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Impact of shared battery energy storage systems on photovoltaic self- consumption and electricity bills in apartment buildings

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Distributed photovoltaics is playing a growing role in electricity industries around the world, while Battery Energy Storage Systems are falling in cost and starting to be deployed by energy consumers with photovoltaics. Apartment buildings offer an opportunity to apply central battery storage and shared solar generation to ag-gregated apartment and common loads through an embedded network or microgrid. We present a study of energy and financial flows in five Australian apartment buildings with photovoltaics and battery storage using real apartment interval-metered load profiles and simulated solar generation profiles, modelled using an open source tool developed for the purpose. Central batteries of 2-3 kWh per apartment can increase solar self-consumption by up to 19% and building self-sufficiency by up to 12%, and shave overall building peak demand by up to 30%. Although the economic case for battery storage applied to apartment building embedded networks is not compelling at current capital prices, with cost thresholds of AU$400-AU$750/kWh compared to AU$750-AU$1000/kWh for individual household systems, there are clear financial benefits to deployment of embedded networks with combined solar and battery storage systems for many sites.
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This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Impact of shared battery energy storage systems on photovoltaic self-
consumption and electricity bills in apartment buildings
Mike B Robertsa,b,1, Anna Brucea,b and Iain MacGilla,c
aSchool of Photovoltaic and Renewable Energy Engineering
bCentre for Energy & Environmental Markets
cSchool of Electrical Engineering and Telecommunications,
University of New South Wales, Sydney 2052, Australia
Abstract
Distributed photovoltaics (PV) is playing a growing role in electricity industries around the
world, while Battery Energy Storage Systems (BESS) are falling in cost and starting to be
deployed by energy consumers with PV. Apartment buildings offer an opportunity to apply
central BESS and shared PV generation to aggregated apartment and common loads through
an embedded network (EN) or microgrid. We present a study of energy and financial flows in
five Australian apartment buildings with PV and BESS using real apartment interval-metered
load profiles and simulated PV generation profiles, modelled using an open source tool
developed for the purpose. Central BESS of 2-3kWh per apartment can increase PV self-
consumption by up to 19% and building self-sufficiency by up to 12%, and shave overall
building peak demand by up to 30%. Although the economic case for BESS applied to
apartment building embedded networks is not compelling at current BESS capital prices, with
cost thresholds of AU$400 AU$750/kWh compared to AU$750 AU$1000/kWh for
individual household systems, there are clear financial benefits to combined PV-BESS-EN
systems for many sites.
Keywords
Photovoltaics, apartments, battery energy storage system, community energy storage,
residential electricity, embedded network.
1. Introduction
1.1. Self-consumption in apartment buildings
Global capacity of solar photovoltaics (PV) now exceeds 400GW [1] and it continues to play a
major role in the transition to a cleaner electricity sector, comprising over half of new
renewable generating capacity in 2017, a greater level of new capacity than net additions of
fossil-fuel and nuclear capacity combined. Unlike other electricity generation options, PV is
also inherently scalable from kW household systems to now GW scale utility projects. Indeed,
Abbreviations: AEMC, Australian Energy Market Commission; BAU, Business as Usual; BESS, Battery Energy
Storage System; BOM, Bureau of Meteorology; BTM, Behind the Meter; CP, Common Property; DOD, Depth of
Discharge; EN, Embedded Network; ENO, Embedded Network Operator; CREN, Community Renewable Energy
Network; CES, Community Energy Storage; DSM, Demand-Side Management; FiT, Feed-in Tariff; GST, Goods and
Services Tax; HVAC, Heating, ventilation and Air Conditioning; NEM, National Energy Market; NPV, Net Present
Value; NSW, New South Wales; PD, Peak Demand; PV, Photovoltaic; SC, Self-Consumption; SOC, State of Charge;
SS, Self-Sufficiency; SGSC, Smart Grid Smart City; TOU, Time of Use; VB, Virtual Building
1 Corresponding author. E-mail address: m.roberts@unsw.edu.au
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
distributed PV represents a significant proportion of the PV market in many jurisdictions.
While early rooftop PV deployment was generally driven by government-subsidised feed-in
tariffs (FiTs), these have now been discontinued in a growing number of jurisdictions, with a
shift to net metering arrangements where self-consumed PV generation saves consumers the
equivalent of their retail tariffs, while any excess ‘exported’ PV generation is paid a market-
based FiT. Falling costs for PV systems and rising retail tariffs have made rooftop PV
deployment an increasingly financially attractive for energy users.
In this context, increased self-consumption of rooftop PV has the potential to benefit energy
users (due to the typical disparity between the volumetric retail tariffs they pay for electricity
from the grid and the far lower FiTs they receive for exports) as well as help manage some of
the network challenges of high PV penetration including voltage rise due to reverse power
flows. Both Battery Energy Storage Systems (BESS) and Demand Side Management (DSM),
when deployed in conjunction with distributed PV, have the potential to significantly increase
self-consumption and there is growing interest in understanding the economic impacts of
these options.
Australia ranks fifth globally for total PV capacity per person [1], with the majority connected
to the low-voltage distribution network, including on 22% of all houses [2]. Moreover, falling
PV costs and high energy prices continue to drive record levels of installations. At present,
BESS deployment is far lower. However, market growth, facilitated by technological advances
and increased manufacturing scale and hence falling costs, is predicted both for BESS installed
in conjunction with new small scale PV and for BESS retrofitted to existing PV systems, globally
and in Australia [3]. The Australian residential market is seen as a key opportunity for both PV
and BESS due to the relatively straightforward regulatory environment and typically the
highest electricity prices paid by energy consumers. Although the anticipated dramatic
reductions in installed costs for BESS are yet to materialize [4], over 20,000 new Australian
household PV systems in 2017 (around 15% of new installations) included BESS [5] and in some
jurisdictions the price of solar plus storage is reported to be less than retail electricity prices
[6].
However, not all Australian households are currently participating in this revolutionary
opportunity to save on electricity bills whilst also reducing environmental impacts. In
particular, the growing opportunity for PV deployment on apartment buildings remains largely
unexploited, despite 60% of the country’s 1.4 million apartments (around 14% of total housing
stock) being in two or three story buildings [7] which are likely to have a high ratio of potential
rooftop solar generation to load [8]. Although this residential sector presents significant
barriers to PV deployment [9, 10], it also presents an opportunity to aggregate apartment
household loads and thereby flatten load profiles, increase self-consumption of on-site PV and
leverage favourable commercial tariffs for imported electricity [11, 12]. The addition of BESS
may further enhance these benefits by increasing overall self-consumption (SC, the proportion
of PV generation used to meet on-site consumption) of PV generation, and self-sufficiency (SS,
the proportion of total consumption supplied by onsite generation), reducing peak demand
(PD) and thereby increasing the value of PV deployment for apartment households.
1.2. Battery energy storage systems (BESS)
There is a wealth of literature examining the potential for behind-the-meter (BTM) BESS to
increase PV SC, maximise SS and create financial benefits for residential customers in stand-
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
alone housing. A review of this literature (predominantly from Europe) from 2015 [13]
suggests that BESS has the potential to increase SC of PV in a residential property by 13-24%
(which is more than the increase achievable through DSM), with the increase tending to grow
with the battery capacity normalised to rated PV power. The review also revealed large
variations in the SC increase between different climate zones, load profiles and initial SC, but
also suggested that SC calculated on hourly (or longer) interval meter data is likely to result in
an overestimate. The authors suggest the ratio of SC to SS is an important metric when
comparing different systems.
There is high variability in the absolute level of SC achievable with PV and storage, because of
its dependence on load profile and climate as well as on PV and BESS energy and power sizing,
but there is widespread agreement that 100% SC (other than for low capacity PV systems) is
not achievable without disproportionately high investment in BESS [14, 15], and that for any
system there is a maximum BESS capacity above which increases in SC are negligible [16].
Some researchers have identified an optimal size of BESS capacity for maximising financial
benefits of a residential PV system [17] or the cost optimal storage capacity normalised by
peak PV power [16]. Studies [18, 19] have shown that unsubsidised PV-BESS systems are rarely
cost-effective with existing tariffs and capital costs while others [14, 19-23] have gone on to
determine a threshold price for BESS that would make such systems viable, with results highly
variable across different contexts (from EUR150/kWh to EUR900/kWh).
Optimal BESS control strategy is application-specific with options including shifting PV
generation (to maximise SC) [18, 19, 24, 25], demand/load shifting, or PD shaving [26, 27].
Other applications, such as retail arbitrage or control of grid voltage and/or frequency [28, 29]
may also be available to customers. The value of appropriate combination and prioritisation
of these functions is dependent on ownership and financial settings [30, 31].
1.3. Shared / community PV and BESS
Aggregation of household loads can flatten profiles and increase self-consumption of PV,
while BESS, added behind the meter either in individual households or at the community level,
may further increase SC and hence energy consumer value [32-34]. A study of 21 detached
houses with PV [35] found that BESS of capacity 2kWh/kWp increased SC by 9% installed
individually or 14% if shared, while a US study [36] found that community energy storage (CES)
only required 65% as much capacity as individual BESS for the same benefits.
Parra et al [37] found that the benefits of CES, including increased round-trip efficiency due to
the lower charge and discharge rates needed to respond to flatter aggregated load profiles,
are sufficient to halve the optimum storage capacity compared to individual BESS, with
breakeven costs of US$360/kWh to US$430/kWh for different tariff settings. A Dutch study
[38] found both CES and individual BESS to be economically unfeasible for capex costs of
EUR1000/kWh. In the UK, Parra et al. [39-41] found that application of CES to shift both PV
generation and demand could reduce the levelized cost by 56% [40] or 37% [41] compared to
a single house BESS.
In Australia, Tomc et al. [15, 42] modelled different arrangements for storage on a residential
microgrid with PV and found a combination of individual PV and storage and communal
storage could reduce grid import by over 90% and reduce aggregated customer bills by 95.5%
(without consideration of capital or operating costs).
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
1.4. PV-BESS in apartment buildings
The studies discussed in Section 1.3 concern the application of shared and/or individual BESS
to a community of detached dwellings with individual PV systems. However, to our
knowledge, there has been little detailed techno-economic analysis of PV-BESS systems that
consider the particular technical and financial characteristics of multi-occupancy residential
buildings. Notwithstanding the considerable variability of apartment load profiles, some
studies suggest that apartments have specific characteristics that differ from those of stand-
alone dwellings, including lower total load, lower load per occupant and higher daily variability
[43-51]. There is also the presence of common property (CP) load for shared areas of
apartment buildings. Moreover, common ownership of roof space and economies of scale
may favour deployment of a single, large PV system applied to aggregated load instead of
multiple small PV systems supplying individual apartments, while installation of a shared BESS
on common property may be safer and more practical than installing individual BESS for each
apartment. These factors, combined with close proximity, shared building structure and
organisational arrangements allowing common ownership, may favour apartment buildings
as local energy communities with potential to exploit the broader socio-economic benefits of
CES, including reduced dependence on fossil fuels, reduced energy bills and higher social
cohesion and local economy[52].
In this paper, we present the results of a techno-economic study of PV-BESS deployment in
five Australian low to medium rise apartment buildings of different sizes and characteristics.
The study combines real apartment load profiles drawn from a study of over 2000 such
households, with those of CP from the five buildings, along with PV generation profiles based
on real-year satellite weather data. We explore a range of PV and BESS system capacities as
well as different possible battery dispatch strategies, comparing the technical and financial
impacts of BESS when applied to individual or aggregated apartment household loads with
onsite PV generation.
We have previously modelled the costs and benefits of applying shared PV to loads aggregated
across apartment buildings under a range of technical arrangements and financial settings
[11]. Here, we explore to what extent and under what conditions the addition of BESS to
apartment PV systems can increase self-consumption and self-sufficiency, shave PD and add
value for customers. A particular contribution of the work lies in the use of a large dataset of
12-month half-hourly apartment load profiles and the analysis of PV-BESS applied with
multiple control strategies to embedded network (EN)2 arrangements that are common in
multi-occupancy buildings.
The remainder of the paper is set out as follows: Section 2 gives a brief outline of some
technical options available for connecting PV-BESS systems in apartment buildings. Section 3
introduces the model, the data and the method used for the study, while Section 4 explains
the energy and financial metrics output by the model. In Section 5 we present an analysis of
the impact of PV on self-consumption, self-sufficiency and peak demand, and in Section 6 we
compare the financial costs and benefits of different scenarios. Finally, in Section 7, we
2 An embedded network (EN) is defined by the Australian Energy Market Commission (AEMC) as “a private
electricity network that serves multiple premises and is located within, or connected to, a distribution or
transmission system through a parent connection in the National Electricity Market (NEM)” [53]. ENs are also
called micro-grids but typically do not have the facility to disconnect from the distribution network.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
present some tentative conclusions and provide some suggestions for future investigations.
2. PV-BESS technical arrangements
In Australia, most apartments are organised under Strata Title, whereby a purchaser buys the
interior space of an apartment and a share of the building structure (including the roof) and
common property (CP) which are managed by a strata body (the Owners’ Corporation or Body
Corporate) on behalf of all owners [54, 55]. The strata body is also responsible for electricity
supply to cover CP loads, which may include lifts, carpark ventilation and lighting, water
heating and pumping for pools and centralised HVAC, water heating for apartments and
lighting for stairwells, corridors and other common areas. Beyond this, apartment residents
(whether owner-occupiers or tenants) are responsible for their own electricity supply
including choice of retailer and paying the bills.
The simplest and most common arrangement for deployment of PV is for the strata body to
install a PV system to meet CP load. For many (particularly high-rise) sites with high CP loads
and relatively low roof area, this arrangement achieves 100% SC for the building. However,
for buildings with low CP load, particularly the 60% of Australian apartments in buildings of
three storeys or leqss [7] with relatively high potential PV capacity, there may be additional
value in application of PV to apartment loads as well as CP. Individually owned PV systems can
be connected behind the meter (BTM) to meet individual unit and/or CP loads as shown in
Figure 1(a). Although this arrangement (btm_i) is technically straightforward, as for stand-
alone dwellings, SC levels are highly dependent on household characteristics, including
appliance ownership and occupancy patterns. SC may of course be increased by addition of a
BESS.
(a) Individual BTM, btm_i,
(b) Embedded Network, en
Figure 1: Possible technical arrangements for PV and BESS3
An alternative arrangement (Figure 1(b)) is to apply shared PV generation to aggregated
building load through an embedded network (EN). In this arrangement, an ‘Embedded
Network Operator’ (ENO) purchases electricity from the grid via a ‘parent’ or ‘gateway’ meter
and on-sells it (along with onsite PV generation) through a ‘child’ meter for each apartment.
This arrangement allows access to economies of scale in PV installation costs and to lower
commercial (rather than residential) tariffs for the grid-purchased electricity, providing a
margin for the ENO which may be shared with the strata body to help offset capital costs.
3 The BESS costs used in the modelling assumed AC coupled BESS, as shown, to allow for addition of BESS to an
existing PV system, although DC-coupled BESS using a hybrid inverter may be more common and could result in
a slight cost reduction.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Application of PV to aggregated building load increases SC (and thereby reduces costs for
residents), while further benefits may be achievable by the addition of a central BESS to the
EN. Although, in Australia as elsewhere, retrofitting an EN to existing residential buildings can
face regulatory and technical barriers [9, 56, 57], the arrangement is widespread for new
developments and may provide a viable mechanism for sharing PV-BESS assets.
3. Dataset and modelling methodology
This study utilises the Multi-Occupancy Residential Electricity with PV and Storage (morePVs)
tool to model electricity flows and financial transactions in apartment buildings with PV and
BESS deployed under a range of technical and financial settings. The Python code has been
made available open source [58] for transparency and a graphical user interface is being
developed to increase accessibility for all interested stakeholders and for application to other
datasets. The model is described in more detail in its on-line documentation [58]. The model
inputs used for this study, including load and generation profiles, network arrangements, BESS
technical and operational parameters, capital and operational costs and tariff settings, are
described below.
3.1. Virtual buildings: load and generation profiles
In common with our previous work [11], this study utilises 12 months of interval load data for
CP and apartments combined from two sources. The CP data, collected for apartment building
energy audits, is for five low- and medium-rise buildings at sites across Sydney with sufficient
roof area to meet a significant proportion of building load from rooftop PV deployment and,
since the period of data collection for the CP load did not match that for the apartment loads,
selected to exclude evident temperature-dependent common load (such as HVAC or
swimming pools).
The apartment load profiles were selected from a publicly available dataset of 2000 NSW
apartments, collected for the AusGrid Smart Grid Smart City (SGSC) trial undertaken over
201215. Details of the trial and the dataset may be found in the various SGSC reports [59-
62], while characteristics of the apartment load profiles, as well as the methodology used to
prepare complete 30 minute load profiles for 2013 from the dataset are described in
forthcoming articles [11, 51].
Because, for most of the apartments included in the SGSC dataset, only limited information is
available about building and household characteristics, we have adopted a stochastic
approach to the modelling. For each of the five sites, fifty ‘Virtual Buildings’ (VBs) were
created, each one combining the actual CP profile for the site with a randomly selected sample
of SGSC apartment load profiles, one for each apartment in the actual building. Table 1 shows
characteristics of the five sites and the total annual load and ‘CP Ratio’ (the ratio of CP load to
total load) averaged across the 50 VBs at each site.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Table 1 Virtual Building Site Characteristics: note that site tags summarise, in order, the number of
apartments, number of floors and percentage of load that is CP.
Site Tag
Apartments
Floors
Total Load
(MWh/year)
max_pv
(kWp)
max_PV
(kWp/
unit)
PV Ratio (%)
PV Systems Modelled
(kWp/unit)
a48_f4_cp09
48
4
190
52.5
1.09
36.5
0.5, 1.0, 1.09
a44_f4_cp17
44
4
180
76.8
1.74
54.7
0.5, 1.0, 1.5, 1.74
a52_f3_cp27
52
3
250
141.5
2.72
78.1
0.5, 1.0, 1.5, 2.0, 2.5, 2.72
a20_f5_cp37
20
5
110
31.5
1.58
41.5
0.5, 1.0, 1.58
a26_f4_cp44
26
4
160
78.5
3.02
67.5
0.5, 1.0, 1.5, 2.0, 2.5, 3.02
For each of the five sites, the maximum size (max_pv) for a potential rooftop PV system was
determined through a visual analysis of the roof area, using multi-viewpoint aerial imagery
[63] to take account of roof orientation, inclination, obstructions and shading. As shown in
Table 1, smaller systems (in steps of 500W / apartment) were designed by successively
excluding roof areas with the lowest insolation.
For each system, the energy generation was simulated for every half hour of the year 2013,
using NREL’s System Advisor Model (SAM) [64, 65] PV Watts module and Australian Bureau of
Meteorology (BOM) satellite-derived irradiance data [66] for each site, along with
temperature and wind speed from the nearest automatic weather station.
The average daily winter and summer PV generation profiles for each modelled system size,
and daily load profiles including CP for each VB (and the overall average for each site across
all 50 VBs) are shown in Figure 2.
a48_f4_cp09
a44_f4_cp17
a52_f3_cp26
a20_f5_cp36
a26_f4_cp43
summer
winter
Figure 2 Average load and generation profiles for 50 VBs at each site over summer and winter the dark
line is the average load profile across all VBs, while generation profiles are for all modelled PV sizes.
For embedded network (en) arrangements, the PV was treated as a single system connected
between the parent and child meters, with the PV energy netted off the aggregate building
load, while for individual behind the meter arrangements (btm_i), a percentage of the total
PV capacity equal to the CP Ratio was applied to the CP load, with the remaining capacity
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
allocated equally to separate systems applied to each individual apartment load.
3.2. Battery energy storage system
The BESS model assumed a Li-ion battery with similar technical characteristics to the Tesla
Powerwall2 [30], i.e. round trip efficiency of 89.0%, maximum state of charge (SOC) of 90%
and maximum depth of discharge (DOD) of 100%. A range of storage capacities were
modelled, corresponding to 1.0, 2.0, 3.0 and 4.0 kWh of storage for each unit in the building,
with maximum charge and discharge rate of 0.38C, equivalent to the 5kW / 13.2kWh
Powerwall.
For embedded network arrangements (en), a single BESS (with capacity Ccentral given by the
number of units (nunits) multiplied by nominal storage per unit) was connected centrally
(between the parent and child meters) and applied to the aggregated building load under a
range of control strategies. For individual arrangements (btm_i), smaller BESSs were applied
to common property (Ccp) and to each apartment (Cunit) with capacities given by Equations (1)
and (2).
C { ∗    | (1,2,3,4)}
Equation (1)
C { ∗ (1  ) | (1,2,3,4)}
Equation (2)
3.3. BESS control strategies
Potential customer benefits from BESS include increased SC of onsite PV generation, increased
SS, reduced PD, provision of emergency power during grid outages, and demand shifting to
off-peak periods. The control and discharge strategy for the BESS is chosen according to the
required apartment household benefits, which may depend on tariffs and other financial
settings as well as on the load and generation characteristics of the overall system. For this
study, a range of BESS control strategies, time periods and charge and discharge rates were
modelled, as shown in Table 2 and described below, in order to assess their technical and
financial benefits.
As discussed earlier, the clearest value proposition for BESS is to maximise SC of PV generation
hence ‘earning’ the retail tariff rather than the lower FiT on exported generation. However,
with some tariff arrangements there can be additional value in targeting particular time
periods or peak demand. The simplest approach to increasing self-consumption is to use a
simple evening discharge (ed) strategy, whereby the BESS is charged from excess PV
generation (after meeting coincident on-site demand) and discharged at the maximum
discharge rate to meet on-site demand during the evening peak period (here modelled as
starting at 16:30, 17:00 or 17:30 and ending at 20:00).
However, because on days of low insolation this approach is likely to result in inadequate
battery SOC to meet some evening peak loads and may result in under-utilization of the BESS,
a range of augmented evening discharge strategies have also been modelled. Adding charge-
priority to an evening discharge strategy (ch_ed) applies PV generation to BESS charging first,
with any excess used to meet on-site loads. A further step to maintain SOC is to use a single
cycle (sc) strategy whereby grid import is increased to charge the BESS during the night-time
off-peak period. A double cycle (dc) strategy maximises BESS utilisation by charging overnight,
discharging to meet morning load peak, charging from PV generation and/or network import
during the day and discharging in the evening peak period.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Table 2 BESS Control Strategies
Tag Description
Charging
Discharge
Source
Grid Charge
Period
3
Rate
Period4 Rate
ed1700_cmax_dmax Evening Discharge
Excess PV
only
None
0.38C
1700-2000 0.38C
ed1730_cmax_dmax Evening Discharge
Excess PV
only
None
0.38C
1730-2000 0.38C
ed1700_c20_d20 Evening Discharge
Excess PV
only
None
0.20C
1700-2000 0.20C
ed1630_c20_d20 Evening Discharge
Excess PV
only
None
0.20C
1630-2000 0.20C
ch_ed1700_cmax_dmax
Charge Priority
Evening Discharge
Gross PV
None
0.38C
1700-2000 0.38C
ch_ed1630_cmax_d20
Charge Priority
Evening Discharge
Gross PV
None
0.38C
1630-2000 0.20C
sc1700_ cmax_dmax
Single Cycle
PV / Grid
0100-0600
0.38C
1700-2000
0.38C
dc1700_cmax_dmax Double Cycle
PV / Grid
0100-0600
1130-1400
0.38C
0700-0900
1700-2000
0.38C
pdt_sc_xx
Peak Demand
Threshold
PV / Grid
0100-0600
0.38C
1400-2000 0.38C
(for xx = 35, 40….80%)
load >= xx% of annual
peak
Where customers are subject to a tariff which includes capacity charges, a single half-hour
period of high demand can have a disproportionate effect on energy bills. A peak demand
threshold strategy prioritises reduction of demand peaks, and only discharges the BESS when
grid imports would otherwise be above a threshold level, here set at a percentage of the
highest PD throughout the year.
Modelled charge and discharge rates are either at the technical maximum of the BESS (0.38C)
for strategies tagged cmax and dmax or at the lower rate of 0.2C (tagged c20 and d20) to
avoid premature draining of the battery.
3.4. Capital and operating costs
Average installed costs (after Federal government subsidies and Federal goods and services
tax (GST) of 10%) for residential and commercial PV installations in NSW of AU$1.01 to
AU$1.84 per Watt were used to calculate the capital costs of the PV systems. Inverter
replacement (assumed necessary every ten years) was included at between AU$0.31/Watt
and AU$1.10/Watt, depending on the size. Other operating costs, including replacement of
electrical balance-of-system components and occasional cleaning, are likely to be low in
comparison to decreases in inverter costs, and have therefore been omitted from this study.
Full details are given in Appendix A.
The costs of retrofitting an EN to an apartment building are highly variable and depend on
building characteristics, particularly the age of the existing electrical installation, as well as on
jurisdictional regulatory requirements. We have explored sensitivity of EN benefits to these
costs elsewhere [11] but for this study we have used a medium cost setting of AU$20,000 per
site (to cover gateway meter installation and switchboard upgrades) plus AU$400 per unit for
the child meter installations. EN opex has been estimated at AU$250 per customer, slightly
4 All charge and discharge periods are shifted by an hour in summer to align with daylight saving time.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
above the estimated operational costs (AU$230 [68]) of a NSW electricity retailer.
Globally, installed cost estimates for Lithium-ion BESS vary between US$200/kWh and
US$1260/kWh [3] (AU$265 AU$1670/kWh5), with average costs predicted by some to drop
to US$160 (AU$212) /kWh by 2025 [68]. In Australia, in part because of supply issues [69] and
controversy around installation standards [70], prices have remained buoyant [4], with
average installed battery costs of AU$930/kWh and average installed battery-plus-inverter
system costs of AU$1290/kWh [71]. Given these disparities, we have modelled a range of
values between AU$200/kWh and AUD$1000/kWh to explore sensitivity to future price
reductions. The BESS has been assumed to have a lifetime of 7300 cycles (as quoted for the
Tesla Powerwall2 [30]) so would not require replacement within the 20-year modelling period
given daily cycling.
The capital costs of installed infrastructure PV, BESS and EN have been amortized over a
20-year period with a discount rate of 6%.
3.5. Tariff structure and rates
Residential tariff arrangements in Australia’s National Electricity Market (NEM) are far from
simple, with hundreds of tariffs offered by some 70 retailers. For this study, we have modelled
‘typical’ arrangements, but note that financial outcomes for actual customers will depend on
their specific retail arrangements. For Business-as-Usual (BAU) and btm_i scenarios, all
customers have been assumed to be paying their retailer a typical market Time of Use (TOU)
tariff equivalent to a 15% discount6 applied to all fixed and volumetric components of a 2017
“standing offer” TOU tariff in the relevant network area [73]. For customers with individual PV
systems (btm_i), we have modelled a flat-rate solar feed-in tariff (FiT) of 8c/kWh, in line with
the state regulator’s ‘all time benchmark’ rate of 8-9c/kWh for 2018-19 [74]. Given recent
downward trends in FiT rates, we have also modelled a zero-rate FiT to explore how this
affects the value of BESS.
A key driver for ENs is that the size of an aggregated building load is likely to trigger access to
a commercial ‘large energy consumer’ tariff (comprising a regulated network component and
a market retail energy component) at the parent meter. These typically have volumetric rates
lower than residential tariffs which act to reduce the value of self-consumed PV, and less
disparity between peak and off-peak rates, which can reduce the value of BESS. However, they
often include a significant capacity charge component which can add value both to BESS and
to PV. For this study, we have used scenarios with high (‘TOU12’) and low (‘TOU9’) market
prices from early 2018, combined with a FiT of 8c/kWh at the parent meter and with no FiT.
More details of these parent tariffs are given in Appendix B.
4. Energy and Financial Metrics
4.1. Self-consumption and self-sufficiency
Luthander et al. [13] define SC and SS as the overlapping part of the generation and load
profiles calculated as a proportion of the total generation and total load, respectively, as
shown in equations (3) and (4), where L(t) and G(t) are the instantaneous load and generation
5 All costs in this paper are AU$ where AU$1.00 = US$ 0.7544 [67]
6 The total bill of a ‘representative customer’ on a ‘representative market offer’ in NSW in 2016 was equivalent
to a 15% discount off the standing offer tariff [72].
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
and S(t) is the power charging to (S(t) < 0) or discharging from (S(t) > 0) the BESS. This
formulation ensures the correct treatment of efficiency losses in the BESS charge-discharge
cycle.
SC ={(),()+()}
()
Equation (3)
SS ={(),()+()}
()
Equation (4)
If L(ti), G(ti) and S(ti) are, respectively, the energy used, generated and discharged from the
battery in discrete time periods ti, (determined by the temporal resolution of the relevant
meters), the measurable SC (i.e. the proportion of total annual on-site PV generation that is
usefully consumed within the building), and the measurable SS (the proportion of the total
annual building load that is met by on-site generation) are given by Equations (5) and (6).
SC ={(),()+ () }
17520
=1 ()
17520
=1 ×100%
Equation (5)
SS ={(),()+ () }
17520
=1 ()
17520
=1 ×100%
Equation (6)
Note that these measurable metrics are likely to be higher than the true SC and SS as ti
increases, because any non-simultaneous imports and exports within the time interval of
measurement (30 minutes) are treated as simultaneous [75].
4.2. Net present value of savings
To understand the effect of BESS on the value of PV, the Net Present Value (NPV) of annual
savings for the whole building, compared to business as usual (BAU) was calculated and
divided by the number of units in the building. Note that for this study, the modelling was
used to assess overall outcomes for the building, without consideration of the distribution of
financial benefits between residents, owners and the strata body. For transparency, and to
avoid dependence on arbitrary projections of future energy costs, only a single year of
operation was modelled with capital expenditure (for EN, PV, and BESS in en arrangements,
or for PV and BESS in btm_i arrangements) amortised at a discount rate of 6% over 20 years
to calculate monthly repayments.
As commonly used in the literature [76], the Net Present Value (NPV), of an initial investment
CO after time T is defined as the sum of cashflows Ft for each time period t and is given by
Equation (7) for a discount rate d.
 =
(1 + )
=1 0
Equation (7)
If Et,s and Ot,s are the electricity cost and operating cost in period t for scenario s and 0, is the
capital cost for that scenario, the NPV relative to the Business as Usual (BAU) scenario is given
by Equation (8).
= ,−,,
(1 + )
=1 0,
Equation (8)
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
5. Results: Energy Flows, SC and SS and PD Impacts of PV and BESS
5.1. Self-consumption and self-sufficiency
Figure 3 shows the SC of on-site generation and the building SS for shared PV generation and
a central battery applied to an EN (en) and for individual PV systems and batteries applied
BTM to each apartment load and CP load (btm_i), using a simple evening discharge control
strategy. The addition of BESS increases SC by 15% - 19% for en and 16% - 22% for btm_i,
depending on the site, but the marginal benefits are reduced for BESS sized at above
1kWh/unit and negligible above 2 - 3kWh/unit. Similarly, SS is increased by up to 12% for en
and 10% for btm_i.
Although application of the PV system to aggregated load rather than individual loads
increases SC and SS, the additional SC achievable from adding a central BESS to an EN with PV
is generally less than for adding individual BESS to BTM PV systems. However, for some sites
(notably a52_f3_cp26, a44_f4_cp17 and a20_f5_cp36) the central BESS increases SS by up to
3% more than the individual BESS.
Figure 3 Self-consumption and self-sufficiency averaged across 50VBs at each site for simple evening
discharge strategy ed1700_cmax_dmax
Figure 4 shows the variation of SC with rated capacity of shared PV systems applied to
aggregated building loads through an EN for a range of shared BESS capacities and control
strategies. The top two rows show simple evening discharge (ed) strategies with different
discharge periods and charge / discharge rates. In all cases starting the discharge period 30
minutes earlier results in an increase in SC, while reducing the charge and discharge rate from
0.38C to 0.2C decreases SC, although both effects are small. The optimum power and time
parameters for this strategy, and the achievable increases in SC and SS are dependent on the
characteristics of the load profile at each site.
When PV generation is applied to BESS charging in preference to on-site load (ch_ed), more
electricity is imported from the grid, particularly on days of low solar insolation, and so self-
consumption is considerably lower than for a simple evening discharge strategy. For PV
systems sized at 1kWp/unit or less, BESS operation with this strategy reduces self-
consumption below the level with no BESS, although increases in SC of up to 5% (compared to
no BESS) are achievable where excess generation from a 4kWp/unit PV system is available to
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
charge the BESS.
When a BESS is charged from the grid, energy losses in the charge-discharge cycle result in
grid imports greater than exports from BESS discharge and therefore reduced self-
consumption compared to no BESS. This is evident for the single cycle strategy (sc) as well as
for the double cycle (dc) strategy for smaller PV systems. However, with a dc strategy applied
where there is a larger PV system (above 1.0 - 1.5 kWp/unit, depending on the site), the BESS
is more often charged from onsite generation, grid import is reduced, and modest increases
in SC of up to 4% can be achieved.
Figure 4 Self-consumption of PV generation for shared BESS applied to EN with shared PV using
augmented evening discharge strategies
5.2. Battery state of charge
Analysis of the hourly and seasonal variations in BESS SOC can assist in optimising BESS
capacity and control strategy.
Figure 5(top) shows the SOC throughout the year and the average SOC for each hour of the
day for a BESS applied, using a simple evening discharge strategy, to an EN at each site with
the maximum charge and discharge rate of 0.38C. PV systems sized below 1.0 kWp/unit are
not shown as generation is largely absorbed by on-site load and the BESS is rarely charged.
The difference between the maximum and minimum of the average daily SOC gives an
indication of the suitability of the BESS capacity, while the density of the annual plot shows
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
the extent of BESS utilisation.
For those sites where a PV sized at 2.0 kWp/unit or above can be installed, a BESS of 1.0
kWh/unit is well utilised throughout the year, while a higher capacity BESS is rarely fully
discharged during the summer months and rarely fully charged during the winter. For smaller
PV systems, the BESS is often insufficiently charged in winter and under-utilised even in the
summer. Note however, that for some battery technologies, low utilisation levels can prolong
battery life and may therefore be advantageous.
The lower chart in Figure 5 shows the aggregate SOC for multiple individual BESS (with the
same total storage capacity) applied seperately to unit and CP loads with individual BTM PV
systems. Note the lower DOD in this arrangement as the energy stored at any time may be on
a different part of the network to the coincident load, and so grid import may be required to
meet demand during the evening discharge period even when SOC is non-zero.
central BESS: en
individual BESS: btm_i
Figure 5 Average daily SOC (red, top axis) and annual SOC (blue, bottom axis) for one VB at each site with
simple evening discharge strategy and charge / discharge rate of 0.38C
Figure 6(top) shows SOC for a central BESS applied to an EN at a single site, operated under
different control strategies. Note the higher levels of BESS utilisation and average SOC when
charging off-peak from the grid (sc or dc) or from PV (ch_ed) in preference to meeting onsite
load.
A BESS of 4kWh operated under a sc or dc strategy rarely reaches its maximum DOD, even in
winter, and therefore retains capacity to meet winter heating loads.
For individual systems, aggregate SOC, as shown in the lower part of Figure 6, is maintained
at a higher level, even though an individual BESS may reach maximum DOD.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
central BESS: en
individual BESS: btm_i
Figure 6 Average daily SOC (red, top axis) and annual SOC (blue, bottom axis) for one VB at one site
under charge priority, single cycle, double cycle and peak demand threshold BESS control strategies
5.3. Peak demand reduction
Table 3 shows the percentage reduction in PD achievable at each site under different BESS
control strategies, for a central BESS applied in en and btm_i arrangements. This is the
maximum reduction achieved in the average of the top ten demand peak peaks averaged
across 50 VBs, for all technical arrangements for each strategy at each site, and in most cases
corresponds to the maximum PV size and BESS capacity of 4.0 kWh/unit.
The variation of PD reductions with BESS capacity for different PV systems is shown in Figure
7. Many of the peaks in the load profiles are due to winter heating loads (Figure 2), while
battery SOC is low in winter (Figure 5) due to a combination of high load and low PV
generation. Therefore, a BESS operated with an evening discharge (ed) strategy has a low
impact on PD if applied to aggregated building load, because the BESS is not able to restore
its SOC between successive winter peaks. Individual BESS applied to individual customer loads,
however, have a greater impact on aggregated PD, as stored energy is applied directly to
individual peaks.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Table 3 Maximum achievable average peak demand reduction across 50 VBs for each site under all
modelled PV and BESS sizes and operational strategies.
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
en
en
btm_i
en
btm_i
en
btm_i
en
btm_i
ed1700_cmax_dmax
1.0
2.7
16.8
3.5
22.0
1.7
10.8
6.3
16.2
ed1700_c20_d20
1.3
4.6
18.3
8.4
23.2
2.7
11.5
9.1
16.6
ed1730_cmax_dmax
1.3
4.0
15.7
6.3
18.1
2.4
10.7
4.9
7.7
ed1630_c20_d20
1.1
3.7
16.5
6.3
22.2
2.0
10.6
7.7
18.4
ch_ed1630_cmax_d20
22.8
31.9
34.1
40.8
39.6
24.7
26.9
39.4
37.7
ch_ed1700_cmax_dmax
9.1
18.0
29.0
25.2
30.8
13.7
23.8
17.1
20.7
sc1700_cmax_dmax
33.5
32.7
32.3
31.2
31.3
26.3
27.1
19.1
20.9
dc1700_cmax_dmax
33.5
32.7
32.3
31.2
31.3
26.3
27.1
19.1
20.9
pdt_sc_60
33.0
32.4
11.3
33.0
20.3
29.4
10.4
23.7
12.7
pdt_sc_65
28.3
28.2
9.0
29.2
18.7
26.8
7.9
23.5
11.2
pdt_sc_70
23.3
23.5
7.3
24.7
17.2
22.8
5.9
21.7
8.6
pdt_sc_75
18.3
18.6
5.4
20.1
16.1
18.1
4.6
18.2
6.4
pdt_sc_80
13.7
13.8
4.0
15.4
14.9
13.7
3.3
13.9
4.9
Both for individual and centralised BESS, much greater reductions are achieved using a charge
priority (ch) strategy, particularly for larger BESS capacity, provided the PV system capacity is
sufficient to maintain the BESS SOC. For a centralised BESS, best results are obtained using a
lower discharge power, while oversizing the BESS compared to the PV system results in low
SOC and reduced capacity to manage peak loads.
For single cycle (sc) and double cycle (dc) strategies, peak reduction increases with BESS
capacity but is not greatly affected by PV capacity (as BESS is charged primarily from the grid).
For capacities of 3kWh/unit or less, greater peak reductions are achieved by applying BESS to
individual loads, while a BESS of 4kWh/unit approaches the maximum possible peak reduction
for this strategy, as the BESS never fully discharges and so has capacity to mitigate all peak
loads.
A peak demand threshold (pdt) strategy has a bigger impact on aggregated PD for a centralised
BESS than if applied to btm_i for most sites. For PV of 1kWp/unit, a threshold of 65% achieves
greatest PD reduction, while one of 80% achieves its maximum effect for a BESS of 1kWh/unit.
Figure 8 shows how the effectiveness of this strategy varies with the chosen threshold, here
described as a percentage. For a shared BESS sized at 1.0 - 2.0 kWh/unit and a shared PV
system of 1.0kWp/unit, a threshold of 65% - 75% can reduce PD by 15% - 22% but note that
the exact threshold is dependent on the characteristics of the building load profile, and choice
of threshold requires fore knowledge of the timing and extent of the annual peak load. For
threshold levels below the optimum, BESS SOC falls below the level necessary for successful
operation of this strategy, so effectiveness is reduced.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
ed1700_cmax_dmax
ch_ed1630_cmax_d20
sc1700_cmax_dmax
dc1700_cmax_dmax
pdt_sc_65
pdt_sc_80
Figure 7 Variation of average of top 10 demand peaks with PV system size for 50 VBs at each site for different
BESS capacities and dispatch strategies
With a larger BESS capacity and a threshold level reduced to 40%-50%, PD reductions of 30%
- 40% can be achieved on some sites. The reason for the sharp increase in achievable PD
reduction for a BESS of 4kWh is that SOC never falls to zero, even in winter, so there is always
energy stored to address winter heating peak loads, unlike, for example, the complete
discharge shown for BESS of 3kWh or less at site a44_f4_cp17 (shown in Figure 6). Note that
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
for en with BESS of 3-4kWh/unit, the achievable percentage PD reduction is lower for sites
with higher load (and higher CP ratio).
Figure 8 Average PD reduction for BESS peak demand threshold strategies
with PV system of 1.0kWp /unit applied to an EN or to individual loads
In contrast, when the same strategy is applied to the equivalent capacity BESS and PV as
individual systems for apartments and common property, aggregate SOC doesn’t reach zero
even for BESS capacity of only 1kWh, yet there may not be energy available to meet PD. With
this arrangement, dispatching energy at lower demand levels is more likely to address peak
loads and optimum thresholds are below 40% in most cases. However, it is important to note
that reducing PD is unlikely to translate into reduced electricity charges for individual on-
market customers as retail tariffs commonly consist only of fixed and volumetric (flat or TOU)
components.
6. Results & Discussion: Financial Outcomes with PV and BESS
Table 4 shows the maximum NPV of annual savings per customer achievable from adding a
central BESS, operated under any of the strategies described above, to an EN with PV. With
no FiT at the parent meter, the greatest savings are achieved on some sites from applying an
evening discharge strategy to maximise SC for high capacity PV systems, with optimum BESS
capacity of 1-2kWh. With an 8c/kWh FiT at the parent meter, however, the benefits of
increased SC are reduced, and greatest value is obtained from applying BESS of capacity
1kWh/unit to shaving PD using a peak demand threshold of 75%-80% of annual peak load for
an EN with PV of 0.5–1.7kWp/unit.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Table 4 Maximum average annual NPV of additional savings from adding BESS to en with PV, showing
optimum PV and BESS capacities, for BESS CAPEX of AU$200/kWh and parent tariff of TOU12. The
maximum achievable NPV for each site is highlighted. [$/unit/year (kWp,kWh)]
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
ed1700_cmax_dmax
No FiT
8.2 (1.1, 1.0)
16.3 (1.5, 1.0)
30.2 (2.5, 2.0)
13.6 (1.6, 1.0)
34.4 (3.0, 4.0)
8c FiT
0.0 (1.1, 0.0)
4.6 (1.7, 1.0)
6.1 (2.7, 1.0)
2.7 (1.6, 1.0)
12.4 (3.0, 2.0)
ed1730_cmax_dmax
No FiT
8.6 (1.1, 1.0)
17.5 (1.7, 1.0)
30.0 (2.7, 2.0)
13.8 (1.6, 1.0)
35.8 (3.0, 2.0)
8c FiT
0.0 (1.1, 1.0)
5.3 (1.7, 1.0)
7.7 (2.7, 1.0)
3.0 (1.6, 1.0)
10.7 (3.0, 2.0)
ed1700_c20_d20
No FiT
5.2 (1.1, 1.0)
13.0 (1.7, 2.0)
24.4 (2.7, 3.0)
9.0 (1.6, 2.0)
29.8 (3.0, 4.0)
8c FiT
0.0 (1.1, 0.0)
2.0 (1.7, 1.0)
5.6 (2.7, 1.0)
0.6 (1.6, 1.0)
8.1 (3.0, 1.0)
ed1630_c20_d20
No FiT
5.1 (1.1, 1.0)
13.9 (1.7, 2.0)
25.4 (2.7, 3.0)
9.6 (1.6, 2.0)
31.2 (3.0, 4.0)
8c FiT
0.0 (1.1, 0.0)
2.0 (1.7, 1.0)
4.4 (2.7, 1.0)
0.2 (1.6, 1.0)
7.6 (3.0, 1.0)
ch_ed1700_cmax_dmax
No FiT
0.6 (1.1, 1.0)
3.7 (1.5, 1.0)
9.1 (2.7, 1.0)
3.8 (1.6, 1.0)
11.5 (3.0, 1.0)
8c FiT
0.0 (1.1, 0.0)
1.3 (1.5, 1.0)
2.5 (2.7, 1.0)
2.0 (1.6, 1.0)
7.1 (3.0, 1.0)
sc1700_cmax_dmax
No FiT
5.5 (0.5, 3.0)
5.4 (0.5, 1.0)
3.4 (2.7, 4.0)
7.1 (0.5, 1.0)
8.6 (3.0, 1.0)
8c FiT
5.5 (0.5, 3.0)
5.4 (0.5, 1.0)
3.4 (2.7, 4.0)
7.1 (0.5, 1.0)
8.6 (3.0, 1.0)
dc1700_cmax_dmax
No FiT
9.3 (1.1, 3.0)
13.1 (1.7, 4.0)
14.1 (2.5, 4.0)
9.5 (1.6, 4.0)
13.1 (3.0, 4.0)
8c FiT
5.9 (1.1, 3.0)
7.8 (1.7, 4.0)
8.0 (2.5, 4.0)
4.2 (1.6, 4.0)
4.7 (3.0, 1.0)
pdt_sc_70
No FiT
9.8 (1.1, 1.0)
9.5 (1.5, 1.0)
0.6 (2.5, 1.0)
13.9 (1.6, 1.0)
10.1 (3.0, 1.0)
8c FiT
9.8 (1.1, 1.0)
9.5 (1.5, 1.0)
0.6 (2.5, 1.0)
13.9 (1.6, 1.0)
10.1 (3.0, 1.0)
pdt_sc_75
No FiT
12.1 (1.1, 1.0)
10.8 (1.7, 1.0)
6.7 (2.5, 1.0)
17.7 (1.0, 1.0)
13.0 (2.5, 1.0)
8c FiT
12.1 (1.1, 1.0)
10.8 (1.7, 1.0)
6.7 (2.5, 1.0)
17.7 (1.0, 1.0)
13.0 (2.5, 1.0)
pdt_sc_80
No FiT
10.5 (0.5, 1.0)
10.4 (0.5, 1.0)
8.4 (1.5, 1.0)
16.6 (0.5, 1.0)
15.2 (0.5, 1.0)
8c FiT
10.5 (0.5, 1.0)
10.4 (0.5, 1.0)
8.4 (1.5, 1.0)
16.6 (0.5, 1.0)
15.2 (0.5, 1.0)
Adding BESS to individual BTM PV systems (Table 5) can achieve greater savings, with optimum
BESS size of 3-4kWh/unit using either an evening discharge strategy to shift PV generation or
single- or double-cycle strategies to reduce consumption during peak periods to take
advantage of the greater difference between peak rates and FiT / off-peak rates7.
Table 5 Maximum average aggregated annual NPV of additional savings from adding individual BESS to PV
btm_i at each site, showing optimum PV and BESS capacities, for BESS CAPEX of AU$200/kWh. The maximum
achievable NPV for each site is highlighted. [$/unit/year (kWp, kWh)]
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
ed1700_cmax
_dmax
No FiT
38.3 (1.1, 2.0)
55.0 (1.7, 3.0)
75.0 (2.7, 3.0)
52.1 (1.6, 3.0)
102.4 (3.0, 4.0)
8c FiT
26.3 (1.1, 2.0)
38.4 (1.7, 2.0)
53.2 (2.7, 3.0)
36.8 (1.6, 2.0)
73.2 (3.0, 3.0)
ed1730_cmax
_dmax
No FiT
35.2 (1.1, 2.0)
49.1 (1.7, 3.0)
66.3 (2.5, 3.0)
48.2 (1.6, 2.0)
90.5 (3.0, 4.0)
8c FiT
23.8 (1.1, 2.0)
34.5 (1.7, 2.0)
46.4 (2.5, 3.0)
34.1 (1.6, 2.0)
65.2 (3.0, 3.0)
ed1700_c20_d20
No FiT
29.2 (1.1, 2.0)
42.5 (1.7, 3.0)
58.9 (2.5, 4.0)
41.8 (1.6, 3.0)
78.3 (3.0, 4.0)
8c FiT
19.0 (1.1, 2.0)
27.5 (1.7, 3.0)
39.8 (2.5, 3.0)
26.9 (1.6, 3.0)
53.7 (3.0, 4.0)
ed1630_c20_d20
No FiT
31.5 (1.1, 2.0)
46.5 (1.7, 3.0)
65.7 (2.5, 4.0)
44.9 (1.6, 3.0)
86.0 (3.0, 4.0)
8c FiT
20.9 (1.1, 2.0)
30.6 (1.7, 3.0)
44.5 (2.5, 3.0)
29.3 (1.6, 3.0)
59.8 (3.0, 4.0)
ch_ed1700_cmax
_dmax
No FiT
29.7 (1.1, 2.0)
35.7 (1.0, 2.0)
47.0 (2.7, 2.0)
39.5 (1.6, 3.0)
62.7 (3.0, 3.0)
8c FiT
23.9 (1.1, 2.0)
29.4 (1.0, 2.0)
37.4 (2.7, 2.0)
33.7 (1.6, 2.0)
51.6 (3.0, 3.0)
sc1700_cmax
_dmax
No FiT
39.7 (0.5, 3.0)
44.5 (0.5, 3.0)
50.8 (0.5, 3.0)
60.5 (0.5, 3.0)
82.1 (0.5, 3.0)
8c FiT
39.6 (0.5, 3.0)
44.5 (0.5, 3.0)
50.8 (0.5, 3.0)
60.5 (0.5, 3.0)
82.1 (0.5, 3.0)
dc1700_cmax
_dmax
No FiT
42.9 (1.1, 3.0)
50.6 (1.0, 3.0)
56.4 (1.5, 3.0)
61.6 (1.6, 3.0)
84.6 (2.5, 4.0)
8c FiT
38.5 (1.1, 3.0)
46.0 (1.0, 3.0)
50.6 (1.5, 3.0)
56.1 (1.6, 3.0)
76.2 (2.5, 4.0)
7 The retail peak rate modelled is 51.3c compared to off-peak 14.2c and FiT of 8c per kWh, while the commercial
peak rate is 20.5c compared to 13.2c and FiT of 8c (see Section 3.5 and Appendix B)
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Despite the relatively slight benefits of adding a centralised BESS, the combined savings from
installing PV-BESS-EN compared to the base case with no EN are significant, as shown in Table
6 for different parent tariffs. Note that a BESS size of 1kWh/unit is optimum in most cases and
that PD shaving is the optimal strategy for all sites if a FiT is available at the parent meter,
while in the absence of a FiT, the relative benefits of shaving PD and maximising self-
consumption are site-dependent.
Table 6 Maximum average annual NPV of savings from combined EN-PV-BESS, showing optimum PV and
BESS capacities and dispatch strategy, for BESS CAPEX of AU$200/kWh. [$/unit/year (kWp, kWh)]
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
TOU9
No FiT
103 (0.5, 1.0)
117 (0.5, 1.0)
183 (1.0, 1.0)
23 (1.0, 1.0)
146 (1.0, 1.0)
pdt_sc_75
pdt_sc_75
pdt_sc_80
pdt_sc_75
pdt_sc_80
8c FiT
116 (1.1, 1.0)
129 (1.5, 1.0)
198 (2.0, 1.0)
34 (1.6, 1.0)
174 (3.0, 1.0)
pdt_sc_75
pdt_sc_75
pdt_sc_80
pdt_sc_75
pdt_sc_80
TOU12
No FiT
53 (1.1, 1.0)
60 (1.0, 1.0)
123 (1.0, 1.0)
-48 (1.0, 1.0)
72 (2.0, 2.0)
pdt_sc_75
ed1730_cmax_dm
ax
pdt_sc_80 pdt_sc_75
ed1700_cmax_dm
ax
8c FiT
69 (1.1, 1.0)
82 (1.7, 1.0)
145 (2.0, 1.0)
-30 (1.6, 1.0)
107 (3.0, 1.0)
pdt_sc_75
pdt_sc_75
pdt_sc_80
pdt_sc_75
pdt_sc_80
Table 7 shows the maximum NPV and optimum system capacities and strategies for individual
PV-BESS connected btm_i. Note that (except for site a20_f5_cp37) the combined savings are
significantly less than can be achieved from EN-PV-BESS systems with a low (TOU9) parent
tariff. However, for a higher parent tariff (TOU12), the advantage of EN is less clear and, for
some sites, individual BTM PV-BESS systems are preferable.
Table 7 Maximum average aggregated annual NPV of savings from combined PV-BESS connected btm_i,
showing optimum PV and BESS capacities and dispatch strategy, for BESS CAPEX of AU$200/kWh.
[$/unit/year (kWp, kWh)]
Site
No FiT
8c FiT
a48_f4_cp09
47 (0.5, 3.0)
sc1700_cmax_dmax
56 (0.5, 3.0)
sc1700_cmax_dmax
a44_f4_cp17
66 (0.5, 3.0)
sc1700_cmax_dmax
75 (0.5, 3.0)
sc1700_cmax_dmax
a52_f3_cp27
78 (0.5, 3.0)
sc1700_cmax_dmax
86 (0.5, 3.0)
sc1700_cmax_dmax
a20_f5_cp37
89 (0.5, 3.0)
sc1700_cmax_dmax
105 (1.6, 3.0)
dc1700_cmax_dmax
a26_f4_cp44
114 (1.0, 3.0)
sc1700_cmax_dmax
151 (2.0, 4.0)
dc1700_cmax_dmax
Figure 9 shows the variation with system size of NPV of annual savings from a combined PV-
BESS-EN installation with a parent tariff of TOU12 in the absence of a FiT. For all these
scenarios, except for site a26_f4_cp44, NPV is maximised for PV sized at between 1kWp and
1.5kWp/unit and BESS at 1.0kWh/unit. Note the significant variability across 50 VBs at each
site, highlighting the dependence of BESS and PV value on the shape of aggregated load
profiles.
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
a48_f4_cp09
a44_f4_cp17
a52_f3_cp27
a20_f5_cp37
a26_f4_cp44
Figure 9 NPV of annual savings from combined EN-PV-BESS with optimum control strategy at each site
for BESS CAPEX of AU$200/kWh, parent tariff TOU12 and no FiT
If the ENO can access a lower TOU9 tariff at the parent meter, the maximum average NPV is
increased by AU$45 - AU$83/unit/year, for optimal PV capacity of 0.51.0kWp, while a FiT of
8c/kWh at the parent meter increases the optimal PV system size to 2.0kWp for site
a52_f3_cp27 and to max_pv for the remaining sites, increasing the average NPV by AU$18 -
AU$45 for BESS of 1kWh/unit.
Value added by BESS ($ / unit / year)
Total System Value ($ / unit / year)
Figure 10 NPV of (top) annual BESS savings and (bottom) annual total system savings for
(left) en and (right) btm_i with kWp, kWh and discharge strategy at each site optimised to maximise NPV, as
shown in Tables 4 - 7
While the foregoing results are based on a projected future BESS capex of $200/kWh, for four
of the sites, the median NPV of additional savings from BESS applied to en remains positive
for BESS CAPEX costs up to AU$400/kWh (Figure 10(a)) but is negative for costs more aligned
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
with current Australian market rates (Section 3.4). By contrast, for btm_i arrangements, NPV
is positive for all sites with BESS capex up to $750/kWh or AU$1000/kWh (Figure 10(b)).
Nevertheless, except for the smallest site (a20_f5_cp36) where the assumed costs of EN
installation exceed any likely benefits, the combined savings from EN-PV-BESS have a positive
NPV even for installed BESS capex of AU$1000/kWh (Figure 10(c)) and, on average, exceed
those from PV-BESS connected BTM in the absence of a FiT (Figure 10(d)).
7. Conclusions
Notwithstanding the complexity of interaction between load profile, PV variability, battery
energy capacity, charge / discharge power and control strategy, our findings demonstrate that
addition of a central BESS to an EN with PV can increase SC and SS and reduce PD. In the
scenarios modelled, application of PV-BESS to aggregated building load achieves higher SC and
SS than application to individual loads, but the impact of adding BESS to an existing PV system
is similar in both cases.
The economic benefits of retrofitting EN-PV-BESS to a brownfield site are evident for all but
the smallest site, while benefits for a greenfield site (with substantially lower marginal capex
costs) will be greater still. Nevertheless, the economic case for BESS in either EN scenario is
not compelling and would require a much lower threshold capex cost than for BESS applied to
individual dwellings, due to the lower volumetric rate and lower TOU disparity of commercial
retail tariffs. ASs electricity prices continue to rise, it would be useful to extend the study to
include a range of higher rates that may be more aligned with future commercial tariffs, and
which may result in a higher threshold BESS capex cost. However, with current (and short-
term future) tariffs and BESS costs, thermal energy storage using hot water systems [77] or
space heating [78] may be a more financially attractive method of storing PV generation in
multi-occupancy residential buildings, and comprehensive techno-economic modelling of this
option would also be a valuable topic for future study.
Although our results suggest that BESS dispatch strategies targeting PD reduction may be the
most appropriate for central BESS in an EN, optimal dispatch strategy design is highly site-
specific and greater benefits may be achievable by combining different strategies with
forecasting of load and generation. Also note that the scale of central BESS compared with
individual household systems may make it better placed to access markets for ancillary power
system services which could improve the economic case. However, as with community energy
generation, it is also important not to overlook the potential broader, non-economic benefits
of CES at household, community and societal levels [52].
Because of the complexity of factors affecting financial outcomes from PV-BESS deployment,
including sensitivity to the size and temporal distribution of building electricity load, building-
specific modelling is necessary to accurately assess the opportunities for a particular site. In
this context, open source tools, such as the one used for this study, may be a helpful resource
to assist stakeholders in decision making, as well as allowing other researchers to compare
outcomes with alternative jurisdictional tariff arrangements.
Acknowledgements
The authors gratefully acknowledge support provided for this research by a grant from Energy
Consumers Australia, a studentship from the CRC for Low Carbon Living and an Australian
Government Research Training Program scholarship. We wish to thank Gareth Huxham of
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
EnergySmart Strata for the provision of the common property load data used in the study.
Declarations of interest: none.
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This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
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This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Appendix A: Capital Cost Data for PV and BESS
Table 8 shows installed costs for residential and commercial PV systems in New South Wales
[79] averaged over 6 months to May 2018 to smooth sudden price fluctuations. These costs
include Federal government subsidies and federal goods and services tax (GST) of 10%
Although PV panels have expected lifetimes exceeding 25 years [80] and 25-year warrantees
are now common, inverter replacement is likely during the lifetime of the system and
constitutes the largest ongoing cost for a PV system [80]. In line with other studies [80, 81],
and given the availability of ten-year warrantees from some manufacturers [82, 83], a lifetime
of ten years for inverters has been assumed, with replacement inverter costs based on median
wholesale prices [84] in each range plus estimated installation costs, and a typical retail margin
of 24% plus GST. It should be noted that although inverter lifetimes significantly greater than
ten years are unlikely to be cost effective in the near future, inverter costs are likely to
continue to fall as manufacturing volumes increase [85].
Table 8 Installed CAPEX costs for PV systems and replacement inverters
PV System Size (kWp)
Installed System Cost ($ / Watt)
Inverter Replacement
Min
Max
Before Subsidy
With Subsidy
($ / Watt)
0
1.5
2.49
1.84
1.1
1.5
2
2.35
1.7
1.1
2
3
2.21
1.56
0.95
3
4
1.9
1.25
0.8
4
5
1.75
1.1
0.83
5
7
1.66
1.01
0.65
7
10
1.73
1.08
0.65
10
20
1.85
1.2
0.65
20
30
1.83
1.18
0.42
30
50
1.81
1.16
0.42
50
70
1.77
1.12
0.31
70
100
1.75
1.1
0.31
100
1.73
1.08
0.31
This is a preprint. The published version of the article Mike B. Roberts, Anna Bruce, Iain MacGill, Impact of shared battery energy storage
systems on photovoltaic self-consumption and electricity bills in apartment buildings, Applied Energy, Volume 245, 2019, Pages 78-95, ISSN
0306-2619 is available at https://doi.org/10.1016/j.apenergy.2019.04.001.
Appendix B: Commercial Tariffs at the Parent Meter
The tariff paid by the ENO at the parent meter would comprise a regulated network
component and a market retail energy component. In the relevant network area of the study,
the network component for a low voltage connection would be EA305 or EA310, depending
on annual load. These network tariffs have a relatively high ratio of fixed and capacity to
volumetric charges, as shown in Table 9, with the daily capacity charge based on the
customer’s peak load in the preceding 12-month period.
The energy and retail component, determined by negotiation with the retailer and therefore
subject to a high degree of variability and to a lack of transparency, is likely to be significantly
lower than the estimated 14.63 c/kWh paid by a representative NSW retail customer [86] in
2017/18, and to include a TOU component. We have used a range of high (‘TOU12’) and low
(‘TOU9’) market prices from early 2018 plus environmental charges of 1.71 c/kWh and GST.
Although avoided transmission use of service costs may be paid for embedded generation
where network benefit is demonstrated [88], it is unusual, though possible, for commercial
customers to receive a FiT applied to PV export. However, for this study, two scenarios were
tested: a FiT of 8c/kWh at the parent meter (in line with the state regulator’s 2018-19 ‘all time
benchmark’ rate for retail FiTs [74]) and no payment for exported generation.
Table 9 Commercial tariffs payable at the parent meter
Component Name
Annual
Energy Use
(MWh)
Fixed
Charge
(c / day)
Peak
Rate
(c / kWh)
Shoulder
Rate
(c / kWh)
Off-peak
Rate
(c / kWh)
Capacity
Charge
(c/kVA/day)
Network
(ex GST) [87]
EA305
160-750 MWh
1905.85
4.95
2.27
1.26
35.74
EA310
> 750 MWh
2403.13
4.40
2.11
1.39
35.74
Retail / Energy
(ex GST)
TOU9
9.00
9.00
6.50
TOU12
12.00
12.00
9.00
Environmental Charges
1.71
1.71
1.71
Combined tariff
(inc GST)
EA305_TOU9
2096.435
17.226
14.278
10.417
39.314
EA305_TOU12
2096.435
20.526
17.578
13.167
39.314
EA310_wTOU9
2643.443
16.621
14.102
10.56
39.314
EA310_TOU12
2643.443
19.921
17.402
13.31
39.314
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