# Degrees of displacement: The impact of household PV battery prosumage on
utility generation and storage

Kelvin Say<EMAIL_ADDRESS>Wolf-Peter Schill<EMAIL_ADDRESS>Michele John<EMAIL_ADDRESS>Sustainable Engineering Group, Curtin
University, Kent Street, Bentley 6102, Australia  Energy Transition Hub.
University of Melbourne, Grattan Street, Parkville 3010, Australia.  German
Institute for Economic Research (DIW Berlin), Mohrenstraße 58, 10117 Berlin,
Germany

###### Abstract

Reductions in the cost of PV and batteries encourage households to invest in
PV battery prosumage. We explore the implications for the rest of the power
sector by applying two open-source techno-economic models to scenarios in
Western Australia for the year 2030. Household PV capacity generally
substitutes utility PV, but slightly less so as additional household batteries
are installed. Wind power is less affected, especially in scenarios with
higher shares of renewables. With household batteries operating to maximise
self-consumption, utility battery capacities are hardly substituted. Wholesale
prices to supply households, including those not engaging in prosumage,
slightly decrease, while prices for other consumers slightly increase. We
conclude that the growth of prosumage has implications on the various elements
of the power sector and should be more thoroughly considered by investors,
regulators, and power sector planners.

###### keywords:

Distributed energy sources, Photovoltaics, Battery energy storage, Prosumage,
Open-source modelling

##  1 Introduction

To mitigate the effects of climate change it is necessary to take advantage of
renewable energy sources and decarbonise energy use [de Coninck et al., 2018].
Continued investments in research and development as well as the massive
deployment of renewable energy technologies has reduced the Levelised Costs of
Energy of PV and wind power in many regions to or below those of conventional
fossil fuel generation [BNEF, 2019, IRENA, 2019b, REN21, 2019]. These ongoing
cost reductions have not only changed how utilities generate their
electricity, but have also opened new opportunities for electricity customers
[Stephan et al., 2016]. In combination with favourable regulatory settings, it
has become increasingly attractive for households to install their own PV
systems in many countries. This not only allows households to reduce their
electricity bills but also decarbonises their energy consumption [Agnew and
Dargusch, 2015].

A similar transition is occurring in the lithium-ion battery sector with
global manufacturing capacity expanding to supply the expected growth in the
battery electric vehicle market [Curry, 2017]. This is driving significant
cost reductions, which are expected to continue decreasing at a lower rate
over the next 20 years [IRENA, 2017, Lombrana, 2019, Schmidt et al., 2017].
These battery cost reductions have led to a growing number of utility and
domestic-scale battery installations in electricity markets worldwide [EIA,
2019, European Union, 2018, IRENA, 2019a]. By storing excess PV generation for
later use, PV-battery systems enable households to increase their overall
share of self-generation. This concept, referred to as prosumage [Bustos et
al., 2019, Green and Staffell, 2017, Schill et al., 2017], can also
significantly reshape grid consumption and retailer revenues [Say et al.,
2019].

This paper aims to quantitatively explore the influence that residential PV
systems with or without batteries could have on the power sector, in
particular on utility-scale generation and storage technologies. We do so by
applying two soft-coupled open-source models to 2030 scenarios in the South
West Interconnected System (SWIS) located in Western Australia. This serves as
a particularly suitable case study, as household PV penetration rates here are
amongst the highest in the world [APVI, 2019] and household PV-battery
installations are also beginning to rise [AEC, 2019]. As it is an island
network,111Island networks face more challenges in matching variable renewable
energy supply with demand compared to interconnected networks, as they lack
the ability of balancing over larger regions. the power sector effects from
prosumage become evident earlier than in larger and interconnected networks.
Firstly, we use a techno-economic simulation model of household prosumagers,
Electroscape [Say et al., 2019], in which a set of heterogeneous households
are driven by economic self-interest to invest in additional PV and battery
capacity while retail price conditions change under different Feed-in Tariff
(FiT) values. By using these households as representatives for the segment of
customers investing in prosumage, we quantify the changes to the ‘residual
network demand’, also known as ‘operational demand’ [AEMO, 2019c] or ‘net
load’ [O’Dwyer and Flynn, 2015]. This serves as an input for a dispatch and
investment model, which determines cost-minimal utility-scale generation and
storage capacity while meeting different exogenous renewable energy targets.

This paper is structured as follows: Section 2 presents a background and
literature review. Section 3 introduces the underlying methodology and
modelling framework. Section 4 describes the input data. Section 5 presents
the results and wider implications on the power sector. Section 6 discusses
some key assumptions of the study and their qualitative impacts on results.
Section 7 discusses and concludes with policy implications and avenues for
future research.

##  2 Background and literature review

Australia currently leads the world in household PV adoption and the
substantial existing PV capacity situated behind-the-meter raises the
potential for an accelerated PV-battery transition, especially as the
financial benefits from PV-battery systems begin to outweigh PV-only systems.
While PV-only and PV-battery systems are both considered as behind-the-meter
Distributed Energy Resources (DER), their grid utilisation and economic
drivers considerably differ and require more detailed analysis. The term
prosumage, in this paper, covers both household PV-only and PV-battery
adoption.222We slightly expand the narrower definition of prosumage used in
Schill et al. [2017] to avoid lengthy verbal differentiations when describing
results for PV-only and PV-battery cases.

###  2.1 Australia as a front-runner in household PV and battery adoption

High levels of solar insolation, relatively high volumetric retail tariffs
[AEMC, 2019], and residential FiT policies333The FiT is only applied to the
amount of excess solar PV energy generated after subtracting the customer’s
underlying electricity demand. FiTs in Australia are typically valued well
below volumetric retail tariffs [AEC, 2019]. FiT payments are funded by
electricity retailers and revised annually (as opposed to fixed-term
contracts) [Poruschi et al., 2018]. have resulted in over 2 million Australian
households (or 20% of all free-standing households) installing solar PV
systems [APVI, 2019]. As of the end of 2018, combined household PV capacity
($7~{}GW_{P}$) accounted for 62% of the nation’s installed solar PV capacity
[APVI, 2019]. As discounted payback periods approach or fall under 5 years in
most Australian capital cities [AEC, 2019], household PV installations are
expected to continue rising [Graham et al., 2018b]. These household PV
systems, that reside behind the meter and are neither centrally monitored nor
controlled, are no longer insignificant and have begun to reshape residual
network demand and system operation [AEMO, 2019b, e, d]. The inability to
control DER systems behind-the-meter [AEMO, 2019b] also effectively grants
household generation the highest dispatch priority on the network, followed by
zero-marginal cost and non-dispatchable utility PV and wind, then conventional
baseload and peaking generation. With regards to battery energy storage, both
utility [Aurecon, 2018, IRENA, 2019a, Neoen, 2017] and household [SunWiz,
2018] installations have begun to rise in Australia, as costs decrease with
increased global production [Schmidt et al., 2017].

The SWIS network in Western Australia has a typical peak demand of $4.4~{}GW$
and $18~{}TWh$ of annual operational consumption. Residential PV capacity
continues to rise and at times in 2019 has been able to supply approximately
45% of the underlying network demand [AEMO, 2019d]. With a household PV
penetration rate above 27%, it has one of the highest rates in the world [AEC,
2019, APVI, 2019]. Its cumulative installed household PV capacity at the end
of 2019 was $800~{}MW_{P}$, and is forecasted to reach $2~{}GW_{P}$ in 2030
[AEMO, 2019f]. Since 2014, household battery adoption has increased year-on-
year and is expected to continue growing at an accelerated rate [AEC, 2019,
AEMO, 2019f]. The SWIS network is islanded and lacks the means to balance load
with neighbouring networks. As installed capacities grow, prosumage households
in the future could have considerable influence on the appropriate mix of
utility-scale generation and storage technologies.

###  2.2 Studies of PV-only and PV-battery households

With household PV-only systems, the timing of all self-generation is tied to
the sun without the ability to store and buffer energy. This means that
changes in residual grid consumption begin to coincide with other households,
thus driving observable grid demand patterns such as the ’duck curve’ [Denholm
et al., 2015, Maticka, 2019]. The use of energy storage, changes the level of
coincidence by making excess PV generation available for later use and
increasing the system’s sensitivity to the type of economic incentives and
differences in household demand. This not only changes the overall residual
grid consumption, but also the effectiveness of existing FiT policies to guide
PV battery adoption [Günther et al., 2019, Say et al., 2019].

By adopting battery storage, households become technically capable of
providing further services to the rest of the power sector. Since the supply
and demand of energy must always be in balance, spare household battery
capacity could be used as a form of dispatchable load or generation to provide
quantifiable system benefits [Lawder et al., 2014, von Appen and Braun, 2018].
However the use of time-invariant volumetric residential tariffs remains
common in many regions, including Australia [Downer, 2016, Nelson et al.,
2018], Europe [Jargstorf et al., 2015], UK [Nicolson et al., 2017], and China
[Lin and Jiang, 2012]. The time-invariant nature of these tariffs does not
give households an incentive to consider wholesale market price signals when
operating their PV battery systems, thus leaving increased PV self-consumption
as the largest financial incentive for households to invest in PV battery
capacity [Rocky Mountain Institute, 2015].

At the household scale, techno-economic models and electricity bill savings
are commonly used to determine the appropriate sizing of household PV-battery
systems. Using project finance metrics, such as Net Present Value (NPV) [Say
et al., 2019, Schopfer et al., 2018, Talent and Du, 2018], Internal Rate of
Return [Parra and Patel, 2016] and Discounted Payback Periods [Akter et al.,
2017], optimal system capacities can be calculated. At the utility scale,
techno-economic models are commonly used for long term energy planning and
renewable energy integration. Using numerical optimisation, many different
objectives can be evaluated, such as least-cost utility-scale renewable energy
portfolios [Jeppesen et al., 2016], coordinating renewable generation, network
and storage expansion [Haller et al., 2012], through to establishing optimal
utility-scale energy storage capacities [Schill and Zerrahn, 2018]. The
objectives of these household and utility models differ, with households
aiming to reduce electricity bills, and techno-economic models aiming to
reduce the overall cost supplying energy. Hence, modelling frameworks that
combine these perspectives are rare in the literature.

###  2.3 Main contributions of this paper

This paper addresses this gap by using two open-source models to link
household PV battery investment decisions and optimal utility-scale generation
and storage decisions from a social planner perspective. A counterfactual
comparison is used to provide quantitative insights into the range of utility-
scale system impacts from household prosumage, including generation and
storage capacities, their dispatch and wholesale price impacts.

The Western Australian (WA) case study investigated in this paper allows the
derivation of relatively undistorted insights into the effects of prosumage,
as WA has a liberalised electricity market and islanded network. Given the
real-world conditions that are currently driving Western Australia’s
significant household PV and growing battery adoption, these scenario analyses
provide a front-runner case of what other markets could expect in the future.
Moreover, the development and provision of the two open-source models also
contribute to the literature by providing transparency and reproducibility for
subsequent research.

##  3 Methods

###  3.1 General setup

Figure 1: Developed methodology that integrates household prosumage and
utility-scale investment and dispatch decisions. Starred parameters are varied
in sensitivity analyses.

We soft-link two open-source techno-economic models to represent the differing
objectives between PV battery investing prosumage households and central
planner investments in utility-scale generation and storage capacity (Figure
1). The first model Electroscape reflects the financial objectives of
prosumage households as retail conditions change over time. These households
consider investing annually in PV and battery systems, given exogenous
assumptions on retail price conditions, installed system costs and three FiT
scenarios (0%, 25%, 50% of volumetric usage charges) between 2019 and 2030.
The second model DIETER-WA adopts a central planner perspective for the
overall power sector, i.e., it determines least-cost utility-scale investment
and dispatch decisions over a range of locally available technologies in 2030.
DIETER-WA uses outcomes from Electroscape and its three FiT scenarios to also
assess the additional impact of varying the Renewable Energy Source (RES)
share between 39%, 49%, and 59% of gross electricity demand, where 49% is a
linear interpolation between Australia’s 2020 renewable energy target of 23.5%
[Commonwealth of Australia, 2019] and an assumed 100% target for 2050.444Based
on Australia’s commitment to the COP21 Paris Agreement [Commonwealth of
Australia, 2016]. By comparing each of these results against reference
counterfactual scenarios (without household PV battery investments) the
effects of household prosumage on the overall system are quantified and
separated.

Both models are soft-linked through an hourly time series of the residual
network demand profile nad household prosumage investments. Electroscape is
solved annually for each of the FiT scenarios between 2019 and 2030 to
determine the resulting net grid utilisation profile in the year 2030 for each
individual prosumage household. These profiles are normalised and scaled to an
assumed number of 500,000 prosumage households, which builds on the
independent system operator’s PV installation estimates for 2030 [AEMO,
2019f]. By subtracting the net grid utilisation changes from prosumage
households from actual SWIS network demand data [AEMO, 2019h], we determine
the overall impact of household PV and battery investments on the residual
network demand. To isolate the effect of prosumage household investments, all
other customers of the SWIS electricity network are assumed to consume the
same amount of energy each year without investing in self-generation or energy
efficiency.

###  3.2 Household PV battery investment modelling (Electroscape)

To determine PV battery investment decisions for residential households,
Electroscape [Say et al., 2019] uses the time-series of underlying household
demand and insolation profiles with projections of retail tariffs, FiTs and PV
battery installation costs and takes into consideration previous PV battery
investments. By evaluating households individually and with real-world energy
meter data, the model avoids biases that can be introduced when using
aggregated or synthesised data [Quoilin et al., 2016, Schopfer et al., 2018].
This model was first introduced in Say et al. [2018] and subsequently used to
evaluate the relationship between household PV battery investments and future
electricity retailer revenues [Say et al., 2019]. The model is implemented in
R with its source code and data available under permissive open-source license
[Say, 2020].

Assuming that prosumage households are economically rational, the model
simulates the installation of each and every PV and battery combination (using
a step size of $0.5~{}kW_{P}$ and $1~{}kWh$ respectively) on a household’s
underlying demand to determine the net grid utilisation and annual electricity
bills. By comparing against a ‘no installation’ case, the expected savings in
electricity bills are calculated and form the basis of a discounted cashflow
for each and every combination. This model assumes a fixed investment horizon
and uses Net Present Value (NPV) to compare each PV battery configuration as
competing investment opportunities. The configuration with the highest NPV
becomes a prime candidate for installation, pending a real options valuation
[Reuter et al., 2012] based on Discounted Payback Periods (DPPs),555The DPP is
used to publicly track potential PV system financial performance in Australia
[AEC, 2019] which is mirrored in this model. the model determines if
sufficient financial returns can be realised to warrant making an actual
investment. If an investment is made, the underlying household demand is
updated, and subsequent PV battery investments must now consider these newly
installed systems. Repeating this process annually allows Electroscape to
simulate sequential and lumpy PV battery investment behaviour that reacts to
changing retail conditions, namely retail tariffs, FiTs and PV battery
installation costs. The model provides the means to model PV battery
investment choices catered to the energy use and solar resources of individual
households. By applying Electroscape across a set of real and heterogeneous
household data, the normalised results are able to provide an approximate of
the net grid utilisation from an average prosumage household.

###  3.3 Power sector dispatch and investment modelling (DIETER-WA)

To investigate the power sector effects of increased prosumage, we devise the
open-source model DIETER-WA. It represents a simplified and adjusted version
of the dispatch and investment model DIETER, which has been first introduced
by Zerrahn and Schill [2017] and Schill and Zerrahn [2018]. The model has a
long-run equilibrium perspective and minimises the total cost of utility-scale
electricity generation for all subsequent hours of a whole year. Its results
may be interpreted from a central planner perspective, or as an outcome of a
frictionless market with perfect competition. The model assumes perfect
foresight and is solved for all consecutive hours of an entire year. It is
implemented in the General Algebraic Modelling System (GAMS). Source code and
input data are available under a permissive license [Schill, 2020].

The model’s objective function covers operational costs which consist of fuel
and other variable costs, as well as annualised investment costs of all
utility-scale generation and storage technologies. An energy balance ensures
that electricity supply satisfies demand in each hour. Generation technologies
comprise both dispatchable thermal and variable renewable generators. The
model is also capable of representing various energy storage technologies and
their respective intertemporal restrictions. In the model version used here,
we ensure that a specified share of yearly gross electricity demand is met by
renewable energy sources, including household PV installations.

Model inputs comprise specific fixed and variable costs of all technologies,
hourly renewables availability factors, as well as the residual network demand
profile, which considers the net grid utilisation profiles of prosumage
households determined by Electroscape. Prosumage PV and battery investments
also enter as exogenous inputs. Endogenous variables include investments in
utility-scale generation and storage technologies and their hourly use.
Further model outputs comprise the total cost of providing electricity and the
shadow prices of the energy balance equation, which we interpret as wholesale
prices.

##  4 Input data

###  4.1 Input data for Electroscape

The main input parameters and data used in Electroscape are summarised in
Table 1.

Input parameter | Unit | Values | Source
---|---|---|---
Scenario forecast period | years | 12 | Own assumption
Simulation time step | minutes | 30 | Own assumption
Financial horizon | years | 10 | Own assumption
DPP evaluation criteria | years | 5 | Own assumption
Initial PV evaluation range | $kW_{P}$ | 0-10 | Own assumption
Initial battery evaluation range | $kWh$ | 0-18 | Own assumption
Battery energy-to-power ratio | ratio | 2.5 | Own assumption
Initial FiT rebate | $AUD/kWh$ | 0-14.5 | Own assumption
Initial volumetric usage charges | $AUD/kWh$ | 0.29 | Infinite Energy [2019]
Yearly change in tariff charges/rebates | % | 4 | ABS [2020]
FiT eligibility limit | $kW_{P}$ | 5 | Synergy [2019]
Yearly discount rate | % | 5 | RBA [2019]
Initial installed PV system cost (residential) | $AUD/kW_{P}$ | 1292 | Solar Choice [2019b]
Initial installed residential battery system cost | $AUD/kWh$ | 1172 | Solar Choice [2019a]
PV cost reduction curves | $AUD/kW_{P}$ | Time series | Graham et al. [2018a]
Battery cost reduction curves | $AUD/kWh$ | Time series | [Schmidt et al., 2017]
Number of unique household profiles | household | 261 | [Ausgrid, 2018]
Underlying household demand profile (per household) | $Wh$ | Time series | [Ausgrid, 2018]
Household available insolation profile (per household) | $Wh$ | Time series | [Ausgrid, 2018]
Table 1: Input parameters and data used in Electroscape

One year of real utility energy meter measurements of half-hourly resolution
‘underlying household demand’ and ‘insolation’ profiles are used to establish
an average representative prosumage household within the SWIS network. This
data was collected from 300 households in Sydney, Australia between $1^{st}$
July 2012 and $31^{st}$ June 2013 [Ausgrid, 2018, Ratnam et al., 2017] and has
been used in other Australian electricity market studies [Ellabban and Alassi,
2019, Babacan et al., 2017, Young et al., 2019, Konstantinou et al., 2016].
Due to similar latitudes and climate conditions, the average annual
consumption and PV generation profiles in Sydney remain consistent with those
of Perth, Australia (which is the primary source of residential demand in the
SWIS network). After removing households with missing time series data, 261
households remain for analysis. Data from Sydney households was necessary as
strict privacy laws prevent SWIS household data from being publicly available.

The battery model is based on lithium-ion residential systems designed for PV
applications, similar to those sold by
Tesla,666https://www.tesla.com/en_AU/powerwall LG
Chem,777https://www.lgenergy.com.au/products/battery and
Sonnen,888https://sonnen.com.au/sonnenbatterie/ with a round-trip efficiency
of 92% and 70% storage capacity remaining at the end of a 10-year operational
lifespan. A fixed energy-to-power ratio of $2.5$ is used based on the average
of these residential battery systems. Battery systems cost reduction curves
are derived from Schmidt et al. [2017] and have been scaled with a factor of
0.73 to fit local price conditions [Solar Choice, 2019a].

The PV generation model assumes a 25-year operational lifespan with 80%
generation capacity remaining. We assume a financial investment horizon of 10
years, reflecting expectations that homeowners typically require profitability
before moving to another residence.999$10.5$ years is the typical duration
that a home is owned before being sold [CoreLogic, 2015]. We further assume
that households extend their home mortgage to access financial capital and a
discount rate of 5% is used, consistent with the average standard variable
home mortgage interest rate over the last 5 years [RBA, 2019]. PV cost
reduction curves are derived from Graham et al. [2018a] and have been scaled
with a factor of $0.78$ to fit local price conditions [Solar Choice, 2019b].

Corresponding to 2019-20 SWIS retail tariffs, volumetric usage charges begin
at $0.29~{}AUD/kWh$ [Infinite Energy, 2019] and increase at 4% per annum,
based on the average annual growth rate of Australian electricity prices over
the previous 10 years [ABS, 2020]. The real options evaluation requires that
at least one investment opportunity has a Discounted Payback Period of under 5
years for an investment to be made.

The value of the FiT plays a significant role in incentivising various
configurations of household PV battery systems,101010Generally higher FiTs
accelerate the adoption of PV but delay the cost-effective tipping point of
PV-battery systems. While lower FiTs initially reduce PV adoption, it also
brings forward the tipping point for PV-battery systems that simultaneously
drive further growth in additional PV capacity [Say et al., 2019]. thus three
FiT scenarios are evaluated using time-invariant FiTs valued at 0%, 25% and
50% of volumetric usage charges (i.e., it only applies to the quantity of
excess PV generation exported to the network). This range is consistent with
Australian retail FiTs in 2019 [Solar Choice, 2019d]. As is standard practice
to maintain hosting capacity on the SWIS network [Synergy, 2019], a
$5~{}kW_{P}$ FiT eligibility limit is used, such that PV systems above
$5~{}kW_{P}$ lose all excess PV generation payments.111111On other Australian
networks, special approval is typically required to connect PV inverters
greater than $5~{}kW$ to the grid [Solar Choice, 2019c].

###  4.2 Input data for DIETER-WA

The residual network demand profile used in DIETER-WA is derived from SWIS
network demand data provided by AEMO [2019h] combined with the scaled net grid
utilization of prosumage households as determined by Electroscape. Historical
time series of hourly wind power availability in the SWIS are provided by AEMO
[2019g]. To ensure utility-scale PV generation remains temporally consistent
with household PV generation, the utility PV availability profile equals the
average PV generation across each of the 261 households.

As for conventional utility-scale generation technologies, we include coal-
and natural gas-fired plants, i.e., combined cycle gas turbines (CCGT) and
open cycle gas turbines (OCGT), as well as bioenergy, onshore wind power, and
utility PV. We further allow for investments in utility-scale batteries and
hydrogen storage.121212Under the parameterisation used here, we find that
bioenergy and ’power-to-gas-to power’ hydrogen storage are never part of the
least-cost portfolio. We accordingly do not report on these technologies in
the following. Key techno-economic input parameters for these technologies are
summarised in Table 2.131313The complete input data is available in the open-
source spreadsheet provided with the model. We ensure that both utility-scale
and household PV and battery storage technologies utilise the same relative
cost reduction curves mentioned in section 4.1. We also include a lower bound
for wind power and utility PV investments corresponding to the capacity
already in place [AEMO, 2018].

Input parameter | Unit | Hard coal | CCGT | OCGT | Bioenergy | Wind power | PV | Li-ion storage | Hydrogen storage |  Source
---|---|---|---|---|---|---|---|---|---|---
Overnight investment costs | AUD/MW | 3,195,000 | 1,254,000 | 877,000 | 12,432,000 | 1,874,000 | 817,000 | 115848 | 2384615 |  Graham et al. [2018a], Schmidt et al. [2017], Pape et al. [2014]
Overnight investment costs (energy) | AUD/MWh | - | - | - | - | - | - | 173773 | 308 |  Schmidt et al. [2017], Pape et al. [2014]
Annual fixed cost | AUD/MW | 53200 | 10500 | 4200 | 131600 | 36000 | 14400 | 2027 | 16694 |  Graham et al. [2018a], own assumptions
Variable OM costs | AUD/MWh | 4.2 | 7.4 | 10.5 | 8.4 | 2.7 | 0 | 0.5 | 0.5 |  Graham et al. [2018a], own assumptions
Thermal efficiency or roundtrip efficiency | % | 40 | 48 | 31 | 23 | - | - | 92 | 41.9 |  Graham et al. [2018a], Pape et al. [2014]
Fuel costs |  AUD/MWhth | 12.06 | 31.68 | 31.68 | 4.5 | 0 | 0 |  |  |  Graham et al. [2018a]
Technical Lifetime | years | 25 | 25 | 25 | 25 | 25 | 25 | 15 | 22.5 |  Graham et al. [2018a], Pape et al. [2014]
Lower bound for investment | MW | 0 | 0 | 0 | 0 | 419 | 202 | 0 | 0 |  AEMO [2018]
Table 2: Input parameters and data used in DIETER-WA

##  5 Results

###  5.1 Changes in residual network demand from investments in PV battery
prosumage

Investments by prosumage households in PV and battery capacity are heavily
influenced by the value of the FiT (Figure 2). Higher FiTs provide greater
returns for excess PV exports, encouraging larger PV systems (up to the
$5~{}kW_{P}$ FiT eligibility limit), while lowering returns for self-
consumption and discouraging the use of battery energy storage. As a result,
the scenario with a FiT equalling 50% of volumetric usage charges drives all
261 households by 2030 to invest in $5~{}kW_{P}$ PV systems with no battery
storage. As the value of the FiT lowers, the value of self-consumption
increases and the cost-effectiveness of battery storage is improved. In the
scenario with a 25% FiT, the increased value of self-consumption results in
7.3% of households (with above average electricity consumption) foregoing
their FiT revenue and installing PV systems above the $5~{}kW_{P}$ FiT
eligibility limit. This raises the average PV capacity per household to
$5.3~{}kW_{P}$ with $5.9~{}kWh$ of accompanying battery storage. In the
scenario without a FiT (or 0% FiT), the lack of financial incentive to export
excess PV generation discourages excessively large PV systems while maximising
the value of household self-consumption. This results in households investing
in slightly smaller PV systems but with even larger battery capacities. The
overall average PV capacity per household is $4.7~{}kW_{P}$ with $8.7~{}kWh$
of accompanying battery storage.141414Qualitatively similar results have been
found for other countries, e.g. for Germany [Günther et al., 2019].

Figure 2: Individual and average installed PV and battery capacities for each
FiT scenario in the year 2030. (a) FiT valued at 50% of volumetric usage
charges, if $\leq 5~{}kW_{P}$. (b) FiT $\leq 5~{}kW_{P}$. (c) Without any FiT.

Each of the FiT scenarios (0%, 25%, 50% of volumetric usage charges) results
in different average configurations of PV battery systems. To assist with
readability, these three FiT scenarios will be respectively referred to as the
‘PVB+ FiT0’, ‘PVB FiT25’ and ‘PV-only FiT50’ scenarios.

The installed PV battery systems affect residual network demand by removing a
household’s load from the network (during self-consumption) and acting as a
negative load (during excess PV exports). In the reference case without
household PV battery investments, the annual residual network demand is
$18.1~{}TWh$. Normalising the 261 households evaluated in Electroscape to a
single representative household and then scaling to $500,000$ households leads
to the following reductions in annual residual network demand. In the ‘PV-only
FiT50’ scenario, with an average of $5~{}kW_{P}$ of PV and no batteries, the
annual residual network demand is reduced to $15.1~{}TWh$ (or $-16.7$%). In
the ‘PVB FiT25’ and ‘PVB+ FiT0’ scenarios, the annual residual network demand
is respectively reduced to $14.7~{}TWh$ ($-17.9$%), and $15.2~{}TWh$
($-15.6$%). Since household PV generation is either self-consumed, exported,
or time shifted (minus round-trip efficiency losses), these annual residual
network demand reductions are predominantly driven by installed household PV
capacity.

While annual residual network demand does not significantly differ between the
FiT scenarios, their influence becomes much more evident at the diurnal scale
(Figure 3). Generally, the minimum residual network demand each day begins to
occur increasingly over midday due to the timing of excess PV generation.
Potential reductions in the early evening peak depend upon the presence of a
battery system. In the ‘PV-only FiT50’ scenario, the peak residual network
demand is delayed until sunset. In absolute terms, the diurnal peak demand can
only be reduced slightly, and only during the summer months with long daylight
hours. In the ‘PVB FiT25’ and ‘PVB+ FiT0’ scenarios, the household battery
systems (that operate only to maximise self-consumption) are able to reduce
peak residual network demand more strongly, and for a longer period of time.
As it uses the PV generation stored during the day, larger battery systems
(for a similar PV capacity) lead to a greater reduction of midday PV exports,
thus reducing the down ramp of demand between the morning and midday, and the
up ramp between midday and the early evening (comparing ‘PVB+ FiT0’ and ‘PVB
FiT25’ in Figure 3 with ‘PV-only FiT50’).

Figure 3: Influence of the FiT scenarios on the SWIS residual network demand
for 500,000 prosumage households across a week. (a) Week of the summer
solstice (17 to 23 December). (b) Week of the winter solstice (18 to 24 June).

###  5.2 Impacts on optimal utility-scale generation and storage capacity

In the reference ‘39% RES share’ scenario (i.e., without prosumage household
investments), $1.16~{}GW_{P}$ of utility PV and $1.61~{}GW$ of wind power are
optimal, along with relatively small utility battery storage of $0.21~{}GW$
and $0.62~{}GWh$ (Figure 4, upper left panel). As the RES share rises to 59%,
utility PV and wind capacity increases to $1.95~{}GW_{P}$ and $2.45~{}GW$
respectively, while conventional generation capacity is reduced. The capacity
of utility batteries raises disproportionately to $0.62~{}GW$ and
$1.72~{}GWh$.

Figure 4: Installed power and storage energy capacity for varying FiT and RES
shares (500,000 households) and the change in capacity with respect to the
equivalent reference (i.e., without prosumage household investments) RES share
scenario.

In the scenarios with prosumage, however, household PV capacity generally
substitutes for utility-scale renewable energy generation capacity. The nature
of this substitution depends upon the FiT values that incentivise different
types of household PV-only or PV-battery investments that subsequently impact
the timing of excess PV exports and the required contribution of utility-scale
generation to the RES share.

In the ‘PV-only FiT50’ scenario and across each RES share, utility PV
experiences the largest reduction in capacity. Here, the cumulative PV
capacity of PV-only prosumage households ($2.50~{}GW_{P}$) causes utility PV
capacity to drop to the assumed lower bound of $202~{}MW_{P}$. In the 39% RES
share scenario, each $MW_{p}$ of household PV substitutes for $0.38~{}MW_{P}$
of utility PV and $0.20~{}MW$ of wind capacity, respectively. By generating at
similar times, household PV capacity generally discourages additional utility
PV capacity. As the RES share rises, relatively more utility PV and less wind
power are substituted, as the respective utility-scale PV capacity in the
reference is also larger. In the 59% RES share scenario, each $MW_{p}$
household PV accordingly substitutes for $0.70~{}MW_{P}$ of utility PV and
only $0.08~{}MW$ of wind power. The significant installed household PV
capacity and absence of installed household battery systems in ‘PV-only FiT50’
also causes an increase of optimal utility battery power and energy storage
capacity. This is because the increase in overall PV capacity and the
corresponding decrease in wind power leads to larger diurnal variations
between the midday and early evening residual network demand (compare Figure
3). This effect is particularly strong in the 39% RES share scenario, which
has the highest PV capacity share, with $0.12~{}MW$ and $0.55~{}MWh$ of
additional utility battery capacity per $MW_{p}$ of household PV capacity.
Conventional generation capacity hardly changes, except for a slight decrease
in gas-fired generation capacity that corresponds to the increase in utility
battery capacity.

In both the ‘PVB FiT25’ and ‘PVB+ FiT0’ scenarios, most effects are
qualitatively similar. The substitution of utility-scale PV is slightly less
pronounced because household battery systems partially balance the daily
export of excess PV generation from household PV installations. Accordingly,
this also slightly reduces the optimal amount of wind capacity. The only
qualitative difference relates to utility batteries. Prosumage now slightly
decreases the installed utility battery power capacity. Utility battery energy
storage capacity, in contrast, remains constant or even increases. Overall,
the substitution of utility batteries by household batteries is very
incomplete, due to their operational focus on maximising self-consumption
rather than wholesale energy arbitrage. Across RES shares, $1~{}MW$ of
prosumage battery power capacity only substitutes for $0.02~{}MW$ to
$0.17~{}MW$ of utility battery capacity; and $1~{}MWh$ of prosumage battery
energy capacity substitutes for at most $0.09~{}MWh$ utility battery capacity
(‘59% RES share’ & ‘PVB+ FiT0’ scenario), but may also trigger an increase of
up to $0.10~{}MWh$ (‘49% RES share’ & ‘PVB FiT25’ scenario). Conventional
generation capacity again hardly changes, aside from a minor substitution
between CCGT and OCGT capacity.

###  5.3 Impacts on optimal yearly utility-scale generation and storage

Figure 5: Yearly generation for varying FiT and RES shares (500,000
households) and the change in generation with respect to the equivalent
reference (i.e., without prosumage household investments) RES share scenario.

As the RES share rises in the reference scenario (upper left panel of Figure
5), wind power becomes an increasingly important resource in terms of yearly
energy provided (30% contribution at a 39% RES share) and eventually begins to
dominate the generation mix (45% contribution at a 59% RES share). The
contribution of utility PV also slightly rises (9% to 14% between 39% and 59%
RES shares). Coal generation has the greatest reduction (51% to 29%) while
CCGT increases its share slightly, and OCGT generation remains generally
unaffected.

In the scenarios with prosumage, wind generation generally experiences a
larger overall reduction in terms of yearly generation when compared to the
capacity effects described above, as wind power’s higher full load hours mean
that capacity reductions have a larger energy impact. Raising the RES share
again leads to a lower substitution of wind generation and a higher
substitution of utility PV generation, slightly tempered with increasing
deployment of household batteries (columns two, three and four of Figure 5).

Overall power generation from coal increases slightly in the cases with
prosumage (Figure 6). This is most pronounced in the ‘PVB FiT25’ and ‘PVB+
FiT50’ scenarios, where household batteries are also deployed. The increase in
coal-fired power generation, combined with a corresponding decrease of natural
gas-fired generation, also causes CO2 emissions to slightly
increase.151515Additional model runs show that this finding disappears if the
binding RES share constraint is relaxed.

Figure 6: Change in power generation from coal and natural gas for varying FiT
and RES shares and effects on CO2 emissions per $kWh$ compared to reference
scenario.

Although the coal-enhancing effect of prosumage is small, an exploration of
its drivers raises complementary insights. To do so, we look at Residual Load
Duration Curves (RLDCs) of the reference scenario and the ‘PVB FiT25’ scenario
with a 49% RES share (Figure 7).161616For earlier and more detailed
applications of residual load duration curves in the context of renewable
energy integration and energy storage, see Ueckerdt et al. [2015] and Zerrahn
et al. [2018]. The blue curves show the residual load that remains to be
served by utility-scale dispatchable generators and utility storage after the
feed-in of all variable renewables. Here, the dashed blue line for ‘PVB FiT25’
considers the net grid interaction of prosumage households, i.e., it takes not
only household PV generation into account, but also the smoothing effect of
behind-the-meter batteries. Comparing the two blue curves shows that PV-
battery prosumage leads to an overall flatter residual load. While this is
generally beneficial from a power sector perspective, it also allows coal-
fired generators that have the lowest variable costs of non-renewable
generators in our case study to slightly increase their production.

Figure 7: Total load and residual load duration curves for the reference
scenario and ‘PVB FiT25’ for a RES share of 49%.

The changes in the residual load curve are driven (i) by an increasing overall
solar PV capacity, and (ii) by the smoothing effect of household batteries.
The dashed orange line shows the RLDC if only the utility-scale PV generation
is taken into account in the reference scenario. The dashed red line then
shows a counterfactual where this PV capacity grows to the size of prosumage
household PV in the ‘PVB FiT25’ scenario, but assuming that it would feed into
the grid as utility-scale PV. That is, we counterfactually abstract from the
smoothing effect of household batteries. The difference between these two
curves thus shows the influence of having more PV in the power sector,
triggered by prosumage. The solid orange curve then illustrates the smoothing
effect of household batteries, i.e. a decrease in residual load on the left-
hand side, and an increase on the right-hand side.

Figure 7 further shows a slightly increasing renewable surplus generation in
the prosumage scenario on the very right-hand side. This is a consequence of
the sub-optimal (i.e., oversized compared to the reference) overall PV
capacity. It also explains why the optimal utility-scale storage energy
capacity does not decrease further, or even increases, as this capacity is
used for shifting surplus energy to other hours. Utility storage power, in
turn, does not decrease further because it is still required for contributing
to peak residual load coverage. Note that the RLDC hardly changes on the very
left-hand side, i.e., the peak residual load stays high. We find that the
utility-scale battery power is optimized such that it exactly covers the
difference between the peak residual load and other dispatchable generators.

###  5.4 Impacts on wholesale prices and system costs

The use of household PV-battery systems not only benefits prosumage households
(by significantly reducing the total amount of grid-imported energy) but also
reshapes the overall residual network demand. This affects wholesale
electricity prices and has implications for the cost of supplying electricity
to different types of customers that have their own specific grid utilisation
profiles. In cost minimisation modelling, the shadow price of a model’s energy
balance is often interpreted as a wholesale electricity market price, which is
for example used for market value analyses of renewable energy
sources.171717See Brown and Reichenberg [2020] for a recent discussion on
this. We use this approach to calculate the weighted yearly average wholesale
market prices for three customer types, using their respective grid
utilisation profiles: (i) 500,000 prosumage households, (ii) 500,000 non-
prosumage households,181818As the Western Australian SWIS network has
approximately 1 million residential customers at the end of 2018 [AEMO, 2019a]
and we have assumed 500,000 prosumager households previously. and (iii) the
remaining Commercial and Industrial (C&I) demand. We assume both prosumage and
non-prosumage households to have the same underlying grid demand as derived
from Ausgrid [2018].191919In this setup, residential household demand equates
to 31% of total network demand and remains consistent with SWIS conditions in
2018 [AEMO, 2019a]. For clarity, we focus on the central ‘49% RES share’ and
‘PVB FiT25’ scenario.

Figure 8: Effect of household PV-battery adoption in the ‘PVB FiT25’ and ‘49%
RES share’ scenario on: (a) Average grid demand per hour for each customer
segment. (b) Average wholesale electricity prices per hour.

By comparing the average hourly grid demand profiles (Figure 8) between
prosumage households, non-prosumage households and C&I, it can be observed
that residential households have a typical double peak profile (with a larger
peak in the early evening, a much smaller peak in the morning and minimum
demand during the night). With PV-battery systems, prosumage households on
average are able to reduce the majority of their grid demand during the day
with PV self-generation while continuing to reduce their grid demand past the
early evening peak and partly into the night with their energy storage. C&I
demand has a relatively more constant profile with higher demand occurring
over the day. In the absence of prosumage households, wholesale electricity
prices are, on average, highest in the early evening as household peak demand
overlaps with declining C&I demand. With prosumage households, the early
evening price peak reduces significantly, while an additional price peak
during the mid-morning emerges. Night-time prices are also increased slightly
on average, due to the reduced contribution of zero marginal cost wind power
in the overall portfolio.

Figure 9: Comparison of average wholesale electricity prices for the three
customer types with and without prosumage for the ‘49% RES share’ and ‘PVB
FiT25’ scenario.

Average wholesale prices per $MWh$ of electricity decrease for prosumage
households, but even more so for non-prosumage households, as they are able to
benefit more from the large reduction in prices over their early evening peak
(Figure 9). Since prosumage households are generally discharging their battery
systems during this time, they receive less advantage from this effect. But as
prosumage households already benefit from significant reductions in their
total net grid utilisation, they are able to obtain much greater overall
wholesale market bill savings than non-prosumage households. Conversely, the
wholesale prices for C&I demand increase slightly, as they are relatively more
exposed to the moderate rise of wholesale electricity prices in mid-morning
and at night-time, and benefit less than households from the reduction of the
evening price peak.

Figure 10: Relative effects of prosumage on overall system costs for each FiT
and RES share scenario.

Overall system costs always increase with prosumage compared to the reference
scenario (Figure 10). This is mainly driven by additional battery deployment,
and also by the fact that household investments in small-scale PV and battery
capacity have higher specific investment costs compared to utility-scale
investments. More generally speaking, prosumage households’ aim to reduce
electricity bills by optimising self-consumption against the regulatory
setting, which leads to a sub-optimal allocation of capital across the power
sector. This is particularly visible in the ‘PVB FiT25’ and ‘PVB+ FiT0’
scenarios where installations of household batteries are high. Remember that
these household batteries are not designed to assist with overall grid
operation and thus hardly substitute utility-scale batteries. Additional
information on system cost calculations are included in A.1.

##  6 Key assumptions and their qualitative impact on results

In the following, we briefly discuss some key assumptions of the analysis and
how these may qualitatively impact the results.

First, our retail price assumptions are independent of the modelled power
sector changes and wholesale market outcomes. This is a consequence of the
research design that soft-links Electroscape with DIETER-WA. In Electroscape,
households’ PV battery investments are driven by an assumed constant increase
in retail electricity prices that is independent of the renewable energy share
and the number of prosumage households. This assumption mirrors a long-running
empirical trend in Australia [ABS, 2020]. If retail price increases were lower
than the 4% per year assumed here, the uptake of household PV-battery systems
would be slower and the accompanying system effects would be smaller (and vice
versa). Increasing RES shares generally implies a greater penetration of zero-
marginal cost generators, such that the wholesale cost of electricity may also
decrease. Yet these savings may not be reflected in retail electricity prices.
Wholesale electricity costs contribute to less than half of retail charges
[AEMC, 2019], and an increasing penetration of variable renewable energy often
requires significant transmission and distribution network upgrades, that
(though not explicitly modelled here) are usually recovered via increases in
retail prices. Likewise, the financing of renewable energy remuneration
schemes may also lead to further increases in retail prices. Accordingly, it
appears justified to assume that the recent trend of increasing retail prices
continues even if RES shares increase. Future research may aim to relax this
assumption by further integrating both model approaches.

Second, we assume that FiTs are only eligible for PV system installations
under $5~{}kW_{P}$. This mirrors the conditions in the SWIS network [Synergy,
2019], with similar arrangements occurring in other Australian networks [Solar
Choice, 2019c]. The $5~{}kW_{P}$ threshold is likely to become even more
prevalent amid concerns with reverse power flows [CSIRO and Energy Networks
Australia, 2017] and revised inverter standards [Stapleton, 2017]. Higher
thresholds would result in higher household PV capacities being installed, and
accordingly larger power sector effects, particularly in the ‘PV-only FiT50’
scenario.202020Compare, for example, the German setting modelled by Günther et
al. [2019], where household PV installations are always at a $10~{}kW_{P}$
threshold whenever the FiT is sufficiently high.

Third, we adopt a long-run equilibrium ’greenfield’ perspective in DIETER-
WA.212121It is not a pure greenfield assumption, as we include a small lower
bound for utility wind and solar power, and the PV lower bound is binding in
some cases ($202~{}MW$). Accordingly, the optimal solution accounts for the
full costs of all generators, both fixed and variable. In reality, existing
conventional plants may not have to recover their full costs, but only the
costs of going forward with the existing capacity. We may therefore
underestimate the capacity of coal-fired power plants, which come with
relatively high fixed costs, and conversely overestimated the capacity of OCGT
plants. At the same time, an opposite distortion may be present, as we
implicitly assume that the overall setting remains stable over time in our
2030 parameterisation. In the real world, investors may expect an ongoing
transition toward higher shares of renewable energy sources or tighter carbon
constraints beyond 2030, such that investments in coal- or gas-fired plants
could be lower.

We further assume that certain shares of renewable energy sources are exactly
met by including respective binding conditions in the model. This allows for
meaningful comparisons of different scenarios and is relevant from an energy
policy perspective, as relative renewable energy targets are common in many
countries. Yet we force the model to deviate from an endogenous, cost-optimal
renewable share. To explore this, we carry out additional model runs with an
endogenous share of renewable energy sources. Given our parameterisation, we
find an optimal222222Note that this is not the optimal share of renewable
energy sources from a social welfare perspective, i.e., if all external costs
were internalised. share of renewables just below 59%, which slightly
decreases with higher FiT values. Qualitatively, most results do not change
compared to our setting with exogenously fixed renewable shares. Yet the coal-
enhancing effect of prosumage described earlier disappears, as prosumage
always increases the renewable penetration compared to the reference scenario,
and accordingly substitutes more electricity from coal compared to the
scenarios with fixed renewable shares.

Next, we abstract from technical limitations or additional costs related to
ramping up and down power generation from conventional plants between one hour
and the next in the DIETER-WA model used here. Thus, we may have not fully
captured the potential system benefits of the ‘PVB+ FiT0’ scenario, which
generally leads to a smoother residual load duration curve and lower ramps for
thermal generators.

We further abstract from including a CO2 price. While this is a meaningful
policy assumption for the Western Australian case modelled here, it somewhat
limits the interpretability of results for other jurisdictions where CO2
pricing is present. In case a sufficiently high CO2 price was introduced,
coal-fired power plants would be substituted by natural gas. Accordingly, the
minor coal-enhancing effect of prosumage would also disappear. Yet overall
results are unlikely to change as the share of renewable energy sources is by
assumption fixed.

Finally, our research design ignores the possibility that residential
batteries could be used for further market or grid services, rather than only
increasing the self-consumption of households’ PV generation. While this
adequately reflects the current setting in Western Australia and many other
markets, household batteries may increasingly become available for additional
uses in the future, enabled by aggregators and new technologies. If
residential batteries become available for such applications, they may
substitute utility-scale storage to a greater extent and thus mitigate the
overall system cost increase from prosumage. Exploring the potentials and
preconditions for this in more detail appears to be a promising avenue for
future research.

##  7 Discussion and conclusions

Using two open-source models, we first determine optimal investments into
residential PV and battery capacities from a financial household perspective
and then analyse their wider power sector effects. Using different FiT values
and RES shares, we illustrate how prosumage changes the residual network
demand and overall utility-scale generation and storage capacity investments
and dispatch. We do so for the Western Australian SWIS island network, which
serves as an example of what many other countries may experience in the
future. Accordingly, the following general outcomes, which are evident across
the range of scenarios and results, should also be of interest to many other
geographical settings.

First, residential PV generally displaces utility-scale PV capacity. This
effect is less pronounced if more residential batteries are deployed, and more
pronounced for higher RES shares. Accordingly, future investments in utility
PV capacity will have to consider the growth of prosumage as it directly
competes against their market dispatch. Therefore, the use of utility PV
capacity in the future may require additional financial certainty by engaging
in hedging agreements, such as contracts-for-difference, rather than relying
solely upon market dispatch revenues from the wholesale energy market.

Second, the optimal wind capacity, in contrast, is generally less affected by
prosumage. As self-generation by prosumage households contributes to the RES
share, it naturally displaces the remaining share of renewable energy required
from utility-scale generators. Across each of the FiT and RES share scenarios
however, reductions in wind capacity are less pronounced than with utility PV
capacity. Furthermore, raising the RES share drives additional wind capacity
over utility PV capacity. From a central planner perspective, investments in
wind capacity may be more resilient to different degrees of prosumage
adoption.

Third, even if a substantial residential battery capacity is deployed, utility
battery power capacity is only displaced slightly while utility battery energy
capacity may even increase. There is therefore a very imperfect substitution
of utility storage by residential batteries. This is, amongst other factors,
driven by the fact that we have assumed prosumage batteries to only be
operated with the objective of minimising the households’ energy costs. This
is also a major source of increasing overall system costs.

Fourth, prosumage causes average wholesale prices to slightly decrease for
both prosumage and non-prosumage households and slightly increase for other
consumers. These distributive implications of prosumage may be of interest
from a policy perspective; yet the overall effects are also dependent on the
design (and potential design changes) of retail tariffs and the pass-through
by retailers to consumers.

Overall, we conclude that prosumage can have substantial impacts on the
overall power sector, which has to be considered by system planners, investors
and regulators alike. Such power sector repercussions should also be taken
into account when designing FiTs, retail tariffs and self-consumption
regulation for residential electricity customers that influence subsequent
household PV-battery investment behaviours. Likewise, an increasing uptake of
prosumage presents a potential opportunity to both better incorporate and
utilise these behind-the-meter PV battery systems as a source of additional
power sector flexibility.

Future work may explore in more detail the distributive impacts of prosumage
and potential grid tariff reform options to mitigate these. Moreover, a
further integration of the two models appears desirable, which would also
allow the incorporation of retail price feedbacks from increased prosumage as
well as investigating the effects of making additional use of prosumage
batteries for grid storage purposes.

## Acknowledgements

We thank researchers from the Energy Transition Hub and the participants of
the Climate & Energy College seminar at the University of Melbourne for
fruitful discussions. Kelvin Say acknowledges the resources provided by The
Pawsey Supercomputing Centre with funding from the Australian Government and
the Government of Western Australia. Wolf-Peter Schill acknowledges funding by
the Federal Ministry of Education and Research via the START project (FKZ
03EK3046E) and the invitation for a research stay at the University of
Melbourne, where parts of this work have been carried out.

##  Appendix A Additional results

###  A.1 System costs

Although not in the focus of this analysis, we also compare overall system
costs. As for all non-prosumage parts of the power sector, this is
straightforward, as respective fixed and variable costs are direct outcomes of
the DIETER-WA model. On the prosumage side, we re-calculate the costs of
investments in household PV and battery capacity determined by Electroscape in
a way that they are comparable to system cost calculations in DIETER-WA. We do
this by summing up the annuities of respective investments in every year
between 2019 and 2030, using a discount rate of 4% (the same as for utility-
scale assets in DIETER-WA) and lifetimes of 10 years for batteries and 25
years for PV installations. In doing so, we consider the higher specific
investment costs of household PV and battery installations compared to their
utility-scale counterparts.

Figure 11: Comparison of total annual system costs.

We find that overall system costs in the scenarios with prosumage are always
higher than in the reference scenario. Generally speaking, this is because the
inclusion of household PV battery systems forces the model to deviate from the
least-cost generation and storage portfolio achieved in the reference
scenario. In particular, prosumage batteries substitute utility battery
storage only to a minor extent (compare Section 5.2), so overall battery-
related investments increase substantially.232323Similar findings have been
made for prosumage scenarios for Germany [Schill et al., 2017]. Accordingly,
system costs increase most in the FiT0 scenario, where we find the highest
prosumage battery investments (Figure 11). Depending on the renewable share,
yearly system costs increase by around 23% in the FiT0 scenario compared to
the reference, but only by 6-7% in the FiT50 scenario without batteries.
Another factor that contributes to increasing overall system costs relates to
higher specific investment costs of households’ PV and battery installations
compared to their utility-scale counterparts.

##  Appendix B Research Data

Source code and input data are publicly accessible from Zenodo repositories
http://doi.org/10.5281/zenodo.3693308 for Electroscape and
http://doi.org/10.5281/zenodo.3693287 for DIETER-WA.

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##  Appendix C Sensitivity analyses

###  C.1 Solar PV system costs ($\pm~{}20\%$)

A 20% reduction in PV system costs generally raises utility and household PV
capacity and reduces wind capacity compared to baseline assumptions (Figure
12). Household PV continues to displace utility PV, but to a slightly lesser
extent. Wind continues to remain the dominant source of renewable energy
generation, but at a slightly lower level (Figure 13). Qualitatively, the
impacts of household PV battery prosumage on utility-scale capacities and
generation remain consistent.

Figure 12:  Installed power and storage energy capacity for varying FiT and
RES shares (500,000 households) and the change in capacity with respect to the
equivalent reference scenario, sensitivity with -20% PV system costs.
Figure 13:  Yearly generation for varying FiT and RES shares (500,000
households) and the change in generation with respect to the equivalent
reference scenario, sensitivity with -20% PV system costs.

A 20% increase in PV system costs generally reduces utility and household PV
capacity and increases wind capacity compared to baseline assumptions (Figure
14). With household battery capacity remaining stable, the higher proportion
of wind generation (Figure 15) drives a general increase in utility battery
capacity. Qualitatively, the power sector implications from household PV
battery prosumage remain consistent.

Figure 14:  Installed power and storage energy capacity for varying FiT and
RES shares (500,000 households) and the change in capacity with respect to the
equivalent reference scenario, sensitivity with +20% PV system costs.
Figure 15:  Yearly generation for varying FiT and RES shares (500,000
households) and the change in generation with respect to the equivalent
reference scenario, sensitivity with +20% PV system costs.

###  C.2 Battery storage system costs ($\pm~{}20\%$)

A 20% reduction in battery system costs leads to greater utility and household
battery capacities. Household PV capacity also increases slightly to take
advantage of the increased energy storage capacity (Figure 16). Utility PV
capacity remains heavily displaced by prosumage while wind continues to remain
the dominant source of renewable energy generation (Figure 17). Qualitatively,
the system implications from household PV battery prosumage remain consistent.

Figure 16:  Installed power and storage energy capacity for varying FiT and
RES shares (500,000 households) and the change in capacity with respect to the
equivalent reference scenario, sensitivity with -20% battery system costs.
Figure 17:  Yearly generation for varying FiT and RES shares (500,000
households) and the change in generation with respect to the equivalent
reference scenario, sensitivity with -20% battery system costs.

A 20% increase in battery system costs leads to reductions in utility and
household battery capacities. Household PV capacity is also reduced (Figure
18). Utility PV capacity remains heavily displaced while wind continues to
remain the dominant source of renewable energy generation (Figure 19).
Qualitatively, the system implications from household PV battery prosumage
again remain consistent.

Figure 18:  Installed power and storage energy capacity for varying FiT and
RES shares (500,000 households) and the change in capacity with respect to the
equivalent reference scenario, sensitivity with +20% battery system costs.
Figure 19:  Yearly generation for varying FiT and RES shares (500,000
households) and the change in generation with respect to the equivalent
reference scenario, sensitivity with +20% battery system costs.

###  C.3 Number of prosumage households ($\pm~{}20\%$)

Lowering the number of prosumage households from 500,000 to 400,000 reduces
the displacement of wind and utility PV capacity and allows most of the
utility PV capacity to recover in the PVB/PVB+ prosumage scenarios (Figure
20). Wind generation continues to dominate as the primary source of renewable
energy generation (Figure 21). Qualitatively, the system implications from
household PV battery prosumage remain consistent.

Figure 20:  Installed power and storage energy capacity for varying FiT and
RES shares (400,000 households) and the change in capacity with respect to the
equivalent reference scenario.
Figure 21: Yearly generation for varying FiT and RES shares (400,000
households) and the change in generation with respect to the equivalent
reference scenario.

Raising the number of prosumage households from 500,000 to 600,000 displaces
further utility PV and wind capacity, while also slightly increasing utility
battery capacity (Figure 22). Wind generation continues to dominate as the
primary source of renewable energy generation (Figure 23). Qualitatively, the
system implications from household PV battery again prosumage remain
consistent.

Figure 22: Installed power and storage energy capacity for varying FiT and RES
shares (600,000 households) and the change in capacity with respect to the
equivalent reference scenario.
Figure 23: Yearly generation for varying FiT and RES shares (600,000
households) and the change in generation with respect to the equivalent
reference scenario.