Foreword
The Clean Energy Council (CEC) is the peak body representing Australia’s Renewable Energy Industry.
Australia is a world leader in renewables. With some of the highest on-grid penetrations of solar PV
in the world, electricity consumers are now driving an irreversible change. However, this change
does not come without challenges.
The continued integration of renewable energy technologies into Australia’s electricity distribution
networks represents one of the largest economic, regulatory and technical challenges that the
industry has faced to date. The extent of this reform brings with it risks and opportunities. The CEC
firmly believes that these challenges are best addressed by considering all facets of the industry
collectively and through constructive stakeholder engagement.
About the FPDI Project
The CEC, in conjunction with its members and other key stakeholders, has scoped a comprehensive
program of work that will begin to address some of these challenges. With the objective of
enhancing the flexibility and resilience of Australia’s electricity distribution systems and the
installations connected to them, the CEC-led Future Proofing in Australia’s Electricity Distribution
Industry (FPDI) project will analyse existing and emerging issues.
Ultimately the project seeks to build the foundations to facilitate the effective and efficient
integration of renewable energy systems for Australia’s electricity distribution industry. A
subsequent goal is to ensure that the benefits of the transformation of this key industry towards a
renewable energy future are accessible by the sector’s various stakeholders.
The project’s detailed scope of work includes technical, economic and regulatory analysis, forums,
knowledge gathering and dissemination of the project outcomes. This approach is intended to
create the environment for well-rounded stakeholder engagement throughout the project that will
reinforce project outputs and target specific beneficial outcomes from each aspect of the project.
Further details of the project, its scope, governance and objectives can be found on the CEC website1
in the FPDI Project area.
About this Report
A part of the industry’s transition is new charging options designed to provide signals which reflect
the costs of providing electricity. These signals are anticipated to create new dynamics in the way we
use electricity.
For small-medium enterprise businesses there is generally a low understanding of the options
available to contain their electricity costs – and the options are increasing. Demand-side
management options for SMEs include embedded generation, battery storage, load shifting and
integrated energy management systems. All of which are facing declining costs – while electricity
costs are increasing.
At the same time there is an expectation that network owners place a greater emphasis on the use
of demand side management in their investment strategies. The same options above might also
assist these crucial stakeholders in managing the utilisation of their network assets. However, there
1
http://www.cleanenergycouncil.org.au
is no immediately evident link between the expectations of DNSPs for demand management, and
that of SME customers who may adopt demand management.
Aimed at all electricity industry stakeholders, the objective of this report is to inform the more
technically minded stakeholders including distribution networks and DSM equipment suppliers. The
CEC hopes that this report will provide a reasonable level of technical detail to increase the
understanding of the options and businesses cases for demand side-management in the SME sector.
Based on this analysis, the accompanying report “Guide to demand side management solutions for
businesses” translates this technical detail to inform SME businesses.
Acknowledgements
This, and the companion report, received funding from ARENA as part of ARENA’s Emerging
Renewables Program.
The CEC thanks Entura for their efforts in preparing this report and the FPDI project Steering
Committee for their time and effort in providing crucial guidance and review of this work. These
stakeholders include ARENA, Alternative Technology Association, Australian Energy Regulator,
CSIRO, Department of Industry, Energex, Energy Networks Association, Energy Retailers Association
of Australia, Marchment Hill Consulting, Pacific Hydro Pty Ltd, AusNet Services and University of
Technology Sydney.
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Prepared by Hydro-Electric Corporation ABN48 072 377 158
t/a Entura 89 Cambridge Park Drive, Cambridge TAS 7170 Australia
Entura in Australia is certified to the latest version of ISO9001, ISO14001, and OHSAS18001.
©Entura. All rights reserved.
Entura has prepared this document for the sole use of the client and for a specific purpose, as expressly stated in the document. Entura
undertakes no duty nor accepts any responsibility to any third party not being the intended recipient of this document. The information
contained in this document has been carefully compiled based on the client’s requirements and Entura’s experience, having regard to the
assumptions that Entura can reasonably be expected to make in accordance with sound professional principles. Entura may also have
relied on information provided by the client and/or other parties to prepare this document, some of which may not have been verified.
Subject to the above conditions, Entura recommends this document should only be transmitted, reproduced or disseminated in its
entirety.
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Document Information
Document title
Analysis of Demand-Side Management Opportunities
FPDI TA-1C
Client organisation
Clean Energy Council
Client contact
Tom Butler
ConsultDM number
E304346
Project Manager
Chris Blanksby
Project number
P508834
Revision history
Revision 2
Revision description
Final
Prepared by
Lucas Thomson
25/11/2014
Reviewed by
Chris Blanksby
25/11/2014
Approved by
Donald Vaughan and Seth
Langford
25/11/2014
Distributed to
(name)
(signature)
(date)
Tom Butler
Clean Energy Council
26/11/2014
(name)
(organisation)
(date)
Current Document Distribution List
Revision
Organisation
Issued to
Date
0 (Draft)
Clean Energy Council
Tom Butler
26/09/2014
1 (Draft)
Clean Energy Council
Tom Butler
10/11/2014
Document History and Status
Revision
Prepared by
Reviewed by
Approved by
Date
approved
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CB
SL/DV
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Executive Summary
The Clean Energy Council (CEC), through the Future Proofing in Australia’s Electricity Distribution
Industry (FPDI) program, is exploring the changes from the continued integration of renewable
energy into Australian distribution networks. This report is a key input to the wider CEC FPDI program
and is intended to build an understanding of the opportunities for the uptake of Demand Side
Management (DSM) options for Small-Medium Enterprises (SMEs). The outcome of the analysis from
this technical report and further business engagement is compiled in the companion report "Guide to
demand side management solutions for businesses" compiled by Moreland Energy Foundation
Limited. In particular, this companion report highlights some broader considerations for uptake of
DSM in SMEs.
The CEC hopes that the publication of this report will inform technically-minded stakeholders and
enhance engagement such distribution network service providers and DSM equipment suppliers.
Combined, the two reports are intended to highlight the business case for various DSM options,
assist SME business owners in understanding the opportunities and risks of implementing these
options and increase the ability of distribution networks to engage with SME customers.
The analysis conducted here was driven by consideration of three key factors affecting DSM
opportunities for SMEs:
•
load profile – a business specific characteristic representing how that business uses electricity
•
technology – the physical mechanism via which the SME can impact on their load profile
•
tariffs – the charging regime for electricity use, which, in conjunction with the load profile, can
determine the impact of the technology.
Inputs from a range of sources were used to represent each of these three factors, to cover a wide
range of SMEs.
To ascertain where opportunities exist, a detailed half hourly model was used with the inputs to
estimate the cost of electricity for each business type (represented by a particular load profile),
technology, and tariff. Financial metrics (net present value and payback period) were compiled for
each to provide a measure of the viability of each DSM option.
A key limitation of this report is that it cannot cover the specific circumstances of all businesses, nor
can it cover all the potential ways of combining and implementing technologies (including their
future variants), or the ways in which this and other factors will impact on tariffs. However, in the
context of the key business drivers for SMEs (short payback, simple to implement technology that
does not distract or impact on core business), such complexities and niche scenarios are unlikely to
be a high priority for most SMEs, and as such, the focus on simple and reliable opportunities is
considered justified1.
This report considers both current and future opportunities for DSM. The key considerations in
looking at future opportunities are likely changes in technology pricing and tariff structures. New
1
While disconnecting completely from the grid is not commercially feasible in the immediate to short term,
consideration of this opportunity for individual SMEs both now and in the future is beyond the scope of this
report. The context of this report is DSM opportunities for SMEs within the grid.
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entrant technologies (i.e. those that are not already in the marketplace) that may initiate a ‘game
changing’ scenario were not considered as these could not be reliably modelled. A watching brief on
new technology entrants needs to be kept as any ‘technology transition’ or ‘pricing transition’ may
occur in the short or medium term.
Tariff structures were also considered to be in a state of transition, and a source of significant
uncertainty. This is partly driven by the impact of DSM on revenues for network service providers,
and hence on tariffs (which in turn affects DSM viability). To attempt to capture this uncertainty,
possible future tariffs were constructed to represent different scenarios, on the basis of cost
reflective pricing. These tariffs included capacity charges, which is increasingly common in smaller
customers, facilitated by time of use and smart metering. The essential difference between the
future tariff scenarios was differing portions of variable cost (i.e. cost per kWh). As the variable cost
component is the strongest driver of DSM viability, there was considerable difference in the
outcomes for DSM under the different tariff structures, representing a high level of investment
uncertainty.
Outcomes and Recommendations
Across most business types, there were two DSM technologies that performed well. These were
energy efficiency and solar PV embedded generation. Solar PV had somewhat better outcomes in
general, however, was more exposed to the uncertainty described above. In particular, it was noted
that whereas under existing tariffs, these opportunities could be said to be viable for most
businesses, the uncertainty of future tariffs could degrade the value of investments in many
instances, particularly for solar PV. While strong opportunities still exist, these are not as universal
and require consideration of specific business circumstances.
More advanced technologies, including battery energy storage systems, fuel cells, and automatic
load shifting technology, were also examined and found to be currently uneconomic. There were
some specific scenarios for a small number of SMEs where these technologies would work, typically
associated with offsetting large network investment or maintenance. While downward capital cost
trends for these technologies indicate a point of broad scale future adoption, this is unlikely to occur
in the short term and may be further delayed by changes to tariff structures.
In general, it is recommended that businesses considering DSM should focus on simple, proven
solutions in energy efficiency, and should consider solar PV with an understanding of the risks and
guidance of a reputable installer, who can take into account their particular circumstances. Other
opportunities using alternate technologies may be presented to SMEs, and businesses may wish to
consider these if they are justified for their particular circumstances.
Though outside the scope of this study, it is also advisable that SMEs regularly review their tariff
structure options against their load profile and select a structure that minimises their costs.
For some businesses, DSM, through energy efficiency and solar PV, presents good opportunities now.
Looking forward, new opportunities are likely to involve more integrated DSM options. We will see
increasing opportunities for businesses to embrace a range of technologies including automated load
shifting, solar PV generation, and potentially energy storage to deliver more services to the electricity
network, and this will be supported by technology improvements, cost reductions, new tariff
structures, and methods to capture the additional value to the network.
Broadly speaking, there are likely to be fundamental changes in the dynamic within the grid that will
need to be understood by all stakeholders.
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SME specific barriers to DSM
In undertaking this study and developing the above findings, a number of barriers for SMEs were
identified.
•
SME consumers are largely disengaged from energy usage and cost of supply other than the
final cost of electricity.
•
Electricity is not a primary cost driver. Small /medium business electricity costs often account
for less than 2- 5% of total business costs. While many DSM options may provide a positive
NPV, the absolute magnitude of the savings may be a barrier to adoption.
•
Technology barriers – smaller load sizes, particularly for load shifting, though often less
standardised than residential loads have a larger relative cost to control.
•
Business portfolio planning – where business is part of a larger corporation often does not
have the flexibility to enact DSM options outside of the capital allocation and planning
schedules that occur at a consolidated level.
•
SMEs generally have less capacity to be directly involved in managing their own DSM than
larger businesses, who can potentially devote a role to this function. Similarly, management
systems designed for larger businesses may not be cost effective for SMEs. Domestic
technology / approaches may be more useful for many SMEs, as these technologies develop
commercially.
•
Ownership structures – access to site infrastructure such as individual site metering and access
to physical roof space in circumstances where SMEs are in leased or shared buildings.
•
Tariff, policy understanding – limited understanding of tariff arrangements, especially likely
future tariffs and impacts on DSM options.
Policy options to support SME adoption of DSM
In consideration of these barriers, and the outcomes of the study, several key policy opportunities
were identified, which would substantially improve DSM opportunities for SMEs. While these
measures are likely to be beneficial, it is important to understand that they are not the core factor
driving uptake of DSM, and are not the main outcome of this study.
•
provide certainty and consistency for investment (environmental and renewable energy
policies, schemes, technology, standardisation for load control, communications, metering)
•
increase retail tariff certainty (persistence of tariff structures and rates) over a medium term
(~5 years) to support investments in DSM solutions – this may best be achieved by policy to
encourage cost reflective pricing at a distribution, and possibly also retail level (note that this
may not make DSM more attractive in the short term, but is intended to provide support in the
longer term)
•
provide clear and consistent (across jurisdictions) rules for tenant access to building
infrastructure, or standardised tenant – landlord agreements to provide the same effect
•
coordinated national approach to support efficient adoption for businesses with multiple sites
•
support technology aggregators to assist SMEs with implementation of DSM – aggregators are
likely to be able to reduce administrative and knowledge burden on SMEs, manage risk over a
portfolio of projects, capture multiple disparate revenue streams, and manage network
support opportunities.
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Glossary
c/kWh Cents per kilowatt hour
CEC
Clean Energy Council
DLF
Distribution Loss Factor
DNSP
Distribution network service provider
DSM
Demand Side Management
The planning, implementation, and monitoring of strategies to manage demand to meet supply in the
medium to longer term2.
DSP
Demand Side Participation
The ability of energy consumers to make decisions regarding the quantity and timing of their energy
consumption that reflect their value of the supply and delivery of electricity. These decisions include
both short-run decisions in response to specific events, and longer-run investment decisions about
energy efficiency3.
DR
Demand Response
A rapid change in electricity use by a consumer in response to changes in the condition of the grid,
changes in energy price or other information.4.
DUOS Distribution Use of System
HV
High voltage
KWh
Kilowatt hour
Large customer
A customer that consumes more than 160 MWh of electricity per year
LRET
Large-scale Renewable Energy Target
LV
Low voltage
MLF
Marginal Loss Factor
MW
Megawatt
2
Australian Standard AS5711-2013 Smart grid vocabulary
3
Australian Standard AS5711-2013 Smart grid vocabulary.
4
Australian Standard AS5711-2013 Smart grid vocabulary.
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MWh Megawatt hours
NEM
National Electricity Market
NER
National Electricity Rules
PV
Photovoltaic
RET
Renewable Energy Target scheme
SME
Small Medium Enterprise
SRES
Small-scale Renewable Energy Scheme
STC
Small-scale Technology Certificate
TLF
Transmission Loss Factor
TOU
Time of Use
TUOS
Transmission Use of System
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Contents
1.
2.
Introduction
1
1.1
Background
1
1.2
Business drivers
2
1.3
Method
3
1.4
Limitations, Assumptions and Exclusions
5
1.5
Carbon price
6
Technologies
7
2.1
Technology options
7
2.1.1 Energy Efficiency
7
2.2
3.
4.
5.
2.1.2 Embedded Generation
11
2.1.3 Battery Energy Storage Systems
13
2.1.4 Automated Load Control
14
First Pass Analysis
19
Businesses
23
3.1
Business Descriptions
23
3.2
Inputs
24
3.3
Business Engagement
25
3.3.1 How can DNSPs engage with SMEs?
25
3.4
Discussion of DSM Technology Applicability by Business Type
25
Tariffs
27
4.1
Tariff Structures
27
4.2
Underlying supply cost drivers and implications for future retail tariffs
29
4.2.1 Feedback Mechanisms
30
4.3
Published tariffs for use in this study
31
4.4
Modelled tariffs for use in this study
34
Modelling
37
5.1
Inputs and Assumptions
37
5.1.1 Economic Assumptions
37
5.1.2 General Modelling Assumptions
37
5.1.3 Technology Assumptions
38
Case Study Results – Retail
39
5.2.1 Energy Efficiency Scenario
40
5.2.2 Solar PV Scenario
41
5.2.3 Sensitivity Analysis – Retail Solar Case Study
42
5.3
Case Study Summary Results
43
5.4
Example Case Study Results – No Solar Export
44
5.5
Example Case Study Results – Solar PV Location, Orientation and Sizing Options
45
5.6
Example Case Study – BESS
47
5.2
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5.6.1 BESS Adoption Point
49
5.7
Example Case Study – Fuel Cells
50
5.8
Example Case Study –Declining Block Demand Tariff
50
5.9
Example Case Study – Discretionary Load Control
53
5.9.1 More General Automated Load Shifting Applications
54
Outcomes and Recommendations
55
6.1
Energy Efficiency Recommendations
55
6.2
Embedded Generation Outcomes
55
6.3
Battery Energy Storage Systems
56
6.4
Automated Load Shifting
56
6.5
Summary for Distribution Network Service Providers
56
6.6
Business Engagement and Barriers
57
6.7
Policy Recommendations
58
6.8
Concluding Remarks
58
6.9
Acknowledgements
59
References
61
Appendices
Appendix A Business
Appendix B Business Modelling Case Study Results
List of figures
Figure 2.1: Energy efficient LED lighting
8
Figure 2.2: Energy efficient controls and heat pump
8
Figure 2.3: Energy efficient solar hot water system
9
Figure 2.4: Power factor correction equipment, capacitor banks
9
Figure 2.5: Embedded generation solar photovoltaic
11
Figure 2.6: Embedded generation small wind turbine
12
Figure 2.7: Battery energy storage system, top left small < 100 kWh, top right 200 kWh, bottom 1.6 MWh large
scale energy storage
14
Figure 2.8: top left Interval meter, top right interval meter used in commercial demand management
installation, bottom left smart circuit breaker, bottom right smart circuit breaker used in commercial demand
management installation
16
Figure 2.9: Example load shifting application
16
Figure 3.1: Business average daily load profiles
24
Figure 4.1: Traditional tariff breakdown by business size
28
Figure 4.2: Sample energy tariffs, energy components only
33
Figure 4.3: Make-up of a typical retail business electricity bill under each of the tariff structures.
35
Figure 5.1: Retail business electricity costs (base case)
39
Figure 5.2: Retail business electricity costs with energy efficiency
40
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Figure 5.3: Retail business returns with energy efficiency
41
Figure 5.4: Retail business electricity costs with Solar PV
41
Figure 5.5: Retail business returns with solar PV
42
Figure 5.6: Solar PV orientation (illustrative only)
46
Figure 5.7: BESS charge and discharge weekday, with excess solar
47
Figure 5.8: Sample weekend charging from excess solar
48
Figure 5.9: Sample peak tariff discharge on Monday following Saturday charge
48
Figure 5.10: Momentum standing offer left, AGL stepped demand tariff right
51
Figure 5.11: Example discretionary load control
53
List of tables
Table 2.1: Summary of potential energy savings from energy efficiency
10
Table 2.2: DSM options first pass analysis
20
Table 4.1: Qualitative opportunities for SMEs associated with different tariff components
29
Table 4.2: Published tariff summary
32
Table 4.3: Declining block demand tariff
33
Table 4.4: Modelled tariff summary
35
Table 5.1: Retail case study sensitivity of payback period to capital cost
43
Table 5.2: Summary of discounted payback periods by business, tariff and technology
43
Table 5.3: Office case study (no export)
44
Table 5.4: Solar resource locational variability
46
Table 5.5: Results declining block demand tariff
52
Table 5.6: Discretionary load control results
54
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Introduction
The Clean Energy Council (CEC), through the Future Proofing in Australia’s Electricity Distribution
Industry (FPDI) program, is exploring the changes from the continued integration of renewable
energy into Australian distribution networks. There are a number of significant changes currently
occurring in the way electricity is priced, new distributed electricity generation options, load control
technology and distributed energy storage. The CEC is analysing how these changes will impact the
shape of the distribution industry. The CEC’s major focus is to enhance the flexibility and resilience of
Australia’s electricity distribution systems and the installations connected to them.
This report is a key input to the wider CEC FPDI program and is intended to build an understanding of
the opportunities for the uptake of Demand Side Management5 (DSM) options for Small-Medium
Enterprises (SMEs). The outcome of the analysis from this technical report and further business
engagement is compiled in the companion report "Guide to demand side management solutions for
businesses" compiled by Moreland Energy Foundation Limited.
The analysis presented here will be used to produce reference material that will equip SME
customers with the knowledge to make informed decisions on what DSM opportunities are available
to them and what these decisions may mean for their energy costs.
The case studies and outcomes presented in this report are intended to provide a guide for
businesses to understand if demand side participation (DSP) is relevant to them, and what DSM
opportunities they should consider. The case studies considered (including sensitivities) are intended
to provide broad coverage of the types of scenarios that may apply to SMEs. However, the individual
circumstances of every business will vary, and the results should be considered in this context. In
particular, specific assumptions are made about the load profile, technology and tariff for each case
study, and the associated outcomes apply directly only to those particular circumstances. Outcomes
for SMEs will vary with location and with individual circumstances.
1.1
Background
Total retail electricity prices have been rising for SMEs, with the dominant cause being the increase in
network costs bought about by capital infrastructure projects required to meet projected increases
in peak demand that occur for a very short period, often less than 0.5% of the year or 50 hours per
year. The CSIRO’s Change and choice6 report provides a concise background on the recent rises in
5
Demand side participation is defined as “The ability of energy consumers to make decisions
regarding the quantity and timing of their energy consumption that reflect their value of the supply
and delivery of electricity. These decisions include both short-run decisions in response to specific
events, and longer-run investment decisions about energy efficiency.” Australian Standard AS57112013 Smart grid vocabulary. In this report, we typically refer to DSM, as this encompasses the actual
planning and implementation process, however, DSP is still relevant in some aspects of the
discussion.
6
CSIRO, Change and choice The Future Grid Forum’s analysis of Australia’s potential electricity
pathways to 2050, 2013.
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retail electricity tariffs. This report also highlights how demand response from customers to these
increasing prices has impacted on the growth in peak demand, and is beginning to have significant
impacts on the way electricity is produced and consumed. A range of future scenarios is presented
that highlight the current uncertainty in the market, and potential for change.
At the same time, a range of technologies is maturing in the market place, which provides customers
with options to address their electricity costs.
With rising electricity tariffs, businesses are exploring their opportunities to contain or reduce their
electricity costs. New technologies offer avenues to do this. A number of new technology options and
tariff changes are allowing businesses to increasingly participate in demand side management in an
attempt to reduce the cost of electricity supply. Any such changes can, however, impact on the
efficiency of the electricity system as a whole.
Significant changes are occurring in the electricity industry, providing opportunities for SMEs to
reduce their electricity costs through greater involvement in both the consumption and generation of
electricity
Retail electricity tariffs are changing from simple bundled flat rates to the introduction of more ‘cost
reflective’ electricity pricing. This change is attempting to price electricity taking into account when,
where and how much electricity is consumed and the cost that this consumption has on the cost of
running the electricity systems as a whole. These tariff changes are resulting in increased complexity
and uncertainty for DSM options for SMEs, however, there are also some niche opportunities.
Another factor increasing the options for DSM is the increasing availability of technologies including
increased information technology, advanced metering, load control systems, energy efficiency, solar
PV generation and battery energy storage systems. These technologies have experienced significant
cost reductions, especially in the case of solar PV providing an opportunity for SMEs to generate
behind the meter, offsetting energy consumption.
Ongoing policy and regulatory change is occurring which will impact on the type and quantity of DSM
opportunities. These changes can either limit or encourage DSM, for example changes to the feed in
tariffs of embedded solar PV generation and in other cases providing opportunities to reduce peak
demand to defer distribution network augmentation upgrades.
1.2
Business drivers
In identifying opportunities for business to participate in demand management, Entura and MEFL
have developed a summary of key drivers, developed from direct engagement with SMEs in regards
to DSP. An understanding of SME drivers is required in evaluating opportunities for DSP in these
businesses. Both the drivers and opportunities for SMEs are significantly different to both residential
and large scale customer. The key findings are drawn from engagement with business around DSP
from the King Island demand side management project, direct business engagement for this project
by MEFL, and ongoing SME interactions and engagement with SMEs by Momentum Energy’s
customer energy services staff.
A summary of key SMEs engagement outcomes includes:
•
2
opportunities require short payback periods, and these vary considerably between businesses
from 2 to a maximum of 7 years in rare cases
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•
simple and easy to use processes, within their internal capacity, are required for adoption of
new DSM technology or solutions
•
SMEs have considerable uncertainty over length of time in premises
•
often premises are leased rather than owned, which may limit access or desire for changes
related to the property, for example roof spaces or electrical infrastructure
•
electricity cost is often a low priority in selection of premises (in comparison to other drivers
such as market location, or workforce)
•
electricity is often a relatively low (<5%) proportion of overall business costs
•
there is focus on core business activities, with limited capacity for other considerations
•
may be part of larger businesses and require alignment with other businesses for planning of
capital investment
•
energy efficiency has historically provided quick payback periods, though has often not been
fully exploited
•
most electricity loads are non-discretionary as they are critical to business functionality, and
hence load deferral or automatic load control options may be limited
•
SMEs have a limited understanding of DSP and the various technologies
•
SMEs have a desire to identify how much they will save on their annual bill.
A more detailed compilation of outcomes is presented in the MEFL companion report "Guide to
demand side management solutions for businesses".
This report focuses on the technology viability and economic drivers for adoption of DSM by SMEs.
This is likely to be a core consideration in any SME decision making around DSM. The MEFL
companion report "Guide to demand side management solutions for businesses" also addresses
broader drivers, as outlined above.
1.3
Method
The stages of work undertaken in completing this project are as follows:
•
Gathering data – identification and collection of data, including business consultation (as
described in the previous section), technology information, tariff / pricing data, and regulatory
background information relevant to SMEs. The data gathered is shown in Section 2, 3, and 4.
•
Policy and Regulatory Considerations Analysis – consideration of any policy or regulatory
barriers to uptake of DSM for SMEs, and to inform policy recommendations arising from DSM
opportunity assessment. The key barriers identified are detailed in the Companion report, with
policy recommendations included in Section 6 of this report.
•
SME Electricity Usage Modelling – using data collected to generate representative load profiles
for selected business types. This is presented in Section 3 and Appendix A.
•
Modelling of Demand Side Management Options and Integration – modelling the operation of
DSM technology in the context of tariffs and load profiles. This is presented in Section 5, and a
more detailed description of the method use is outlined below.
•
Calculation of Economic Viability Triggers – using the modelling results to estimate the
economic viability of different DSM options, including trigger points for when capital cost
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reductions to increase adoption for DSM technology that was not currently viable. This is
presented in Section 5, with discussion of the outcomes in Section 6.
•
Writing of this Technical Report, and the companion report "Guide to demand side
management solutions for businesses".
The following is a more detailed discussion of the method used in analysing opportunities for DSM,
arising from consideration of the interplay between tariffs, load and technology.
DSM opportunities can vary widely based on a variety of factors including the load type, geographical
location, renewable resource, technology access, business type, policy / regulation and tariff
structures. To capture this variation in a meaningful way, a number of case studies have been
modelled to describe the opportunities and assist businesses to align the outcomes with their
individual circumstances.
A brief overview of currently available DSM technologies and the application of these technologies
are included to assist businesses in identifying which technologies are currently available and how
they may be used for demand side participation. A summary of applications, capital and operational
and maintenance costs along with associated value streams were compiled.
An initial cost benefit analysis of identified DSM options screened out any opportunities that
currently do not present an opportunity under an optimistic scenario. Detailed case study modelling
was undertaken on the remaining DSM options.
Load Profile
In developing the case studies for detailed modelling of the DSM options, load profiles for a cross
section of business types were selected based on 30 minute measured interval data. The SME
business types were selected to cover a wide cross section of potential load profiles. The load profiles
and associated metadata were used to characterise the business operations.
Load profiles are infinitely variable, both in the pattern and volume of consumption. Thus, the results
include reflection on the sensitivity of opportunities to variation in load profile. This may not,
however, capture scenarios where the load profile is considerably different to the case studies.
Tariffs
A range of current retail tariff structures has been sourced suitable for small / medium businesses
defined as electricity consumption below 160 MWh per annum. The tariffs were chosen to provide
coverage of the current and future types of tariff applicable to SMEs, including flat and time of use
tariffs with bundled and unbundled costs. A number of tariffs were created based on 2013/14 spot
market data to examine a range of issues including exposure to energy market volatility and potential
cost reflective pricing models. In looking at tariffs, one of the key factors to consider is the
apportionment of costs between fixed and variable components, and the impact this has on
technology impact. Section 4.2 provides a discussion on the impact of this apportionment.
Actual retail tariffs currently available offer a huge range of choice, with a very large number of
options available in most areas. Typically, these are structured to offer marketing advantages, and do
not necessarily represent underlying costs. Tariffs that do not reflect underlying costs are generally
more sensitive to changes in the demand structure (e.g. a flat rate is more likely to change under a
falling demand scenario than a tariff with a higher fixed or capacity charge).
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Modelling
Half hourly time series modelling was conducted to determine the economic opportunities of DSM
for a range of options. The modelling included overlaying the DSM option (solar, battery energy
storage) with the load profile, solar resource, temperature, import and export tariff, capital and
operational costs to determine the relative benefits compared to a base case. The modelling package
utilised was the Homer Energy Micro power Optimisation Model, Version 2.81. The opportunities
identified in this modelling were assessed against our understanding of SMEs drivers and barriers.
The resulting opportunities have been included in the outcomes and recommendations of
opportunities for DSM.
1.4
Limitations, Assumptions and Exclusions
Businesses with annual electricity consumption in excess of 160 MWh are considered large
customers, and attract alternative retail or separately negotiated tariffs and have been excluded
from this report.
The opportunities identified are based on repeatable opportunities that are available to the market
as a whole and exclude site specific or individual opportunities. For example, incentives that are only
available in specific geographical areas, including payments for avoided upgrades of network
distribution, are not included. Also, individual opportunities such as where businesses close to a
threshold seek to change consumption to be reclassified into another tariff class are not included.
In general, the focus is on opportunities that are available to a broad cross section of SMEs, however,
where there are potential opportunities for niche applications, these are noted (but not investigated
in detail). Given the business drivers for SMEs (in particular, focus on core business) niche
opportunities are not necessarily likely to be identified by the SMEs they are relevant to, and are
likely to rely on approaches from third parties, including aggregators, retailers, or distributors.
In focussing on widely available opportunities, pricing signals used to inform the economic
proposition for a particular technology were based on existing and potential tariff structures.
Estimating the benefits of network support services, of the type where a specific issue exists, in a
specific part of the network, that can be addressed using some form of DSM, are the subject of
separate FPDI work and are not addressed here. As part of consultation processes, it is likely that
DNSPs would engage directly with local SMEs (and others) to determine a solution and value
attribution where this exists.
There is a significant amount of uncertainty in the current policy environment surrounding
embedded renewable generation and the electricity industry in general. While we have attempted to
cover the range of sensitivities through considering different tariff structures, the possibilities
represented by this uncertainty have the potential to significantly change the outcomes presented
within this report.
Technology options and costs are subject to significant change. The outcomes presented in this
report are based on currently available information and may change in the short-medium term.
There are a number of additional DSM options that have not been included in this report as they are
still in development or early commercialisation trials and are not currently widely and commercially
available.
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Carbon price
There is currently no legislated price on carbon in Australia. However, businesses may want to
consider the possibility of a carbon price in the future. If so a carbon price is likely to have a minor
positive impact on the economics of DSP by businesses. The magnitude of any future carbon price is
unknown, though from the 2013/14, at a price of $24.15 per tonne of Co2-e, it had an approximate
impact on retail electricity bills in the order of 9%7. The impact of this is within the tariff sensitivities,
and can be reflected in an increase in a retail tariff improving the economic returns. Refer to
section 5.2 for more details.
7
Australian government, Department of Industry,
http://www.industry.gov.au/Energy/EnergyMarkets/Documents/ELECTRICITY-PRICES-FACTSHEET.pdf, accessed
30th October 2014.
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2.
Technologies
2.1
Technology options
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DSM provides a way for SMEs to be involved in managing their electricity demand and can assist in
reducing costs for the individual business and electricity market as a whole. DSM can take on a
variety of forms, or applications, with specific technology associated with each. This section outlines
the currently available applications and technology that can be utilised by SMEs to participate in
demand side management (DSM) activities.
DSM typically involves either shifting or reducing electricity consumption from the grid (reduction
also includes self-generation). Energy efficiency and Solar PV are examples of technology for reducing
electricity consumption and direct load control is an example of changing the time of consumption to
occur at times of lower price.
This section provides a brief overview of the current technologies and links them to their
applications.
2.1.1
Energy Efficiency
Energy efficiency is intended to reduce the amount of energy required for a business to operate.
Energy efficiency involves either installing equipment such building insulation, or changing
behaviours such as turning off lights.
Energy efficiency technology and behavioural change strategies are not covered in detail in this
report. As there are many sources of publically available information8,it is recommended that all
businesses investigate energy efficiency as a starting point to reduce their overall cost of energy.
Examples of energy efficiency technology include:
•
Lighting
o
8
Efficient lighting, technologies such as LED lighting or lighting control ensuring light is
only provided when necessary such as times when building is occupied or when natural
light is low.
Further information on energy efficiency for SMEs can be found at: http://www.eex.gov.au
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Figure 2.1: Energy efficient LED lighting
•
Building envelope
o
•
Building insulation, draft stopping, window treatments etc.
Heating Ventilation Air Conditioning (HVAC)
o
Insulation and building design
o
Installation or retrofit of more efficient compressors and control.
o
Automation or behavioural change to insure only heating/cooling in the amounts and at
the time required is delivered.
Figure 2.2: Energy efficient controls and heat pump
•
Energy efficient appliances
o
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Upgrading or augmentation of appliances from domestic appliances through to process
equipment e.g. variable speed drivers for motor loads.
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Solar hot water
o
Solar hot water systems (SHWSs) are an example of distributed generation and energy
efficiency which business should consider as a cost effective measure to reduce energy
demand. Current government support, under the Small-Scale Renewable Energy Scheme
for eligible solar water heaters and air source heat pumps are entitled to a number of
small-scale technology certificates (STCs).
Figure 2.3: Energy efficient solar hot water system
•
Power factor correction
o
In larger premises power factor correction technology such as capacitor banks can be
used to reduce the real power consumed reducing the premises cost and also the load
on the distribution network. Power factor correction charges is currently only applicable
to large businesses consuming in excess of 160 MWh per annum, which has the power
factor charge unbundled. It is possible the unbundling of power factor may reduce over
time potentially creating opportunities for smaller customers to reduce costs through
power factor correction.
Figure 2.4: Power factor correction equipment, capacitor banks
The Federal Government Energy Efficiency Opportunities (EEO) Program (http://eex.gov.au/)
contains outstanding data and advice on the specific opportunities presented by energy efficiency.
While this data is primarily derived from large business reporting and experiences, there is a range of
specific information relevant to SMEs, including detailed technology descriptions, typical cost of
implementation of the technology, and actual energy savings.
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For the purposes of this study Entura looked at generic estimates for the impact of energy efficiency
measures, to enable comparison against other opportunities. Energetics9 studied energy efficiency
savings opportunities for a range of SMEs using EEO data and other databases10. Entura has reviewed
the opportunities for savings for SMEs reported in this work, and allocated these to the relevant
business categories as summarised in Table 2.111.
Table 2.1: Summary of potential energy savings from energy efficiency
Business type
Potential energy savings as a
% of total consumption
Retail
18.9%
Accommodation
19.3%
Hospitality
18.9%
Warehouse
17.8%
Office
20.1%
Manufacturing
15.3%
It is notable that the potential impact of energy efficiency is similar across all businesses (with
manufacturing and warehousing being slightly lower). Given the small sample size on which this data
is based, the difference between these values is not significant and Entura considers it more robust
to assume the same impact across all business types. That is, on average, it is assumed that cost
effective energy efficiency measures can reduce demand/consumption by up to 18.4%.
The Energetics work also summarised the range of capital cost to energy savings ratios associated
with various energy efficiency technologies (i.e. how much does it cost to achieve these savings).
These ratios show a decreasing rate of return, and not all available measures would typically be
implemented. Typically, only those measures with a payback of 3.3 years or less were found to be of
interest to SMEs, though payback of up to 4 years were considered. To model energy efficiency,
Entura calculated the average capital cost required to give a 4 year payback on a flat tariff, for each
business. This capital cost was maintained when considering the impact of other tariffs.
Energy efficiency measures will typically have a lifetime of between 15 and 25 years (typically
equipment such as HVAC and lighting will be at the lower end of this range, and building envelope
improvements will be at the higher end of the range).It is assumed here that capital outlays on
equipment are repeated at 15 years.
9
Energetics (July 2012) Energy use and energy efficiency opportunity data for commercial sector and
small/medium businesses – summary of results, Energetics report J/N 110359 (commissioned by Department
of Climate Change and Energy Efficiency) downloaded 4/11/2014.
10
It is notable that the Energetics identifies an upper limit on the payback period of 4 years for businesses in
general, and 1-2.5 years for SMEs. This is supported by EEO data that shows approximately 85% of all adopted
savings have payback periods of less than 4 years (Energy Efficiency Opportunities – Continuing Opportunities
2011. Results of EEO Assessments reported by participating corporations)
11
Further savings were possible with additional energy efficiency measures, however, these had a diminishing
impact and were not cost effective enough for implementation in the cases presented.
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Embedded Generation
Centralised generation (predominantly large coal power stations) has historically dominated
electricity generation in Australia. The alternative is on-site (or embedded) generation of electricity
behind the meter, such as by the business itself. Embedded generation has the ability to reduce
consumption of electricity from the grid, and can also result in export of excess energy to the grid.
Embedded generation is rising rapidly in Australia, with in excess of 3,600 MW of domestic solar PV
in the Australia market, and the opportunities that it represents to offset electricity costs is one of
the key aspects of this study. It is notable that embedded generation currently attracts government
incentives in the form of Small-scale Technology Certificates (STCs), which effectively reduces the
capital cost of this technology. There is, however, some uncertainty about future changes in
incentive structures.
•
Renewable Embedded Generation
o
Solar Photovoltaic (PV) generation is the most common embedded generation source in
Australia accounting for in excess of 3,600 MW of installed capacity. Solar PV converts
the solar irradiance incident onto a PV module into DC power which is inverted to grid
frequency AC. Solar PV is both suitable for urban and rural environments and has the
advantages of being modular allowing it to be cost effectively installed for a range of
sizes suited for a SME, from single kW through to hundreds of kW systems. Solar PV has
historically shown a rapidly decreasing capital cost of equipment.
Solar PV typically has a system life of 25 years, though inverter replacement after 10-15
years is typical (13 years has been assumed in this study).
Figure 2.5: Embedded generation solar photovoltaic
o
Small wind turbine generation is the conversion of wind energy into electrical energy.
While it has the advantage of producing more energy per installed kW, it is generally
more expensive than solar and not usually suitable for the urban environment.
Wind has not realised significant uptake in small scale embedded generation. Wind
turbine projects at a smaller scale, suffer from disproportionate costs (planning and
permitting, erection, transport, maintenance, skills, non-modular and supply costs) and
are often uncompetitive with solar PV.
Wind turbines are more appropriate in rural areas where they can be installed on masts
up to 30 m high (this will still often require planning approval). In instances where
planning approval can be readily obtained and a high wind resource exists, small wind
turbines may be an effective embedded generation source for rural businesses. Smaller
direct roof mounted technology has a higher levelised cost of energy, though wind
energy provides advantages such as renewable resource diversification.
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While there are a number of new small wind technologies entering the market, the
historical reduction in cost has been modest.
Small wind typically has a system life of 10-15 years.
Figure 2.6: Embedded generation small wind turbine
o
Diesel generation or backup generation already installed and configured for
synchronisation with the grid is not competitive with the retail or socket price of energy.
For example diesel at a cost of $1.60 per litre, with a generating efficiency of
3.5 kWh/litre and an operations and maintenance cost of $25/MWh would result in a
short run cost of energy of between $0.40 and $0.50/kWh. In some limited, site specific
cases there may be an opportunity to utilise existing generation at peak times and
obtain a network support payment from a DNSP.
Diesel generation is a mature technology, and any future cost reductions are expected
to be modest. The typical life of a diesel generator depends on the run-time (and
maintenance). In typical DSM applications for SMEs, and with proper maintenance, a
diesel generator may last 15-20 years.
•
Fossil fuel embedded generation
o
In broad terms, fuel cells can generate electricity from a fuel source, and some types can
also use electricity to synthesise a fuel. Generator only fuel cells typically use fuels such
as natural gas, methanol, ethanol, or propane, and serve a similar function to diesel
generators. In comparison to diesel generators, fuel cells typically have higher efficiency,
lower emissions, lower noise, lower maintenance, a shorter operating life, and currently
have higher capital costs.
Fuel cells that synthesise fuel (or both generate electricity and synthesise fuel) typically
use hydrogen as the fuel, and can serve a similar role as battery storage (or in some
senses, operate like vehicle to grid technology by generating a non-stationary energy
source).
Given the similarities between the function of fuel cells and other technologies, it is
principally the economics that provide differentiation. At a simple level, the economics
of the generator function can be summarised as follows:
12
−
Commercial fuel cells typically operate to a maximum efficiency of about 60%
when producing electricity (when serving both electrical and heat load, this can
increase to 85%).
−
Considering natural gas as a fuel source, current prices of approximately 2.5c/MJ
mean that a fuel cell could generate electricity at approximately 15c/kWh.
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−
with zero capital cost, the volume rate for electricity from the grid (or other
sources) would need to exceed this 15c/kWh for fuel cells to be viable (assuming
no change in gas price)
−
Fuel cells have a limited operating life in the order of 15,000 hours. Considering
the example of the BlueGen fuel cells, which currently cost $33,000 for a 1.5 kW
system, this equates to approximately $1.50/kWh
−
Hence, for fuel cells to become viable, there needs to be a considerable reduction
in capital costs, and volume rates for electricity need to be below the equivalent
cost of alternative fuel.
−
For storage applications, the fuel may be essentially free if it is generated by
excess energy from solar (for instance). However, fuel cells that can synthesise
fuels are considerably more expensive and require greater maintenance due to
the additional compression and storage equipment required
Fuel cells are commercially available within Australia, however, supply options are
limited. Very little uptake has occurred and fuel cell technology remains a niche
technology. Significant uncertainty in future gas prices is likely to have an impact on the
adoption of this technology. However, the potential for fuel cell backing to firm up
supply (as an alternative to battery storage) from distributed PV generation remains an
option, as does employment of fuel cells in off-grid applications or areas of network
support, though any detailed consideration of these is outside the scope of this report.
2.1.3
Battery Energy Storage Systems
Battery energy storage systems (BESS) comprise energy storage (usually lead acid or lithium cells), a
charger/inverter and a communications and control system that can be used to provide a range of
energy storage and grid support functions. BESS can be used for a variety of applications including to
store excess solar PV generation that may not have otherwise been injected into the grid, to provide
ramp rate control, to reduce consumption of energy at critical peak times, to island a system in the
event of a grid outage, or to enable fully off-grid operation.
Lithium based energy storage technology has been used in this report. Lithium batteries typically
have greater cycle life, greater depth of discharge, greater energy density (they require less space),
lower toxicity, and lower maintenance costs than lead acid batteries, but are currently more costly.
The factors in favour of lithium batteries improve their chances of being employed in modular mass
produced systems, and hence the potential for cost reductions as the technology matures (removing
the remaining advantage of lead acid batteries). Lithium technology is thus considered the likely
technology to be deployed at scale in DSM, were BESS to be adopted.
Businesses should note that BESS as a technology for demand management is still in its infancy
across all applications. Use of BESS in demand management scenarios is limited to trials, early
adopters and there is only limited commercially available BESS. However, given the flexibility of
applications associated with BESS, there is considerable potential for opportunities, initially in niche
situations, as the technology matures and cost reduces.
It noted that BESS is composed of much more than just the battery cell technology itself, and
includes the inverter, control system, and housing that are required to deliver opportunities for DSM.
It is also noted that there is uncertainty of cell life (highly dependent on discharge patterns), fire risk,
physical size and weight considerations with BESS.
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BESS cells currently warranted lifetimes of up to 10 years. The warranty is also highly dependent on
the charge/discharge characteristics. In this study, it is assumed that BESS cells will be replaced after
10 years.
BESS capital costs are currently very high in comparison with the incentives provided by current retail
tariffs. The immediate opportunities for BESS will require a range of incentives derived from both
power and energy applications, for example: network support payments, critical peak price energy
charge avoidance, reducing peak demand to defer high value network augmentation and the like.
Figure 2.7: Battery energy storage system, top left small < 100 kWh, top right 200 kWh, bottom
1.6 MWh large scale energy storage
2.1.4
Automated Load Control
Automated load control is the reduction of demand, often for infrequent and short periods of time,
up to 50 hours per year, in response to price signals. These price signals can range from retail time of
use (TOU) tariffs, exposure to critical peak energy prices or avoidance of peak network events. An
example of load shifting from a peak to shoulder tariff period can be seen in Figure 2.9.
The ability to shift load and the cost to do so varies significantly between businesses, location and
time of operation. Applications of load control include:
•
Demand shifting – or scheduling of load to occur at times of lower prices. This can either occur
on a short or longer timeframe. An example is allowing the air-conditioning temperature to
rise at critical peak times to avoid high time of use network charges or critical peak prices. Or
to precool a building prior to a likely extreme price event.
•
Direct load control –the direct control of loads. This can vary from simple direct control of
circuits via electrical relays to more sophisticated integration with control systems to partially
interrupt or reduce the energy required to a given load. A range of commercial providers exist
that have either technology solutions or service providers known as an aggregator of DR to
provide both the technology and operating regime for the business with the intention of
sharing the savings between the customer and the service provider.
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•
Demand Interruption – the demand for electricity at times of very high prices. This can
incorporate either direct load control by a 3rd party or aggregator of DSM or discretionary
interruption.
•
Inverter discretionary load control – some inverters provide output signals that allow the
switching on or off of discretionary loads in response to the solar generation power output.
This provides an opportunity to switch discretionary loads, for example a pool pump at an
accommodation business, in response to solar generation. SMEs have a relatively limited
number of discretionary loads, and these tend to be of small size relative to their total load.
This, combined with solar that has limited excess capacity may limit the opportunities
presented by inverter load control.
Opportunities for inverter load control exist in site specific circumstances and are assisted by:
excess solar generation capacity already installed, high utilisation and large discretionary loads
and where grid exports are limited or of little value. The value of load control may be
enhanced when coupled with intelligent sensors and control systems that can predict or ‘learn’
customer loads to maximise internal consumption for non-discretionary loads. An example of
this is pre-cooling rooms utilising excess solar generation, while knowing the cooling load will
be needed shortly.
Additional inverter functionality such as grid management functions (for example; power
factor correction, load control and ramp rate control) may offer benefits to the network
service provider and customers, and if priced into tariffs or other incentives, utilisation of
these features should be considered.
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Figure 2.8: top left Interval meter, top right interval meter used in commercial demand
management installation, bottom left smart circuit breaker, bottom right smart circuit breaker
used in commercial demand management installation
An appropriate location specific incentive model may create an opportunity for customers to utilise
grid management functionality, assisting in reducing network costs by potentially delaying network
investment or allowing additional solar to be installed, where this is required. However, these
benefits are not generally reflected in existing tariffs, and are currently more likely to be negotiated
directly where potential network benefits exist.
Figure 2.9: Example load shifting application
In this report only loads that do not create a significant burden, or high opportunity costs on the
business, such as minor changes to HVAC settings, are included. Only a small portion of business
electricity demand or processes are suitable for automated load control. This is because there is
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significant sunk investment in existing business equipment that is likely to only be replaced on
failure, or the incentive for the load reduction is disproportionally low compared to the business core
processes being interrupted.
This report focuses on fully automatic control. There are simple, alternate solutions to load control
that require manual intervention, such as ‘energy orbs’, which change colour to signal electricity
price, and hence to suggest manual load adjustment, or email/phone/SMS notification for 24 hour
advance notification of high load periods, again requiring manual load adjustment. However,
individual loads in a business context are generally too small to warrant this level of attention
(i.e. the business processes and labour required to implement this solution are unlikely to be cost
effective). Hence, while these solutions are noted, the focus here is on automated technology
options.
Typical technology used in providing automatic load control includes:
•
Advanced Metering Infrastructure (AMI) (Interval / smart meters)
•
Local Area Networks (Wi-Fi, wired IP, Zigbee)
•
Receivers (smart switches, control systems)
•
Demand Response Enabled Devices
•
Back end data management and control systems.
Demand Response enabled Devices (DREDs) are loads/ devices that are able to respond to Demand
Response commands, integrated into a LAN and controlled from a centralised aggregator.
Examples of currently available DRED devices compliant with AS4755, applicable to SMEs include:
•
HVAC domestic / commercial
•
Electric water boilers
•
Electric boost solar hot water heaters.
DRED enabled devices, such as those listed above, respond to control signals to reduce their
consumption e.g. Compressor off, <50% capacity or <75% of rated capacity. Depending on customer
preferences the control signals can originate from a number of sources, including:
•
local energy management control system
•
DNSP
•
electricity retailer
•
a third party DSM aggregator (a business that aggregates individual loads to form a
commercially tradeable DSM service).
Before considering automated DSM a business may want to ask the following of any proposals
received by demand side management aggregators;
•
Is the existing plant and equipment (primarily water heating, HVAC and pumping systems)
capable of being interrupted, without impacting on the equipment or processes reliant upon
it?
•
What is the risk associated with a failed reduction in load?
•
What is the cost to the business processes of a load reduction, deferral or interruption?
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•
What are the costs in implementing demand response technology, labour costs, complexity
and involvement?
•
Contact your DNSP to find out if you are located in an area where there is a network capacity
constraint where peak load deferral may be of interest?
•
Do I have the ability to manually override the automated DSM and if so what are the cost
implications of this?
•
If you are going to participate in automated DSM a mature service offering by a reputable
provider, clearly demonstrating the cost savings and potential impact on your individual
businesses.
SMEs are unlikely to have DRED devices currently in operation in their businesses apart from a
limited number of split system air conditioning units. Businesses will likely require some form of
Advanced Metering Infrastructure to be able to participate in load control. Other equipment would
require either retro fitting or once off integration such as commercial scale compressors requiring
additional time, cost and complexity, further reducing the likelihood of adoption. Current automated
DSM providers are traditionally industrial companies provided multi megawatt reductions in single
processes. Significant change and cost reductions would be required to see large scale automated
DSM adoption by SMEs.
Automated demand response revenue is derived from short term payments, occurring for less than
50 hours per annum. The daily shifting of energy using automated DSM is unlikely to be a cost
effective option. SMEs that have a high ratio of peak load to average load, high proportion of
discretionary loads, or loads that are easily interrupted, low risk of technology failure, low
productivity losses and are located in a constrained network area are best paced to benefit from
automated DSM.
DNSPs have been evaluating non–network options to reduce the cost of network upgrades, including
automated load control. Though, it is often a high cost method when compared to traditional
alternatives such as power factor correction or embedded generation, many projects have remained
deferred, due to unrealised growth.
The South West Interconnected System (SWIS) is observing slower uptake in load shifting, even with
capacity payments (i.e. where electricity customers are paid to reduce load and hence avoid network
capacity constraints) in the order of $135/ kW. This indicates the load shifting market still has
significant cost reductions or access to more revenue streams to be competitive with existing
systems. While this scheme is designed for large participants (MW loads), and has considerable
administrative overheads that would not be manageable for an SME, it does provide an indication of
the approach and limitations.
2.1.4.1
•
Other automated load control technologies
Vehicle to grid or V2G technology is the two way transfer of energy to the electricity grid
which uses the energy storage in an electric vehicle to perform a number of DSM services.
Including services such as Frequency Control Ancillary Services (FCAS) services, generation
smoothing and energy shifting. This is excluded from consideration as the services are not
currently available.
It is important to distinguish between V2G technology, which is essentially an automated load
control technology aimed at providing network support, and normal operations of electric
vehicles, including in a business fleet (noting that most SMEs are likely to have at most a small
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fleet of vehicles). Under normal operation of EVs, they act as a load, much like any other load,
however, there may be some level of discretion on timing of charging (it is not fully
discretionary as this would impact on the primary purpose of the investment – as transport).
The opportunities presented by discretionary loads are discussed in Section 5.9. Broadly
though, charging EVs from excess solar that would otherwise be exported to the grid is likely to
present a good opportunity (when it does not constrain use of the car for other business
functions. Installing solar (or additional solar) specifically for the purposes of charging EVs is
potentially a good opportunity, depending on usage patterns of the vehicle and times available
for charging. This opportunity assessment is very similar to the more general assessment of
solar opportunities presented in Section 5.3, which prove to be more attractive when the load
(charging) coincides with generation (i.e. this opportunity is likely to be viable when the vehicle
is not in use during the middle of the day). Broadly though, the case for use of EVs versus
conventional vehicles is not within the scope of this DSM project, and in this context, charging
of EVs should be considered as a load like any other (though with some discretion).
EVs can also act as temporary storage, providing a similar function as BESS (i.e. energy
arbitrage), within the limits of other use of the vehicle. The opportunities presented by BESS
are discussed in Section 5.6. Again, the case for use of EVs versus conventional vehicles is not
covered here. It is noted though using and EV as a BESS would reduce battery life available for
transport (though would increase low battery utilisation usual as a BESS), but that less
opportunities for energy arbitrage may be available with some being used to charging
opportunities being used to charge the EV for transport.
The V2G specific automated load control technology is unlikely to represent a significant
opportunity in the context of present tariff structures capacity payments. It is more likely to be
of value in specific network support operations, as outlined earlier. As noted previously,
valuation of these is underway as a separate FPDI study, and is outside the context of this DSM
study.
•
Building Energy Management Systems (BEMS) have traditionally been designed for large and
high energy use buildings to perform a number of functions though including energy efficiency
energy and scheduling and control of major building equipment functions such as lighting,
ventilation and temperature.
These systems are fairly complex requiring installation, software, sensors, integration and
system operation, though are highly flexible and can perform a variety of DSM functions such
as energy efficiency, sub-metering, load scheduling and load interruption.
BEMS are only suited to larger loads, in excess of 160 MWh, due to the initial capital costs and
complexity of these systems. BEMS are a more attractive opportunity where BEMS already
exist and are being replaced as part of a building upgrade and where the value of BEMS can be
attributed to multiple functions, such as security, sub-metering, energy efficiency and DSM.
2.2
First Pass Analysis
This section narrows down the range of DSM options from those considered in the preliminary
analysis in the preceding sections, based on the suitability for small/ medium size businesses. This
section focuses broadly on current opportunities, but also considers the potential for changes in
technology to change the situation. DSM options considered suitable form the focus of detailed
analysis. In some cases, DSM options not considered suitable are still of potential interest for a
variety of reasons, and in these cases, specific examples are used in Section 5 to explain these cases.
The screening of opportunities is presented in Table 2.2.
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The criteria used to determine whether a technology is suitable are based on experience with SME
customers, engineering judgment, industry knowledge, outcomes from industry reports and
hindsight having modelled scenarios for most of the technologies here. Key evaluation criteria
include:
•
simplicity: technology, planning, organisational and commercial complexity
•
site independent location; preference for generic applications that can apply to the majority of
sites suitable for both urban and rural installations and appropriate to the scale of demand
•
availability
•
economic return, payback periods of less than 7 years sought
•
certainty of commercial outcome.
Table 2.2: DSM options first pass analysis
Technology
Comment
Analysis type
Energy
Efficiency
Energy efficiency is a mature, moderately simple
technology to implement, and solutions are available
for most locations. It is widely available and well
understood, and there are a number of solutions
available with short payback. The commercial returns
are moderately easy to estimate in advance of an
investment. Future technology developments will likely
increase the range of options.
This technology is likely to offer opportunities for a
wide range of SMEs.
Detailed (see
Section 5)
Solar PV
Solar PV is a mature, simple to implement technology,
which is widely available and well understood. The
commercial returns vary with location, business type,
tariff, and sizing, and payback periods vary accordingly
but are generally modest. Despite this variability,
returns can be predicted relatively well for a specific
business. Decreasing system prices will improve the
attractiveness of new installations over time.
This technology is likely to offer opportunities for a
wide range of SMEs.
Detailed (see
Section 5)
Wind
Small wind is a moderately mature technology, but is
complex to implement and highly site specific. It is
generally available to most SMEs, however, payback
periods tend to be moderate even for businesses that
are suited for it. Accurate predictions of commercial
return are difficult or require disproportionate up-front
investment. Planning requirements may present
limitations. Future technology developments are likely
to result in modest improvements in returns, but may
not address other issues.
This technology is likely to offer opportunities only for
a very small proportion of SMEs, likely in rural areas.
Preliminary (see
Section 2.1)
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Technology
Comment
Analysis type
Fuel Cells
Fuel cells are a mature technology, can be relatively
easy to implement and are moderately site
independent (though use for heating loads may vary).
Its availability is currently low. Certainty of commercial
outcomes is moderately easy to estimate, though gas
price uncertainty is an issue. However, capital costs are
extremely high, and make the payback period on this
technology non-existent. Future technology
improvements may reduce capital costs significantly.
This technology is likely to be adopted by few SMEs, in
niche situations or where there are other drivers for
their use (such as energy security).
Example case
(see Section 5.7)
Co/TriGeneration
Co and Tri-generation is a mature technology,
however, it is complex to implement and integration is
highly site specific. Availability of co/tri-generation is
currently moderately good, and payback periods can
be moderate in businesses where it is suited, however,
it can be difficult to predict commercial outcomes
associated with heating and cooling loads in a small
premises, and commercial outcomes are also
dependent on forward gas prices. Technology
developments are likely to gradually reduce price,
however, the issues of complexity and site specific
suitability may prevail.
This technology is likely to offer opportunities to a few
SMEs in niche situations, where they are willing to
divert attention from core business to the assessment
and implementation of the system.
Preliminary (see
Section 2.1)
Battery
Energy
Storage
Systems
(BESS)
BESS that are entering the market are relatively simple,
and often integrated with solar inverters. They are
moderately site independent, though require some
space and aim to take advantage of excess solar (or
grid) energy, so are load specific. Commercial certainty
is moderate, as it can be difficult to predict battery
performance and life. Most importantly for BESS,
payback period is currently non-existent. Though BESS
are relatively immature and there is the potential for
significant capital cost reductions, however, there are a
number of factors that would need to alter before BESS
can active reasonable payback periods.
This technology is likely to offer opportunities to a few
SMEs in niche situations, and may become more widely
viable in the future, however, there are a number of
barriers for this to occur and BESS are not considered
as near term opportunities in general.
Example cases
(see Section 5.6)
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Technology
Comment
Analysis type
Building
Energy
Management
Systems
(BEMS)
Building energy management systems are typically
complex systems aimed at larger businesses and
requiring active involvement. They are moderately site
specific and generally available, however, at an SME
scale, they are likely to have relatively long payback.
Future developments may see this technology migrate
towards smaller customers, however, it is not yet clear
how the control requirements would be addressed cost
effectively.
This technology is likely to offer opportunities to a few
SMEs in niche situations, and may become more widely
viable in the future, however, there are a number of
barriers for this to occur and BEMS are not considered
as near term opportunities in general.
Preliminary (see
Section 2.1)
Load control
Applications of smart load control such as scheduling,
deferring or interruption of loads is currently only used
in very site specific locations that generally have a
distribution network constraint. Smart load control
relies heavily on existing infrastructure such as
communications networks, interval meters with load
control functionality and equipment that is demand
response enabled. Commercial certainty is low and
payback periods are long, except where there is a
specific network constraint to support. Future
developments may see greater penetration of smart
grid enabled devices into the network, in which case
there may be opportunity to deploy this technology
more widely.
This technology is likely to offer opportunities to a few
SMEs in niche situations, and may become more widely
viable in the future, dependent on a range of factors
Example case
(see Section 5.9)
Vehicle to
Grid (V2G)
V2G are relatively immature but moderately simple to
implement (though some degree of user input may be
required in some circumstances) where standardised
solutions are offered, which is currently relatively rare
at present. Commercial certainty is complex as it needs
to consider use of the vehicle as well as network
support. Payback periods are long or non-existent
under current tariffs, but may be lower in network
support, however, costing of this benefit is outside the
scope of this work.
Future developments of the technology may address
the issues for implementation, however, there are a
range of issues to address and it is difficult to estimate
how this role will develop. SMEs may look to niche
opportunities in this area, particularly if they purchase
EVs for other reasons.
Preliminary (see
Section 2.1)
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3.
Businesses
3.1
Business Descriptions
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The following general business types have been used for modelling:
•
retail
•
accommodation
•
food (Hospitality)
•
warehouse
•
office
•
manufacturing.
One load profile was selected to represent each business type. While these profiles were taken from
actual businesses within that category, there is significant variation between individual businesses of
a single category. Thus, the business type, description of energy use, average consumption, and load
profiles should be considered as a guide only.
Across the six business types, the range of load profiles varies considerably. This provides good
coverage of the range of possible load profiles, and largely captures the possible variation due to
load profile (i.e. the opportunities available to SMEs will fall within the coverage of opportunities
considered by looking at these load profiles, and it is possible that some businesses may identify
more closely with a case study of a different business type). The load profiles are summarised in
Figure 3.1.
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Mean Daily Profile
40
Accommodation
Office
Hospitality
Manuf acturing
Retail
Mean Value Power (kW)
30
20
10
0
0
6
12
18
24
Hour of Day (Hours)
Figure 3.1: Business average daily load profiles
Considerable geographic variation in loads for individual businesses will occur due to differences in
cooling and heating loads. These will tend to be seen as seasonal variations rather than daily
variations. With tariffs being based on daily time of use (and the load profiles normalised as
described in the next section), these geographic differences have little impact on the outcomes of
the assessment.
3.2
Inputs
The sample business data presented here is taken from actual businesses within Victoria and New
South Wales (where the required data was accessible). The actual business used to generate the
profile is not explicitly identified on request of data providers.
The businesses used for each case study were selected from available data, as being from a particular
business type, and having typical operating conditions. ‘Typical’ performance was determined by
qualitative comparison of load profiles form at least 3 businesses of each business type, and also
considering US Department of Energy12 average profiles for that business type.
The load profiles were taken from measured 30 minute interval meter data for the 2013/2014 year.
All load profiles were scaled to give an annual consumption of 100 MWh (considered a mid-level
baseline of consumption for SMEs) to enable comparison. No other processing was applied to the
load data.
This data was supplied by individual businesses or by Momentum Energy from its customer database.
It is noted that most of these businesses are based in Victoria because this provided the best
12
http://energy.gov/eere/buildings/commercial-reference-buildings
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available detail in the data. As noted earlier, it is expected that there will be considerable variation in
seasonal load with geographic location. However, this has limited impact on the daily load profile,
and as it is the daily load profile that primarily interacts with tariffs and technologies, it is expected
that this geographic variation will have limited impact on the opportunities available. It is further
noted that the normalisation of annual load to 100 MWh removes some of the potential geographic
variation in load.
For details of the case study load profiles and business summaries, refer to Appendix A Business .
3.3
Business Engagement
As described in Section 1.2, a range of discussions were entered into with individual SMEs, to
understand their individual drivers (including values, economic considerations, and actual electricity
usage).
3.3.1
How can DNSPs engage with SMEs?
DNSPs may wish to engage with SMEs to encourage specific DSM actions that support the network.
Given the business drivers for SMEs, and in particular the focus on core activities and the relatively
low portion of costs associated with electricity supply, SMEs are generally unlikely to engage direct
with DNSPs, and the primary means of influence over SMEs’ DSM actions is through tariffs. That is, in
the majority of cases, the only means of influence that DNSPs will have over SMEs DSM is by setting
the network use of service charges to reflect their interests, and encouraging these to be passed on
where possible through retailers.
Standard consultation processes of DNSPs considering network upgrade options are also likely to be
ineffective in attracting the attention of SMEs (i.e. SMEs are generally unlikely to even attend
advertised consultation forums), for the previously described reasons. If there is a specific network
issue where an SME can significantly provide benefit through DSM, then a direct approach or offer
from a DNSP to that SME may be an effective form of engagement. However, such situations where
the benefits outweigh the efforts in such engagement are unlikely to be common.
Capacity payment systems such as in the SWIS are unlikely to be of interest to SMEs because of the
associated administrative burden and the above reasons. Over time, if such systems can be simplified
and automated, there is the potential that these may form a basis for engaging with SMEs.
Both direct approaches to SMEs and SME participation in capacity payment systems may be more
attractive to both DNSPs and SMEs if facilitated by an aggregator role. This may be a retailer or
independent third party who can access multiple value streams (including from DNSPs) by identifying
suitable opportunities across multiple SMEs and providing access to these. DNSPs may be better
served by consulting with an aggregator where network benefits can be achieved through DSM, than
by attempting to engage directly with SMEs.
3.4
Discussion of DSM Technology Applicability by Business Type
Businesses in this case study can generally apply energy efficiency practices reasonably uniformly
across the board and should be considered a first step in managing demand and reducing electricity
costs, due to the relatively fast pay back periods. Energy efficiency technology solutions are wide
ranging and applicable to nearly all business types. They typically have diminishing returns across the
range of solutions, so it is generally possible for businesses to choose one or two solutions with a
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smaller impact and shorter payback, or more solutions with greater impact but longer payback. The
energy reduction is generally results in a direct ‘flat’ reduction across the daily load profile, as
opposed to targeted reductions at peak times. The small niche batch manufacturer in this case study
was considered to have a lower ability due to the changing short run batch processes that are less
likely to allow for energy efficiency returns to be realised over short manufacturing periods.
Embedded generation opportunities, and in particular solar PV, may provide a benefit worth
investigating across a wide range of business types, though not all business load profiles. Benefits are
driven by daily load profile being coincident with the solar resource, ability to offset consumption in
peak times under flat tariffs, relatively flat daylight hour demand and demand occurring 7 days per
week to maximise the utilisation of generated electricity. Future tariff structures are highly uncertain
and may significantly impact on the economic benefits of solar PV.
The accommodation case study is less advantageous for solar PV, due to load generally occurring
outside of daylight hours, either in the morning or the evening, combined with seasonal operations.
In this case the solar PV is sized to match the daily load and is relatively smaller compared to peak
load occurring in the evening, this minimises lower value exports.
The ability to shift or interrupt load is linked to the major business processes such as water heating or
HVAC, either pre-heating\cooling (load shifting) or short term load interruption. The quick service
restaurant modelled here has limited ability to alter load as the processes of preparing food and
beverages often uses just in time preparation processes using specialised equipment. HVAC
requirements often require very consistent temperature profiles for food storage and customer
comfort and often require significant capital investment to alter their operation and control.
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4.
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Tariffs
There are a large number of current retail electricity tariffs available across Australia. This section
covers the current and proposed types and structure of tariffs a business may be exposed to rather
than analysis or optimisation of all the available tariffs. The tariffs are intended to indicate the
sensitivity of differing DSM options to changing tariffs structures, rather than any sort of reflection
on the absolute value of a particular tariff.
It is noted that other sources of value other than the tariff exist for SMEs, though these are currently
site and business specific and are not available to the majority of SMEs. Business specific value
sources may include energy security, marketing, or personal values, and these are discussed further
in the Companion Report. Site specific value sources are typically non-tariff based incentives for
avoided or deferred network upgrade costs, which are currently typically implemented by
consultation by the DNSP. The value of such opportunities is part of other work in the FPDI program,
and is not specifically addressed here.
4.1
Tariff Structures
Traditionally tariffs on offer to businesses below the 160 MWh annual consumption thresholds have
been simple bundled retail tariffs. These bundled tariffs combined all the electricity costs (retail,
wholesale energy, distribution/transmission and environmental) into a single volume consumption
rate, expressed as c/kWh.
As new technologies such as interval meters are installed there is an increased offering of more
sophisticated tariffs that can reflect the volume and timing of energy network cost components. Cost
reflective pricing is an attempt to apportion the actual costs for individual customers and provide a
retail price that reflects these costs. With cost reflective pricing, there are typically three components
of electricity supply that can be included in a tariff (depending on the metering installed and what
data it records):
•
fixed fee (also known as daily/monthly supply charge) – a fee that is fixed for the billing period
($, $/day or $/month)
•
volume fee – a fee that is proportional to the energy consumed ($/kWh)
o
flat rate – the same rate is charged at all times during a billing period (the ‘traditional
tariff)
o
off peak – a different rate is charged at defined ‘off-peak’ times compared to the regular
tariff (effectively a peak / off peak two rate system)
o
time of use (TOU) – different rates are charged at different times of day or week
(typically three rates - peak, shoulder and off-peak)13
13
TOU pricing is actually very similar to ‘off-peak’ pricing, except that it typically uses three rates instead of
two. And confusingly, many published TOU tariffs may have the same shoulder and peak rates. However, it is
important to keep the distinction.
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o
•
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block – a different rate is charged when the total energy use in the billing period
exceeds a given threshold
−
inclining block – where the rate increases above the threshold
−
declining block – where the rate decreases above the threshold
capacity fee – a fee that is proportional to the instantaneous demand ($/kW or $/kVA)
o
flat rate – the fee is proportional to the maximum instantaneous demand for the billing
period
o
TOU – different rates apply to instantaneous demand at different times of day or week
(typically three rates – peak, shoulder and off-peak).
o
block – a different rate is charged when the peak demand in the billing period exceeds a
given threshold (inclining or declining as above)
There is also a fourth component:
•
Feed in – a payment that is proportional to the energy fed back into the grid, typically from
solar PV ($/kWh)
o
flat rate – the same rate is paid at all times during the billing period
o
TOU - different rates are paid at different times of day or week (typically three rates peak, shoulder and off-peak)
o
capped – a rate (flat or TOU as above) is paid up to a capacity or volume limit
Retailers can include these components in any number of ways, resulting in a myriad of options.
Often, retailers will package these in ways that offer a marketing advantage (i.e. to differentiate from
competitors, or offer perceived savings to customers). Retailers will set the rates for each component
so that at predicted customer demand, they will achieve their required cost recovery and profit
margin, however, the component costs may not reflect underlying costs.
Traditionally, more complex ‘unbundled’ tariffs were used for larger businesses, however,
increasingly as the metering technology, retailer modelling and data processes develop, these are
becoming available to smaller businesses (and even households), as shown in Figure 4.1.
Figure 4.1: Traditional tariff breakdown by business size
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In qualitative terms, the potential opportunities for SMEs for associated with each tariff component
are set out in Table 4.1.
Table 4.1: Qualitative opportunities for SMEs associated with different tariff components
Tariff
Rate
component
Opportunity
Technology / behaviour
Fixed
-
Off-gridb
Solar-battery-diesel (or fuel cell),
Energy efficiency
Volume
Flat
Reduce consumption
Energy efficiency, embedded
generation
Off-peak
Reduce peak consumption,
shift peak to off-peak
Energy efficiency, Embedded
generationa, storage, load control
TOU
Reduce peak and shoulder
consumption, shift peak and
shoulder to off-peak
Energy efficiency, Embedded
generationa, storage, load control
Inclining
Block
Limit total consumption
Energy efficiency, Embedded
generation
Declining
Block
Off-gridb
Solar-battery-diesel (or fuel cell),
Energy efficiency
Flat
Reduce peak demand
Energy efficiency, embedded
generationc, load control
TOU
Reduce peak and shoulder
demand, shift peak and
shoulder demand to off-peak
Energy efficiency, Embedded
generation, storage, load control
Flat
Export energy to grid,
system size to suit business
case
Embedded generation
Capacity
Feed-in
TOU
Capped
a
Particularly where the embedded generation coincides with peak or shoulder tariffs.
b
There are actually limited opportunities here to reduce costs, hence the only way of avoiding
costs here is to go off-grid. Though if business output is dependent on cost of energy, there
may be advantages in increasing consumption (rare for SMEs).
c
Embedded generation is typically variable, and may be low at times of high demand. Thus, it
may have a low impact on capacity charges.
4.2
Underlying supply cost drivers and implications for future retail tariffs
This section is intended particularly for network service providers to understand the implications of
the opportunities available to SMEs, and to plan appropriate responses. As identified previously,
SMEs are focussed on their core activities, and economic decisions around DSM will be determined
by pricing signals in their tariffs. Thus, pricing signals through tariffs will be the primary economic
factor under their influence that will affect take-up of DSM, and hence the primary means of
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directing SME behaviour to support the network. Some specific direct consultation with SMEs may be
possible where specific network issues exist, however, this is likely to be the exception, rather than
the norm.
To assist network service providers to plan tariffs that will support SME decisions that support the
network, this section highlights the factors driving electricity supply cost, which will impact on retail
tariffs, with a view to better understanding possible future tariff scenarios and tariff uncertainty. This
is expanded further in the following section, where we look at how possible future tariffs will affect
technology take-up, through feedback mechanisms. Note that Entura has not specifically modelled
the broader economic drivers of network pricing (this is addressed in other FPDI studies). The
discussion presented here is based on Entura and Momentum’s experience with retail tariff pricing,
background information on current industry trends, and the implications of our modelling in this
study on net revenues.
In simplistic terms, the underlying costs of electricity supply vary in the following ways:
•
peak demand drives investment in transmission and distribution infrastructure
•
spatially, the cost of electricity supply depends on the drivers at each particular node in the
network, and can vary considerably
•
standards govern the safety of electricity networks, and changing standards can impact on
operations and maintenance costs, as well as new infrastructure.
On a broad scale, recent experience has seen expectations of rising demand causing significant new
infrastructure development. While this can be linked to peak demand levels (and in particular, peak
air conditioner loads), the reality is that this investment is now a sunk cost, and is effectively a fixed
cost component of the electricity supply system (i.e. past investment costs do not change with
current volume or capacity demands). With current falling demand (generally), network charges are
generally transitioning towards higher fixed, and lower volume based components in order to
maintain revenue (capacity charges are also increasing as will be explained shortly).
At the same time, this trend is not universal, with parts of the network still experiencing demand
growth. Furthermore, new standards particularly in relation to fire safety represent an additional
cost to network services providers, which impacts more on some areas of the network than others.
4.2.1
Feedback Mechanisms
In modelling the impact of different tariffs, there were several interesting observations that were
likely to drive future tariff structures:
•
Load shifting – Viability of a technology that shifts load from high cost times to lower cost
times depends on the time based pricing differential (energy arbitrage). Implementation of
that technology causes a reduction in volume and capacity load at high cost times. Wide
spread adoption, is therefore likely to reduce the driver for the high energy arbitrage.
For instance, installing batteries to shift peak usage to off-peak usage reduces peak loads, and
hence was likely to reduce the underlying cost associated with higher peak charges. As
adoption increases, the arbitrage on offer was considered likely to decrease.
This was considered a stabilising feedback loop.
•
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Load reduction – Viability of a behaviour change, or technology that reduces consumption
depends on the rates of avoided volume charges (and to some extent capacity charges).
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Implementation of load reduction causes a reduction in volume (and to some extent capacity)
charges, and corresponding reduction in revenue to the industry. Where the reduction in
industry revenue exceeds the variable cost of supplying that energy, industry margins suffer
and tariff changes are likely with increased fixed or other charges.
For instance, recent reductions in load associated with energy efficiency and embedded
generation have resulted in lower volume rates and higher capacity rates or fixed fees.
This was considered a stabilising feedback loop. However, the cycle may be long in line with
the pricing cycle for network service providers, particularly given lag in consumer behaviour
and mixed signals in pricing passed through to customers by retailers.
Any load reductions that are viable and can be implemented where the reduction in industry
revenue is less than the variable cost of energy supply will likely result in widespread adoption,
without necessarily introducing a feedback loop, resulting in a progressive, stable adoption.
If load reduction is extended to going off grid, a reinforcing feedback loop may result. Moving
off-grid means fixed costs of supply are apportioned over a narrower base, increasing costs to
other users, and thus the incentive for them to move off-grid.
•
Tariff selection – changing from one tariff to another to get a lower total bill, without changing
load, is currently a viable option for many SMEs. This would result in reduced net revenue to
industry, without any actual reduction in cost. The consequential changes in the tariff
(increase, or change in structure) would be designed to offset this revenue loss, and hence this
was considered a stabilising loop.
In summary, all of the identified actions, with the exception of going off-grid, were considered as
stabilising loops, such that where incentives drive adoption of a behaviour or technology, the
adoption causes a decrease in those incentives in the longer term. The period of feedback is linked to
the pricing cycle.
Ultimately, however, where the cost of DSM results in a real reduction in energy supply costs of the
same or greater amount, it should remain viable and uptake should increase. At present, however,
only some DSM strategies appear to have reached this threshold in very limited parts of the network.
4.3
Published tariffs for use in this study
At any given location in the NEM14, an SME may have access to something in the order of 50 tariffs
from around 10 retailers15. And between locations, there will be further variation in tariffs available.
As such, it is not possible to consider the full range of tariffs available to SMEs. Instead, the approach
here uses two examples of current published tariffs for common tariff structures. For these two
tariffs, we will investigate the opportunity presented by various technologies for each business type.
We also investigate some alternative tariffs to understand the sensitivities of these results to
variation in the tariffs, with the aim of capturing the majority of scenarios.
•
14
The published tariffs considered are the traditional or conventional pricing and the more
recent TOU tariff. These specific tariffs selected were Momentum Energy tariffs for the
In Western Australia, the tariff choices are more limited, but still offer some flexibility.
15
From a review of various locations on http://www.energymadeeasy.gov.au/ - actual numbers vary widely
with location
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AusGrid network (somewhat arbitrary, though the basis for these tariffs was best known to
Entura). Once again, sensitivity studies were undertaken to understand how other tariffs
would affect the viability of opportunities for SMEs. The selected tariffs, are presented in
Table 4.2. Assumed all business have a Type 5 standard interval meter for customers
consuming less than 100 MWh.
Table 4.2: Published tariff summary16
Name
State Retailer
DNSP
Tariff Peak
Type volume
charge
(c/kWh)
Tariff 1
NSW
Momentum
AusGrid Flat
(Standing
Rate
Offer E
(Anytime) MOM14126SS)
Tariff 2
NSW
Momentum
AusGrid Time
(Momentum
of
Standing Offer
Use
D (LoadSmart)
MOM14130SS)
Shoulder
volume
charge
(c/kWh)
OffPeak
volume
charge
(c/kWh)
capacity
charge
(c/kW/
day)
Daily
supply
charge
(c/day)
29.39
n/a
n/a
n/a
153.42
24.37
18.35
13.43
33.83
577.85
A comparison of these tariffs on a time of use basis is shown in Figure 4.2. Also shown in Figure 4.2
are Tariffs 3 and 4, which are described in Section 4.4., however, it is important to understand that
this figure does not tell the full story, as the actual charge associated with these two tariffs can only
be determined by considering the load profile (i.e. total cost to customers does not depend only on
the volume rate of energy, and other components can be significant). This is considered in
subsequent sections.
16
http://www.momentumenergy.com.au/system/files/documents/PPIS/2014/August_4/NSW%20Business_Stan
ding%20Offer%20v2.pdf
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Figure 4.2: Sample energy tariffs, energy components only
4.3.1.1
Declining block demand tariff
Declining block demand tariffs (or capacity tariffs in the Australian Standard smart grid vocabulary)
have been implemented in some instances as a response to solar PV, to better reflect supply costs.
While the effect of this tariff is somewhat similar to other tariffs considered, Entura has included a
declining block demand tariff as a case study (i.e. this is not used for all businesses, and only for solar
embedded generation) to highlight the impact of this tariff. The selected tariff is shown in Table 4.1.
Table 4.3: Declining block demand tariff
State Retailer
SA
DNSP
AGL
SA
(General Networks18
Supply
Stepped
Demand
GSSD17)
Tariff
Type
Volume Daily supply
(c/kWh) charge
(c/day)
Declining 24.651
block
demand
tariff
27.92
Daily capacity charge
(c/kVA/month)
First 100 KVA/mth
2,240.7
Next 150 KVA/mth
1,469.6
Next 750 KVA/mth
1,179.2
Balance KVA/mth
986.7
Additional
demand
597.3
As described previously, a declining block demand tariff, charges customers based on the maximum
demand (kVA) with the higher demand the lower the charge. For the selected tariff, the volume
component is charged at a flat rate.
17
Retail tariff, Small Business, South Australia, AGL, General Supply Stepped Demand GSSD, July 2014.
18
Network tariff, SA Networks, Low Voltage Stepped Demand (KVA), July 2014
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Modelled tariffs for use in this study
The tariffs identified previously do not necessarily reflect the likely range of future tariff structures.
Thus, Entura designed two possible ‘future tariffs’, which may better reflect underlying costs of
supply. It is notable that costs of supply vary widely, and information on these costs was quite
speculative. Thus, the intent here is to capture a wide spread of possible variations.
The future tariffs were developed from component costs (cost of generation, ‘green scheme’ costs,
use of network service costs and retail costs) and implement a few features:
•
more granular, cost reflective pricing on cost of generation (volume charge), based on 5
minute spot market pricing with cap
•
inclusion of a fixed fee, time of use volume, and flat demand charge.
The method for deriving these tariffs, and the cost structure they represent, is described below:
•
5 min spot market data NSW node 2013/14 year
•
Negative price events removed.
•
Critical peak pricing capped at $1,500/MWh or 150 c/kWh for Tariff 3 and $2,030 /MWh
or 203 c/kWh for Tariff 4.
This is designed to provide a price signal to which a business may be incentivised to respond
but to limit the extent of the exposure and potential volatility on quarterly electricity bills.
•
Tariff compiled into 30 minute blocks
•
Defined additional charges are added to the cost of generation, including:
o
TOU network costs based on published Ausgrid DNSP distribution tariffs 2014/15,
EA302, Low Voltage LV 40-160 MWh (System) TOU network costs. Including network
access charges and network capacity prices fully unbundled.
o
Other costs such as retail, margins, head room, NEM fees, partial hedging and
administration costs.
o
Renewable Energy Target and in particular Small Scale certificates remain available.
o
Carbon price is removed from the wholesale energy price, based on the NEM carbon
intensity and published carbon price for 2013/14.
o
Assumed Distribution Loss Factor (DLF) of 7%.
•
At this point, the total cost of electricity, for a typical retail business, was approximately 82% of
that under the published TOU tariff (Tariff 2 described in the previous section). The difference
can be accounted for by various forms of bundling and margins applied to these costs. To
include these costs, and enable investigation of sensitivity to changes in tariff structure, these
remaining costs were apportioned either as fixed fees (Tariff 3) or volume charges (Tariff 4). It
is notable that the magnitude of potential future changes in distribution of charges between
fixed, volume and capacity is not known, and could be larger than in these tariffs, however, the
selected tariffs are considered plausible and provide the necessary guidance on the impact of
such changes.
•
It is assumed that sites have interval meters, necessary for full TOU tariffs.
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Table 4.4: Modelled tariff summary
Name
Tariff Type
Minimum
volume
charge
(c/kWh)
Maximum
volume
charge
(c/kWh)
Network
costs (peak
/ shoulder /
off peak)
(c/kWh)
Capacity
Daily
charge
supply
(c/kW/day) charge
(c/day)
Tariff 3
TOU energy and
network (fixed)
7
150
12.2/5.7/3.5
33.8
1,880
Tariff 4
TOU energy and
9
network (volume)
203
12.2/5.7/3.5
33.8
577
The distribution of charges for a typical retail business, under the four tariffs being considered (two
published as per Section 4.3, and two modelled described here), are shown in Figure 4.3. Other
business types will have slightly different bill compositions, which are presented in Appendix B.
Figure 4.3: Make-up of a typical retail business electricity bill under each of the tariff
structures.
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Modelling
Time series modelling was performed to optimise the impact of the DSM option and calculate the
economic return of the DSM option in relation to the base case (business as usual or no DSM) for
various tariffs and business types. This section presents the results of this analysis. As described in
Section 2.2, energy efficiency and solar PV are assessed for each combination of tariff and business,
and example cases for fuel cells, BESS and automatic load control.
Example cases are also included to cover variation in location (impact on solar resource only),
orientation of the solar PV, no export tariff for solar, and discretionary load control.
5.1
Inputs and Assumptions
5.1.1
Economic Assumptions
The following assumptions were used in creating indicative scenarios, please note this modelling is
general in nature only and may not be relied upon for making financial decisions. All systems
modelled require case by case modelling to determine the individual project returns.
•
Project Life: 25 years.
•
Discount rate of 7%19 was utilised considering a range of general indicators. However, the
relevant discount rate is business specific, and SMEs should consider their own circumstances
in determining whether this is appropriate.
•
Grid electricity prices were based on RET analysis by ROAM20 indicating flat real energy price
growth in the mid-term.
•
All charges and tariffs are exclusive of GST.
5.1.2
General Modelling Assumptions
•
Resolution of the time steps: 30 minutes
•
No net metering (in Homer model)
•
Grid sales at a flat feed in tariff of 5 c/kWh21
19
Note the discount rate provided is in general in nature only, before considering a project a specific project
discount rate would need to be determined.
20
ROAM Consulting, RET policy analysis for the Clean Energy Council, CEC0011, May 2014
21
Based on the lower bounds of the NSW, Independent Pricing and Regulatory Tribunal (IPART), Draft
determination on solar feed-in tariffs in 2014/15. The lower estimate is due to significant uncertainty regarding
future retail tariffs.
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Technology Assumptions
Energy storage
•
lithium technology22
•
life time 10 years
•
maximum cycles 4,00023
•
round trip efficiency 85%
•
minimum state of charge 20%
•
power / energy rating 8 kW / 8 kWh
•
capital cost $1,650 per kWh assuming the power output rating of the inverter is equal to the
available energy storage capacity. i.e. 1 kW power rating / 1 kWh energy storage capacity (or
the BESS can store 1 hour of maximum solar output)
•
operations and maintenance 1.5% of capital cost
Solar Assumptions
•
Location, Sydney, NSW, Latitude 33.9° S, Longitude 151.2° E
•
Temperature profile of Sydney, BOM monthly profile
•
SME businesses have access to roof space and are owner occupiers.
•
Solar data from the NASA SSE World dataset, were used at the capital city locations in the
following regions, at an individual cell resolution of 1°(Homer converts this to half hourly data
coincident with load on a statistical basis). The solar irradiance at Sydney is equivalent to
4.4 kWh/m2/day
Sydney was chosen as it aligns with areas where both the retail and network tariffs selected.
Sydney’s solar resource is slightly below the Australian average. Sensitivity to solar resource is
investigated in Section 5.5, by looking at a specific case for other Australian locations.
•
Fixed tilt at latitude of 34°
•
Inverter requires replacement once during the 25 year life
•
Module efficiency 16% at standard test conditions
•
Temperature degradation at -0.5%/°C
•
97% inverter and 85% rectifier efficiencies
•
PV technology capital costs are discounted assuming that small scale certificates are sold
upfront to the installer. The purchase capital cost is $1.50/watt24 installed, net of small scale
technology certificates (STCs).
22
As noted earlier, lithium technology has a number of advantages over lead acid batteries in BESS
applications, however, lead acid is certainly another available technology. Using lead acid batteries in the study
changes capital cost and other assumptions slightly, but does not affect the conclusions around this
technology.
23
In Entura’s experience, this manufacturer specified cycle life may not be achieve in practice.
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Inverter sizing is fixed to match the module output. Separate analysis of decoupling inverter
sizing and PV orientation is provided in Section 5.5.
The PV system was sized based on an optimisation of the net present value over the 25 year
project period. This approach generally results in PV sizing that avoids large amounts of export
and maximises self-consumption. Typically, solar capacity will equal or somewhat25 exceed
midday average load.
•
Inverter represents 20% of installed PV costs (post discounting for STC)
•
Operations and maintenance was defined as 2% of capital cost26
5.2
Case Study Results – Retail
This section presents a description and discussion of results developed for the Retail business. The
following section presents results for all businesses, however, this section on the Retail case is
included to highlight the reasons behind various outcomes, which are common across all business
types.
The annual cost of electricity figures for the Retail business for the four different tariffs are presented
in Figure 5.1.
Figure 5.1: Retail business electricity costs (base case)
Figure 5.1 shows that the different tariffs have different amounts of fixed, volume and demand
charges, which will impact on the savings that can be made under these tariffs.
24
A reduction in technology prices is subsequently considered as part of the sensitivity analysis and discussion.
25
In most cases, solar capacity would only slightly exceed midday average load, but for cases that have
relatively low midday average load, the installed capacity is up to twice the midday average load.
26
Based on Entura’s experience
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Energy Efficiency Scenario
Energy efficiency measures are assumed to reduce consumption across the board (i.e. a constant
reduction in load profile). With energy efficiency installed, the new electricity costs for the Retail
business are shown in Figure 5.2 and returns are shown in Figure 5.3. These results show:
•
savings vary with tariff type, primarily because the fixed charges are unaffected
•
both capacity charges and volume charges are reduced by energy efficiency solutions
•
net annual savings reflect actual reductions in electricity charges plus payback of capital outlay
and operations and maintenance costs
•
net annual savings are relatively small compared to total electricity costs, and hence very small
relative to total costs of the business.
Figure 5.2: Retail business electricity costs with energy efficiency
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Figure 5.3: Retail business returns with energy efficiency
5.2.2
Solar PV Scenario
This retail load profile is suited to solar generation due to the high coincidence of load and solar
generation, resulting in high utilisation of generated solar and off-setting higher retail volume energy
charges.
Figure 5.4: Retail business electricity costs with Solar PV
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Figure 5.5: Retail business returns with solar PV
The retail tariff type greatly impacts the returns of a solar PV system, higher volume energy charges
provide a higher return, and higher fixed charges provide a lower return due to limited ability to
reduce them through solar PV.
Under the solar PV scenario the annual savings from solar when moving from a flat tariff to a TOU
tariff show a reducing value to the business. This is due to the value of the energy the solar is
offsetting declining as solar generation occurs during the day, generally at off-peak or shoulder rates.
The solar annual savings are linked to the amount of cost apportioned to either the fixed or variable
components, the former unable to be offset from embedded generation resulting in lower annual
savings and Net Present Value (NPV).
These four tariff options highlight the significant variation of an investment in solar PV, depending on
the tariff type. Longer term tariff certainty, including the length of time the tariff is available,
provides a level of certainty over the returns as the variable energy charges are known and also the
proportion of fixed charges.
5.2.3
Sensitivity Analysis – Retail Solar Case Study
The business case presented above for energy efficiency and solar will be sensitive to a range of
factors. Sensitivity to tariff structures is already addressed through consideration of four variants.
Sensitivity of solar PV to location (and hence solar resource) is presented in Section 5.5. Sensitivity to
load profiles is addressed through consideration of the six different businesses, each with a different
characteristic load profile. The other key sensitivity is variation in capital cost of technology, which is
presented here.
The sensitivity analysis, shown in Table 5.1, details the relative economic impact from changing the
capital costs of solar PV. This highlights that the business case for solar is more sensitive to the
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different tariffs considered, than to the range of capital costs. However, it also highlights that within
any of the non-traditional tariffs (tariffs 2-4), a change in capital cost can have a considerable impact
on outcomes.
For SMEs, while this highlights the risk of tariff variation, it also highlights the potential benefits of
capital cost reduction in reducing this risk.
Table 5.1: Retail case study sensitivity of payback period to capital cost
Tariff type
Tariff 1
Tariff 2
Tariff 3
Tariff 4
Sensitivity
Capital 110 %
10
18
no payback
21
Base case Capital
100 %
9
15
no payback
18
Sensitivity
Capital 90 %
8
11
22
15
5.3
Case Study Summary Results
The results of each business case study are shown in Table 5.2. As can be seen, significant variation in
the payback time is apparent for solar PV installations. Full modelling results, including a range of
other metrics which should be considered in assessing the business case, are available in Appendix B.
Table 5.2: Summary of discounted payback periods by business, tariff and technology
Tariff Type
Tariff 1
Tariff 2
Tariff 3
Tariff 4
Solar PV size 70 kW
9
15
no payback
18
Energy Efficiency
4
5
6
5
Solar PV size 30 kW
9
17
no payback
23
Energy Efficiency
4
6
10
7
Solar PV size 40 kW
7
11
no payback
16
Energy Efficiency
4
7
9
7
Solar PV size 40 kW
10
17
no payback
18
Energy Efficiency
4
6
8
6
Solar PV size 50 kW
7
10
23
14
Energy Efficiency
4
5
8
6
Retail
Accommodation
Hospitality
Warehouse
Office
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Manufacturing
Solar PV size 90 kW
8
12
no payback
16
Energy Efficiency
4
5
6
5
*”no payback” refers to cases where the technology does not pay for itself over the 25 year project
life.
In considering the above results, it is clear that energy efficiency presents good opportunities for
each business type, and that while these are somewhat sensitive to non-traditional tariffs (payback
period increases), there is still a good case for investment on this grounds alone. Solar PV generally
has longer payback periods, often in excess of what would be considered of interest for an SME. This,
coupled with the higher sensitivity of Solar PV to non-traditional tariffs means that solar PV
opportunities are likely to be limited to some businesses, particularly those with loads that coincide
with solar resource, such as an office type environment. As noted in the previous section, sensitivity
to capital costs means that these payback periods are likely to reduce with declining technology
costs.
Table 5.2 presents the primary results for this study. However, a number of alternate scenarios, with
particular applications, were also considered. These are presented in the following sections.
5.4
Example Case Study Results – No Solar Export
The no solar PV export case study is a slight variation on the standard case, but represents scenarios
that have recently become more common. For example, in the Energex network in Queensland, a
solar PV system is given priority connection agreement processing on the condition that it does not
export to the grid. This case example is presented for the office load profile only.
Case Study Inputs
The inputs are as per the previous office case studies with differences below:
•
Location: Brisbane, the solar resource and temperature profile for Brisbane are used for the
solar PV modelling.
•
Export to the electricity grid is not allowed.
Table 5.3: Office case study (no export)
Tariff Type
Tariff 1
Tariff 2
Tariff 3
Tariff 4
Annual Cost ($)
29,269
23,865
23,666
23,884
Net Present Cost ($) 25 years
429,572
350,263
347,338
350,534
Annual savings ($/yr)
5,717
2,254
-510
1,742
Total Net Present Value ($)
83,911
33,076
-7,488
25,565
Discounted payback (years)
8
15
n/a
16
Base Case
Solar PV (kW)
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Solar PV on a flat tariff provides a discounted payback in the no export scenario of 8 years compared
with 7.1 years if export is allowed at a rate of 5 c/kWh. While the results are sensitive to the value of
the feed in tariff, other factors such as the tariff type and capital cost have a larger impact on the
NPV.
5.5
Example Case Study Results – Solar PV Location, Orientation and Sizing Options
The previous case studies assume that solar PV is oriented to maximise the annual generation of
energy, while limiting system size (and hence capital costs) to match the maximum site demand
(noting that with current low and decreasing feed-in tariffs, generation for export is not cost
effective). For example a consistent 20 kW day time peak load ideally would be matched with a
20 kW solar system to maximise the daily load offset and avoiding exporting electricity to the grid,
considering that current feed in tariffs are significantly below the current LCOE. However, it may be
more effective in some situations to use alternative system configurations to better match the daily
load profile of a business.
A number of alternative system designs can be considered on an individual site basis. These options
should be discussed with a solar professional taking into account the load profile, solar resource and
retail tariff. These options can include:
•
installing peak PV generation in excess of the site load
•
installing a proportion of PV modules facing east and west to increase generation in the
morning and afternoon’s coincident with the load to maximise the site consumed electricity.
Solar PV generation capacity in excess of site capacity
Installing solar PV modules where the peak module output exceeds the inverter output results in
limiting or clipping the peak output, while increasing generating at times of less than peak solar
resource, increasing the annual generation utilisation on site. In effect, this approach meets more of
the demand using on-site generation, and trades-off the additional grid export capacity (which may
attract a low or zero tariff) by reduced inverter capital costs. An example of this is may be installing a
25 kWp PV system coupled to a 20 kW inverter to meet a 20 kW peak load, which is fairly flat across
the middle of the day. This approach may be beneficial where a high flat tariff exists, the inverter can
technically accommodate over capacity installation, and real cost savings from using an undercapacity inverter.
Solar PV Orientation
Orientating a small portion of PV modules in an east, and / or west direction, with the bulk of the
installation oriented in a northerly direction, can achieve a flatter generation curve, which will better
match the load profile for some businesses. This is shown in Figure 5.6. Businesses should consider
their individual load profile, tariff and solar resource when determining if this option may be suitable
for them. An experienced solar installer is also required to design the system correctly. This option is
improved where: exports to the grid receive a low feed in tariff relative to the cost of purchased
energy, the load profile has a relatively high, but flat daytime load.
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Figure 5.6: Solar PV orientation (illustrative only)
Solar PV Location
Businesses considering embedded solar PV generation as a DSM option, should consider the
variation in solar resource in capital cities across Australia. An indication of this variation is shown in
Table 5.4, along with the average solar resource and the impact on discounted payback periods. This
table highlights the sensitivity to the location in terms of temperature and solar resource only. It
shows relatively high sensitivity, with discounted payback periods varying from 9 years in Darwin to
18 years in Hobart. The modelling is based on the Retail case study utilising the NASA SSE World
dataset solar resource, BOM temperatures and Tariff 2 (the published time of use tariff from NSW).
Table 5.4: Solar resource locational variability
Location
46
Average solar
irradiance
(kwh/m2/day)
Discounted payback
period (years)
Sydney
4.4
15
Canberra
4.6
14
Brisbane
4.8
15
Darwin
6.1
9
Perth
5.6
10
Adelaide
4.6
14
Melbourne
4.1
16
Hobart
3.7
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Example Case Study – BESS
Modelling BESS with any of the tariff structures or business types shows no payback over the project
life. Thus, the scenarios under which future changes in the technology may create an opportunity
were examined.
Case study inputs
•
as per the retail case study with 70 kW of solar PV included
•
battery efficiency and lifetime assumptions as per Section 5.1.3
•
BESS was sized to store 1 hr of peak output from the solar PV system, in the retail system this
was modeled as the ability for 70 kWh of effective storage (post efficiency losses).
Operational conditions
The battery was modeled to charge when excess solar generation was available and only discharge
during peak prices, based on Tariff 2 (published TOU). This attempted to ensure the maximum energy
arbitrage value could be achieved. In this case the additional revenue from discharging the battery is
the difference between 5 c/kWh (the value of exported solar), and 24.7 c/kWh (the peak price), an
amount of 19.7 c/kWh.
Results
Figure 5.7, Figure 5.8 and Figure 5.9 show the solar profile, load, retail tariff and battery charge /
discharge cycling for three representative days under this scenario.
G rid Pow er Price ($/kWh)
0.26
Grid Pow er Price
0.24
0.22
0.20
0.18
0.16
0.14
0.12
50
Power (kW)
40
0.6
30
0.4
20
0.2
10
0
Global Solar (kW/m2)
0.8
Global Solar
AC Primary Load
Battery Charge Pow er
Battery Discharge Pow er
0.0
0
6
12
October 9
18
24
Figure 5.7: BESS charge and discharge weekday, with excess solar
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0.26
G rid P o w er P rice ($/kW h)
Grid Power Price
0.24
0.22
0.20
0.18
0.16
0.14
0.12
40
0.8
Power (kW)
30
0.6
20
0.4
10
0.2
0
Global Solar (kW/m2)
1.0
Global Solar
AC Primary Load
Battery Charge Pow er
Battery Discharge Pow er
0.0
0
6
12
October 13
18
24
Figure 5.8: Sample weekend charging from excess solar
Grid Pow er Price
0.24
0.22
0.20
0.18
0.16
0.12
60
0.6
50
0.5
40
0.4
30
0.3
20
0.2
10
0.1
0
Global Solar (kW/m2)
0.14
Power (kW)
Grid Power Price ($/kWh)
0.26
Global Solar
AC Primary Load
Battery Charge Pow er
Battery Discharge Pow er
0.0
0
6
12
October 15
18
24
Figure 5.9: Sample peak tariff discharge on Monday following Saturday charge
The following observations are made:
•
•
48
Low utilisation of the BESS occurred, reducing the amount of the opportunities to receive
revenue from energy arbitrage. The low utilization, in which the battery only performed the
equivalent of 172 full discharges per year or 47 % of the time, was a result of:
o
no occurrences of peak tariff costs on the weekend, this results in charges occurring on a
Saturday not being discharged until Monday evening when peak prices next occur (as
per Figure 5.9)
o
many days of either no solar resource or no excess solar generation above and beyond
the site load.
The battery wear costs are in excess of the value of the energy arbitrage at current capital
costs, when charging from excess solar and discharging at the peak tariff rate (costs of solar
are not considered as they are considered sunk).
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•
In testing sensitivity to capital costs, large reductions to capital costs were applied but,
operations and maintenance costs were still a significant barrier in overcoming the relatively
low available energy arbitrage.
•
Energy arbitrage value represented <6% of the annual electricity bill.
•
BESS required cell replacement three times during the 25 year project life, due to the
maximum limit of 10 year. At 10 years, the batteries had completed only about 43% of their
maximum cycles (underutilised).
•
Round trip efficiencies of 85% also limit the opportunities to extract maximum value.
•
BESS, in this case did not reduce capacity fees associated with the tariff, as the storage size
was insufficient to achieve this across the full peak period (and increasing storage size is
unlikely to be practical given the additional capital cost, and physical system size).
5.6.1
BESS Adoption Point
Economic trigger points are highly uncertain, not only in relation to BESS technology, but also
though the feedback impact on the electricity network, and potential tariff adjustments. However,
ignoring feedback effects, a capital cost reduction (including a corresponding operations and
maintenance cost and replacement capital cost reduction) in excess of 70% was likely to make BESS
economically viable.
Energy storage is likely to be adopted earlier in niche locations that can combine value from a
number of sources:
•
minimising solar export, potentially in regions where export may be actively discouraged
•
energy arbitrage
•
avoiding critical peak energy spot prices
•
network support payments
•
avoided network investment
•
reduced network capacity payments.
These events must be able to deliver:
•
events where the cost of electricity arbitrage (i.e. reduction in cost of electricity by shifting
consumption from one time to another via a BESS) is less than the cycle cost of a battery
•
high frequency of utilisation of the battery, as minimum cycle cost can only be achieved with
frequent (daily) cycling, hence the previous point must be satisfied on such frequent intervals. .
Under a scenario where these can be reasonably combined, capital cost reductions of the order of
10% are likely to make BESS economically viable, and cost reductions of up to 50% may be required
before payback periods are of interest to SMEs. This is based on modelling with the following inputs:
•
Warehouse business (to maximise disparity of load profile to solar generation)
•
Tariff 4 (to pass through critical peak pricing)
•
zero feed in tariff (to maximise the energy arbitrage from excess solar)
•
high solar radiation (to maximise potential solar excess generation)
•
other inputs as per previous modelling
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Example Case Study – Fuel Cells
Modelling fuel cells with any of the tariff structures or business types shows no payback over the
project life. Thus, the scenarios under which future changes in the technology may create an
opportunity were examined.
Inputs
As per retail study base case, Tariff 2, but including:
•
1.5 kW fuel cell with life of 15,000 hours or 2 years, and a cost of $33,000
•
gas fuel cost of 2.5 c/MJ.
Results
Capital reductions in the order of 90% were required to deliver a positive NPV for this project. Given
such a significant requirement for reduction, there is considerable uncertainty around this estimate,
which may be impacted by ancillary benefits of the technology, and by gas (or other fuel source)
prices.
5.8
Example Case Study –Declining Block Demand Tariff
A declining block demand tariff is included to highlight the impacts of this particular tariff, which has
recently been introduced in one instance to better manage demand response. Some DNSPs require
customers who modify their metering, for example installing solar generation for example, to switch
to a declining demand block tariff.
Inputs
This case study focuses on the installation of embedded solar generation in the retail case. Utilising a
South Australian declining block demand tariff, based upon the network tariff.
•
Retail tariff, Small Business, South Australia, AGL, General Supply Stepped Demand GSSD, July
2014.
•
Network tariff, SA Networks, Low Voltage Stepped Demand (KVA), July 2014
•
Base case, SA, Momentum Standing Offer E\QBSR\MBSR - MOM14433SS
•
All other inputs are as per the retail case study inputs and assumptions.
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Results
A comparison of the standing offer tariff and the stepped demand tariff, when solar is installed,
highlights the significant increase in the proportion of charges that are recovered from the demand
charges (largely fixed) as opposed to the energy charges (volume based). In this case higher demand
charges reduce the ability for a business installing solar to reduce their bill, as the demand charges in
effect act as fixed charges that cannot be avoided, without additional DSM options.
Energy (volume charges)
Energy (volume charges)
Demand charges
Demand charges
Fixed charges
Fixed charges
Figure 5.10: Momentum standing offer left, AGL stepped demand tariff right
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Table 5.5: Results declining block demand tariff
Tariff
Standard
tariff
Declining
block
demand
tariff
Comparison
between change
from standard
offer to declining
block demand
tariff
No solar
Case
Annual
Cost ($)
32,772
35,638
Net
Present
Cost ($) 25
years
480,993
523,050
Solar PV
(kW)
60
60
Annual
savings
($/yr)
8,021
4,702
117,725
69,017
7
10
Total Net
Present
Value ($)
Discounted
payback
(years)
13
Note: under current arrangements, a customer would typically start on a standard tariff, and be
converted to the declining block demand tariff after installing solar, hence the grey cells may not be
available options, and actual payback from the solar would be as determined by switching from the
standard offer to the declining block demand tariff with solar, as shown above.
Outcomes
The declining block demand tariff results in longer payback periods for businesses that wish to install
solar. The longer payback periods are the result of higher fixed portion of the bills compared with the
energy portion, this can be seen in Figure 5.10. It is noted the declining block volumes of demand
where the price reduces are quite high and effectively all businesses considered in this report would
fall into the highest cost, first block. Under a declining block demand tariff, opportunities exist for
DSM that results in a permanent reduction in demand. This favours energy efficiency solutions that
have a permanent effective on reducing demand, such as improved insulation. It is noted the
declining block demand tariff does not have a time of use component, all energy and demand were
effectively charged at a flat rate.
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Example Case Study – Discretionary Load Control
Discretionary loads refer to loads that require a set amount of energy per day, though the timing of
the energy being delivered is totally flexible, such that there is no impact on the consumer if the load
is operated in the morning, evening or overnight. This provides opportunities for businesses to
change the timing of discretionary loads to save on electricity costs. This can take on the form of a
simple timer to operate the load on an off-peak tariff, or ensuring the load is not coincident with
maximum demand to limit capacity charges. A more sophisticated discretionary load control utilises
the solar Inverter and other equipment to integrate a discretionary load and schedule operation at
times of excess solar generation.
The payback periods of discretionary load control are often very short (<2 years) and are an
attractive DSM option. However, SME generally have very small or no discretionary loads. Examples
of discretionary loads include pumping water into tanks or pool pump operation (or to some extent,
charging of EVs). Business that typically exhibit these loads include rural businesses with independent
water supplies and accommodation businesses that have a swimming pool.
Case Study Inputs
As per the retail case study with Solar PV though with the following additions;
•
Power of discretionary load 2 kW, equivalent to a small/mid-sized pumping load.
•
Energy required per day 8 kWh, equivalent to 4 hours operation per day.
•
Discretionary consumption period, 24 hours.
•
Load capacity 100%, the pump required the full power rating of 2 kW to operate.
•
Capital costs required to gain discretionary load control $550 / 2 kW load, this includes
hardware, installation and time associated with identifying the load to be managed. It assumes
the solar inverter has suitable outputs for discretionary load control.
Outcomes
60
AC Primary Load
Defer. Served
Inverter Output Power
Power (kW)
50
40
30
20
10
0
Jul 5
Jul 6
Time
Figure 5.11: Example discretionary load control
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Table 5.6: Discretionary load control results
Tariff
Annualised cost savings
Simple payback years (discretionary load
control only)
1
2
3
4
321
136
82
137
2
4
7
4
The operation of a discretionary load can be seen in green in Figure 5.11, with energy being delivered
at times of excess solar generation or when the load requires a minimum amount of daily energy, the
latter shown as small spikes in the early hours of July 5, where the discretionary load can be run at
off peak times.
The discretionary load size is relatively small at only 3% of the peak load in this instance. However,
the simple payback period is very short providing an attractive opportunity, particularly in cases
where an SME has sizeable discretionary loads.
5.9.1
More General Automated Load Shifting Applications
Discretionary load control, as per the previous case, is a specific example of automated load shifting,
which is an opportunity, though likely to be of low value, where such discretionary loads exist. More
generally, automatic load control can allow a range of other revenue streams including, in particular:
•
minimising solar export – shifting load to times of solar generation (discretionary load control)
•
avoiding critical peak energy spot prices
•
receiving network support payments for site specific avoided network investment
•
reduced network capacity payments
These revenue streams, and network support payments in particular, could potentially be an order of
magnitude larger than discretionary load control, however, they are not expected to be generally
available to SMEs for the following reasons:
•
Network support payments will typically be very site specific, and negotiated direct with
customers27.
•
Other interruptions or rescheduling of business loads has the potential to incur an opportunity
cost by impacting on other business operations (i.e. load shifting options are likely to be
limited for SMEs).
Where such opportunities do exist for specific SMEs, these should be considered. It is likely that such
opportunities will increase with the roll-out of smart metering, and as load control technology
becomes smarter and more able to integrate seamlessly into normal operations of SMEs.
27
As noted earlier, these the value of these is the subject of separate FPDI work
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Outcomes and Recommendations
The outcomes and recommendations of this report are based on the results of modelling, business
discussions and experience. They are intended to provide an overview of DSM opportunities for
SMEs. This technical report focuses on the opportunities represented by economic viability of a DSM
approach. It is targeted towards the electricity industry, with the intent of aiding understanding of
the opportunities available to SMEs, to enable them to plan appropriately. A companion document,
the Business Guide Engagement Report, targets SMEs, and has a broader focus, including
consideration other drivers (such as personal or corporate commitments) for DSM uptake.
6.1
Energy Efficiency Recommendations
Energy efficiency is a cost effective proposition for business to reduce overall energy use and
electricity costs. The solutions are mature and will continue to provide opportunities to reduce the
total energy use and peak demand.
Businesses will likely focus on simple low upfront capital solutions that have minimal disruption and
involvement to deploy energy efficiency technology. This is likely to include programs such as
installing LEDs and altering HVAC settings. Replacement of larger capital equipment such as HVAC
with more energy efficient options are likely to only occur on failure, as opposed to upfront
replacement of plant and equipment.
Existing energy efficiency, either associated with newer buildings, or previous adoption of energy
efficiency measures, may limit the uptake of this option. Similarly, limited access to building
infrastructure (for tenants) may restrict uptake for some.
6.2
Embedded Generation Outcomes
Solar PV is currently the leading embedded generation option, and for many businesses it provides
an ability to offset electricity consumption and, under current tariffs, to reduce costs. However,
externalities such as uncertain future retail tariffs and feed-in tariffs, as well as the impact of policy
change, create significant uncertainty in the likely NPV outcomes over the project life (and given this
modelling, in most cases there is a reasonable chance that an investment would not be viable).
Businesses more suited to embedded solar generation include those with a repetitive load profile
that occurs during the day and is used up to 7 days per week. Example businesses include offices,
retail and some day-time hospitality businesses are well placed to adopt solar PV. Businesses less
suited to solar include peaky or highly variable energy consumption that occurs in the evening this
includes Hospitality (bars and evening restaurants) and accommodation.
Flat bundled retail tariffs, with a significant proportion of costs recovered on a volume basis, will
generally offer the highest incentive for businesses. The solar resource is a secondary consideration
when considering the economic return of solar PV for SMEs.
Downward trends in prices for installing solar PV mean that, in the future, the downside risk of solar
PV will reduce. However, future trends in capital costs have no influence on the outcome of an
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investment decision made now. And as such, a decision to defer investment in solar PV may be
appropriate for some SME businesses, although it does represent a real opportunity for others. To
identify and take advantage of these requires consideration of individual circumstances.
6.3
Battery Energy Storage Systems
SMEs considering investment in BESS should understand the likely negative returns for this
investment, and weigh this against their individual circumstances, and other drivers for this
investment. With anticipated declining battery costs, the NPV should become positive in time, and
payback periods should decline to a level acceptable for SMEs. The economic trigger point for
adoption is likely to require a reduction in capital costs in the order of 70%, and may be impacted by
load and tariff changes. There may be specific opportunities where BESS are viable now or in the
near term, however, these are only likely to be relevant to a very small portion of SMEs.
6.4
Automated Load Shifting
Automated load shifting is still a relatively new technology for SME businesses, and is currently used
predominantly at commercial and industrial sites, or in location specific residential trials. The value of
automated load shifting is very site specific, though currently does not provide a compelling service
for wide scale adoption at the SME scale.
6.5
Summary for Distribution Network Service Providers
Based on the identified opportunities for SMEs, the following implications may be anticipated for the
network:
•
energy efficiency uptake is expected to continue, resulting in declining volumes of energy.
•
many technology options exist for businesses to alter the amount and timing of consumption,
though uptake will be low in the short term, apart from solar PV
•
high rates of solar PV uptake may continue based on good business cases with current tariffs.
However, uncertainty of future tariffs creates significant downside risk and is likely to reduce
uptake as businesses defer decisions
The means by which DNSPs respond to these implications will depend on a wide range of factors,
including their own objectives and planning, as well as the DR in other sectors of the market,
however, the following responses may result in DSM that better supports the network:
•
network use of service charges structured to cover costs in an environment of declining
demand (while being robust enough to deal with increasing demand), without large changes in
each pricing cycle, escalating the demand decline
•
provide charges that are able to be applied to technologies, tariffs and efficient capital
deployment
•
increase unbundling of tariffs and provide exposure and opportunities for businesses to alter
their demand to benefit the distribution system, noting the requirement for short term returns
on investment for business (and the fact that tariffs are the primary means of engaging with
SMEs)
•
load shifting programs need to be simple and clear and will require significant education of
SME businesses to encourage adoption.
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Business Engagement and Barriers
This section highlights some major points from business engagement and draws on experiences in
energy audits, system studies, the demand side management project on King Island and Moreland
Energy Foundation Limited’s numerous business engagement programs.
General barriers for demand side participation
•
Lack of standardisation
•
Ability to capture often disparate value of DSM
SME-specific barriers to DSM
•
SME consumers are largely disengaged from energy usage and cost of supply other than the
final cost of electricity. This is likely to lead to the majority of smaller businesses requiring
integrated solutions that rely on a DSM aggregator to manage the system while delivering cost
savings to the final business consumer, largely through processes that have little or no impact
on the final business. DSM is quite low in competing business priorities that may be achieved
with the same level of involvement and financial commitment.
•
Electricity is not a primary cost driver. Small /medium business electricity costs often account
for less than 2- 5% of total business costs. While many DSM options may provide a positive
NPV, the absolute magnitude of the savings may impede adoption.
Integrated DSM services that are simple and efficient may assist in overcoming this by
aggregating many revenue streams and services including tariff optimisation, data metering,
energy efficiency, embedded generation and load control all provided by a single energy
services provider with on-bill financing.
•
Technology barriers - smaller load sizes, particularly for load shifting, though often less
standardised than residential loads have a larger relative cost to control.
Modular and plug and play technology solutions may assist in overcoming this.
•
Business portfolio planning - where business is part of a larger corporation often does not have
the flexibility to enact DSM options outside of the capital allocation and planning schedules
that occur at a consolidated level.
•
SMEs generally have less capacity to be directly involved in managing their own DSM than
larger businesses, who can potentially devote a role to this function. Similarly, management
systems designed for larger businesses may not be cost effective for SMEs. Domestic
technology / approaches may be more useful for many SMEs, as these technologies develop
commercially.
•
Ownership structures - access to site infrastructure such as individual site metering and access
to physical roof space in circumstances where SME are in leased or shared buildings.
Standing policy / agreements that provide business tenants access to infrastructure is likely to
encourage DSM.
•
Tariff, policy understanding - limited understanding of tariff arrangements, especially likely
future tariffs and impacts on DSM options. Significant change has occurred in public policy and
there is a need for independent advice or services that address the knowledge gap in providing
integrated DSM energy services.
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Understanding the current and likely future tariff options is an essential step in determining
the suitability of DSM for a business. Costs can be substantially reduced by selecting the tariff
that gives the lowest annual electricity costs when considering you particular load profile.
Accessing interval meter data and using government comparison websites can assist (one
example can be found at http://www.energymadeeasy.gov.au/).
6.7
Policy Recommendations
Detailed policy recommendations for improving DSM are the focus of other sections of the CEC
Future Proofing of the Distribution Network work. The policy recommendations here are limited to
desirable policy outcomes and attributes that would encourage DSM by small and medium sized
businesses. Details of how these policies could be structured or implemented are not included.
Desirable policy outcomes for business
•
reliable and low cost electricity supply
•
certainty and consistency for investment (environmental and renewable energy policies,
schemes, technology, standardisation for load control, communications, metering)
•
provide clear and consistent (across jurisdictions) rules for tenant access to building
infrastructure, or standardised tenant – landlord agreements to provide the same effect
•
coordinated national approach to support efficient adoption for businesses with multiple sites
•
efficient and effective deployment of capital
•
DSM consistent value reflective pricing
•
increase retail tariff certainty (persistence of tariff structures and rates) over a medium term
(~5 years) to support investments in DSM solutions - this may best be achieved by policy to
encourage cost reflective pricing at a distribution, and possibly also retail level
•
support technology aggregators to assist SMEs with implementation of DSM - aggregators are
likely to be able to reduce administrative and knowledge burden on SMEs, manage risk over a
portfolio of projects, capture multiple disparate revenue streams, and manage network
support opportunities.
6.8
Concluding Remarks
Demand side participation for small medium / business has been rapidly increasing in recent years,
and has been seen most prominently in the implementation of energy efficiency and embedded solar
PV DSM installations. In the current environment, these two technologies still provide the most
viable DSM opportunities for SMEs.
Looking forward, the range of DSM options is expanding rapidly with many new products and
services aimed at reducing or shifting demand to reduce costs for the benefit of the business. At the
same time, the uptake of existing DSM options (energy efficiency and solar PV) has impacted
significantly on overall demand, and hence the revenues of DNSPs and others. The possible pricing
response to this impact, as well as policy changes at state and federal level, has created considerable
uncertainty in the industry as a whole, and particularly for DSM (which is both causing some of the
uncertainty, and is affected by it). In this environment, businesses may want to focus on simple
proven solutions with low risk. If DSM is going to be adopted, advice from a reputable service
provider is recommended, considering the SME’s specific situation. Even considering the uncertainty,
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energy efficiency measures will generally be viable, and there will be many cases where solar PV is
also viable. However, an uncertain future means greater scrutiny will be required prior to investing.
Inherent uncertainties and the broad range of SME characteristics make it impossible to make
blanket recommendations as to what technology types will be viable for certain business types, or
when.
Newer technologies of smart load control and battery energy storage technology were found to be
not viable in general, even ignoring the current uncertainty. While cost curves for these technologies
mean they are likely to become viable at some point, the required changes are considerable and the
broad scale adoption point is not likely to occur in the short term. However, because of the diversity
of the network and business situations, it is likely that a small number of SMEs could benefit from
these technologies now.
Independent of any DSM opportunities, it is recommended that businesses investigate the most
efficient tariff type applicable to their individual business profile. This is a relatively simple measure
that can reduce costs.
A further recommendation is to ensure the CEC continues to provide linkages for businesses seeking
advice on DSM, as it is becoming increasingly complex and difficult to identify the best value
proposition for a business.
6.9
Acknowledgements
Acknowledge and thank the steering committee for their input and assistance.
This report was prepared in collaboration with Moreland Energy Foundation Limited who is providing
the business facing engagement report.
Entura would like to acknowledge the input from within the Hydro Tasmania Group (particularly
Momentum Energy and Hydro Tasmania) for assistance in supply data and access to demand side
management outcomes from the King Island Renewable Energy Integration Project (KIREIP)
http://www.kireip.com.au.
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7.
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References
[1.]
CSIRO, Change and choice The Future Grid Forum’s analysis of Australia’s potential electricity
pathways to 2050, 2013
[2.]
AEMC 2012, Power of choice review - giving consumers options in the way they use electricity,
Final Report, 30 November 2012, Sydney
[3.]
George Wilkenfeld and Associates, Appliances Get Smart: But how and when? , 2 February
2014
[4.]
Climate Works Australia, Industrial demand side response potential, discussion paper 2014
[5.]
ROAM Consulting, RET policy analysis for the Clean Energy Council, CEC0011, May 2014
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A
Business Profiles
A.1
Load Profiles
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The business represented in these case studies aims at achieving significant diversification of daily
and seasonal load profiles, for small and medium sized businesses. Significant coverage of services
industries is included along with an example of a small niche manufacturer, business types and load
descriptions are summarised in Table A.1 and Section 3.2.
Table A.1: Business types and corresponding load profiles
Business Type
Load description
Retail
Daily consistent high utilisation profile (weekdays)
Accommodation
Peaky and seasonal variation
Food (Hospitality) Peaky high utilisation
A.1.1
Warehouse
Non daylight hour peaks, 7 day operation
Office
Daily highly predictable profile (weekdays)
Manufacturing
Peaky consistent daily profile, 7 day operation
Retail
Business Description
The Retail business type is characterised by single floor retail premises that operates Monday to
Friday with reduced trading hours on a Saturday. The floor area is in the order of 800 m2 and the
business is likely to be a specialist small retailer this may include business such as clothing stores,
large chemists and medium sized specialty stores. They are likely to have a significant number of part
time employees, though generally less than 5 - 10 full time equivalent staff.
Key sources of energy use, in order of consumption, may include:
•
HVAC often standalone split systems either small commercial or multiple residential grade
units.
•
Lighting general lighting as well as additional retail specific lighting
•
Retail equipment
•
Hot water
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Table A.2: Load Summary: Retail
Description
Quantity
Days of operation
6
Units
days / week
Days of operation per year 257
days / year
Daily Minimum Demand
2
kW
Daily Average Demand
11
kW
Daily Maximum Demand
61
kW
Annual Energy
100,000
kWh
Mean Diurnal Profile
35
Load
30
Load (kW)
25
20
15
10
5
0
0
6
12
18
24
Hour of Day (Hours)
Figure A.1: Average workday daily load profile
A.1.2
Accommodation
Business Description
The accommodation business type is characterised as small to medium sized accommodation with
less than 50 rooms.
The business operations are seasonal with periods of prolonged high utilisation followed by
significantly lower utilisation, with full time, 7 days per week operation during peak season. In this
case study the accommodation has a winter peak load followed by an early year high cooling load.
The daily profile shows periods of high energy use outside of daylight hours, whether early in the
morning or late in the evening. Examples of the accommodation types could include small to medium
sized chain businesses offering both accommodation and meals.
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Key sources of energy use, in order of consumption, may include:
•
HVAC often centralised or multiple small commercial units.
•
Equipment for hospitality and entertainment
•
Lighting
•
Hot water often using gas
Table A.3: Load Summary: Accommodation
Description
Quantity
Units
Days of operation (during peak season) 7
days / week
Days of operation per year
365
days / year
Daily Minimum Demand
2
kW
Daily Average Demand
11
kW
Daily Maximum Demand
45
kW
Annual Energy
100,000
kWh
Mean Diurnal Profile
20
Load
Load (kW)
15
10
5
0
0
6
12
18
24
Hour of Day
Figure A.2: Average daily load profile
A.1.3
Hospitality
Business Description
The quick service restaurant is characterised by single floor mid-size chain ‘fast food’ outlet operating
6 days per week. The weekly load profile is consistent with set operating hours and has minor
seasonal variability. A business of this size is likely to have generally less than 10 full time equivalent
staff.
The floor area is in the order of 50 m2. This may include business such as, chain coffee shops, fast
food ice-creameries and cafes.
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Key sources of energy use, in order of consumption, may include:
•
HVAC often standalone split systems either small commercial or multiple residential grade
units.
•
Equipment largely food processing equipment
•
Hot water
•
Lighting general lighting as well as additional retail specific lighting
Table A.4: Load Summary: Hospitality
Description
Quantity
Days of operation
6
Units
days / week
Days of operation per year 324
days / year
Daily Minimum Demand
3
kW
Daily Average Demand
11
kW
Daily Maximum Demand
24
kW
Annual Energy
100,000
kWh
Mean Diurnal Profile
16
Load
Load (kW)
12
8
4
0
0
6
12
18
24
Hour of Day
Figure A.3: Average workday daily load profile
A.1.4
Warehouse
Business Description
The warehouse business type is characterised as a medium sized non refrigerated storage and single
floor distribution business. This case study has been chosen to identify the effect of a load profile
that is dominated by overnight, off-peak and non-solar coincident demand.
The business operations week to week are fairly consistent with slight variations and peaks in winter
for a winter heating load and lighting load. Examples of this business type include self-storage,
distribution centres. Note this load profile is of a self-storage warehouse and exhibits strong energy
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consumption in the mornings and evenings, an example of a load not coincident with the solar
resource.
The total floor area of a warehouse of this size is in the order of 2,000 m2.
Key sources of energy use, in order of consumption, may include:
•
lighting
•
equipment
•
HVAC
•
refrigeration.
Table A.5: Load Summary: Warehouse
Description
Quantity
Units
Days of operation (during peak season)
7
days / week
365
days / year
Daily Minimum Demand
2
kW
Daily Average Demand
11
kW
Daily Maximum Demand
38
kW
100,000
kWh
Days of operation per year
Annual Energy
Mean Diurnal Profile
16
Load
Load (kW)
12
8
4
0
0
6
12
18
24
Hour of Day
Figure A.4: Average workday daily load profile
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Office
Business Description
The office business type is characterised as a medium sized office space.
The business operations are highly consistent with 5 operating days and a strong diurnal profile
coincident with the solar resource. Seasonal variations are apparent and highly dependent on
climatic conditions to control the internal working climate and variability associated with office days
of operation.
Examples of the office business type includes small to medium sized offices with either a single or
multiple floors, having a floor area in the order of 800 m2.
Key sources of energy use, in order of consumption, may include:
•
HVAC often centralised or multiple small commercial units.
•
Lighting
•
Equipment for example computers servers, lifts, printers
•
Other
Table A.6: Load Summary: Office
Description
Quantity
Units
Days of operation (during peak season) 5
days / week
Days of operation per year
255
days / year
Daily Minimum Demand
3
kW
Daily Average Demand
11
kW
Daily Maximum Demand
31
kW
Mean Diurnal Profile
20
Load (kW)
Column 1 (kW)
15
10
5
0
0
6
12
18
Hour of Day
Figure A.5: Average daily load profile
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Manufacturing
Business Description
The manufacturing business type is characterised as a small to medium sized manufacturer.
The business operations are variably with 7 operating days and a diurnal profile coincident with the
solar resource.
The load is exhibits significant daily peaks compared to the average load. Seasonal variation is related
to both temperature and batch utilisation of the facility. With significant inter day and inter hour
variation including non-standard operating weeks, for example 7 day operation followed by periods
of weeks with less than 5 days of operation.
This business type includes very small specialist manufacturers, using non energy intensive
manufacturing processes on a small single floor building with a floor area in the order of 400 m2.
Businesses in this group may include niche batch manufacture ring that exhibits a day time load, with
limited energy inputs to their processes largely dominated by labour inputs. An example of
businesses in this group includes small electronic design and assembly or integrated building parts
producers. This load group specifically excludes large scale manufacturing and large food
manufacturing as these operations are largely energy intensive and have more continuous load
profiles.
Key sources of energy use, in order of consumption, may include:
o
Equipment
o
HVAC
o
Lighting
o
Other
Table A.7: Load Summary: Manufacturing
Description
Quantity
Units
7
days /
week
Days of operation per year
300
days /
year
Daily Minimum Demand
0.5
kW
Daily Average Demand
11
kW
Daily Maximum Demand
65
kW
100,000
kWh
Days of operation (during peak
season)
Annual Energy
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Mean Diurnal Profile
40
Scaled_100
Load (kW)
30
20
10
0
0
6
12
18
Hour of Day (Hours)
Figure A.6: Average workday daily load profile
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B
Business Modelling Case Study Results
B.1
Business, Technology and Tariff Modelling Summary
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Table B.1: Net annual savings modeling summary
Tariff Type
Tariff 1
Tariff 2
Tariff 3
Tariff 4
Retail
Solar PV size 70 kW
Net annual savings ($/yr)
6,700
3,031
-436
1,854
Energy Efficiency
Net annual savings ($/yr)
3,476
2,617
1,743
2,618
Solar PV size 30 kW
Net annual savings ($/yr)
3,106
940
-768
162
Energy Efficiency
Net annual savings ($/yr)
3,496
1,901
733
1,399
Solar PV size 40 kW
Net annual savings ($/yr)
6,126
2,658
- 78
1,594
Energy Efficiency
Net annual savings ($/yr)
3,476
1,679
741
1,528
Solar PV size 40 kW
Net annual savings ($/yr)
3,365
1,173
- 411
931
Energy Efficiency
Net annual savings ($/yr)
3,476
2,058
1,070
1,872
Solar PV size 50 kW
Net annual savings ($/yr)
6,939
3,563
299
2,408
Energy Efficiency
Net annual savings ($/yr)
3,476
2,298
1,170
2,043
Solar PV size 90 kW
Net annual savings ($/yr)
10,775
4,977
-160
3,262
Energy Efficiency
Net annual savings ($/yr)
3,476
2,952
2,126
3,087
Accommodation
Hospitality
Warehouse
Office
Manufacturing
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Table B.2: Net present value modeling summary
Tariff Type
Tariff 1
Tariff 2
Tariff 3
Tariff 4
Retail
Solar PV size 70 kW
Total net present value ($)
98,335
44,491
-6,398
27,206
Energy Efficiency
Total net present value ($)
51,013
38,415
25,582
38,417
Solar PV size 30 kW
Total net present value ($)
45,581
13,794
-11,276
2,380
Energy Efficiency
Total net present value ($)
51,305
27,899
10,761
20,526
Solar PV size 40 kW
Total net present value ($)
89,907
39,018
-1,148
23,390
Energy Efficiency
Total net present value ($)
51,015
24,644
10,880
22,429
Solar PV size 40 kW
Total net present value ($)
49,394
17,213
-6,062
13,669
Energy Efficiency
Total net present value ($)
51,015
30,204
15,698
27,477
Solar PV size 50 kW
Total net present value ($)
101,835
52,287
4,387
35,345
Energy Efficiency
Total net present value ($)
51,015
33,723
17,170
29,981
Solar PV size 90 kW
Total net present value ($)
158,138
73,042
-2,344
47,876
Energy Efficiency
Total net present value ($)
51,015
43,328
31,201
45,312
Accommodation
Hospitality
Warehouse
Office
Manufacturing
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B.2
Case Study Results – Retail
Figure B.1: Base case tariff cost
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Figure B.2: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.3: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.3: Detailed modeling results
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.30
0.27
0.27
0.27
29,827
26,711
26,711
26,711
437,765
392,020
392,035
392,034
29,267
18,795
13,220
17,973
-
5,807
6,629
6,629
560
2,109
6,862
2,109
29,827
26,711
26,711
26,711
6,700
3,031
-
436
1,854
98,335
44,491
-
6,398
27,206
9
15
12,551
6,697
4,582
7,046
-
4,857
5,687
5,687
560
2,109
6,862
2,109
13,111
13,663
17,131
14,842
O&M cost
2,100
2,100
2,100
2,100
Annualised capital cost
7,951
7,951
7,951
7,951
DSM cost
10,051
10,051
10,051
10,051
Total costs
23,162
23,714
27,182
24,893
3,476
2,617
1,743
2,618
51,013
38,415
25,582
38,417
4
5
6
5
23,882
15,335
10,787
14,666
-
4,738
5,410
5,410
560
2,109
6,862
2,109
24,442
22,182
23,059
22,185
2,057
2,057
2,057
2,057
26,499
24,239
25,116
24,242
Annual Retail Cost ($)
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 70 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
no payback
18
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM cost
Total costs
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B.3
Case Study Results – Accommodation
Figure B.4: Base case tariff cost
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Figure B.5: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.6: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.4: Accommodation detailed modeling results
Tariff
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.30
0.23
0.21
0.2
Annual Cost ($)
29,934
22,816
21,223
20,086
439,337
334,871
311,485
294,797
29,374
17,088
10,057
13,673
-
3,619
4,304
4,304
560
2,109
6,862
2,109
29,934
22,816
21,223
20,086
3,106
940
-
768
162
45,581
13,794
-
11,276
2,380
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 30 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
9
17 no payback
23
21,976
11,868
6,553
9,219
-
3,607
4,304
4,304
560
2,109
6,862
2,109
22,536
17,584
17,719
15,632
900
900
900
900
Annualised capital cost
3,408
3,408
3,408
3,408
DSM cost
4,308
4,308
4,308
4,308
26,844
21,892
22,027
19,940
3,496
1,901
733
1,399
51,305
27,899
10,761
20,526
4
6
10
7
23,969
13,944
8,207
11,157
-
2,953
3,512
3,512
560
2,109
6,862
2,109
24,529
19,006
18,581
16,778
2,057
2,057
2,057
2,057
26,586
21,063
20,638
18,835
Demand charges
Fixed charges
Annual tariff cost
O&M cost
Total
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM Cost
Total
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B.4
Case Study Results – Hospitality
Figure B.7: Base case tariff cost
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Figure B.8: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.9: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.5: Hospitality detailed modeling results
Tariff
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.30
0.22
0.21
0.21
Annual Cost ($)
29,827
21,611
21,267
20,790
437,763
317,180
312,133
305,140
29,267
17,592
11,895
16,171
-
1,910
2,510
2,510
560
2,109
6,862
2,109
29,827
21,611
21,267
20,790
6,126
2,658
-
78
1,594
89,907
39,018
-
1,148
23,390
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 40 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
7
11 No payback
16
17,418
9,400
6,440
9,045
-
1,721
2,320
2,320
560
2,109
6,862
2,109
17,978
13,230
15,622
13,474
O&M cost
1,200
1,200
1,200
1,200
Annualised capital cost
4,544
4,544
4,544
4,544
DSM cost
5,744
5,744
5,744
5,744
23,722
18,974
21,366
19,218
3,476
1,679
741
1,528
51,015
24,644
10,880
22,429
4
7
9
7
23,882
14,355
9,706
13,196
-
1,559
2,048
2,048
560
2,109
6,862
2,109
24,442
18,023
18,616
17,353
2,057
2,057
2,057
2,057
26,499
20,080
20,673
19,410
Demand charges
Fixed charges
Annual tariff cost
Total
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM Cost
Total
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B.5
Case Study Results – Warehouse
Figure B.10: Base case tariff cost
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Figure B.11: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.12: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.6: Warehouse detailed modeling results
Tariff
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.30
0.24
0.23
0.23
Annual Cost ($)
29,827
23,670
21,223
20,791
437,765
347,397
338,318
332,578
29,267
17,595
12,132
16,494
-
3,966
4,057
4,057
560
2,109
6,862
2,109
29,827
23,670
23,051
22,660
3,365
1,173
-
411
931
49,394
17,213
-
6,062
13,669
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 40 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
10
17 no payback
18
20,178
11,036
7,191
10,211
-
3,629
3,686
3,686
560
2,109
6,862
2,109
20,738
16,774
17,739
16,006
O&M cost
1,200
1,200
1,200
1,200
Annualise capital cost
4,544
4,544
4,544
4,544
DSM cost
5,744
5,744
5,744
5,744
26,482
22,518
23,483
21,750
3,476
2,058
1,070
1,872
51,015
30,204
15,698
27,477
4
6
8
6
23,882
14,358
9,900
13,459
-
3,236
3,311
3,311
560
2,109
6,862
2,109
24,442
19,703
20,073
18,879
2,057
2,057
2,057
2,057
26,499
21,760
22,130
20,936
Demand charges
Fixed charges
Annual tariff cost
Total
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM Cost
Total
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B.6
Case Study Results – Office
Figure B.13: Base case tariff cost
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Figure B.14: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.15: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.7: Office detailed modeling results
Tariff
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.29
0.25
0.24
0.24
Annual Cost ($)
29,828
24,973
23,596
23,587
429,553
366,522
346,315
346,184
29,268
19,345
13,195
17,939
-
3,519
3,539
3,539
560
2,109
6,862
2,109
29,828
24,973
23,596
23,587
6,939
3,563
299
2,408
101,835
52,287
4,387
35,345
7
10
23
14
14,615
9,125
6,258
8,893
-
3,023
3,023
3,023
560
2,109
6,862
2,109
15,175
14,257
16,143
14,025
O&M cost
1,500
1,500
1,500
1,500
Annualised capital cost
5,680
5,680
5,680
5,680
DSM cost
7,180
7,180
7,180
7,180
22,355
21,437
23,323
21,205
3,476
2,298
1,170
2,043
51,015
33,723
17,170
29,981
4
5
8
6
23,882
15,785
10,767
14,638
-
2,872
2,888
2,888
560
2,109
6,862
2,109
24,442
20,766
20,517
19,635
2,057
2,057
2,057
2,057
26,499
22,823
22,574
21,692
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 50 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Total
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM Cost
Total
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B.7
Case Study Results – Manufacturing
Figure B.16: Base case tariff cost
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Figure B.17: Top solar PV net present value, below annual electricity cost breakdown
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Figure B.18: Top energy efficiency net present value, below annual electricity cost breakdown
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Table B.8: Manufacturing detailed modeling results
Tariff
Tariff
1
2
3
4
Base Case
LCOE ($ / KWh)
0.30
0.28
0.29
0.29
Annual Cost ($)
29,827
28,530
28,792
29,264
437,770
418,723
422,575
429,503
29,267
19,451
14,533
19,758
-
6,970
7,397
7,397
560
2,109
6,862
2,109
29,827
28,530
28,792
29,264
10,775
4,977
-
160
3,262
158,138
73,042
-
2,344
47,876
Net Present Cost ($) 25 years
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Solar PV 90 kW
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
8
12 no payback
16
5,615
2,860
2,944
4,747
-
5,707
6,268
6,268
560
2,109
6,862
2,109
Annual tariff cost
6,175
10,676
16,074
13,124
O&M cost
2,700
2,700
2,700
2,700
Annualise capital cost
10,223
10,223
10,223
10,223
DSM cost
12,923
12,923
12,923
12,923
Total
19,098
23,599
28,997
26,047
3,476
2,952
2,126
3,087
51,015
43,328
31,201
45,312
4
5
6
5
23,882
15,872
11,859
16,122
-
5,687
6,036
6,036
560
2,109
6,862
2,109
24,442
23,668
24,757
24,267
2,057
2,057
2,057
2,057
26,499
25,725
26,814
26,324
Demand charges
Fixed charges
Energy Efficiency
Net annual savings ($/yr)
Net Present Value ($)
Discounted payback (years)
Energy (volume charges)
Demand charges
Fixed charges
Annual tariff cost
Annualised DSM Cost
Total
97