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Naomi Stringer, Luke Marshall
Open Source Model for Operational and Commercial Assessment of Local
Electricity Sharing Schemes in the Australian National Electricity Market
Naomi Stringer1, Luke Marshall1, Rob Passey1,2, Anna Bruce1,2, Iain MacGill2
1 School of Photovoltaic and Renewable Energy Engineering, University of New South Wales,
Sydney NSW 2052, Australia
2 Centre for Energy and Environmental Markets, School of Electrical Engineering and
Telecommunications, University of New South Wales, Sydney NSW 2052, Australia
E-mail: n.stringer@unsw.edu.au, luke.marshall@student.unsw.edu.au
Abstract
Local electricity sharing schemes have the potential to play an increased role in the Australian
National Electricity Market as the penetration of distributed energy resources (DERs) continues
to grow. These models allow participants to share energy between separately owned and
operated DERs, however are largely untested. While embedded networks have generally been
established for specific circumstances such as shopping centres and airports, there is growing
interest in their wider application in providing a framework for local sharing of energy
resources. However, the potential operational and commercial implications for key stakeholders
(including consumers, network operators and retailers) are not well understood. An example of
one such proposal is within the Byron Arts and Industrial Estate through which the community
owned retailer, Enova, is seeking to offer a bespoke energy solution to its customers within the
estate.
In this paper, a new open source software model for assessing technical and commercial
outcomes of local electricity sharing is presented. The model is applied to the Byron Arts and
Industrial Estate case study which demonstrates the relevance of modelling to support
appropriate investment and operational decision-making.
1. Introduction
With significant reductions in photovoltaics (PV) and battery energy storage (BES) system
costs over recent years, a range of new business and community models are emerging in
electricity industries around the world, specifically designed to cater to a new class of
consumer: participants that both generate and consume energy from distributed energy
resources. These new approaches allow customers to buy and sell energy between each other
(rather than from a centralised retailer or generator), and to aggregate their consumption to
access a more beneficial interface with networks and electricity markets.
This includes a range of ‘peer to peer’ energy models, ‘local energy trading’ models, and new
retail offerings in the Australian context (for example Powershop’s “Your Neighbourhood
Solar” scheme). While arrangements may vary significantly between specific scheme
implementations, distributed technology types and jurisdictions, in this paper we focus on a
subset of models that involve trading and aggregation between consumers in the same local
network area (e.g. zone substation). We refer to these broadly as ‘local electricity sharing
schemes’, defined below.
'Local electricity sharing scheme' is a broad term for any contractual structure under which
locally
1
generated (or stored) electricity can be shared
2
between consumers within some
subset of the grid.
Proponents of local electricity schemes point to a range of benefits. For instance, where they
are community-owned, by enabling the sharing of PV electricity, they may act as a central focal
point for communities wishing to attain some level of autonomy from electricity utilities. They
may also provide increased reliability through islanding the local area during brownouts and
blackouts. Furthermore, they may be able to provide network support and thereby be of value
to DNSPs through the deferral of network augmentation or asset replacement.
Embedded networks are a subset of local electricity sharing schemes. The Australian Energy
Market Commission (AEMC) which oversees rule making in the Australian National Electricity
Market (NEM) describes an embedded network as follows:
“Embedded networks are private electricity networks which serve multiple customers and are connected to
another distribution or transmission system in the national grid through a parent connection point. A party,
other than the registered local network service provider (LNSP), owns and operates the private electricity
network that customers connect to. The party is known as an embedded network service provider. Generally, the
embedded network service provider also purchases electricity at the parent connection point and onsells it to
customers within the embedded network.” (AEMC, 2017)
Examples include semi-autonomous community-owned ‘mini-grids’, which can enable the
sharing of PV electricity within a community (Bowyer, et al., 2016), solar sharing within
apartment blocks (Roberts, et al., 2016), greenfield urban developments, caravan parks,
shopping centres and airports.
Embedded networks are commonly operated by an Embedded Network Operator (ENO) that is
responsible for operating and maintaining the network infrastructure, and for buying electricity
from an external retailer, which it then on-sells to households and businesses on the embedded
network. Many embedded networks also have an Embedded Network Manager (ENM) that
performs the market interface services for embedded network customers, which includes
helping embedded network customers access an external electricity retailer if they wish to do
so.
The expansion of interest in embedded networks has resulted in the AEMC initiating a review
focussed on ensuring that embedded network customers have equivalent rights and access to
competition as other ‘on-market’ customers. The most relevant outcomes of the Review’s draft
findings for the work presented here are that (i) any party that sells electricity to a consumer in
a new embedded network must hold a retailer authorisation from the AER (i.e. be a retailer) or
be exempted under a narrow set of circumstances, and (ii) a retailer of an on-market embedded
network customer can pay the ENO a network tariff that is equal to the standard published
network tariff. These changes mean that for new embedded networks, ENOs are likely to hand
1
'locally' means within this subset of the grid, or behind a single point of connection
2
'shared' could include actually sharing (giving to local library for example), trading in a market place, netting off
for a fixed tariff, or assigning under some bilateral agreements (selling to a specific friend down the road for
example), or some other arrangement.
over the role of selling electricity to a conventional retailer (as the cost of obtaining a retail
licence may be prohibitive), which must then pay the ENO’s network component of the tariff
applied to the parent connection point.
In cases where network services are to be provided, the role of the ENO may extend to operating
and maintaining additional assets, such as PV systems (both rooftop and ground-mounted) and
batteries, connected to the embedded network. This added functionality increases the financial
complexity of operating an embedded network, making the ENO responsible for reconciling
internal payments for local generation and subsequent use within the embedded network (for
example, through ‘peer-to-peer’ trading).
While embedded networks are widely used in the current market, models that use the regulated
utility network for local energy sharing, but are otherwise quite similar to an embedded network
(‘pseudo embedded networks’), are of particular interest. They allow the creation of what are
essentially embedded networks on existing networks, which may increasingly occur given the
emerging regulatory limitations on embedded networks.
It is important to note that whether the benefits of local electricity sharing schemes can be
realised is likely to be highly dependent upon the local conditions as well as the specific
business models to be applied, and therefore the merits of local electricity sharing projects will
need to be assessed on a case-by-case basis. This paper therefore presents a preliminary
exploration of commercial and technical impacts on participants and operators in a local
electricity sharing scheme. The case study examined is located in the Arts and Industrial Estate
(A&IE) in Byron Bay, New South Wales, incorporating distributed photovoltaics (PV) as well
as battery energy systems (BES). It is a ‘pseudo-embedded network’, where the DNSP Essential
Energy intends to retain ownership of the internal network. While the proposed scheme does
not meet the AEMC’s standard definition of an embedded network, it involves similar
commercial design decisions relating to the ownership of various assets, the tariffs applied,
metering and billing approach, and technical design decisions relating to the selection, sizing
and operation of distributed energy assets such as PV and batteries
3
.
This paper is structured as follows; Section 2 provides background on embedded networks and
local electricity sharing schemes, Section 3 presents the open source model, Section 4 provides
preliminary results from the Byron A&IE case study and Section 5 sets out some discussion
and conclusions.
2. Method
A case study in the Byron Bay Arts and Industrial Estate (Byron A&IE) was used as a
preliminary exploration of some of the operational and commercial implications of local energy
sharing models. A Python model for calculating energy and financial flows was created and
applied to the case study. The tool is open source and was designed to be extensible, so that it
can in future be extended to model other local electricity sharing schemes, including embedded
networks. A brief background on the case study is provided in section 2.1, the model is
described in section 2.2, and the inputs and PV/battery size scenarios used to apply the model
to the case study are described in section 2.3.
3
Noting also that many design decisions are interrelated, for instance a battery’s charge/discharge strategy will be
directly impacted by which party owns the battery asset.
2.1. Case Study Background
In March 2015, Byron Shire announced its intention to become Australia’s first zero emissions
community by 2025. Enova Community Energy commenced operations in mid 2016, with the
express aim to be community-owned and to promote the uptake of renewable energy. Enova
Community Energy is now planning a pilot local electricity sharing scheme in the Byron Arts
and Industrial Estate, within Essential Energy’s existing distribution network. Although not an
embedded network in the regulatory sense (due to ongoing Essential Energy ownership and
operation of grid assets) this project has many characteristics similar to that of an embedded
network, including the presence of a gate meter at the interface with the local network to the
wider grid.
The pseudo embedded network will include a large battery owned by Essential Energy so it can
be partially included in their Regulated Asset Base and used for network support, but partially
leased to the retailer Enova, potentially for use as a physical hedge against high spot prices. The
battery will be located at the point of connection to the wider network, with the expectation that
the subset of the network behind the parent meter may be operated in isolation from the main
grid if needed. It will also include a number of large roof-mounted behind-the-meter PV arrays.
A total of 69kW PV (with an inverter capacity of 54kW) is already connected, and the intention
is to install sufficient additional PV to be able to meet the embedded network load in most
circumstances. The battery capacity will ideally be sufficient to ride through the types of
blackouts most likely to be experienced.
The case study presented here provides some preliminary analysis of the network load and
financial impacts of the proposed scheme. It is intended that the results and the model may be
used by parties like Enova and Essential Energy to support business decisions such as the
structure of the internal tariffs and the required PV and battery capacities.
2.2. Model
A summary of inputs accepted, outputs generated, and the underlying logic to generate the
outputs (‘the model’) is provided in Figure 1. The model logic consists of two packages of
calculations. The first handles only electricity flows, calculating the volumes of kWh bought or
sold by each participant in the scheme in each time period, and the state of charge of any
batteries. The second package focuses on financial flows; it calculates the resultant costs and
revenues for each participant, based on the preceding electricity flow calculations.
4
Figure 1 High level overview of the model components
4
The code for the model can be found here https://github.com/luke-marshall/embedded-network-model
2.2.1. Electricity flows
In line with the AEMC’s definition of an embedded network, the model allows for multiple
participants that can import and export electricity to and from a central network, as well as
electricity flows between participants on what is termed the ‘local network.’ The model allows
for both ‘participant’ and ‘centralised’ DERs such as batteries and solar PV as shown in Figure
2.
Figure 2 Energy flows for single participant
The local network may also include other loads or DERs that are not participants in the local
electricity sharing scheme. The model takes interval load data and either net or gross metered
PV generation data as input. Seven categories of electricity flow are recorded, these are shown
for a single participant (orange) in Figure 2:
1. Electricity consumed from the grid
2. Electricity exported to the grid from the participant’s PV
3. Electricity consumed from the central battery
4. Electricity exported to the central battery from the participant’s solar PV
5. Electricity consumed from another participant’s PV export on the local network
6. Electricity exported from one participant’s solar PV to another participant
7. Electricity consumed from the central solar PV on the local network
An overview of the electricity flow calculations is provided in Appendix A.
Battery Discharge
In a local electricity sharing scheme, the discharge strategy for a central battery is likely to
depend on which stakeholder owns and operates the battery, as well as any commercial
agreements that may be in place. For this study, the battery is modelled to charge from locally
generated solar electricity that would otherwise be exported via the parent meter and discharge
when there is any load on the local network.
PV generation preferences
In the model, PV generation by a participant is ‘used’ where possible as follows, in order of
preference:
1. Self consume
2. Sell to ‘local load’ (other participant within the local network)
3. Sell to central battery
4. Export to grid
The order of preference above does not change actual electricity flows and is primarily an
accounting concept, however it determines the volume of energy sourced from local generation
(etc.) that is recorded and therefore is relevant in the electricity flow calculations.
Local Electricity Allocation Algorithms
Any proposed local electricity sharing scheme must apply a rule to allocate locally generated
energy to each participant and determine the subsequent amount of ‘local’ tariff revenues and
charges that should be allocated to each generator and consumer. In the course of designing the
local electricity sharing model for this study, a range of potential allocation rules were
examined.
The simplest two rules were fractional allocation (local electricity is allocated in proportion to
share of total consumption) and quota allocation (each participant currently consuming
electricity is allocated an equal ‘quota’ of local electricity to consume). Under the quota
allocation rule, if a participant uses less energy than their quota allows in a given time period,
the remaining energy is added back to the pool of energy and re-allocated to other participants.
For this case study, the quota rule was used to allocate local PV generation and battery export
to participants, and to allocate local load to the available PV generation.
2.2.2. Financial flows
Financial outcomes are calculated for each participant (for each of the seven types of electricity
flows listed above), the party managing the network, and the party managing the retail function
of participant interface. Retail and network tariffs for each participant are applied to grid
electricity. Separate tariffs are applied to ‘locally traded electricity’ (e.g. solar electricity
generated by one participant and consumed by another participant behind the parent connection
point). For the case study, network charges (NUOS) apply to each individual participant’s load
profile, as indicated in Figure 3.
Figure 3 Case study embedded network NUOS payments
Figure 4 Typical embedded network NUOS payments
This is the main point of difference in the financial flows that apply for this case study and a
typical embedded network arrangement, where the embedded network service provider pays
NUOS based on load at the parent connection point meter, then passes through costs to
individual participants indirectly via tariffs. Figure 4 provides a simplified view of this NUOS
application, where the consumption information at the parent connection point informs the
NUOS payment for electricity imported from the grid. The detailed data inputs, tariffs and
scenarios for the case study are now described.
2.3. Modelling of the Case Study
Half hourly load data from eleven existing sites within the Byron A&IE local network for
March and April 2017 were input to the model. PV is installed on four of the sites, with capacity
shown in Table 1. Half hourly PV data was not available and so publicly available rooftop PV
datasets from neighbouring systems (obtained from pvoutput.org) were normalised and then
scaled to the known nameplate capacities of each system.
Table 1 Key participant inputs
Participant
Enova Retail tariff*
Essential Energy Network tariff
Solar capacity
(kW DC)
Gross demand over
the period (kWh)
1
Business TOU
LV TOU <100MWh
0
5,984
2
Business TOU
LV TOU <100MWh
0
217
3
Business TOU
LV TOU <100MWh
0
35,476
4
Business TOU
LV TOU <100MWh
26
961
5
Business TOU
LV TOU <100MWh
0
3,470
6
Business TOU
LV TOU <100MWh
14.8
2,047
7
Business TOU
LV TOU <100MWh
0
744
8
Business TOU
LV TOU <100MWh
27.5
1,174
9
Business Anytime
LV Small Business Anytime
3
687
10
Business Anytime
LV Small Business Anytime
0
515
11
Business Anytime
LV Small Business Anytime
0
538
The relevant Enova retail, and Essential Energy network tariffs applied are listed in 1. Specific
details of these and the local electricity tariffs used are plotted in Figure 4 and detailed in
Appendix B.
Figure 5 TOU tariff structures: retail and NUOS charges compared with local trading tariffs
A local solar tariff of 12c/kWh is paid for solar consumed locally by other participants or the
central battery, in line with available on-market solar feed-in tariffs. A 6c/kW retail margin and
a 6c/kWh local network charge are applied to these transactions, resulting in 24c/kWh paid for
consumed solar. This falls between the energy component of the standard retail tariff and the
solar feed-in-tariff on the wider network, except during off-peak times when the cost of
importing electricity is only 17.8c/kWh. An approach similar to this would likely be used in a
simple ‘local energy trading’ scheme, in order to encourage participation in the scheme. A
2c/kWh margin is added to the 24c/kWh local tariff for energy consumed by participants from
the central battery. DNSP ownership, and addition of the battery to the DNSP’s Regulated Asset
Base is assumed, allowing the battery to be operated at very low cost to the participants. In
practice, the tariffs chosen (including payment for the battery) will be the outcome of
negotiation between Enova and Essential Energy. There is, of course, significant room for
innovation in local electricity sharing tariffs, including the possibility of more complex price
signals or market structures to incentivise certain behaviours.
The scenarios shown in Table 2 were used to explore the impact of varying the centralised
battery size from 5kWh/5kW to 30kWh/30kW. The battery featured a simple dispatch strategy,
charging when excess solar energy was available and dispatching as soon as electricity would
otherwise be drawn from the grid. Local electricity sharing occurred in all non ‘business as
usual’ (BAU) scenarios.
Table 2 Scenarios modelled
Scenario
Central battery capacity
(kWh/kW)
Description
BAU* no PV
0
Assume all participants have no PV installed, no central battery
and no central solar. Normal retail tariffs applied.
BAU
0
Participants have PV as above, no local electricity sharing
arrangements in place and no local electricity trading occurs,
normal retail tariffs applied.
1
0
Local electricity sharing arrangements in place so local
electricity trading occurs, however no central battery present.
2
5 / 5
Local electricity sharing arrangements in place so local
electricity trading occurs. Central battery charging occurs
when there is excess solar generation (which is not being
consumed by a Participant). Central battery discharge occurs
when the following conditions are met:
- It is not charging and
- There is energy in the battery and
- There is load on the local network which would
otherwise be supplied by grid import
3
10 / 10
4
15 / 15
5
20 / 20
6
25 / 25
7
30 / 30
3. Results
3.1. Network Impacts
The most likely implications of local electricity sharing at the Byron A&IE in terms of network
operational and investment costs, changes to import and export volumes at the parent
connection point, and impact on peak demand days are investigated in this section.
The average daily load profile at the parent connection point is shown in Figure 6 for the BAU
scenarios (with and without PV) and for the largest battery scenario. The change to average
load due to the local energy sharing scheme with battery is small in comparison to the change
due to the installed PV. This is largely because of the excellent match between the PV and the
load, and high local consumption of PV energy, reducing the utility of the battery.
On the peak electricity export day (for the case where no battery was present), Figure 7 shows
that adding a battery reduces network export during morning hours and subsequently reducing
import from 3:30pm to 6:30pm. The peak electricity import day (Figure 8) occurred during an
extreme weather event that resulted in widespread flooding in the region, while the peak load
was more than three times the average peak load. On this day, PV had minimal impact and
adding a battery had no impact on network usage, as the battery was unable to charge due to all
PV electricity being consumed by participants at the time of generation. It should be noted that
the battery algorithm allowed charging only from excess locally generated PV, limiting the
ability of the battery to reduce peak demand in this case.
Figure 9 shows that increasing battery size resulted in a minimal reduction in grid imports,
primarily due to good match between PV and load resulting in (immediate) consumption of
local PV behind the parent meter, and also use of PV electricity by other loads within the pseudo
embedded network. Similarly, as the battery size was increased, there was minimal reduction
Figure 6 Average day, 30kWh/30kW battery
Figure 7 Peak solar export day (9th April 2017), 30kWh/30kW
battery
Figure 8 Peak network import day (30th March 2017),
30kWh/30kW battery
in electricity exported to the grid. Figure 10 however indicates a substantial reduction in the
proportion of the time in which export of PV to the wider grid occurs.
Figure 9 Grid impacts: change in electricity imports
and exports with change in battery capacity
Figure 10 Grid impacts: proportion of time importing
and exporting with change in battery capacity
Figure 11 shows more clearly the decrease in PV exported to the grid and increase in battery
electricity consumption as the battery size is increased. While the changes observed are small
with respect to overall electricity consumption, this is largely due to lack of opportunity to
charge given good match between PV and load and relatively low PV capacity. If more PV
were to be installed then battery utilisation would likely increase.
Figure 11 Grid impacts: PV exports and locally consumed battery electricity
3.2. Financial outcomes
It is important to recognise that the financial outcomes presented here are entirely dependent
on the choice of tariff design, which has not been investigated in this case study (which instead
focused on the impact of varying battery size) and therefore only high level financial results are
presented.
Considerable bill reductions for participants were observed under the local electricity sharing
scheme compared to business as usual (with PV) as shown in Table 3. A 10% aggregate bill
saving was achieved in the 30kWh/30kW battery local electricity sharing case (scenario 7)
compared with the BAU case (with PV). These savings were not however distributed equally
across all participants, with a minimum saving of 1% and a maximum saving of 163% (i.e.
some cases resulted in a net payment rather than net bill). Those with high levels of PV export
benefitted more than those with low export or without rooftop PV, due to the more profitable
local solar tariffs. It is worth noting that participant 3 enjoyed the greatest absolute saving (in
dollar terms), despite not having PV, because participant 3 had the largest BAU electricity bill,
and benefited most from the purchase of locally generated PV electricity. Furthermore, it is
important to note that all savings observed by Participants are at the expense of the DNSP and,
to a far lesser extent, the retailer. This is explored in greater detail below.
Table 3 Participant financial outcomes
Participant
PV capacity
(kW DC)
Total bill over the period
Difference***
Percentage
difference***
BAU no PV
BAU
Local Electricity
Sharing Scheme**
1
0
$2,122
$2,122
$2,041
-$80
-4%
2
0
$475
$475
$469
-$6
-1%
3
0
$9,458
$9,458
$8,893
-$564
-6%
4*
26
$685
$185
-$117
-$303
-163%
5
0
$1,357
$1,357
$1,279
-$77
-6%
6*
14.8
$965
$499
$375
-$124
-25%
7
0
$614
$614
$603
-$12
-2%
8*
27.5
$712
$308
-$63
-$371
-120%
9*
3
$319
$193
$164
-$29
-15%
10
0
$266
$266
$235
-$31
-12%
11
0
$273
$273
$242
-$31
-11%
* This participant has solar installed (refer to Figure 1)
** Only results for scenario 7 are shown here, where battery capacity is 30kWh/30kW
*** BAU bill compared with local electricity sharing scheme bill.
All participants experienced some savings on their overall bills as central battery capacity was
increased, but for non-PV participants this was negligible (< 1%). This is the expected result;
the benefits of battery export are shared among all participants, while battery import payments
only accrue to those with surplus rooftop PV. Subsequent simulations with a more equal share
of rooftop PV among all participants demonstrated that the savings as a result of a central
battery are more equally distributed.
Figure 12 shows the aggregate financial impact on each of the stakeholder groups (participants,
DNSP, central battery, retailer). The increased capacity of a central battery appeared to have a
negligible impact on the aggregate energy spend of the participants, especially compared to the
large initial saving that participants gained by entering into the local solar sharing arrangement,
or indeed installing PV. This is probably because PV exports and battery use are relatively
minor. The tariff settings applied in this case study result in benefits for retailers compared with
the BAU (with PV) scenario since they are no longer paying a tariff for exported PV and instead
are earning revenue on locally traded PV. In contrast, the DNSP is significantly worse off,
whilst the central battery earns minimal revenue (a maximum of $99 over the two month study
period when a 30kWh battery is installed). As discussed above, these outcomes are a function
of system sizing and operation and tariff design, which has not been optimised for this case
study.
Figure 12 Financial outcomes for stakeholder groups (with varying battery capacity)
4. Discussion and Conclusions
The Byron A&IE case study demonstrates that the benefits of a local electricity sharing scheme
for participants can be unevenly distributed. While financial outcomes of different tariff designs
were not explicitly tested in this study, under the simple tariff arrangements modelled, PV-
equipped participants were seen to benefit disproportionately from both local solar sales and
the presence of a central battery system, highlighting the need for consideration of the desired
social, community and fairness outcomes when designing local electricity sharing schemes. The
results also indicate that tariff design has significant implications for total DNSP and retailer
revenue, and should ideally reflect their costs and benefits. While Essential Energy would bear
the greatest revenue reduction, it does not appear to receive significant network benefits in the
form of reduced peak or network import/exports.
The installation of a 30kWh/30kW battery reduced total electricity import from the grid by only
1.6%, and reduced the total proportion of time which grid imports were required by 2.9%, with
approximately zero import reduction on the peak day of the simulation period. Overall, there
were minimal opportunities to use the battery given good PV-load match and low local PV
exports. This highlights the importance of PV-battery sizing and the battery dispatch algorithm
if the intention is to use the battery to provide network support services, which would be
required if the Australian Energy Regulator is to allow the battery to be included in Essential
Energy’s RAB as proposed. Testing higher PV penetrations and using more sophisticated
algorithms that target network benefits could enable the business case for a central battery to
be tested.
If adopted widely, local electricity sharing schemes featuring significant generation and battery
storage capacities may also have technical implications for network operators and the
Australian Energy Market Operator as demand and generation profiles change across regions.
Such schemes may present a useful tool for technical, economic and social integration of DERs,
which involve a number of key stakeholders, namely individual consumers, communities,
DNSPs and retailers. However, a number of regulatory challenges remain as local electricity
sharing schemes evolve, in particular ensuring adequate customer protections and retail
competition. Given these challenges, further work to understand the implications of such
schemes is required, and robust, verifiable and open source modelling of different local
electricity sharing scheme arrangements is likely to play an important role.
References
AEMC. (2017). Review of regulatory arrangements for embedded networks - Draft Report.
Bowyer, J., Bruce, A. and Passey, R. (2016) 'Regulatory and Retail Arrangements for
Community-owned Embedded Networks', Asia-Pacific Solar Research Conference
2016, ANU Canberra, Australia.
Roberts, M., Huxham, G., Bruce, A., & MacGill, I. (2016). Using PV to help meet Common
Property Energy Demand in Residential Apartment Buildings. Paper presented at the
2016 Australian Summer Study on Energy Productivity, Sydney.
‘Grid’ Icons made by https://www.flaticon.com/authors/google Google, from
https://www.flaticon.com/ Flaticon, is licensed by http://creativecommons.org/licenses/by/3.0/
Creative Commons BY 3.0
‘Battery’ Icons made by https://www.flaticon.com/authors/iconnice Iconnice from
https://www.flaticon.com/ Flaticon is licensed by http://creativecommons.org/licenses/by/3.0/
Creative Commons BY 3.0
Acknowledgements
The authors gratefully acknowledge A. Crook, S. Harris and V. Barrett of Enova Energy for
ongoing support and the provision of data, C. Waddell and R. Byatt of Essential Energy for the
provision of data, and N. Haghdadi of UNSW for the provision of data.
Appendix A: Model Electricity Flow Calculation Overview
Figure 13 Model electricity flow calculations
Appendix B: Retail and network tariffs applied in the case study to electricity imported
from the grid Table 4 Enova retail tariffs applied in case study
Tariff component
Units
LV Small Business Anytime
LV TOU <100MWh
Connection charge
($/day)
$1.65
$6.55
Block 1 charge
(c/kWh)
0.3113
-
Block 2 charge
(c/kWh)
0.278
-
Block 1 volume
(kWh/day)
54.7945
-
Peak charge
(c/kWh)
-
0.29
Shoulder charge
(c/kWh)
-
0.29
Off-peak charge
(c/kWh)
-
0.178
Peak 1
(hr)
-
07:00 – 09:00
Peak 2
(hr)
-
17:00 – 20:00
Shoulder 1
(hr)
-
09:00 – 17:00
Shoulder 2
(hr)
-
20:00 – 22:00
Off-peak
(hr)
-
22:00 – 07:00
Solar feed in tariff 1
(c/kWh)
0.10
Solar feed in tariff 2
(c/kWh)
0.06
Solar feed in tariff cut-off
(kWp)
10
Table 5 Essential Energy NUOS tariffs applied in case study
Tariff component
Units
Business Anytime
Business TOU
Connection charge
($/day)
$0.7789
$6.1644
Flat charge
(c/kWh)
0.13851
-
Peak charge
(c/kWh)
-
0.135409
Shoulder charge
(c/kWh)
-
0.123165
Off-peak charge
(c/kWh)
-
0.061697
Peak 1
(hr)
-
17:00 – 20:00
Shoulder 1
(hr)
-
07:00 – 17:00
Shoulder 2
(hr)
-
20:00 – 22:00
Off-peak
(hr)
-
22:00 – 07:00
Table 6 Local Electricity Sharing Scheme Tariffs
Electricity source
Tariff
component
Description
Case study
values
Local solar
Energy
Energy component for locally generated solar. Paid to the
participant who owns the solar generator.
12 c/kWh
Retail
Retail component for locally generated solar. Paid to the
retailer (or ‘embedded / local network service provider’) when
local solar is consumed.
6 c/kWh
NUOS
Network component for locally generated solar. Paid to the
party owning and operating the local network when local solar
is consumed.
6 c/kWh
Total
Total tariff paid by participants for consumed local solar.
24 c/kWh
Central battery
local solar import
Energy
Energy component for local solar used to charge the central
battery. Paid to the participant which owns the solar
generator.
12 c/kWh
Retail
Retail component for local solar used to charge the central
battery. Paid to the retailer (or ‘embedded / local network
service provider’).
6 c/kWh
NUOS
Network component for local solar used to charge the central
battery. Paid to the party owning and operating the local
network.
6 c/kWh
Total
Total charge paid by central battery operator for local solar
imported to charge the battery.
24 c/kWh
Central battery
export
Energy
Energy component for central battery export consumed by
participants locally. Paid to central battery operator.
12 c/kWh
Retail
Retail component for central battery export. Paid to the
retailer (or ‘embedded / local network service provider’).
6 c/kWh
DUOS (TUOS?)
Network component for central battery export. Paid to the
party owning and operating the local network.
6 c/kWh
Profit
Profit earned by central battery. Paid by participants
consuming battery export to central battery operator.
2 c/kWh
Total
Total charge paid by participants for central battery export.
26 c/kWh
