Table 9: New decentralised utility model ....................................................................... 91
vi
List of Abbreviations
ABARE Australian Bureau of Agricultural and Resource Economics
AEMO Australian Energy Market Operator
AER Australian Energy Regulator
AHI aqueous hybrid ion
ARAB aqueous rechargeable alkali-metal ion batteries
ARENA Australian Renewable Energy Agency
ASX Australian Securities Exchange
BREE Bureau of Resource and Energy Economics
CEFC Clean Energy Finance Corporation
C&I commercial and industrial
CPD Critical Peak Demand
CRF Capital Recovery Factor
DMIS Demand Management Incentive Scheme
DNSP Distribution Network Service Provider
DoD depth of discharge
EBITDA Earnings before interest, tax, depreciation and amortisation
EES Electrical Energy Storage
GESS grid energy storage system
GHG greenhouse gas
GWh Gigawatt hour
kVA kilo-volt ampere
kW kilowatt
kWh kilowatt hour
LCC life cycle cost
LCOE levelised cost of energy
LCOS levelised cost of storage
vii
LGC Large-scale Generation Certificate
ms millisecond
MW megawatt
MWh megawatt hour
MPC Market Price Cap
NaS sodium sulphur
NEM National Electricity Market
NiCd nickel cadmium
NiMH nickel metal hydride
NEO National Electricity Objective
PV photovoltaic
RAB regulated asset base
RET Renewable Energy Target
RWE Rheinsch-Westfalisches Electrizitatswek
SMES superconducting magnetic energy storage
STC Small-scale Technology Certificate
TCC Total Capital Cost
tCO2-e tonnes of carbon dioxide equivalent
V voltage
Wh Watt hour
ZnBr zinc bromine
viii
Acknowledgements
I would like to express my utmost gratitude to my supervisor, Dr Manickam Minakshi,
for his valuable suggestions and patient guidance throughout the process of completing
this dissertation. It has been an honour to learn from such a great teacher and world
class researcher in the field of energy storage materials.
Special thanks to Graeme Weller and Energy Price Solutions for providing access to
their energy consumption and management software and NEM-Watch.
Completing study while working full-time has proved to be quite a marathon, and has
only been possible with the continuous encouragement, prayers and support from my
family and friends.
1
1. Introduction
1.1. Background
Centralised electricity grids around the world have been constructed on the basis
that electricity will be supplied by fossil-fuelled base load generators and used when
generated. Recent changes in the energy market and technological innovations have
challenged this assumption. As the electricity sector is the largest emitter of
greenhouse gases (GHGs) in Australia, efforts to decarbonise the grid to combat
climate change have resulted in the increasing penetration of distributed renewable
energy generators – principally wind turbines and solar photovoltaic (PV) arrays.
However, the stochastic nature of renewables creates a challenge for grid operators,
whose aims are to provide a stable and reliable energy system by balancing
electricity demand and supply. Given its ability to enable firm supply, "electrical
energy storage (EES)" is increasingly viewed as a solution to this limitation, as
energy can be stored and released when needed (Luo et al. 2015).
Within Australia’s National Electricity Market (NEM), renewable generation grew
by 69% in the 3 years to 2012-13 (BREE 2014). This growth in renewables
combined with declining demand has placed downward pressure on electricity
wholesale prices. This has reduced conventional generator profitability. The
development of affordable EES to support renewable generation therefore has the
ability to further reduce grid demand and the load factor of conventional generators,
and in the process threaten their current business models.
2
1.2. The Case for Storage
In addition to proving firm capacity for renewable generation, there are a range of
other factors that are driving the need for energy storage:
Rising prices: Energy prices have increased by approximately 60% in the 10
years to 2013 (Productivity Commission 2013). This has provided a strong
incentive for end users to improve their energy efficiency and seek greater
energy independence through the installation of renewable energy systems.
Renewable energy storage facilitates energy shifting to reduce peak
electricity charges.
Increasing renewable energy penetration: Renewable energy generation –
specifically rooftop solar - is forecast to contribute 17% of total NEM
generation capacity by 2022-23 from 2% in 2013-14 (AER 2014). Figure 1
illustrates the pattern of declining electricity consumption – partly in
response to rising electricity prices as well as the introduction of more
efficient appliances and lighting - versus rising solar generation.
Figure 1: Annual NEM electricity demand vs solar generation (AER 2014)
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
175,000
180,000
185,000
190,000
195,000
200,000
205,000
210,000
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
NE
M S
ola
r G
ener
ati
on
(G
Wh
)
NE
M C
on
sum
pti
on
(G
Wh
)
NEM Energy Consumption vs Solar
NEM Demand Solar
3
Energy storage ameliorates some of the issues caused by renewable
generation including poor power quality and reliability.
A combination of declining or absence of feed-in-tariffs, and declining costs
of both renewable and EES technologies means that energy storage improves
the value of renewable energy to end users, and will be critical to the growth
of renewables in the long term.
Energy management: Energy storage facilitates effective load management.
Figure 2 illustrates how storage can be effectively used for applications
including peak shaving, balancing supply and demand, as well as
maintaining voltage and frequency.
Figure 2: Example of EES operation and application (Griffith Sciences 2015)
4
It also has positive spill-over effects for other energy consumers and network
operators by enabling the deferment of costly network augmentation to meet
peak demand, which has been the key driver of rising energy costs.
Aging infrastructure: Renewable energy storage facilitates the market exit
of aging emissions intensive generation without compromising supply
reliability. 40% of the NEM’s generation fleet will be 40 years old by 2030,
which equates to 74% of coal-fired generation (Origin Energy 2014).
Despite an abundance of fossil fuels, banks are increasingly reluctant to
finance investment in new fossil-fuelled generators due to their high capital
costs and reputational risk (Vithayasrichareon, Riesz, and MacGill 2015).
This provides an incentive for investment in alternative technologies that
may not be penalised by future carbon pricing.
These drivers and their relationships are illustrated in Figure 3.
1.3. Scope
While much attention has been given to utility-scale and residential scale
applications of EES, the scope of this dissertation will be limited to the application
of EES technologies to support renewable energy generation for commercial energy
users within the NEM. Figure 4 depicts the NEM which comprises the east coast of
Australia and Tasmania.
5
Figure 3: System drivers for energy storage
Rising electricity
prices
Replacing aging
infrastructure
Rising peak demand
Climate change policies
Renewables Deployment
Declining renewable costs
Declining subsidies
Energy storage
Declining storage technology prices
Improved reliability, power quality, demand
management
6
Figure 4: Map of the National Electricity Market (AEMO 2014)
These technologies will also have broader off-grid applications for energy users in
remote areas. Emphasis is placed on electrochemical i.e. rechargeable batteries and
electromagnetic energy storage technologies, which are suited to behind-the-meter
applications for commercial energy users, at a maximum of 1 megawatt (MW).
The impact of increased deployment of these technologies on utilities’ business
models will focus on AGL Energy and AusNet Services as case studies. AGL
Energy is a private vertically integrated energy retailer and the largest owner of both
7
renewable and conventional generation in Australia, while AusNet Services is a
distribution network service provider in Victoria.
1.4. Objectives
The primary research question of this dissertation is: Among the available EES
technologies, which are the most appropriate to enable renewable energy integration
for large commercial energy users? To meet the needs of commercial energy users,
EES technologies need to be cost effective, low maintenance, have excellent cycling
stability and efficiency; and be environmentally sustainable. The outcome of this
dissertation will be:
Information on both emerging and commercially available EES technologies
suitable for commercial sector applications;
A techno-economic comparison of these technologies; and
The identification of the technical, regulatory and economic barriers that need to
be overcome for their widespread deployment.
The capacity of EES as an emerging technology to transform the NEM will also be
explored with the following key objectives:
Examining the impact of increased rates of renewable energy storage on
established gentailers and electricity network service providers; and
Providing the regulatory and policy reforms needed to facilitate the growth of
renewable EES.
8
1.5. Dissertation Structure
The dissertation structure is as follows: Chapter 2 will provide the methodology
used to meet the objectives listed in section 1.4 as well as issues raised by authors in
the literature. Chapter 3 will provide a description and techno-economic comparison
of EES technologies suitable for supporting renewable energy for commercial
energy users. Chapter 4 will provide an overview of the NEM. Chapter 5 will
explore the implications of the increased deployment of renewable EES for utilities
including an assessment of the business models suitable for adaptation to the new
energy paradigm. Chapter 6 will explore the current barriers faced by renewable
energy storage and will provide policy options that will enable positive outcomes for
both utilities and commercial energy users in the long-term. Chapter 7 provides the
dissertation conclusions and recommendations.
9
2. Methods and Analytical Framework
2.1. Literature Review
2.1.1. Electrical Energy Storage Technologies
A literature review was used to conduct a techno-economic assessment of the EES
solutions suitable for commercial energy users which could be used to support
renewable energy integration. Key performance parameters examined were:
System power rating and energy capacity;
Storage time;
Response time;
Energy density;
Power density;
Depth of discharge (DoD)
Cycle life;
Cycle efficiency;
Environmental impact; and
Cost
Publications reviewed for the selected parameters are listed in Table 1. These are
explored in Chapter 3.
10
Table 1: Publications used in literature review of EES technologies
Author Technologies
Akhil et al. 2013
Lead-acid, Li-ion, NaS, NiCd, Vanadium Redox, ZnBr,
Supercapacitors, SMES, Zinc-air
altE Store 2015
Amendola et al. 2013
Carnegie et al. 2013
Hybrid Aqueous Batteries – Aquion AHI
Zinc-air
Lead-acid, Li-ion, NaS, NiCd, Vanadium Redox, ZnBr,
Supercapacitors, SMES
Cho, Jeong, and Kim 2015 Lead-acid, Li-ion, NaS
Doughty et al. 2010 Li-ion
Eos Energy Storage 2015 Zinc-air
IEC 2014 Zinc-air
IRENA 2015 Li-ion, NiMH, Supercapacitors, SMES
Kim et al. 2014 Hybrid Aqueous Batteries
Koohi-Kamali et al. 2013 NaS
Kousksou et al. 2014 Lead-acid, Li-ion, NaS, NiCd, Vanadium Redox, ZnBr,
Supercapacitors, SMES
Lund et al. 2015 NiMH
Luo et al. 2015 Lead-acid, Li-ion, NaS, NiCd, Vanadium Redox, ZnBr,
Supercapacitors, SMES
Ma, Yang, and Lu 2014 Supercapacitors
Pei, Wang, and Ma 2014 Zinc-air
Shukla et al. 2012 Supercapacitors
Whitacre et al. 2012,
2014, 2015
Hybrid Aqueous Batteries – Aquion AHI
Zakeri and Syri 2015 Lead-acid, NaS, NiCd, Supercapacitors, SMES
2.1.2. Cost Comparison
The cost parameter is a key factor in the deployment of EES. While a number of
technology reviews (including those listed in Table 1) provide details of the capital
cost in dollars per kilowatt ($/kW) or dollars per kilowatt hour ($/kWh), these do
not always account for the full cost of storage over the life of the system for
commercial applications. Therefore, average cost calculations for the Total Capital
11
Cost (TCC), Life Cycle Cost (LCC) and Levelised Cost of Energy (LCOE) were
performed following Equations 1 to 8 from Zakeri and Syri (2015).
Total Capital Cost (TCC)
The TCC covers the cost of purchasing and installing the EES unit including the
cost of the power conversion system, storage section and balance of plant costs. It is
calculated as the per unit power rating (Cost/kW) or per unit energy rating
(Cost/kWh):
Ccap = CPCS + CBOP + Cstor (1)
Where
Ccap is the total capital cost
CPCS is the cost of the power conversion system
CBOP is the balance of plant costs
Cstor is the storage section
Life Cycle Costs (LCC)
The LCC includes the TCC as well as the variable and fixed operating and
maintenance costs, finance costs and the cost of disposing or recycling the system.
It is presented as a levelised annual cost in $/kW. The LCC can be calculated by
annualising the TCC (Ccap,a) expressed as a cost/kW-yr and then accounting for the
present value of money and the interest rate (i) over the lifespan (T) by applying a
capital recovery factor (CRF):
Ccap,a = TCC*CRF (2)
12
Where
CRF = i (1+i)T
(3)
(1+i)T - 1
The total fixed and variable operating and maintenance costs are summed and
multiplied by the annual discharge cycles (n) and discharge hours (h) as a cost/kW–
yr:
CO&M,a = CFOM + CVOM x n x h (4)
The annualised replacement costs (CR,a) are based on the replacement period (t) and
the number of lifetime replacements (r):
r
(CR,a) = CRF x Σ (1+i)-kt
x (CR x h) (5)
k=1 ηsys
Where
h is the charging/discharge time
ηsys is the efficiency
These are based on a single complete battery cycle at its rated depth of discharge.
The annualised disposal costs (CDR,a) in cost/kW-yr have the plant lifetime (T) and
interest rate (i) factored in:
CDR,a = CDR x i (6)
(1+i)T-1
To calculate the annualised life cycle costs represented by CLCC,a in cost/kW-yr all
of the cost items are added:
13
CLCC,a = Ccap,a + CO&M,a + CR,a + CDR,a (7)
Levelised Cost of Energy (LCOE)
The LCOE for the storage system measured in cost/kWh can be calculated if the
annual life cycle costs (CLCC,a), number of cycles (n) and operating hours (h) of the
system are known:
LCOE = CLCC,a (8)
n x h
Levelised Cost of Storage (LCOS)
Energy density is a critical factor for large-scale storage but is typically inversely
correlated with cycle life, which impacts the economic viability of energy storage
(Whitacre et al. 2014). This is accounted for in the LCOS in cost/kWh in Equation
9 based on Whitacre et al. (2014). It accounts for the storage technology’s total
capital cost (TCC) per kWh over its lifetime, the number of cycles (n), system
efficiency (ηsys), and depth of discharge (DoD).
LCOS = TCC/kWh (9)
n x ηsys x DoD
The cost metrics used are in US dollars and assume an interest rate of 8% based on
a finance rate of 5% and a long term inflation rate of 3%.
14
2.1.3. The National Electricity Market (NEM)
The NEM forms the system boundary for this study. Information on the governance
structures as well as data on the current state of market with respect to generating
capacity versus demand and market pricing was gathered from reports by the
Australian Energy Market Operator (AEMO), the Australian Energy Regulator
(AER), the Australian Securities Exchange (ASX) and the Bureau of Resources and
Energy Economics (BREE).
Several studies have focussed on the impact of renewable energy generation on the
NEM including pathways to 100% renewable electricity (Hou, Ho, and Wiley 2014;
Abdullah, Muttaqi, and Agalgaonkar 2015; Hasan, Saha, and Eghbal 2014; Elliston,
MacGill, and Diesendorf 2013). Recent challenges raised by the literature also
include managing peak demand, low wholesale prices, an excess of generating
capacity, and rising network prices (Brinsmead, Hayward, and Graham 2014). These
issues were explored in depth by the Productivity Commission (2013) and the AER
(2014). The impact of these issues on utilities business models in relation to EES
technologies were examined in Chapters 5 and 6.
2.2. Selected Case Studies
A comparative case study was performed to critically examine how well the
business models of NEM market participants - AGL and AusNet Services - are
responding to the emergence of EES technologies and the changing energy market
versus their equivalents in the German energy market - E.ON and Rheinsch-
Westfalisches Electrizitatswek (RWE). Data was gathered from company reports,
corporate presentations, submissions and media reports.
15
Particular emphasis was placed on each utility’s business model in terms of
customer interface, value proposition, revenue model and infrastructure (Richter
2012). Richter (2012) noted that the advantage of this approach is that it has been
tested extensively, and has been successfully applied to renewable energy
technologies.
2.3. Analysis Methods
2.3.1. Innovation Systems Theory
Many EES technologies can be defined as innovations i.e. new products that offer
comparative advantage to relying completely on the grid to potential adopters.
While some EES technologies have a history dating back to the nineteenth century,
what we are interested in here is their rate of adoption, and common barriers and
enablers to their widespread market diffusion. The adoption of a technology and its
success is typically illustrated in Figure 5 as an S-curve over time (Rogers 1983).
Figure 5: Technology adoption and market diffusion
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Level
of
Ad
op
tio
n
Adoption Time (Years)
Technology Adoption and Diffusion Curve
16
The S-shaped curve rises slowly at first with a few early adopters. Once an
innovation is adopted by 10-15% of the market, it experiences rapid market
diffusion. This concept was applied to EES technologies in Chapter 3 to analyse the
current stage of the EES market in the product adoption life cycle, and options for
increased deployment in Chapters 5 and 6.
2.3.2. Systems Thinking
Systems thinking was used to formulate policy and regulatory options to encourage
EES as a pathway to increasing levels of renewable energy integration. Systems
thinking involves taking a broad view of an issue to include the overall structures,
patterns, dynamics and cycles rather than focussing on a specific component of an
overall system (Senge 2006). The interactions of system components rather than
individual components are of primary importance. This concept was used to map a
pathway to a new business model for the selected utilities as a means of adapting to
the diffusion of EES technologies and renewables in Chapter 5. It was also used to
map the current energy market structure and policy framework within the NEM in
Chapter 6 and identify areas of possible intervention.
2.4. Limitations
It is not possible to explore all available EES technologies in this study. The scope
has been limited to lead-acid, sodium sulphur, nickel cadmium, nickel metal
hydride, lithium ion, redox flow, zinc-air and hybrid aqueous batteries as well as
supercapacitors and superconducting magnetic energy storage systems. Depending
on the application, while there is broad agreement across the literature on the
technical performance of these systems, there is a wide range in the reported costs.
17
In addition, the actual performance of these systems in a commercial setting may
vary from that in a controlled environment. The location, grid electricity costs, and
size of the storage system will all impact the economic results for individual
commercial users, and data for commercial scale applications is limited due to a
small number of deployments.
How utilities are adapting their business models to the emergence of renewable
EES and its impact on the energy market is rapidly evolving. This leaves a great
deal of scope for future research.
18
3. Electrical Energy Storage Technologies
This chapter details the suitable applications and categories of EES technologies for
commercial energy users. Their relevant technical characteristics will be described
followed by a techno-economic comparison noting the most appropriate
technologies for particular applications and their costs.
Relevant attributes for describing EES systems include:
System power rating and energy capacity: The power rating is the amount of
instantaneous reserve power that the device can supply in megawatts (MW),
while the energy storage capacity is the amount of energy that is accessible,
following charging, in megawatt hours (MWh).
Storage time: Depending on the required application, energy may need to be
stored for minutes to months.
Response time: The speed at which a storage device absorbs and delivers
energy (Kousksou et al. 2014).
Energy density: This is the quantity of energy stored as a ratio of the storage
device mass (Kousksou et al. 2014). Often measured in watt hours per kg or
Wh kg-1
, it determines the size of storage system, which is critical where
space is limited.
Power density: This is the quantity of power stored as a ratio of the storage
device mass and is measured in watts per kg or W kg-1
. It determines how
much power can be delivered.
Depth of discharge (DoD): This describes the amount of charge that a
storage device holds. For example, a battery with a DoD of 20% has
19
delivered 20% of its available energy. The higher the DoD, the lower the
battery lifespan.
Cycle life: This refers to the quantity of charge/discharge cycles that a device
is able to sustain prior to loss of performance and is a critical factor that
impacts the overall cost of the system. An energy storage technology's cycle
life is dependent on ambient temperature and DoD, and will have a particular
operating temperature range and DoD for optimum cycle life.
Cycle efficiency: This is proportion of system energy output to the energy
input required to store the energy.
Cost: This includes the capital and operating costs.
Environmental impact: Selection of energy storage based on its
environmental impacts is of critical importance given some systems’ release
of toxic chemicals, which can impact air, water and soil quality (Yekini
Suberu, Wazir Mustafa, and Bashir 2014).
3.1. Applications for Commercial Users
3.1.1. Power Quality and Reliability
EES devices can be used to protect end-user loads from sharp increases and
temporary moments of low voltage and distortions that can disrupt production at
commercial sites and affect the performance of equipment (Kousksou et al. 2014).
These fluctuations are not uncommon for renewable energy systems, which are
weather dependent. EES technologies can be used to absorb and inject power to
smooth out any fluctuations (Akhil et al. 2013). EES technologies can also be used
to support loads during power outages to provide uninterrupted energy supply
(Kousksou et al. 2014).
20
3.1.2. Demand Management
The following example demonstrates how energy storage can be used for demand
management to reduce or avoid demand costs during peak periods specified by the
network. Peak demand in AusNet Services’ network area is specified as being
between 2:00 pm and 6:00 pm Australian Eastern Standard Time on 5 ‘critical peak
demand’ days nominated by the network between November and March.
Customers are provided 24 hours’ notice by the network of a potential critical peak
demand (CPD) day. The average of their demand over these 5 days becomes the
peak demand that they are charged each month for the next 12 months. The
potential exists for them to reduce their demand charges to zero if they are able to
curtail their load.
An example of this application is illustrated in Figure 6 for a site in Victoria. Site
specifications:
Site use: Meat wholesaler
Annual usage: 1,861 MWh
Demand: 326 kVA
Network tariff: NSP76
Annual network costs (2015 rates): $137,160
Figure 6 shows the benefit of using solar based energy storage during one of the 5
CPD days where the demand was reduced to 181 kVA, as indicated by the dotted
line.
21
Figure 6: Example of demand management using energy storage on critical peak demand day
By reducing the critical peak demand to an average of 176 kVA on all 5 CPD days,
this site is able to save over 17% on network charges per annum. Reducing the
demand to 0 kVA could save over 20%. Calculations are itemised in Appendix A.
3.1.3. Energy Shifting
EES technologies allow renewable energy produced to be delivered when required.
End users can charge their EES systems during lower priced off peak intervals and
release this energy during more expensive peak intervals as a means of reducing
energy costs (Sue, MacGill, and Hussey 2014). This application is illustrated in
Figure 7.
0
50
100
150
200
250
300
350
400
22/0
1/2
01
5 0
:30
22/0
1/2
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:30
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1/2
01
5 2
:30
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5 3
:30
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1/2
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:30
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5 5
:30
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1/2
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5 6
:30
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1/2
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5 7
:30
22/0
1/2
01
5 8
:30
22/0
1/2
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5 9
:30
22/0
1/2
01
5 1
0:3
0
22/0
1/2
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5 1
1:3
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1/2
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5 1
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0
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5 1
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0
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1/2
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5 1
4:3
0
22/0
1/2
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5 1
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0
22/0
1/2
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0
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5 1
7:3
0
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1/2
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5 1
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0
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1/2
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5 1
9:3
0
22/0
1/2
01
5 2
0:3
0
22/0
1/2
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5 2
1:3
0
22/0
1/2
01
5 2
2:3
0
22/0
1/2
01
5 2
3:3
0
Dem
an
d (
kV
A)
Half Hourly Time Interval
Demand Management: 22 January 2015
Demand
Demand
with EES
22
Figure 7: Energy supply shift incorporating storage (Carnegie et al. 2013)
EES may also be used to store excess renewable generation for later use. This
enables the overall load to be smoothed, which is also beneficial when negotiating
commercial retail electricity contracts, as utilities provide preferential pricing to
sites with high load factors.
A summary of the required characteristics to support these applications is listed in
Table 2 below as per Carnegie et al. (2013):
Table 2: Required storage characteristics for commercial applications
Application
Required
Response
Time
Discharge
Cycles
Storage
System
Power (MW)
Duration of
Power
Discharge
Power Quality - Short Duration 20 ms 100 times/year 1-50 5 secs
5 times/day
Once an hour
Power Quality - Long Duration 20 ms Once a year 1-50 4 hrs
Demand Management 10 mins 50-500 1-50 1-4 hrs
3 Hour Energy Shift 10 mins 60 days/year 1-200 3 hrs
Once a day
10 Hour Energy Shift 10 mins 250 days/year 1-200 10 hrs
Once a day
Source: Carnegie et al. 2013
23
3.2. Storage Categories
EES technologies are typically categorised according to the form of energy stored
i.e. electrochemical systems including batteries, kinetic, potential and
electromagnetic (Luo et al. 2015). Figure 8 illustrates these categories:
Figure 8: Energy storage categories
For the purpose of this study, only those EES technologies suitable for use by
commercial energy users will be compared, namely electrochemical i.e. battery and
electromagnetic technologies.
3.3. Battery Energy Storage
Rechargeable batteries consist of two electrodes – a positive cathode, a negative
anode, and an electrolyte which allows ionic transfer between the electrodes (Alias
and Mohamad 2015). During discharge, the anode releases electrons to the cathode
and electrolyte ions through an oxidation reaction, while a reduction reaction occurs
simultaneously at the cathode using the electrons from the anode and the ions from
Energy Storage
Potential
Pumped Hydro
Compressed Air
Kinetic Flywheel
Electrochemical Batteries
Lead-acid
Nickel
Nickel Cadmium
Nickel Metal
Hydride
Lithium Ion
Sodium Sulphur
Flow Batteries
Vanadium Redox
Zinc Bromine
Metal Air
Aqueous Rechargable
Electromagnetic
Capacitors & Supercapacitors
Superconducting Magnetic Energy
Storage
24
the electrolyte (Luo et al. 2015). Electric current is generated as the ions are
transported through the electrolyte and via electrons flowing to the cathode through
an exterior circuit as illustrated in Figure 9. The reverse occurs during charging
when an external voltage is applied to the electrodes (Luo et al. 2015). Figure 10 is
an example of battery charge-discharge behaviour with respect to battery cell
voltage and storage capacity. In this example, a Zn-MnO2 cell comprises a MnO2
cathode and a Zn anode in an aqueous KOH electrolyte. This particular cell reveals
a discharge curve with a reduction in voltage from 1.55 V to 1.45 V which then
reduces to a steady voltage at 1.4 V before dropping sharply to 1.0 V (Minakshi
2008).
Figure 9: Schematic of battery operation (Luo et al. 2015)
25
Figure 10: Example of battery charge discharge behaviour (Minakshi 2008)
3.3.1. Lead Acid Batteries
Lead-acid batteries were the world’s first rechargeable batteries and have a history
dating back to 1859, when they were invented by Gaston Planté (Cho, Jeong, and
Kim 2015). Their market predominance is due to their technical maturity, relatively
low cost and reliability (Luo et al. 2015). They are easy to manufacture and can
provide power in kilowatts to megawatts (Cho, Jeong, and Kim 2015).
Lead-acid batteries may be flooded (electrodes submerged in liquid) or valve-
regulated (electrolyte is gel-based) (Carnegie et al. 2013). Sulphuric acid is used as
the electrolyte (Kousksou et al. 2014). During operation, lead dioxide (PbO2) is
formed at both electrodes with an oxidation reaction at one electrode (Pb →Pb2+
+
2e-) and a reduction reaction at the other (Pb
4+ + 2e
- → Pb
2+) (Cho, Jeong, and Kim
2015). They have a nominal voltage of 2 V and a cycle efficiency of 70 to 90%
(Kousksou et al. 2014; Luo et al. 2015; Carnegie et al. 2013). Disadvantages
include their short lifetime; difficulties in supplying repeated cycling; and poor
0 50 100 150 200 250 300 350
1.0
1.2
1.4
1.6
1.8
2.0
Charge
discharge
Cel
l P
ote
nti
al
/ V
Cell storage capacity / mAh g-1
26
performance at very low or high temperatures (Kousksou et al. 2014; Luo et al.
2015). Lead acid batteries also contain toxic materials that pose environmental and
safety risks, requiring safe disposal (Carnegie et al. 2013)
Advanced lead-acid batteries have reduced maintenance requirements and greater
cell uniformity which provides longer lifetime (Carnegie et al. 2013). The ultra-
battery is an example of this, which combines the lead-acid battery with a capacitor
for a longer lifespan (Carnegie et al. 2013). Advantages over the traditional lead
acid battery include a 60% improvement in charging power and a 50%
improvement in discharging power (Cho, Jeong, and Kim 2015). However, these
advantages add to the cost.
3.3.2. Nickel Batteries
Nickel based batteries are dry cells, each containing a positive nickel-based
electrode and a negative electrode (Carnegie et al. 2013). The main varieties are
nickel-cadmium (NiCd) and nickel-metal hydride (NiMH) (Kousksou et al. 2014).
NiCd batteries are a proven robust, and low maintenance substitute for lead-acid
batteries with a lifespan of over 3,000 cycles at 100% DoD (Yekini Suberu, Wazir
Mustafa, and Bashir 2014). Their high life expectancy, reliability, energy density as
well as high power capability have made NiCd the most popular nickel batteries for
utility energy storage (Carnegie et al. 2013). NiMH batteries were originally
developed as an environmentally friendly alternative to NiCd batteries due to the
toxicity of cadmium (IEC 2014). They have the positive properties of NiCd
batteries, but a lower power capacity (IEC 2014).
27
Limitations include the need to be fully charged and discharged in order to avoid
the ‘memory effect,’ where the battery will remember the previous state of charge
and discharge on this basis, and reduce the battery life (Yekini Suberu, Wazir
Mustafa, and Bashir 2014). NiMH batteries also have a high self-discharge of 5-
20% within 24 hours of full charging, and a sensitivity to deep cycling (Luo et al.
2015).
3.3.3. Lithium-Ion Batteries
Figure 11 shows the schematic view of a lithium-ion battery. Most lithium-ion
based batteries have a positive metal-oxide electrode, a carbon based negative
electrode, and a solution of lithium (Li) salt combined with organic solvents as the
electrolyte, and a polymer separator (Kousksou et al. 2014; Carnegie et al. 2013).
During charging, Li ions flow to the negative electrode from the positive metal-
oxide electrode (Carnegie et al. 2013). The overall chemical reactions can be
summarised as follows (Cho, Jeong, and Kim 2015):
Positive electrode: Li-xCoO2 + xLi+ + xe
- ⇌ CoO2
Negative electrode: Li-xC6 ⇌ xLi+ + xe
- + C6
Overall reaction: LiC6 + CoO2 ⇌ C6 + Li CoO2
28
Figure 11: Li-ion schematic view showing battery function (Carnegie et al. 2013)
Li-ion batteries are ideal for an application requiring a fast response time i.e.
milliseconds, low weight and small dimensions (75 - 200 Wh kg-1
,
150 - 2,000W kg-1
(Luo et al. 2015). They also have a low self-discharge rate (5 per
cent) (Kousksou et al. 2014). Their nominal voltage (3.7V) is higher than many
other battery types, which means that fewer cells are needed for the same level of
power output (Carnegie et al. 2013). The recent use of cathodes made from lithium
iron phosphate (LiFePO4) and lithium titanium oxide (Li4TiO12) anodes has been
viewed as promising as these materials have a good capacity (~170 mAh g-1
), avoid
thermal runaway in high temperature operations, and are lower cost and more
environmentally friendly than other Li-ion batteries (Cho, Jeong, and Kim 2015).
Their main disadvantage is the additional cost associated with their packaging and
internal protection to prevent overcharging (Kousksou et al. 2014). In order to be
operated at MW levels, efficient thermal management is required to maintain safety
due to the use of a flammable electrolyte (Cho, Jeong, and Kim 2015). As their
29
lifetime is dependent on their DoD, they are not recommended for full discharge
applications (Carnegie et al. 2013).
An example of a commercial-scale Li-ion EES system within the NEM is
Transgrid’s iDemand project in West Sydney, which includes 400 kWh/100kW of
Lithium Manganese Nickel Cobalt (LiMnNiCoO2) batteries from Magellan Power;
98 kW of solar panels; and a 3-phase bi-directional inverter (Transgrid 2014). The
site is expected to have a peak summer demand of 400 kW and its demand
management system is expected to reduce the site’s electricity usage by up to half
during peak periods (Transgrid 2014). According Magellan Power, the batteries
have a 4,000 cycle life at 80% DoD and three phase 100 kVA output (Magellan
2015). The demonstration site includes a live monitor detailing system operations.
Figure 12 shows the system operations on 6 June 2015 at 11.59 am:
Figure 12: Transgrid's iDemand energy \system configuration including renewables and storage
(Transgrid 2014)
30
3.3.4. Sodium Batteries
A higher energy density alternative to Li-ion batteries is sodium sulphur (NaS)
batteries. NaS batteries employ a molten sodium (Na) cathode, a liquefied sulphur
(S) anode, and a solid beta alumina ceramic membrane (Al2O3) forms the
electrolyte (Kousksou et al. 2014; Cho, Jeong, and Kim 2015). Figure 13 provides a
schematic illustration of the NaS battery.
The chemical reactions during battery charging are as follows (Cho, Jeong, and
Kim 2015):
Positive electrode: xS + 2Na+ + 2e
- ⇌ Na2Sx
Negative electrode: 2Na ⇌ 2Na + 2e-
Overall reaction: 2Na + xS ⇌ Na2Sx
Figure 13: NaS schematic view (Carnegie et al. 2013)
31
They are regarded as a very promising technology for both high power and energy
storage (Luo et al. 2015; Carnegie et al. 2013). They have approximately 65% of
the market share for load-levelling as well as peak shaving (Cho, Jeong, and Kim
2015). Advantages of NaS batteries include energy densities of 150-300 Wh l-1
, a
rated capacity of 244.8 MWh, that makes it higher than other battery types; and the
use of inexpensive materials that are 99% recyclable (Luo et al. 2015). Cycle life is
2,500 at 90% DoD (Kousksou et al. 2014; Cho, Jeong, and Kim 2015).
Disadvantages include high cost (>$2,000/kW and $350/kWh) due to additional
system controls that are required for operating at high temperatures (300-350°C),
which presents a potential safety hazard (Kousksou et al. 2014).
3.3.5. Flow Batteries
Flow batteries are charged and discharged through a chemical reaction that is
reversible and occurs between two tanks containing liquid electrolyte (Kousksou et
al. 2014). Electricity is produced through a redox reaction as the electrolyte is
transported through an electro-chemical reactor (Kousksou et al. 2014). Figure 14 is
a schematic view of this arrangement in a Vanadium redox battery.
The chemical reactions can be described as follows (Cho, Jeong, and Kim 2015):
Negative electrode: V2+
⇌ V3+
+ e-
Positive electrode: VO+
2 + 2H+ + e
- ⇌ VO
2+ + H2O
Overall reaction: VO+
2 + 2H+ + V
2+ ⇌ VO
2+ + H2O + V
3+
32
Figure 14: Schematic view of a redox flow battery being discharged (Cho, Jeong, and Kim 2015).
The energy and power of the battery can be specified separately by virtue of the
storage of the electrolytes outside the reactor (Kousksou et al. 2014). Their scalable
design means that energy capacity can be readily expanded by increasing the
electrolyte levels in the storage tanks, which lowers the installation costs as the
system size is increased (Kousksou et al. 2014). They have a low self-discharge rate
and are able to be fully discharged without any deterioration, which allows for a
long system life and the ability to store energy over long periods of time (Kousksou
et al. 2014).
Commercialised flow batteries include vanadium redox batteries and zinc bromine
(ZnBr) batteries. Vanadium redox flow batteries were invented by Professor Maria
Skyllas-Kazacos and her colleagues from the University of NSW (NewSouth
Innovations 2015). Vanadium redox batteries are the most competitive of the flow
batteries in terms of system life, maintenance and safety. They are well suited to
frequency and voltage regulation, load levelling and stabilising renewable energy
output as a result of their ability to respond rapidly and deeply; and have low
33
operating costs while idle (Kousksou et al. 2014). Technical challenges that need
addressing include low quality energy density due to the low level of electrolyte
stability and solubility (Luo et al. 2015).
ZnBr batteries consist of two aqueous bromine electrolytes that react with zinc
coated electrodes separated by a microporous membrane (Carnegie et al. 2013).
They have a nominal voltage of 1.8 V, and have a high energy density and lower
cost due to the use of zinc (Carnegie et al. 2013). Lifetime is estimated at 10-20
years (Luo et al. 2015; Carnegie et al. 2013). In Australia, a ZnBr battery
commercialised by RedFlow - the ZBM - can deliver up to 240 kW of continuous
power and 600 kWh. The ZBM systems are available in 10 foot or 20 foot shipping
containers. A 50 kWh battery has been developed by ZBB Energy Corporation and
Premium Power Corporation which has recently been tested up to 2 MW (Luo et al.
2015). Disadvantages include corrosion of materials due to the use of liquid
bromine which reduces lifetime, a tendency to form dendrites, and a narrow
operating temperature range (20°C to 50°C) which limits their applications (Luo et
al. 2015). Flow batteries only operate when the electrolyte flows through the cell
stacks. However, ZnBr batteries have a high self-discharge rate if kept in this mode
i.e. an operating state for immediate charge or discharge. This makes them
unsuitable for continuous use as an energy or power buffer, and more suitable for
energy shifting.
3.3.6. Metal Air Batteries
Unlike other battery types, metal air batteries only require one electrode usually
made of zinc (Zn), lithium, aluminium or magnesium – metals with high oxidation
34
tendencies that readily release electrons (Koohi-Kamali et al. 2013; Akhil et al.
2013). In Zn-air batteries, oxygen is taken from the air surrounding the batteries to
generate an electric current (Akhil et al. 2013). The electrolytes are typically a good
OH- ion and may be a liquid or a solid polymer membrane (Koohi-Kamali et al.
2013). During operation, the air electrode is discharged and produces hydroxyl ions
within the electrolyte (Akhil et al. 2013). This leads to the oxidation of the Zn
electrode that releases electrons, producing an electric current (Akhil et al. 2013).
This operation is illustrated in Figure 15.
Figure 15: Zinc-air battery operation (Akhil et al. 2013)
The chemical reactions during discharge are as follows at each of the electrodes as
per Cho, Jeong, and Kim (2015).
Negative electrode:
Zn → Zn2+
+ 2 e−
35
Zn2+
+ 4 OH− → Zn(OH)4
2− E0 = 1.25 V
Zn(OH)42−
→ ZnO + H2O + 2 OH−
Zn + 2 H2O → Zn(OH)2 + H2 (possible)
Positive electrode: O2 + 2 H2O + 4 e− → 4 OH
− E0 = 0.4 V
Overall reaction: 2 Zn + O2 → 2 ZnO E0 = 1.65 V
Advantages of Zn-air batteries are based on the use of Zn, which is inexpensive,
non-toxic, and safe (Amendola et al. 2013). Eos Energy Storage’s claims its Eos
Aurora 1,000/4,000 Zn-air system is designed to provide grid-scale energy storage
with a discharge capability of 4 hours. The Aurora has a 4 MWh energy capacity
with a projected cycle life of 10,000 cycles for a 30 year lifespan (Eos Energy
Storage 2013). It has a roundtrip efficiency of 75% at full DoD. Significantly, its
price point is $160/kWh (Eos Energy Storage 2013). However, there are questions
regarding Eos's claims regarding its battery cycle life which has been estimated at
2,700 rather than 10,000 (Pei, Wang, and Ma 2014).
There are still a number of challenges that Zn-air batteries need to overcome.
Excess water loss can result in low humidity conditions, which can cause the
battery to fail as the electrolyte concentrations increase (Cho, Jeong, and Kim
2015). Excess water gain under high humidity conditions reduces electrochemical
activity which can also lead to battery failure (Cho, Jeong, and Kim 2015). Other
issues include the formation of dendrites at the Zn electrode; low oxygen solubility
within the electrolyte; Zn dissolution; and limited cathode electrochemical stability
during charging (Cho, Jeong, and Kim 2015).
36
3.3.7. Hybrid Aqueous Technologies
In the future, hybrid aqueous batteries may be a cost-effective and safe alternative
to Li-ion batteries. Kim et al. (2014) provided a review of the suitability of aqueous
recharchable alkali-metal ion batteries (ARABs) for large-scale applications. They
note that while Li-ion batteries have been optimised for energy storage for portable
electronics i.e. storing a large quantity of energy in a short space of time for a given
volume, for large-scale storage, cost, cycle life and safety are more important than
high energy density (Kim et al. 2014). ARABs resolve these challenges and offer
improved safety as it removes the issue of a flammable organic electrolyte. They do
not require the same level of rigorous manufacturing conditions and the electrolyte
solvent and salts are relatively low cost. Aqueous electrolytes have twice the ionic
conductivity of other electrolytes resulting in high round trip efficiency and energy
density; and are environmentally benign given their use of an aqueous electrolyte
(Kim et al. 2014).
Batteries that utilise sodium based electrodes and aqueous based neutral pH
electrolytes are also currently showing great promise. They are suitable for large-
scale storage given that the aqueous electrolyte is safe, environmentally benign, can
achieve higher power compared to organic electrolytes, and are cost-effective due
to the use of water and sodium which are natually abundant (Kim et al. 2014). The
use of water also reduces disposal issues and safety.
Aquion has developed a aqueous hybrid ion battery – the Aquion AHI™, which
uses a manganese oxide (MnO2) cathode that hosts intercalation, a carbon
composite anode with pseudocapacitive reactions, and a sodium sulphate (Na2SO4)
37
electrolyte (Whitacre et al. 2012). Figure 16 is a schematic detailing the internal
features of the AHI battery which may be stacked or module based for large scale
storage.
Figure 16: Internal features of the Aquion AHI battery (Whitacre et al. 2015).
The AHI has a a round trip efficiency above 90% at a 10 C-rate, and a
demonstrated cycle life of over 5,000 cycles at 100% DoD (Whitacre et al. 2012).
This has since been improved through the use of a composite anode based on
activated carbon/ sodium titanium phosphate (NaTi2(PO4)3) as an alternative
(Whitacre et al. 2015). This next generation battery has an energy density that has
two to three times the energy density of the previous version, while optimising the
electrode cost and withstanding partial charge with minimal self-discharge
(Whitacre et al. 2015).
38
3.4. Electromagnetic Energy Storage
3.4.1. Capacitors and Supercapacitors
While batteries involve chemical storage, energy is statically stored in capacitors
(Carnegie et al. 2013). Capacitors comprise a dielectric sandwiched between two
conducting plates (Shukla et al. 2012). They store energy as an electrostatic charge
with positive charges on a conducting plate and negative charges on the other,
which creates a voltage difference. When connected to an external load, the current
flows until the charges are balanced and the stored energy released (Shukla et al.
2012). Importantly, as the charges are stored without any phase changes, the process
can be performed repeatedly and reversed (Shukla et al. 2012). Significantly, their
capacity to be continuously cycled without a reduction in lifespan makes them well
suited to renewable energy applications such as solar PV, where batteries often need
to be replaced every 1 to 3 years (Shukla et al. 2012).
Current supercapacitor energy storage units have power outputs up to 0.05 MW
(Zakeri and Syri 2015). However, the stored energy will only supply the load for a
few seconds or minutes. Supercapacitors are entering the commercial stage and are
competitive with batteries for uninterruptible supply applications requiring high
power (Kousksou et al. 2014). While supercapacitors have self-discharge rates of 5 -
40% per day, they have cycle times of more than 105 and cycle efficiencies of 84 -
97% (Luo et al. 2015).
39
3.4.2. Superconducting Magnetic Energy Storage
Superconducting magnetic energy storage (SMES) involves storing electrical
energy inside a magnetic field without converting it into chemical or mechanical
energy (Kousksou et al. 2014). As illustrated in Figure 17, SMES systems are
typically composed of a "superconducting coil unit", cryogenic refrigeration and
vacuum subsystems as well as power conditioning systems (Luo et al. 2015). The
superconducting coil's direct current is cryogenically cooled which generates a
magnetic field in which energy is stored (Luo et al. 2015).
Figure 17: Schematic view of SMES system (Carnegie et al. 2013)
SMES systems are characterised by high power densities of up to 2,000 W kg-1
,
millisecond response times, complete discharge in under 1 minute, cycle efficiency
of 95 - 98% and a lifetime of up to 30 years (Luo et al. 2015; Zakeri and Syri
2015). One of their key advantages is their ability to be fully discharged with
minimal deterioration after several thousands of cycles (Luo et al. 2015). Their
main disadvantages include a self-discharge rate of 10-15% per day; loss of energy
due to slight temperature variations; and the negative consequences of their
powerful magnetic field (Luo et al. 2015).
40
3.4.3. Hybrid EES Technologies
One option to extract the maximum value of EES technologies may be to combine
their advantages to form a hybrid system. While battery storage is best suited to
supplying loads that are low and steady, battery charging from renewable outputs
are problematic as they may fluctuate substantially depending on weather
conditions (Ma, Yang, and Lu 2014). In addition, rapid power supply or load
fluctuations reduce battery lifetime, as they have a low power density and this
results in a low state of charge, which necessitates more frequent charge/discharge
cycles (Ma, Yang, and Lu 2014).
Supercapacitors may be used to resolve these issues due to their greater cycle life
and improved charging/discharging efficiency (Ma, Yang, and Lu 2014). Batteries
and supercapacitors can be combined in hybrid systems where the battery is used to
import and supply long-term continuous power and the supercapacitor responds to
rapid power fluctuations (Ma, Yang, and Lu 2014). Hybrid systems are therefore
well suited to renewable energy systems to enable both reliable supply and
extending the lifetime of the energy storage system components.
Ma, Yang and Lu (2014) used a hybrid system comprised of a wind turbine, PV
array, inverter, battery and supercapacitor and compared it to a standalone system
with a single type of storage device (Ma, Yang, and Lu 2014). The supercapacitor,
with its high power density, was activated and charged when the charging power
was suddenly increased. When charging was absent, the current flow of the
supercapacitor was changed and stored energy was released to the battery. During
charging, the battery’s low current steadily increases and then declines to a value
41
equal to that of the supercapacitor in the off state. This occurs in the opposite
direction, so that energy continues to be consumed from the supercapacitor. This
results in the battery being under charge during the whole cycle, so that the effect of
variable charging on the battery is reduced by the supercapacitor (Ma, Yang, and
Lu 2014). The results showed that the hybrid system could absorb and deliver 2.5
times more power than a system using only battery storage (Ma, Yang, and Lu
2014).
3.5. Techno-Economic Comparative Assessment
To assess their suitability for the required applications, commercial energy users
need to consider the technical and economic feasibility of storage technologies that
are suitable for their needs. In terms of performance, EES technologies can be
compared according to their rated power, energy and discharge duration, as
illustrated in Figure 18.
Figure 18: Comparison of rated power, energy and discharge duration (Carnegie et al. 2013)
42
EES technologies on the left of the chart including capacitors and SMES provide
high power for short durations while those on the right including lead-acid, Li-ion,
NaS and flow batteries provide higher energy over longer periods of time.
Table 3 provides a comparison of the technical characteristics of EES technologies.
Given their low cost, technical maturity and suitability for several applications,
lead-acid batteries are still the benchmark that other EES technologies are judged
against. NaS batteries compete well with lead-acid batteries in terms of system
capacity, efficiency, energy density, storage time and cycle life. While the cheapest
Li-ion batteries are more expensive than lead-acid batteries, their low operating
costs and longer cycle life makes them competitive on a life cycle basis. Where
space is limited, they are the more suitable as they have a higher energy density.
They also compete well with other battery types in terms of cycle life and
efficiency. SMES, supercapacitors and Li-ion batteries exhibit the highest cycle
efficiencies and also have very fast response times. Compared to batteries,
supercapacitors and SMES systems have much greater cycle times and can charge
instantaneously, while batteries can store large amounts of energy for longer. While
lead-acid batteries are the most mature with a low capital cost, they have high
operating costs and a low energy density. In terms of energy density and lifespan,
NiCd batteries compete reasonably well with lead-acid batteries and require less
maintenance (Kousksou et al. 2014). However, they are more expensive, contain
toxic materials and have a high self-discharge (Kousksou et al. 2014). Hybrid
aqueous and Zn-air batteries are superior to other EES technologies in terms of
environmental impact. Zn-air batteries exhibit safety and cost advantages over Li-
ion, flow redox, NaS, and other metal-air battery types (Pei, Wang, and Ma 2014).
43
Table 3: Comparison of EES Technical Characteristics
Lead-
Acid NiCd Li-ion NaS
Van.
Redox ZnBr
Zn-
Air
Hybrid
Aqueous
Super-
capacitor SMES
System
capacity (MW)
0 - 40 0 - 40 1 - 100 0.4–
244.8 0.5 - 100 0.05 - 10 1 0.03+ 0.3+ 0.1 -10
Voltage
(V) 2.1 1.3 3.7 2.1 1.4 1.8 2.1 ~2 0.5 N/A
Operating Temp. (°C)
-10 - 40 -40 - 45 -10 - 50 300 - 350 0 - 40 20 - 50 0 - 50 -5 - 40 -40 - 85 < - 200
Cycle
efficiency
(%)
70 - 90 60 - 83 85 - 98 75 - 90 85 65 - 75 75 >85 90 - 97 95 - 98
Power
density
(W kg-1)
75-300 50-1000 50-2000 150-230 166 45 N/A N/A 100,000 500-
2,000
Energy density
(Wh kg-1)
25 - 50 40 - 65 75 - 200 150 - 250 30 50 130 -
200 ~46 3 - 10 0.5 - 5
Storage time
Mins - Days
Mins - Days
Mins - Days
Hours - Months
Hours - Months
Hours - Months
Hours Hours Secs - Hours
Mins - Hours
Response
time ms ms ms ms
<1/4
cycle
<1/4
cycle ms ms ms ms
Lifetime (cycles)
250 - 1,000
2,000 - 3,500
2,000 - 10,000
2,500 - 4,500
13,000 2,500 2,700 - 10,000
>3,000 100,000 -
500,000 100,000+
Lifetime
(years) 5 - 15 10 - 20 5 - 15 5 - 15 10 - 15 10 - 20 30 5+ 10 - 30 20+
Capital
cost ($/kWh)
200 -
11,50
400 –
2,400
500 -
2,500 300 - 500
150 –
1,000
150 –
1,000 310 550
300 –
6,000
1,000 -
10,000
Capital
cost ($/kW)
300 -
4,900
500 –
1,500
900 -
4,100
1,000 –
3,000
600 –
1,500
700 –
2,500 1,238 1,950 300 - 450
500 -
7,200
Operating
costs
($/kW/yr)
4 - 50 2 - 12 7.9 - 25 ~80 7.7 - 10 5.7 9.2 - 0.005 -
13.1 18.5 -
22.2
Environ.
impact Negative Negative Negative Negative Small Small Benign Benign Small Negative
References Carnegie
et al. 2013
Kousksou
et al. 2014
Cho,
Jeong,
and Kim
2015
Cho,
Jeong,
and Kim
2015
Akhil et
al. 2013
Kousksou
et al. 2014
Akhil et
al. 2013
Whitacre
et al. 2014
IRENA
2012
Carnegie
et al. 2013
Cho,
Jeong,
and Kim
2015
Luo et al.
2015
Doughty
et al. 2010
Koohi-
Kamali et
al. 2013
Kousksou
et al. 2014
Luo et al.
2015
EOS
Energy
Storage
2015
altE Store
2015
Kousksou
et al. 2014
IRENA
2012
Kousksou
et al. 2014
Zakeri
and Syri 2015
Kousksou
et al. 2014
Kousksou
et al. 2014
Luo et al.
2015
Zakeri
and Syri
2015
IEC
2014
Luo et al.
2015
Kousksou
et al. 2014
Luo et al.
2015 Luo et al.
2015
Luo et al.
2015
Zakeri
and Syri
2015
Pei,
Wang
and Ma
2014
Ma, Yang,
and Lu 2014
Luo et al.
2015
Zakeri
and Syri
2015
Zakeri
and Syri
2015
Zakeri and
Syri 2015
Zakeri
and Syri
2015
44
3.5.1. Application Comparisons
Table 4 compares EES systems depending on their application. The results reveal
that the same technologies yield different results in terms of lifetime and cost for
different applications.
Table 4: Comparison of EES technical characteristics and applications based on
Carnegie et al. (2013)
EES Power
(MW)
Capacity
(MWh)
Discharge
Time
(hrs)
Efficiency
(%)
Lifetime
(Cycles) Maturity
Total
Cost
($/kW)
Cost
($/kWh)
Renewable Integration Adv. Lead
Acid 50 200 4 85 - 90 2,200 Commercial
1,700 -
1,900 425 - 475
Lead Acid 20 - 50 200 5 85 - 90 4,500 Mature 4,600 -
4,900 920 - 980
Lead Acid 100 400 4 85 - 90 4,500 Demo 2,700 675
NaS 50 300 6 80 4,500 Commercial 3,071 512
Vanadium
Redox 50 250 5 65 - 75 >10,000 Demo
3,100 -
3,700 620 - 740
ZnBr 50 250 5 60 >10,000 Demo 1,450 -
1,750 290 - 350
Zn-air 50 250 5 75 >10,000 Early
Commercial
1,440 -
1,700 290 - 340
Power Quality Adv. Lead
Acid 1 - 100 0.25 - 25 0.25 - 1 75 - 90 >100,000 Demo
950 -
1,590
2,770 -
3,800
Li-ion 1 - 100 0.25 - 25 0.25 - 1 87 - 92 >100,000 Demo 1,085 -
1,550
4,340 -
6,200
Vanadium
Redox 0.2 0.7 3.5 68 >10,000 Demo 5,213 1,490
Supercapacitors 0.3+ 0.075 -
0.3 0.25 - 1 90 - 97
100,000 -
500,000 Demo 300 - 450
300 -
6,000
SMES 0.1 - 10 0.025 -1 0 0.25 - 1 95 - 98 >100,000 Early
Commercial
500 -
7,200
1,000 -
10,000
Reliability; Demand Management & Energy Shifting
Adv. Lead
Acid 1 - 12 3.2 - 48 3.2 - 4 75 - 90 4,500 Demo
2,000 -
4,600
625 -
1,150
NaS 1 7.2 7.2 75 4,500 Commercial 3,200 -
4,000 445-555
NiCd 40 200 5 60 - 73 2,000 -
3,500 Commercial
500 -
1500
400 -
2,400
Li-ion 1 - 10 4 - 24 2 - 4 90 - 94 4,500 Demo 1,800 -
4,100
900 -
1,700
Vanadium
Redox 1 - 10 4 - 40 4 65 - 70 >10,000 Demo
3,000 -
3,310 750 - 830
ZnBr 1 - 10 5 - 50 4 60 - 65 >10,000 Demo 1,670 -
2,015
340 -
1,350
Zn-air 1 5.4 5.4 75 4,500 Early
Commercial 1,238 310
Hybrid
Aqueous
Batteries
0.03+ 25 20 >85 >3,000+ Early
Commercial 1,950 550
45
Power Quality and Reliability
Given that power quality requires a response time within milliseconds, Table 3
reveals that suitable technologies include lead-acid, Li-ion and NaS batteries,
SMES systems and supercapacitors (Luo et al. 2015). The good cycling ability,
high power density and efficiency of supercapacitors and SMES systems make
them good options (Chambers and Rozali 2013). While supercapacitors have a high
self-discharge rate and a low energy density, they have a shorter charging and
discharging time than batteries and a higher power density (Luo et al. 2015; Shukla
et al. 2012; Ma, Yang, and Lu 2014). These qualities are particularly useful for
improving power quality (Luo et al. 2015; Zakeri and Syri 2015).
Reliability applications require technologies with a fast response that can discharge
over long periods. While supercapacitors and SMES systems have fast response
times, their inability to store energy for long periods precludes them from this
application. Batteries and flow batteries are suitable (Luo et al. 2015).
Demand Management
The higher power ratings of Li-ion, advanced lead-acid, NiCd and flow batteries,
coupled with a response time of up to 1s, and the ability to provide power supplies
for up to several hours to allow for the switching of one power source to another,
make these technologies suitable for demand management (Luo et al. 2015). The
lower cost of hybrid aqueous batteries makes them an attractive option.
46
Energy Shifting
The fast response time and energy capacity of conventional and flow batteries make
them suitable for energy shifting (Luo et al. 2015). The higher energy capacity,
storage time, efficiency and lower cost of Li-ion, Zn-air and hybrid aqueous
batteries make them competitive.
3.6. Cost Comparison
Cost estimates across the literature varies widely due to varying assumptions
regarding the system scale, discount rates, performance data, cycle life dependent
on DoD and a lack of standard test conditions. Estimates also vary depending on
the application. Based on the literature, Figures 19, 20 and 21 summarise the
average total capital cost, average life cycle cost and average levelised cost of
energy (LCOE) of selected EES technologies suitable for commercial long duration
storage, load shifting and renewables integration.
Figure 19: Total capital cost of storage technologies ($/kWh) including installation, power
conditioning and balance of plant based on the literature for long duration storage costs and Eq. (1)
0
100
200
300
400
500
600
700
800
900
1,000
Advanced
Lead
Acid
NaS Vanadium
Redox
ZnBr Zn-air Hybrid
Aqueous
Li-ion
$/k
Wh
Battery Types
Total Capital Costs
47
Lead-acid batteries are often pointed to as a low cost storage solution, and this
certainly holds true for commercial-scale, long duration storage. When comparing
the total installed costs however, Zn-air batteries are the most competitive. Zn-air
batteries are cost competitive by virtue of their use of zinc, which is a low cost
metal (Akhil et al. 2013). The next lowest cost batteries are the NaS batteries.
These costs are expected to fall as more systems are installed (Akhil et al. 2013).
Li-ion batteries exhibit a wide range in costs depending on their overall system
capacity. The Li-ion battery in this comparison has a much lower system energy
capacity, which results in a higher total cost per kWh.
Figure 20: Life cycle costs of large-scale energy storage ($/kW-year) are calculated from Eq. (7)
assuming a discharge of at least 4 hours, discount rate of 8% and 365 cycles per annum.
On a life cycle basis Figure 20 shows that Zn-air batteries are the most cost
effective on an annual basis followed closely by hybrid aqueous batteries.
Replacement and operating costs have a significant impact on life-cycle costs.
Hybrid aqueous batteries are more competitive than Li-ion batteries on this metric,
0
50
100
150
200
250
300
350
400
450
500
Advanced
Lead Acid
NaS Vanadium
Redox
ZnBr Zn-air Hybrid
Aqueous
Li-ion
$/k
W-y
ear
Battery Types
Life Cycle Costs
48
as they require less frequent replacements and due to their use of low cost, naturally
abundant resources i.e. sodium and water, and minimal maintenance requirements.
Care needs to be taken when comparing batteries on a life cycle cost basis. The life
cycle costs of batteries are subject to a number of uncertainties including the
lifetime of emerging technologies, which is strongly related to the operating
conditions, DoD and charge/discharge cycles (Zakeri and Syri 2015).
Figure 21 reveals that the lowest LCOE storage technologies are Zn-air, followed
closely by NaS and hybrid aqueous batteries. Zakeri and Syri (2015) note that
LCOE estimates are sensitive to the discharge time. For example Li-ion batteries
may be more cost effective over a 2 hour discharge time, and the most expensive
for discharge times of 4 hours. In this comparison, the higher replacement costs of
the Li-ion battery results in a higher LCOE, which also reflects it higher overall
costs for long duration storage applications.
Figure 21: Levelised cost of energy ($/kWh) calculated from Eq. (8)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Advanced
Lead Acid
NaS Vanadium
Redox
ZnBr Zn-air Hybrid
Aqueous
Li-ion
$/k
Wh
Battery Types
Levelised Cost of Energy (LCOE)
49
Figure 22 illustrates the levelised cost of storage (LCOS) for the selected
technologies. The LCOS cost metric is far more useful than the LCOE metric as it
accounts for overall battery efficiency, cycle life and DoD. Using this cost metric,
hybrid aqueous and flow batteries prove to be the most cost effective based on their
longer cycle life and lower lifetime costs.
Figure 22: Levelised cost of storage ($/kWh) based on Eq. (9)
3.7. Future Outlook
Managing energy costs is one of the key drivers for commercial energy users’
interest in energy storage. While energy storage costs have reduced, they are not yet
at a level sufficient to encourage widespread deployment. Storage costs require a
capital cost of less than $250/kWh in the near term at an efficiency of greater than
75% and a cycle life of more than 4,000 cycles (Cho, Jeong, and Kim 2015; BASF
2015). In the long term they need a cost of under $150/kWh with an efficiency
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Advanced
Lead Acid
NaS Vanadium
Redox
ZnBr Zn-air Hybrid
Aqueous
Li-ion
$/k
Wh
Battery Types
Levelised Cost of Storage (LCOS)
50
greater than 80% and a cycle life of over 5,000 cycles (Cho, Jeong, and Kim 2015;
BASF 2015).
Tesla’s announcement on April 30, 2015 of the launch of its small-scale Powerwall
and commercial-scale Powerpack Li-ion batteries are regarded as ground breaking
in terms of their costs. The commercial-scale Powerpack comes in 100 kWh battery
blocks and is priced at $US250/kWh (AFR 10 May 2015). If Tesla’s claims about
its batteries can be independently verified, then Li-ion batteries will already be at a
cost point predicted for 2020. Li-ion battery cells are expected to experience the
most rapid decline in costs driven by increasing manufacturing economies of scale
due to their current favourable status for use in electric vehicles and research and
development funding, as indicated in Figure 23.
Figure 23: Projected battery cell costs for bulk energy storage by Navigant Research
(IRENA 2015)
A significant factor that has received limited attention is the finite reserves of
lithium. While globally there are more than 39 million tonnes of lithium resources,
680
550
350
600 550
500 550
300
200
535 535 500
0
100
200
300
400
500
600
700
800
2014 2017 2020
US
D /
kW
h
Lowest Projected Battery Costs
Flow batteries
Advanced
lead-acidLi-ion
NaS
51
only 13 million tonnes are considered to be economically recoverable (Bradley and
Jaskula 2014). Fortunately, lithium can be repeatedly recycled, which reduces the
need for continuous mining of a finite resource (Bradley and Jaskula 2014). The
limited nature of the resource however, does make a good case for Zn-air and hybrid
aqueous batteries.
There are also technical obstacles that need to be overcome including improvements
in storage capacity, lifetime, energy density, power performance and safety. The
monitoring and control equipment presents a challenge, as the available energy
content and system safety may be monitored easily and inexpensively in some
systems, while this may require more effort in others (Kousksou et al. 2014). The
different requirements of storage systems i.e. thermal monitoring for Li-ion or NaS
batteries, means that standardisation of battery management systems is challenging,
which adds to the cost (IRENA 2015). The energy storage market is expected to
experience significant future growth with worldwide revenue growing from
USD 220 million in 2014 to USD 18 billion in 2023 (IRENA 2015). With battery
storage expected to grow from 360 MW to 14 GW over this period, applications to
support renewable energy are expected to provide 40% of revenue (IRENA 2015).
In terms of EES deployment in Australia, Figure 24 shows that lead-acid battery
storage still dominates.
52
Figure 24: Australian installed battery energy storage (DOE 2015)
With improving manufacturing economies of scale, Marchment Hill Consulting
(2012) estimates that the potential market for energy storage in Australia could
grow from less than 500MW including pumped hydro storage in 2012 to more than
2,500 MW by 2030. Improvements in performance and declines in cost are
expected to drive the global growth in energy storage deployment, which will
encourage increased uptake in Australia to support renewable energy integration
(Chambers and Rozali 2013). Irrespective of which EES technologies are
technically superior, as the prices of Li-ion batteries reduce, it is reasonable to
expect that their market share in Australia will grow and likely dominate.
Significantly, they are also the technology currently favoured by the largest energy
retailers in the NEM looking to offer renewable based energy storage from 2015 –
AGL and Origin Energy.
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Lead-acid Battery Lithium-ion Battery Flow Battery Other
kW
Installed Battery Energy Storage in Australia
53
4. The National Electricity Market
4.1. Market Overview
The NEM acts as a wholesale spot market that connects generators, transmission
and network service providers, retailers and end users along the electricity supply
chain indicated in Figure 25.
Figure 25: Electricity supply chain (AEMO 2010)
Electricity is traded through pool arrangements where electricity produced by
approximately 322 generators is aggregated, transmitted by 5 transmission network
service providers and 13 network services providers to meet end user demand in
each of the NEM jurisdictions (AER 2014). Table 5 lists key statistics on the NEM
(AER 2014).
54
Table 5: Key NEM Statistics for 2013-14
Regions covered
ACT, Qld,
NSW, Vic, SA
and Tas
Generating capacity 47,779 MW
Generators 322
End users 9.5 million
Revenue $10.8 billion
Electricity generated 194 TWh
National maximum summer demand 33,610 MW
National maximum winter demand 30,114 MW
Source: AER 2014
Figure 26 shows that generation within the NEM is dominated by fossil fuels,
principally black and brown coal, which makes up 85.37% of generation capacity,
while renewables only make up 14.63% (BREE 2014).
Figure 26: NEM generation by fuel type (BREE 2014)
Black Coal
111,491
44.8%
Brown Coal
47,555
19.1%
Natural Gas
51,053
20.5%
Oil Products
4,464
1.8% Other
1,945
0.8%
Bagasse, Wood
1,550
0.6%
Biogas
1,600
0.6%
Wind
7,328
2.9%
Hydro
18,270
7.3%
Solar PV
3,817
1.5%
National Electricity Generation By Fuel Type (GWh)
55
The dominance of fossil-fuelled generation can be explained by Australia’s
abundance of fossil fuel reserves, which has shaped the direction and outcomes of
its energy policy. The results of Geoscience Australia’s (2010) assessment of
Australia’s demonstrated fossil fuel resources are listed in Table 6.
Table 6: Australia's fossil fuel resources
Resource Economically demonstrated
resources (PJ)
Total demonstrated
resources (PJ)
Black coal 883,400 1,046,500
Brown coal 362,000 896,300
Conventional gas 122,100 180,400
Coal seam gas 16,590 46,590
Source: Geoscience Australia 2010
By comparison, Australia’s renewable energy resources have been estimated at an
average of 58 million petajoules of solar radiation per annum and more than
600,000 km2 of wind resources at average wind speeds of 7ms
-1 or higher
(Geoscience Australia and ABARE 2010). This provides a naturally strong basis
for future growth of renewable energy technologies. AEMO’s 2015 National
Electricity Forecasting Report (NEFR) forecasts a strong growth in commercial
PV, with an increase from 497 MW in 2014-15 to 2,942 MW in 2024-25, largely
due a greater area of available roof space and increased economic viability
(AEMO 2015).
56
4.2. Key Market Participants
4.2.1. Gentailers
Despite governments disaggregating the energy sector in the 1990s, vertical
integration of generators and retailers to create ‘gentailers’ emerged as an
increasing trend within the NEM (AER 2014). The AER (2014) notes that AGL
Energy, Origin Energy and EnergyAustralia own 46% of the NEM’s generating
capacity and also supply more than 75% of the retail energy market.
Vertical integration allows gentailers to manage volatility in the spot market and
reduces participation in the hedging market (AER 2014). However, this reduces
market liquidity which can reduce the competitive incentives needed for non-
vertically integrated retailers and generators to enter or expand within the market
(AER 2014). While this may reinforce the market power of the existing gentailers,
it also means that they may have more to lose from further reductions in grid
demand due to higher penetrations of renewable energy storage.
4.2.2. Networks
Networks within the NEM are valued according to their regulated asset base
(RAB), which indicates the network’s replacement cost and new investment minus
depreciation (AER 2014). Within the NEM, electricity networks apply to the AER
to assess their forecast revenue and expenditure required to attain a favourable
return while ensuring the lowest efficient costs (AER 2014). The network’s
allowable revenue is determined by the AER in five year regulatory periods, and is
based on its capital, maintenance and operating expenditure, taxation liabilities,
depreciation costs and return on capital, which is often the largest component of
57
revenue (AER 2014). This was estimated at $54 billion for the distribution
networks and $17 billion for the transmission networks in 2014 (AER 2014).
Given that they are monopoly businesses, networks are regulated by the AER, who
is guided by the National Electricity Objective (NEO) under the National
Electricity Rules, which is to “promote efficient investment in, and operation of,
electricity services for the long term interest of consumers” (AER 2014). While the
NEO aims to ensure economic efficiency within the NEM, the current rules also
encourage an increase in capital expenditure (CAPEX) to ensure reliability and
security (Sue, MacGill, and Hussey 2014). This has resulted in network prices
increasing sharply over several years as networks increased CAPEX on the basis of
replacing aging infrastructure, meeting more stringent reliability standards,
forecasts of increasing peak demand and increased finance costs following the
global financial crisis (AER 2014). The lack of incentives to seek least cost
alternatives conflicts with the benefits that EES offers to end users through reduced
demand (Marchment Hill Consulting 2012).
4.3. State of the Market
4.3.1. Wholesale Prices
Following the introduction of the carbon tax in July 2012, electricity spot prices
increased by an average of approximately $20/MWh, as a result of increased
operating costs for the NEM's fossil-fuelled generators. However, assessing the
underlying spot prices excluding carbon, spot prices have been very low as
illustrated in Figure 27.
58
Figure 27: NEM quarterly volume weighted average quarterly spot prices (AER 2014)
These low prices reflected reduced demand, increased end user energy efficiency in
response to higher energy prices and the uptake of renewable generation (AER
2014). These outcomes flowed through to the electricity futures market, where
hedge contracts between generators and retailers are traded on the Australian
Security Exchange (ASX). While details of commercial users' retail contracts are
confidential, the futures market provides a reasonable approximation of the prices
that end users pay excluding retailer margins. Figures 28 to 31 reveal a trend of
rising prices across all NEM states over the past 12 months for calendar year 2016
futures.
59
Figure 28: NSW electricity futures (ASX Energy 2015)
Figure 29: Qld electricity futures (ASX Energy 2015)
$30.00
$32.00
$34.00
$36.00
$38.00
$40.00
$42.00
$44.00
$46.00
$48.00
$50.00
01
-Sep
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16
-Sep
-14
01
-Oct
-14
16
-Oct
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31
-Oct
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15
-Nov
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30
-Nov
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15
-Dec
-14
30
-Dec
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14
-Jan
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29
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13
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28
-Feb
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15
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30
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14
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15
29
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15
14
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13
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15
28
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15
12
-Aug
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27
-Aug
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11
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$/M
Wh
NSW Base Cal Futures 2016
$40.00
$42.00
$44.00
$46.00
$48.00
$50.00
$52.00
$54.00
$56.00
$58.00
$60.00
01
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31
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28
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12
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27
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11
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26
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$/M
Wh
Qld Base Cal Futures 2016
60
Figure 30: SA electricity futures (ASX Energy 2015)
Figure 31: Vic electricity futures (ASX Energy 2015)
$40.00
$45.00
$50.00
$55.00
$60.00
$65.00
$70.00
$75.00
$80.00
01
-Sep
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16
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01
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16
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31
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11
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$/M
Wh
SA Base Cal Futures 2016
$30.00
$32.00
$34.00
$36.00
$38.00
$40.00
01
-Sep
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16
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$/M
Wh
Vic Base Cal Futures 2016
61
4.3.2. Demand-Supply Balance
In recent years, electricity grid demand has declined, resulting in an excess of
generating capacity in the NEM as renewable generation is continually added. In
response to this, utilities have announced the withdrawal of 4,550 MW of fossil-
fuelled generating capacity from the NEM by 2022 since AEMO's 2014 Electricity
Statement of Opportunities (AEMO 2015b). AEMO now forecasts a sharp
reduction in surplus capacity by 2023-24, as illustrated in Figure 32.
Figure 32: NEM forecast surplus generating capacity (MW) (AEMO 2014a, 2015b)
While electricity consumption reduced from 2008-09, commercial consumption is
expected to experience a slower rate of decline, with evidence that the repeal of the
carbon tax resulting in lower electricity prices is causing a slight increase which is
being moderated by rooftop PV (AEMO 2015).
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5. Implications for Utilities
Increased deployment of renewable generation supported by EES technologies
presents both a challenge and an opportunity for utilities. The following section
uses AGL Energy, a vertically integrated gentailer in the NEM and AusNet
Services, an electricity network service provider as case studies to assess the
implications of this change on utilities' business models.
Given that the German energy market has a renewables penetration of 30% and a
predicted increase in EES installations from 15,000 in 2014 to 100,000 by 2018
(Germany Trade & Invest 2015), the NEM utilities business models will be
compared to the German utilities E.ON and RWE in terms of their customer
interface, value proposition, infrastructure and revenue models. This comparison
will be used to determine which business models are best suited to meeting the
risks posed by renewable EES and taking advantage of the opportunities it presents.
5.1. Implications of Increased Renewable Energy Storage
5.1.1. Reduced Conventional Generation Market Share
Assumptions regarding the full implications of renewable energy storage on
utilities business models depend on forecasts of their grid penetration rates.
Rooftop PV generation has already passed a tipping point in the residential market
and signs of stronger growth in the commercial market are starting to emerge
(Brazzale 2015). Data from AEMO’s 2015 National Electricity Forecasting Report
in Table 7 forecasts a growing increase in PV generation as a proportion of NEM
consumption, with variations in each NEM region depending on available solar
resources and electricity prices (AEMO 2015).
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Table 7: PV generation as a proportion of NEM consumption
Qld NSW SA Vic Tas
2014-15 5.7% 2.4% 8.4% 2.7% 3.0%
2017-18 9.1% 3.7% 11.9% 4.4% 4.9%
2024-25 16.0% 6.3% 22.1% 8.6% 11.0%
2034-35 20.2% 9.3% 28.5% 13.7% 17.4%
Source: AEMO 2015
AEMO’s forecast reveals that the tipping point for distributed generation i.e. more
than 10% penetration, occurs first in SA in 2017-18, followed by Qld, Tas, Vic and
NSW. AEMO’s 2015 forecast on the uptake of energy storage in the NEM is
influenced by the uptake of PV. This forecast is outlined in Table 8 (AEMO
2015a).
Table 8: AEMO NEM Forecast Energy Storage Capacity (MWh)
Qld NSW SA Vic Tas
2017-18 129 201 2 188 9
2024-25 982 1,043 206 1,131 83
2034-35 2,046 2,482 484 2,774 196
Source: AEMO 2015
As it does not factor in the willingness of users to retrofit existing systems with
EES, AEMO's estimates may be conservative. In addition, it does not account for
the uptake of EES in the commercial sector, where time-of-use tariff structures
provide stronger economic incentives. Sue et al. (2014) note that the NEM’s current
structure does not support the integration of renewable EES technologies. However,
their study focussed more specifically on the integration and application of EES
technologies at the utility level. Similar observations have been made on the
NEM’s regulatory structure when considering residential energy users during the
Smart Grid, Smart City trials, which between 2010 and 2014 tested a number of
64
smart grid technologies including integrating solar PV and battery storage (Norris
et al. 2014). Results of the trial found that while price reductions were expected for
residential users of EES technologies, current electricity pricing structures made the
deployment of EES unviable in the long term (Norris et al. 2014).
The same trial showed that this was not the case for commercial users whose
higher consumption, time-of-use pricing structures including network capacity and
critical peak demand pricing, provided suitable incentives for distributed energy
storage (Norris et al. 2014). Current tariff structures along with declining PV and
EES costs may suggest that the commercial and industrial sector may represent the
biggest market potential for renewable EES technologies. Figure 33 details data
from Green Energy Markets (Brazzale 2015) comparing PV installations for the
residential and commercial sectors. The data reveals that commercial PV is growing
and in 2014 represented 16.5% of installations by MW, providing a key market
driver for EES.
Figure 33: Residential and commercial solar PV installations (MW) (Brazzale 2015)
0.0%
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5.1.2. Excess Generation Capacity Reduces Wholesale Electricity Prices
The increased penetration of renewables has resulted in an excess of generation
capacity by contributing to reduced grid consumption. Declining consumption
presents revenue risks to utilities such as AGL. In the 2014 financial year, only
1.02% of AGL’s commercial customers had renewable generation on site compared
to 10.5% of its residential and small business customers (AGL Energy 2014b).
AGL’s proportion of commercial customers with distributed generation is
comparable to the broader NEM rate of 1.1% (Clean Energy Council 2015). In
terms of volume of electricity sold, 53% is sold to the residential market and 47%
to the commercial market (AGL Energy 2014b). Should these customers install
EES systems, they may be able to further reduce their grid consumption.
Reduced consumption has placed downward pressure on wholesale electricity
prices. The NEM volume weighted average spot price in 2006-07, prior to the
introduction of the expanded Renewable Energy Target, was $59/MWh while in
2014-15 it was $41.80/MWh - a reduction of $17.20/MWh. This represents a
substantial reduction in revenue for generators in real terms.
German utilities have faced the same pressures as Australian utilities in the NEM -
declining or flat grid consumption following the global financial crisis, which has
been compounded by increased renewable energy penetration and improved energy
efficiency (Cohen 2015). These factors have combined to reduce revenues for
Germany's two largest utilities - E.ON and RWE. Large incumbent utilities have
been hardest hit by these challenges as the load factors of their fossil fuelled
generation have decreased by 26%, reducing profits (Leger and Vahlenkamp 2014).
66
In its 2014 company report, RWE's CEO noted that wholesale prices in the German
electricity futures market have declined from €49 in 2012 to €32 in 2014, and
unless prices increased, RWE's generators would suffer operating losses (RWE
2015).
5.1.3. EES Enables Reduction in Demand and Energy Shifting
Germany's policy of transforming its economy to meet its climate change objectives
- Energiewende - aims to increase renewables while minimising costs to consumers,
encouraging innovation, and transitioning from a reliance on nuclear power by
2022 (Pegels and Lütkenhorst 2014). Encouraged by this policy, increased
penetration of PV has resulted in the disappearance of the midday peak demand in
sunny regions, which is a key period where conventional generators have
historically derived much of their revenue when prices are higher (Cohen 2015).
In terms of energy consumption, a similar phenomenon is starting to emerge in the
NEM as increasing numbers of consumers install distributed generation. More
significantly for retailers though, commercial customers may be able to use
renewable EES to both reduce and shift their energy consumption to reduce energy
charges, as set out in Appendix B. In this hypothetical example, PV reduces peak
consumption; and the EES system is charged during off peak periods and
discharged during peak periods. Even without optimising the use of renewable
energy storage, this results in overall energy contract savings of 12.45%. This
would erode AGL’s profit margins as more end users adopt the use of EES as a
demand management or energy shifting tool.
67
Within the NEM, Figure 34 shows the trend of decreasing consumption in AusNet
Services’ distribution network, where the penetration of renewables has increased.
However, it also reveals rising network peak demand. While solar PV can reduce
energy demand, peak generation does not necessarily coincide with network peak
demand, as Figure 35 shows.
Figure 34: Declining energy consumption and rising peak demand (AusNet Services 2015b)
Figure 35: Solar PV generation versus AusNet Services' network load profile (AusNet Services
2015c)
68
However, EES can be used to optimise PV generation and reduce peak demand.
This benefits both owners of EES and energy users more broadly as rising peak
demand has been a key driver of increasing energy prices as networks have had to
augment their infrastructure to cater for increasing peak demand, even though
energy consumption has decreased.
5.2. Business Model Comparison
5.2.1. Value Proposition
In terms of their value proposition, AGL, E.ON and RWE highlight their strengths
as vertically integrated utilities with large customer bases. This is captured by
Figure 36, which illustrates RWE's value proposition throughout the energy supply
chain.
69
Figure 36: RWE's energy supply chain (RWE 2015)
AGL is keen to emphasise that unlike its competitors, it offers energy solutions –
not just energy, and its position as Australia’s largest ASX listed owner and
operator of renewable generation (AGL Energy 2014a).
70
Like utilities in the NEM, E.ON's traditional business model was based on
centralised, commodity-oriented generation (E.ON 2015). In this model, the value
proposition that generates revenue involves the strategic use of generation assets,
competitive costs, and its ability to deliver superior outcomes in its operations, its
engineering and bulk trading capabilities, and large volumes of sales to end users
(E.ON 2015).
At the end of 2014, E.ON announced that it was adopting a new strategy aimed at
adapting to the changing energy markets and providing additional value to
customers titled "Empowering customers. Shaping markets" (E.ON 2015). The
strategy involves splitting the company into two: a New Company focussed on
centralised conventional generation based on coal, gas and nuclear; energy
transmission and global commodity markets; while the future E.ON will focus on
renewables, distributed generation and customer solutions (E.ON 2015).
E.ON's decision to develop its new business model is in response to what it sees as
three key market trends: rising global demand for renewables, the evolution of
distribution networks as a key platform for distributed energy solutions including
EES, and changing customer expectations as they move from being simply
consumers of energy, to being both consumers and pro-active producers of energy
(E.ON 2015). E.ON sees itself as adding value to these areas where its competitive
advantage is its growing renewables business combined with its skills in project
development and execution which are regarded as industry-leading (E.ON 2015).
71
Unlike the other 3 utilities, AusNet Services does not retail or generate energy due
to ring-fencing rules within the NEM. As a regulated monopoly that transmits and
distributes energy, AusNet Services’ value proposition is based on delivering
energy safely and reliably to 679,213 customers in northern and eastern Victoria in
the most efficient manner (AusNet Services 2015a).
5.2.2. Customer Interface
All 4 utilities have embraced digital communication strategies to connect with
customers. This makes sense given their sizable customer bases. E.ON has 6.3
million customers across Germany (E.ON 2015a). A core objective of its new
business model is a focus on customer solutions, where E.ON aims to respond to
customer needs for a more sustainable energy supply, and innovative solutions
including renewable technologies and energy management solutions. While the
traditional customer interface was based on telesales to end users who were more
passive in relation to their energy use, the new customer interface involves a
number of additional digital communication strategies aimed at enhancing customer
engagement with consumers who have come to expect more from their energy
providers.
AGL’s primary channel for customer communication is through online channels.
Commercial and industrial customers are able to monitor and manage their energy
use; and estimate future electricity usage and costs by creating hypothetical
scenarios. AGL’s Merchant Energy division services approximately 19,100
commercial and industrial customers, with large accounts assigned a dedicated
account manager (AGL Energy 2014a). Its Energy Services team facilitates a
72
number of energy productivity services including demand response initiatives and
renewable generation installations. Apart from dedicated account management,
AGL’s energy management services provide its best opportunity to develop the
depth of relationships that it claims to be working towards.
RWE highlights what it regards as proof of the success of its customer interface.
Imug's market survey of utilities in 2014 ranked RWE first for the quality of its
customer service based on its availability, friendliness and query and complaints
processing (RWE 2015).
All 4 utilities still use call centres. AusNet Services’ notes that its customer
engagement is largely by telephone and its customer satisfaction rating was
reported as 85% in 2014-15 (AusNet Services 2015a). While networks have
typically relied on retailers to communicate with end users, AusNet Services has
found that building direct relationships is increasingly important to avoid the
danger of network bypass. It has initiated a customer engagement programme made
up of customer surveys, focus groups and community forums (AusNet Services
2015).
5.2.3. Infrastructure
AGL, E.ON and RWE's generation portfolios are all dominated by large-scale
carbon intensive plants. AGL’s generation portfolio scheduled to operate in the
NEM for 2015 includes 8,150 MW of conventional black coal, brown coal and fuel
oil generation, 155 MW of large-scale solar, 630 MW of hydroelectric generation;
and wind farms totalling 771 MW (AEMO 2015). As illustrated in Figure 37,
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following its purchase of Macquarie Generation, AGL Energy is the largest
generator in the NEM with a generation capacity of 10,628 MW (Jackson 2015).
Figure 37: Owners of generation in the NEM (Jackson 2015).
With a carbon intensive generation portfolio, AGL produced 25.7 million tonnes of
GHG emissions in 2013-14 (AGL Energy 2014b). Its 2015 GHG policy affirms a
commitment to not extend the life of any of its existing coal-fired plants beyond
2050 or acquire any conventional coal-fired power stations without carbon capture
and storage (AGL Energy 2015).
In South Australia where most of its wind farms are located, AGL has partnered
with transmission network company ElectraNet and Worley Parsons with funding
from the Australian Renewable Energy Agency (ARENA) to examine the role of
large-scale EES i.e. 5 – 30 MW in renewable energy grid integration (ARENA
2014). The Energy Storage for Commercial Renewable Integration project will
examine how storage can optimise AGL’s renewable portfolio in South Australia,
which tends to generate large amounts of power overnight (ARENA 2014).
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E.ON has taken the most radical option of the 4 utilities to prepare for a carbon
constrained future. E.ON's conventional generation including 17.5 GW in
Germany, exploration and production and energy trading resources will be divested
into a New Company. Following the split, the new E.ON will have approximately
4.4 GW of renewables with 15 GW in the pipeline worldwide (E.ON 2015). It will
also retain its distribution network which covers over 411,000 km in Germany
(E.ON 2015).
In recognition of the growth of digitisation, E.ON's technology and innovation
(T&I) division was embedded into the existing business in 2014 with tasks
including increasing the cost effectiveness of renewables; developing energy
storage and energy intelligence solutions; and conducting trials of pre-market
products in real-life conditions (E.ON 2015). The company is also developing
strategic partnerships with venture capital funds to combine the innovation of start-
up companies with E.ON's resources and solid customer base.
Figure 38 outlines RWE's generation output by fuel type for 2013 and 2014. It
reveals a very low proportion of renewables generation. RWE has 4,133 MW of
renewable generation, mostly from wind (2,165 MW), while solar PV only makes
up 1 MW (RWE 2015). Over 97% of its generating capacity in Germany is from
conventional generation, with coal accounting for 72.6% and nuclear accounting for
22.5% (RWE 2015). RWE's coal-fired generation is supported by its lignite
production in the Rhineland, where they produced 93.6 million tonnes in 2014
(RWE 2015). RWE also operates 343,750 km of the electricity distribution grid in
Germany (RWE 2015).
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Figure 38: RWE German generation by fuel type (RWE 2015)
In an attempt to adapt to the challenges posed by a decentralised energy supply
chain, RWE has a 75% stake in Innogy Venture Capital (IVC), which it uses as a
vehicle to source and fund energy innovations including energy storage from
European start-up companies. Figure 39 depicts IVC's areas of focus, which
includes energy storage, decentralised generation, and microgrids.
Figure 39: RWE Innogy venture capital portfolio (RWE 2015a)
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AusNet Services’ assets, valued at $12.1b, include 6,573 km of transmission lines;
13,000 towers; and 50,987 km covering its electricity distribution network, is
depicted in Figure 40.
Figure 40: AusNet Services infrastructure assets (AusNet Services 2015a)
5.2.4. Revenue
All 4 utilities are in varying stages of adapting their business revenue models to a
decentralised energy market. At present AGL’s revenue is derived from its
electricity generation and upstream gas portfolio and sales of electricity and gas to
end users. Revenue in financial year 2014 was $9.543b with an underlying profit of
$562m, which was 3.9% lower than the previous year, attributed to overall
declining energy consumption and strong competition in the commercial segment
(AGL Energy 2014a).
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AGL has established a ‘New Energy’ division, which will leverage on AGL’s
experience in energy markets and form strategic partnerships with software
developers, solar monitoring providers and battery manufacturers to deliver
distributed generation and EES technologies. Its new revenue model illustrated in
Figure 41 reveals its vision as a future “systems integrator” and energy service
provider, which can finance new technologies on the strength of its strong balance
sheet, and act as a “decentralised trader” in the NEM by leveraging its trading
experience.
Figure 41: AGL's new business revenue model (England 2015)
The New Energy division aims to deliver 600 GWh per annum by 2020 which
equates to 400 MW of rooftop solar supported by EES with $400m in revenue
(England 2015). Its small-scale solar (3-4.5 kW) and EES system (6 kWh of usable
storage) will be offered to early adopter residential customers in Queensland 2015 –
where it sees the most economic value, not the C&I market (Parkinson 2015). This
will be followed by a roll-out in New South Wales and then Victoria.
78
Given that AGL’s gross margin per commercial account based on volume of
electricity sold is 10 times larger than that for a residential account (AGL Energy
2014a), it is not surprising that it is unwilling to encourage growth in renewable
EES for commercial energy users at present, which would result in a larger decline
in conventional generation energy volumes sold and contribute to further
weakening of wholesale electricity prices.
While E.ON's existing revenue model is based on largely on energy sales, the future
E.ON will derive revenue from its distribution network and providing sustainable
energy products and services to end users as illustrated in Figure 42.
Figure 42: E.ON customer centric model (E.ON 2014)
Of the 4 utilities, RWE has been the slowest to adapt its business model to a
distributed future, blaming political intervention for the decline in its margins and
the load factor of its conventional generation plants (RWE 2015). In 2014
electricity production declined by 4.5% compared to 2013 (RWE 2015). Over the
same period Earnings Before Interest, Tax, Depreciation and Amortisation
79
(EBITDA) declined by 9.8% (RWE 2015). Given the pressures on its conventional
generators, the company is focussing on also expanding its investment in
renewables, networks and using its thermal generation to provide security of supply
as renewables expand (RWE 2015). However, revenue from its distribution
networks in Germany also declined by 1.6%, where RWE notes that 15.1 GW of
PV and wind output was fed into its grid (RWE 2015).
Unlike AGL, E.ON and RWE who derive most of their revenue from electricity
generation and sales, 86% of AusNet Services’ revenue is derived from regulated
sources (AusNet Services 2015a). Comparing the changes in allowable revenue
between the AER’s 2011-15 determination to its previous five year determination,
AusNet Services’ annual electricity network revenue increased by 39%, largely as a
result of its regulated asset base (RAB) increasing by 36% to replace ageing
infrastructure and annual operating expenditure increasing by 48% (AER 2014).
Like other NEM electricity networks, AusNet Services faces the risk that declines
in grid energy consumption and maximum demand will reduce its network
investment and therefore its RAB – a key source of revenue. Based on its 2014
annual report, it appears that AusNet Services is managing this risk. Despite a
reported 1.9% decline in energy volumes for its electricity distribution business,
which it attributed to changing consumer behaviour and the take up of distributed
generation, AusNet Services reported EBITDA increasing by 23.7% (SP AusNet
2014).
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AusNet Services reported a 7.9% increase in revenue in its 2015 Annual Report
despite a 1.6% decline in energy volumes and a 1.6% increase in customer
connections (AusNet Services 2015a). Its increase in revenue for 2014 was driven
by a 5% growth in its RAB to $8.6b (AusNet Services 2015a).
5.3. Discussion
5.3.1. Value Proposition
Electricity is generated from large-scale fossil fuel generators in the traditional
utility business model. Up until recently, utilities have opted for a model where the
value proposition remains the same except that generation is now derived from both
large-scale conventional and renewable generation and fed into the grid.
AGL’s new business model may allow it to gain a first mover advantage in the
NEM relative to its competitors as it develops a strategy to take advantage of the
increased penetration of renewables and interest in EES. This allows it to position
itself ahead of the innovation diffusion curve. Though not as radical as E.ON's,
AGL has also adopted a value proposition where it will continue to be the largest
owner of centralised conventional and renewable generation in the NEM, while also
providing innovative products and solutions to customers to accommodate
increasing decentralisation. This represents a modest rather than fundamental shift
to prepare AGL for a future decentralised market. However, targeting commercial
rather than residential users provides a better value proposition given the roof space
available on commercial buildings and from an operational point of view it would
be far easier to manage several thousand commercial installations rather than
hundreds of thousands of smaller residential installations.
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Like AGL, RWE's value proposition can be characterised as a utility-side rather
than a customer-side business model. This allows RWE to promote the company's
environmentally friendliness without making substantial changes to its value
proposition. The company established an 'Innovation Hub' management unit in
2014 which will develop new business models focussed on a decentralised energy
system by 2020 (RWE 2015). RWE foresees the company developing into a
decentralised energy manager which offers consulting services; finance, installation
and maintenance services for customer distributed generation and storage assets;
and as an aggregator that unifies a network of virtual power plants (RWE 2015).
AusNet Services' critical peak demand (CPD) tariffs are delivering a genuinely
unique value proposition to commercial energy users that delivers genuine savings
and assists the network to meet its reliability obligations. CPD tariffs apply to 2,000
commercial customers, and has resulted in a 13% reduction in load for these
customers over the 2013-14 summer with average savings of 15% per customer
(AusNet Services 2014). While aimed at improving demand management, CPD
tariffs actually improve the economic viability of EES, which will be accelerated as
technology costs decline.
5.3.2. Customer Interface
AGL believes that its digital strategy is enhancing its customer engagement. Figure
43 reveals that its customer satisfaction rating has been trending upward and it had
the highest score relative to other Tier 1 energy retailers.
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Figure 43: Customer satisfaction score (AGL Energy 2014b)
While AGL boasts that its customer churn rate in 2014 was 15.4% compared to
20.5% in the rest of the market (AGL Energy 2014b), this is still a fairly high rate
and indicative of the challenge of maintaining existing customers in a competitive
market. Similarly, while RWE ranks well compared to its competitors in terms of
customer service, this has not been sufficient to stem its declining revenue as it
competes with smaller, more flexible distributed energy providers (Wassermann,
Reeg, and Nienhaus 2015).
5.3.3. Infrastructure
Key resources in the traditional business model typically include large centralised
power plants. However, in a decentralised model they are located on customers'
premises i.e. solar PV on roofs and this necessitates a different structure and a
different set of key activities (Richter 2012). This requires increased investment in
information and communication technologies (ICT) to support efficient and reliable
energy flows.
6.20
6.40
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Q1 F
Y2
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014
Customer Satisfaction Score
AGL Energy
EnergyAustralia
Origin Energy
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By shifting generation to renewables, utilities are able to meet customer
expectations for better environmental outcomes, which improves their corporate
image and customer levels of trust (Richter 2012). AGL and AusNet Services
responded to this trend earlier than their German counterparts and moved to
incorporate renewables into their business planning. This is evidenced by AGL's
position as the largest owner of renewable generation in the NEM, and AusNet
Services' investment in a mini-grid planned in Mallacoota supported by embedded
generation, storage and network controls. The network notes the benefits of the
mini-grid may outweigh traditional network supply infrastructure in remote
locations facing the challenges of increased peak demand and reduced reliability
(AusNet Services 2015c).
Actions taken by AusNet Services indicate that it is well positioned to deal with an
increased penetration of renewable generation supported by EES. AusNet Services
commissioned its 1MW/1MWh Li-ion Grid Energy Storage System (GESS) in
2015. The pilot system is connected at 22 kV to a feeder from its Watsonia zone
substation and also includes a 1 MW diesel generator (Vashishtha 2015). GESS
will be used to provide capacity during periods of high demand to defer network
upgrades, improve power quality and provide voltage support, and can operate in
grid or island mode (Vashishtha 2015). Conclusions of the trial to date show that
while the technology is currently expensive and complicated, should the two year
trial prove to be successful and technology costs reduce, the network sees the
potential for wider deployment for low emissions grid support (Vashishtha 2015).
The behaviour of this system will provide valuable knowledge of the systems
interaction with the network, which could benefit commercial end users.
84
E.ON and RWE have been relatively slow to incorporate renewable generation into
their portfolios which is evidenced by their low levels of ownership in Germany.
For example, of the installed capacity of PV in Germany, private consumers own
39.4%; farmers own 21.2%; industry owns 19.2%; while E.ON and RWE together
with the other 2 major utilities only own 0.2% (Wassermann, Reeg, and Nienhaus
2015).
5.3.4. Revenue
While the traditional revenue model for utilities is based on maximising consumer
energy consumption, energy efficiency measures and distributed renewable
generation owned by third parties presents a threat to that revenue model (Richter
2012). While decoupling revenue from sales volume might provide a theoretical
incentive for utilities to encourage customer-side renewable technologies, it is not a
concept readily embraced by utilities themselves (Richter 2012). The utilities in this
study have adopted a hybrid utility-side/customer side approach to innovative
technologies. AGL's New Energy strategy and investment in renewables will act as
a hedge against declining energy consumption in the NEM which erodes the
capacity factor of its conventional generation and reduces spot prices.
Several authors have pointed to the correlation between high renewable penetration
and reduced electricity spot prices (Clean Energy Council 2015; Cludius, Forrest,
and MacGill 2014; Forrest and MacGill 2013; Molyneaux et al. 2013). This is
particularly marked in South Australia, which has a renewable penetration rate of
40% (Clean Energy Council 2015). However, they fail to mention that there are
also a number of times when South Australian spot prices meet the market price cap
85
(MPC), which in 2014-15 was set at $13,500/MWh. This typically occurs when
renewable penetration is insufficient to meet demand. While low spot prices often
coincide with high renewable generation penetration, high spot prices also often
coincide with low renewable penetration, which allows thermal generators to
recoup some of their losses, as illustrated in Figure 44.
Figure 44: High spot prices in SA (Global Roam 2015)
86
In Figure 44, South Australia’s spot price reached $13,500/MWh at 9.35am on 16
April 2015 when demand was 1,484 MW and wind generation was 278.38 MW and
solar generation was not contributing to meet demand at all. While most of the
demand was met by gas-fired generation, and AGL’s brown coal-fired generators
were only generating 180.05 MW, AGL was able to set the price at the MPC to
meet the demand as other generators were constrained by their ramp up rates
(AEMO 2015). This example lends credibility to recent modelling which suggests
that coal-fired plants will continue to complement renewables in the NEM until
2030, albeit with reduced capacity factors and asset values (Vithayasrichareon,
Riesz, and MacGill 2015). In that model, the optimum level of conventional
generation by 2030 was 25% to manage the potential risk of future carbon and gas
pricing uncertainties, while also reducing GHG emissions (Vithayasrichareon,
Riesz, and MacGill 2015).
Rolling out its renewable EES in Qld first enables AGL to compete in a market
where it has limited generation capacity (554 MW of peaking plants) running at an
average capacity factor of 4%, and its lowest number of customers (AGL Energy
2015c). This allows it to test and refine its new business model, without
cannibalising itself in parts of the NEM where it dominates. Qld has excellent solar
resources and relatively high electricity prices, which will allow for greater
extraction of value for customers, and allows AGL to promote its products to
enable greater consumer choice.
Declining revenues encouraged E.ON to take the decision to split its company into
two in the second half of 2016. E.ON is expected to lose $5.5 billion in 2015 due to
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the issues in its conventional generation division (Lacey 2014). By splitting the
company in two, E.ON aims to eventually divest its investment in conventional
generation, where overinvestment during a period of high demand has resulted in
high debt at an average cost that is the highest in the industry (Dickens et al. 2014).
However, until EES is at a scale that can provide a secure and stable energy supply,
the New Company will still have an important role to play given the need for
thermal generation to provide back-up supply.
AusNet Services has to some extent been more sheltered from the impact of the
changing energy market driven by previous regulatory revenue determinations.
AusNet Services has noted distributed generation supported by EES presents a risk
of “network bypass” as consumers take greater control of their energy needs
(AusNet Services 2015a). Around 12% of its distribution customers already have
solar PV (AusNet Services 2015b). As a means of managing this risk, AusNet
Services is diversifying its revenue base and adapting to changing customer
behaviour. It has developed an Energy Solutions division, which is focussed on
developing products and services related to energy use, efficiency and EES (SP
AusNet 2014a).
AusNet Services is also responding to forecast flattening revenue by flattening its
future expenditure forecasts and improving its operational efficiency, as illustrated
in Figures 45 and 46. Improving operational performance will be a key requirement
for utilities in the new energy paradigm, where revenue is increasingly decoupled
from volumetric sales of electricity.
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Figure 45: AusNet Services projected revenue (AusNet Services 2015b)
Figure 46: AusNet Services' flattening expenditure (AusNet Services 2015b)
5.3.5. The New Business Model
While it is too early to judge the effectiveness of E.ON's new business model, it is
the most promising in terms of meeting the challenges posed by the new
decentralised energy paradigm. Cohen (2015) notes that E.ON’s share price
increased after the company's announcement and investment bank UBS provided a
positive assessment of the company split-up and called it:
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...the most radical transformation E.ON could have chosen, but we think
it makes strategic sense and could create more value and growth than the
traditional integrated business model.
After increasing, following the company split announcement in December 2014,
E.ON's stock price has reduced again and is far from its peak in 2008, as shown in
Figure 47. This warrants further analysis after the planned company split in 2016.
Figure 47: E.ON stock price history (Reuters 2015)
Separating the company also means that the company will be split according to
cultural divisions - with employees that are committed to customer-centric
decentralised generation, and those that remain committed to utility-side centralised
generation. E.ON's Chief Executive Officer acknowledged the difficulty of
effectively responding to the changing energy system without addressing the
culture of the organisation: "We're already experiencing how difficult it is to
combine these two very different cultures in a single organisation" (Cohen 2015).
The decision to split the company ensures that the future E.ON will be able to
overcome any systemic internal opposition to sustainable innovations within its
E.ON Stock Price History 1996 - 2015 (Euros)
€
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own organisation, and competing objectives. This is the definitive element of
E.ON's business model that sets it apart from other incumbent utilities and provides
the greatest potential to drive the growth in renewable EES. Table 9 is a summary
of the new business model which is emerging based on a decentralised energy
system. This model is supported by a framework of systems thinking, which can
enable us to identify the greatest leverage points in a utility’s social system so that
it can best meet the challenges and opportunities presented by the new energy
paradigm of which renewable energy storage is an integral part. This can provide a
sustainability roadmap for utilities, as per Doppelt (2003):
i) "Change the dominant mind-set" out of which the system arose i.e. its
frame of reference (Doppelt 2003). These are the stated and unstated
assumptions held by the majority of people. It is challenged by pointing
out the failure of the current mode of thinking and articulating a new
one. In the case studies presented, the primacy of centralised generation
is challenged by decentralised distributed generation technologies.
ii) Rearrange its parts – this involves engaging new people with different
perspectives and skills and reshaping the way that work is accomplished.
To this end, the utilities are co-investing with technology start-ups to
foster sustainable innovation.
iii) Alter the system goal, which focuses the energy and attention of its
members. In the new energy paradigm, the goal of increasing energy
sales is replaced by the goal of delivering greater value to end users
through innovative solutions.
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iv) Restructure the rules of engagement by developing new operational and
governance strategies. E.ON has taken the most radical option by
divesting its centralised generation operations.
v) The information available to people shapes their understanding and their
ability to make decisions. Utilities' new energy divisions are focussed
solely on sustainable energy solutions.
vi) By correcting feedback mechanisms, change can be effected by
continually rewarding learning and innovation to increase knowledge.
vii) Align the organisational chart, performance and incentive systems, as
well as the policies and procedures with sustainability. The future E.ON
is solely focussed on sustainable solutions.
Table 9: New decentralised utility model
Model Component
New Decentralised Model
Value Proposition
Shift from vertical integration to utility as energy
aggregator, connector and flow manager.
Enables greater customer insights and choices to ensure
reliable, sustainable energy supply.
Customer Interface
Active customer engagement; relationship management
built on cross channel sales
Digital interface to facilitate increased communication
transactions
Infrastructure
Large number of distributed renewable generators behind-
the-meter coupled with storage
Shift from physical to virtual infrastructure:
o Information and communications technology, software
capable of managing multidirectional rather than one-
way energy flow
Utilise external expertise; collaboration and co-innovation
Revenue
Shift from volumetric energy sales to volumetric solutions
built on fee for service
o Energy consulting services providing advice, finance
and maintenance of distributed technologies
Cost reflective energy prices
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6. Policy and Regulatory Options
6.1. Regulatory and Policy Context
Australia's energy policy is part of a complex regulatory and policy framework with
overlapping jurisdictions and responsibilities between Federal and State
governments (Byrnes et al. 2013). Having multiple actors with sometimes
competing objectives makes formulating a singular coherent approach to energy
policy challenging. This framework and relationships between these bodies is
illustrated in Figure 48.
Figure 48: NEM governance and regulatory structure adapted from Byrnes et al. (2013)
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The NEM's current policy and regulatory framework, presents both barriers and
enablers to the deployment of energy storage. Key barriers include:
Technical, economic and policy uncertainty;
A lack of standards to integrate energy storage technologies; and
Cultural resistance.
Though not specifically targeted at energy storage, at present the principal policy
driver for energy storage is the Federal government's Renewable Energy Target
(RET). This is because an increase in the uptake of renewable energy provides an
incentive to maximise its value to end users by enabling increased self-consumption
of the renewable energy generated through the use of electrical energy storage.
Electrical energy storage provides firm capacity for renewable generation. Other
policy frameworks that can enable the deployment of energy storage include:
The Clean Energy Finance Corporation (CEFC); and
The Demand Management Incentive Scheme.
However, these policy frameworks also present some limitations which will be
outlined along with policy options to overcome them. These options are specifically
aimed at deployment in the commercial sector by overcoming cultural resistance,
and reducing policy, economic and technical uncertainty for end users and utilities.
6.2. Overcoming Cultural Resistance
6.2.1. Framework for Understanding Technology Adoption
The market diffusion of any innovative technologies such as EES systems is a
social process, and competing economic, political and technical drivers all occur
within a social system, as depicted in Figure 49.
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Figure 49: Technology's place in the social system
This means that the extent to which EES technologies will be deployed depends not
just on economic and technical factors, but also social inertia and industry cultural
resistance to change. The evolution of social acceptance of EES innovations can be
explained by the technology adoption life cycle. The diffusion curve can be divided
into 5 categories of system members, where innovativeness is defined as the degree
to which an individual or business is relatively earlier in adopting new ideas than
other members as depicted in Figure 50. These groups are: 1) innovators, 2) early
adopters, 3) early majority, 4) late majority, and 5) laggards (Rogers 1983).
Social Framework
Political System
The Economy
Regulatory Framework
Energy Storage
Technologies
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Figure 50: Moore's technology adoption life cycle (Moore 2001)
Innovators: The adoption process begins with a small number of individuals who
actively seek out new ideas and technology products (Moore 2001). In the
commercial sphere, these are the small number of businesses that are more willing
to take risks to gain first mover advantage, and are capable of absorbing possible
losses due to the new venture (Rogers 1983).
Early Adopters: Once the benefits of the innovation become apparent, early
adopters’ interest can be piqued. These businesses are always searching for a means
of gaining a competitive advantage. They are able to comprehend the benefits of
new technologies without relying upon external references when making their
purchasing decisions (Moore 2001). These businesses may be interested in trials
and play an important role in decreasing uncertainty within a social structure
(Rogers 1983).
Early Majority: Early majorities are pragmatists looking for productivity
improvements but will not act without solid proof (Moore 2001). They are risk
averse and require external references before investing. Their purchasing decision
96
period is lengthier than that of the innovators and early adopters but play an
important role in the diffusion process (Rogers 1983). Offers to these businesses
need to focus on lowering the entry cost and guaranteeing performance.
Late Majority: These are the conservative pragmatists who are risk averse and
uncomfortable with new ideas. Their primary drivers are economic necessity and
fear of being left behind (Rogers 1983). The late majority will typically wait until a
particular product has become the established standard and will purchase from
established companies (Moore 2001). As the late majority are a large market
segment, having utilities as the distribution channel assuages the concerns that they
may have.
Laggards: They see high risk in any new product or behaviour. Their decisions are
often based on what has worked in the past and once they do adopt an innovation, it
may have been superseded (Rogers 1983).
6.2.2. The Importance of Utilities in EES Market Diffusion
While utilities can be the main obstacle to the growth distributed EES technologies
coupled with renewables, they are now also a key distribution channel that can
ensure broader market diffusion. Moore (2001) argues that a chasm exists between
the early adopters of a new technology and the early majority, and for a technology
product to enter the mainstream, it needs to cross this chasm by providing a
reasonably priced value proposition. It is not surprising then that the market
segment that AGL and other NEM retailers are initially targeting with their
combined solar PV and EES system offerings are the early adopters (Parkinson
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2015). Learning gained from targeting this segment will then form the base to target
the early majority. Customers benefit from this approach as utilities will be able to
provide cheaper sustainable technologies by virtue of their purchasing power
(Richter 2012). Utilities are also well placed to overcome some of the key barriers
to the adoption of innovations such as EES technologies:
Cost: One of the single biggest barriers to purchasing innovative products is
the high upfront costs, which limits market growth (Clean Energy Group and
SmartPower 2009). High upfront costs limit market growth with the value
equation i.e. the relationship between the perceived benefits of a product to
its costs, not being strong.
Reliability: Concerns about the reliability of new technologies also limits
market growth. Renewable energy technologies have long been viewed as
niche rather than mainstream products and face persistent claims that their
intermittency means that they cannot be relied upon.
Complexity: The perceived complexity of new technologies serves to
reduce their uptake due to uncertainty regarding their purchase and
installation (Clean Energy Group and SmartPower 2009).
Inertia: The complexity of clean energy technologies contributes to
purchasing inertia with considerable time passing between the decision to
investigate new technologies and the actual purchase. Research by Clean
Energy Group and SmartPower (2009) found that it was common for
purchasing decisions on solar PV installations to take up to 2 years. This
concept may be applicable to EES technologies.
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For utilities to effectively enable the diffusion of renewable EES technologies to the
mainstream though, the appropriate policy and regulatory support is still required.
6.3. Policy Certainty
6.3.1. Renewable Energy Target
Marchment Hill Consulting (2012) notes that there is a reciprocal feedback between
the deployment of renewable energy and the deployment of EES. This means that
increasing renewable generation within the NEM requires energy storage solutions,
and these in turn require increasing deployment of renewable generation.
Furthermore, any policies to support the deployment of EES systems need to be
closely aligned to renewable energy policies. This is because EES systems charged
by fossil fuels defeats the purpose of decarbonising electricity generation. Within
the NEM, in the absence of a price on carbon, the key policy driver which serves
this purpose is the Renewable Energy Target (RET). The RET, originally legislated
as the Mandatory Renewable Energy Target in 2001, targeted the generation of an
extra 9,500 GWh of renewable generation by 2010 (Parliament of Australia 2010).
In 2009 the RET was amended to ensure that renewable generation would make up
20% of Australia’s electricity supply by 2020. In 2009 this equated to a target of
45,000 GWh of electricity from large-scale renewable generation such as wind and
hydropower with the remainder to be met through small scale renewables. Under a
later revision of the scheme the target was split with Large-scale Generation
Certificates (LGCs), equivalent to 41,000 GWh, created through the installation of
renewable energy plants greater than 100 kW, for each megawatt hour of energy
produced, or through the creation of Small-scale Technology Certificates (STCs),
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equivalent to 4,000 GWh, from the installation of small-scale renewable energy
plants, principally solar PV. Electricity retailers have a mandatory obligation to
purchase LGCs and STCs to meet the targets and surrender them to the Clean
Energy Regulator.
The Federal government initiated a review of the RET following in February 2014.
The Warburton Review, headed by self-proclaimed climate sceptic Dick
Warburton, resulted in a freeze in investment in new large-scale renewable projects
as the government and opposition tried to reach an agreement on a new target. A
new large scale target of 33,000 GWh by 2020 with an uncapped small scale
renewable energy scheme was legislated in June 2015.
Policy Option
Policy uncertainty due to competing priorities and political differences between
Federal and State governments presents difficulties in achieving a unified approach,
resulting in onerous compliance regimes that block entry for new market
participants and technologies (Byrnes et al. 2013). While it may never be possible
to eliminate political differences within a liberal democracy, it may be possible to
mitigate these differences by setting long term targets for renewable EES beyond
2020. For example Germany, which has been a frontrunner in the deployment of
renewable energy and storage, has a target of 80% by 2050 and feed-in-tariffs that
last for 20 years (Sühlsen and Hisschemöller 2014).
Linking renewable targets and energy storage targets is required to overcome
ongoing concerns regarding reliability. Specific targets could be set for 2030 and
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2050 to facilitate a transition from fossil-fuelled generation, by which time much of
the current fleet of conventional generation will need to be retired. This will
provide the certainty required by businesses and utilities for long-term investment
planning. It also goes some way to reducing the perceived risks by financiers of
providing funding for projects.
A key criticism of the RET is that it favours more mature technologies such as
wind, and also provides assistance to non-renewable resources such as waste coal
mine gas (Byrnes et al. 2013). This serves to crowd out investment in emerging
technologies and undermines the credibility of the scheme through providing
subsidies to non-renewable sources (Byrnes et al. 2013). A key reform for the
scheme would therefore be a stricter definition of what constitutes a renewable
resource i.e. derived from natural, sustainable sources and practices and set a strict
emissions intensity baseline. To further the deployment of EES systems, a means of
rewarding renewable projects that incorporate EES could be devised, on the basis
that they enable additional renewable generation capacity and therefore contribute
to reduced emissions.
6.3.2. Clean Energy Finance Corporation (CEFC)
The CEFC is an independent government funded body that uses a commercial
approach to provide targeted finance for renewable and low emission technologies
at the later stages of their development, and that can provide a positive return but
require assistance to overcome any market barriers and encourage investment
(Clean Energy Finance Corporation 2014). Under the Commonwealth's CEFC Act,
the CEFC receives $2 billion each year from 1 July 2013 to 30 June 2017 (Clean
101
Energy Finance Corporation 2014). Investments made by the CEFC to date are on
track to provide an investment return of 7% and abate 4.2m tonnes of carbon
dioxide equivalent each year at a return of $2.40 per tonne (Clean Energy Finance
Corporation 2014). The CEFC's future has been uncertain since the Abbot federal
government tried unsuccessfully to abolish it. In July 2015, the federal government
issued a directive to the CEFC requesting that it cease investments in wind power
and small scale solar, and instead support large-scale solar and emerging
technologies (Winestock 2015).
Policy Option
Given that the CEFC has an investment mandate to support technologies that are in
the "later stages of development," while also providing a financial return, excluding
small-scale solar PV which is well matched with EES, would eliminate an
important source of funding for commercial end users. For example, $100m of
CEFC funding has recently been allocated to the NEM's largest retailer - Origin
Energy, to provide power purchase agreements to businesses and households to
enable greater access to solar PV without the need to purchase the technology itself
(Clean Energy Council 2015b). If support is still required to assist greater access to
solar PV, then it is of even greater importance for solar PV coupled with energy
storage, which adds to the upfront capital cost. Therefore, the continued operation
of the CEFC is vital until these technologies decline further in cost and there is
enough industry experience, public knowledge and private finance sector
understanding of their costs and benefits.
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6.4. Technical and Regulatory Certainty
6.4.1. Standards
Several authors have noted the importance of implementing technical standards for
EES technologies (Marchment Hill Consulting 2012; Sue, MacGill, and Hussey
2014; Clean Energy Council 2015a). AECOM (2015) notes that the current focus in
relation to standards has been limited to grid connection via updating AS4777.
Existing standards that could form the basis for future energy storage standards
development include AS4755, which provides a framework for demand response
including enabling technologies (Marchment Hill Consulting 2012). The e-waste
standard AS5377 also deals with end of life battery processing and recovery
(AECOM 2015).
Industry accepted standards that set the appropriate guidelines for the safe
installation, testing, maintenance and disposal of energy storage systems are
required along with an accreditation programme for installers (Clean Energy
Council 2015a). To this end, the Clean Energy Council has formed a "Storage
Integrity Working Group" and along with ARENA has commissioned the CSIRO to
undertake a study on international best practices to be implemented by Standards
Australia (Clean Energy Council 2015a).
6.4.2. Grid Stability, Planning and Simpler Connection Processes
A number of studies have been carried out to establish the feasibility of
incorporating energy storage systems into power systems. Not all studies have been
favourable. For example, the European Union is favoured to achieve its renewable
energy targets with investment in grid extensions to optimal renewable resource
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sites rather than investment in energy storage, which is only preferable from an
economic perspective during times of high renewable generation and low demand
(Anuta et al. 2014). Furthermore, while some energy users may favour the
installation of energy storage systems for economic reasons, their systems may not
be in the optimal location to relieve grid congestion (Anuta et al. 2014). Within the
NEM, Sue et al. (2014) note that the quality of network information available for
opportunities for the deployment of storage is poor and there is a lack of available
real-time demand data at appropriate spatial and temporal scales.
Policy Option
Requiring greater transparency from networks on areas of grid congestion that
could be solved by energy storage as part of their revenue proposals to the AER
could be used to facilitate increased deployment in agreed optimal locations. This
also serves to improve long-term network planning. Requiring networks to provide
real-time demand data mapping across their network, much like AEMO does at a
state-by-state level online, would also alleviate the existing information
assymetries. With greater transparency on the optimal locations for renewable
energy generation and storage, the network connection process can be simplified
with reduced connection application processing timeframes as the technical
assessments, including system capacity, would already have been carried out by the
network.
To ensure greater grid stability, regulators could introduce a conditional subsidy
scheme for energy storage coupled with renewables as operates in Germany. In
Germany, where 12% of solar PV systems are already coupled with storage thanks
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to subsidies, renewable EES system owners need to ensure that the system operator
has open access to the EES system to facilitate grid services (IRENA 2015b). This
approach could address questions of equity. Opponents of renewable energy
subsidies often claim that those without access to renewable energy systems are
subsidising those who do, or are not paying their full share of transmission and
distribution costs.
6.5. Improving Economic Outcomes
6.5.1. Demand Management Incentive Scheme
The current and expected future growth of commercial installations of renewable
generation warrants greater attention in terms of its impacts on the grid in terms of
transmission and distribution planning. The Productivity Commission (2013) noted
that there is a systematic bias towards focussing on network side solutions and
investment in order to manage peak demand. This is understandable given that
customer demand response, distributed generation and energy efficiency measures
reduce their revenue. However, a promising initiative that challenges this bias and
could be used to promote the deployment of EES is the Demand Management
Incentive Scheme (DMIS). The AER provides economic incentives for networks to
consider non-network alternatives to augmentation to meet demand through the
DMIS (AusNet Services 2015c). AusNet Services notes that the scheme helps to
mitigate "the financial risks associated with investing in new and innovative
technologies which may offer substantive long-term value for DNSPs and their
customers" (AusNet Services 2015c). AusNet Services has used this allowance to
invest in large and small-scale EES. The AusNet residential storage trial indicates
that where storage is located in areas where 4 days of overload are experienced per
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annum, these systems could contribute more than $900 per annum of reduced
energy risk (AusNet Services 2015c). In addition, a concentrated storage roll-out
within the feeder boundary could defer a single large feeder augmentation worth
$0.5m for 2 years (AusNet Services 2015c).
Policy Option
Given the promising results of the AusNet Service's trial, the AER could require a
specific focus on storage solutions as part of the DMIS. This measure also goes
some way to counter network concerns that increasing renewable generation creates
voltage and frequency regulation issues which necessitates increased investment to
meet reliability standards, and does not substantially reduce peak demand and defer
network expenditure (Productivity Commission 2013).
Focussing on providing economic incentives for networks and retailers to support
commercial energy users to install combined renewable generation and EES
systems will serve to promote the shift from utilities deriving the bulk of their
revenue from volumetric sales of electricity. For example, networks could be
allowed to include EES systems installed on commercial premises as part of the
regulatory asset base, and provide reduced connection fees for premises that
provide grid support services such as peak demand management, power quality or
reliability. This is a far more productive approach than allowing networks to rely on
increasing fixed connection charges to recover asset capital costs in the face of
declining volumetric sales.
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6.5.2. Improving Economic Certainty for Utilities
Policy support for the increased deployment of EES to firm renewable energy
generation supports one of the key goals of the National Electricity Objective -
economic efficiency in electricity market operations to ensure affordable and
reliable energy supplies for end users (Sue, MacGill, and Hussey 2014).
Affordability for end users also requires economically efficient investment by
utilities, as higher costs for utilities are passed through to consumers. Considering
the possibility of a future price on carbon and increased gas prices, modelling by
Vithayasricheron et al. (2015) found that an electricity generation portfolio
comprising 60% renewables represents the lowest cost by 2030. Within this model
coal-fired plants operate at a lower capacity factor and provide support for
renewables without significantly increasing emissions or costs (Vithayasrichareon,
Riesz, and MacGill 2015). However, uncertainty around renewable energy pricing
and a lack of specific policy support for energy storage, may present additional
capital funding costs for this transition. Renewable energy policy uncertainty for
example, has been reported to create variations in the cost of utility debt by 2% -
6% (Simshauser 2014).
A key barrier for utilities to transition to decentralised generation is the substantial
sunk cost of existing conventional infrastructure and barriers to closing fossil-
fuelled generation. The result is that 75% of conventional generators in NEM are
still operating beyond their engineering lifespan and utilities have adopted a "do
nothing" approach rather than take a "first-mover disadvantage" relative to their
competitors, or pay for site remediation costs, which could total $100m - $300m
(Nelson, Reid, and McNeill 2014).
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Policy Options
Given that the lowest cost to utilities by 2030 is a generation portfolio consisting of
60% renewables, this may appear to be a reasonable target for the RET backed by
energy storage by 2030. However, setting targets is a vexed issue. Taylor et al.
(2010) point to the risks that policymakers face in formulating strategies based on
current market conditions, noting that market volatility can lead to highly variable
return rates for those who invest in storage, making it an unattractive option for
market participants (Anuta et al. 2014).
A measure to address the barriers to exit of aging conventional generation could
involve regulations that prescribe generation emissions intensity that decline over
time. This could be coupled with government funding to close conventional plants,
provided that they are replaced with lower emissions generation, including
renewable generation backed by EES to ensure supply reliability. This could
provide further incentives for the development of utilities as distributed energy
aggregators/service providers, where end user sites are combined to form a 'virtual
power plant.' A programme for conventional plant closure would also ensure an
"orderly" market exit while ensuring security of supply and government payments
could be sufficient to cover the costs of site remediation, loss of revenue or both
(Nelson, Reid, and McNeill 2014).
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7. Conclusion and Recommendations
7.1. Conclusions
The ability of EES technologies to enable firm supply from renewables is a
significant development in terms of supporting efforts to decarbonise the grid. This
dissertation provided a techno-economic comparison of EES technologies to
determine which were most appropriate for large commercial energy users. In
addition to renewable integration, key applications, where the value of EES
technologies could be best realised for commercial energy users, were power
quality, reliability of supply, demand management and energy shifting. Practical
examples, illustrating the potential applications, were provided, including energy
cost savings and emissions reductions.
Different technologies provide different advantages and no single technology may
be able to meet all of the requirements of commercial end users in terms of
providing multiple applications at a low cost with excellent cycling stability,
efficiency, environmental sustainability and also with low maintenance.
Furthermore, the full benefits of each EES technology may be difficult to quantify,
as their actual performance in a commercial setting will depend on how they are
used, i.e. their purpose, charge/discharge cycles and the operating environment
within which they are deployed. Most of the investment into manufacturing at an
international level favours Li-ion batteries as they are currently the best battery type
for electric vehicles due to their light weight. This provides a much larger market for
Li-ion batteries. This combined with the fact that NEM utilities currently favour
batteries with Li-ion chemistries for their deployment of EES offerings to end users,
means that Li-ion batteries are expected to dominate the NEM. This growth is
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expected to accelerate as international manufacturing economies of scale improve
and costs decline through to 2020 and beyond.
An examination of the business models of NEM utilities AGL and AusNet Services
as case studies, versus their German counterparts E.ON and RWE, found that the
NEM utilities are better placed to adapt their business models to the changing
energy paradigm rather than being forced to change as their German counterparts
have been. The case studies were used to point to a new business model capable of
adapting to the risks presented by renewable EES and taking advantage of the
opportunities that they present. The new model is a digital one, providing a shift in
focus from physical to virtual infrastructure capable of managing multidirectional
energy flows; and revenue built on providing volumetric solutions to energy users
rather than volumetric energy sales.
The results of the dissertation also found that utilities are well placed to facilitate the
increased deployment of renewable EES technologies provided that the right policy
incentives are in place. Policy options that were identified include setting specific
long term renewable energy targets beyond 2020, for 2030 and 2050, so that both
utilities and commercial energy users have the certainty required for long term
investment planning. Providing additional regulatory support for networks under the
Demand Management Incentive Scheme also provides shared benefits for both
utilities and commercial energy users. Further economic certainty for utilities is also
required to ensure that conventional generation exits the grid in a manner that does
not shift the cost burden to end users. A policy option that could achieve this
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outcome involves regulations that prescribe declining generation emissions intensity
over time.
7.2. Recommendations
Further research of actual EES deployments within the commercial sector
and their performance under Australian environmental conditions is
warranted. Publication of these results and validation of their performance
and safety will both guide further research for EES developers and build
confidence for commercial end users.
The development of standardised costing models that accurately quantify the
economic costs and benefits of EES technologies within the NEM will assist
commercial end users in their purchasing decisions.
Follow-up research on the results of the business model changes of NEM
utilities more broadly would also be beneficial; as will further analysis of
E.ON's planned company split in 2016 and lessons learned from its business
model transformation. This may be particularly beneficial to smaller utilities
and new entrants to the market who are not limited by ownership of
centralised fossil fuelled generation.
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Appendix A: Demand Management Calculations
Demand Management Analysis
Site: Victoria
DNSP: AusNet Services Savings: $23,336
0.17013756 17.01%
Billing Period Consumption (kWh)
From To Days Peak Shoulder Off Peak Total
1/04/2014 31/03/2015 365 576,217 364,515 920,766 1,861,498
Demand (kVA): Capacity 480 Critical Peak 326
Current Network Tariff - NSP76 (Critical Peak Demand Multi-Rate)
Consumption Charges kWh Rate (c/kWh) Cost ex GST
Peak 576,217 6.5415 $37,693
Shoulder 364,515 3.8942 $14,195
Off Peak 920,766 2.9089 $26,784
Demand Charges kVA $/kVA pa
Capacity 480 52.0800 $24,998
Critical Peak Demand 326 86.7600 $28,305
$/ pa
Standing Charge
5,184.42 $5,184
Total Cost $137,160
Costs With Solar + Energy Storage
Consumption Charges kWh Rate (c/kWh) Cost ex GST
Peak 433,483 6.5415 $28,356
Shoulder 264,515 3.8942 $10,301
Off Peak 1,020,766 2.9089 $29,693
Demand Charges kVA $/kVA pa
Capacity 480 52.0800 $24,998
Critical Peak Demand 176 86.7600 $15,291
$/ pa
Standing Charge
5,184.42 $5,184
Total Cost $113,824
Notes: Calculations based on AusNet Services 2015 network tariff rates (AusNet Services 2015d); use of
a solar PV system reducing peak energy by 142,734 kWh; battery storage reducing demand by 100kVA.
112
Appendix B: Retail Contract Savings
Retail Contract Savings Analysis
Site: Victoria
Retailer: AGL Energy Savings: $8,538
Savings: 12.45%
DNSP: AusNet Services
kWh Savings: 142,734
tCO2-e
Savings: 168
Billing Period Consumption (kWh)
From To Days Peak Off Peak Total
1/04/2014 31/03/2015 365 940,732 920,766 1,861,498
AGL Retail Energy Contract
Consumption Charges
kWh Rate
(c/kWh) Loss Factor
Cost ex
GST
Peak 940,732 4.3592 1.05976 $43,459
Off Peak 920,766 2.5245 1.05976 $24,634
$/month
Retail Service Fee
40.5000 $486
Total Cost: $68,579
AGL Retail Energy Contract with Solar + Storage Energy Shifting
Consumption
Charges kWh
Rate
(c/kWh) Loss Factor
Cost ex
GST
Peak 697,998 4.3592 1.05976 $32,245
Off Peak 1,020,766 2.5245 1.05976 $27,309
$/month
Retail Service Fee
40.5000 $486
Total Cost: $60,041
Notes:
A 99 kW solar PV system reduces peak energy by 142,734 kWh.
Battery charged using off peak energy (100,000 kWh) and discharged during peak period.
113
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