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Weather conditions and domestic consumption belong to the essential boundary conditions in the optimal dimensioning of residential battery storage systems. In future, both factors will undergo transitions due to climate change and efficiency enhancement of domestic appliances. This study seeks to assess potential developments in climate and consumption loads on the battery flows and residual loads for the near-time future. For this purpose, a land surface processes model with an integrated domestic energy system component is applied. Three scenarios project changes in consumption loads and meteorological conditions for the year 2040. The study area includes 4906 buildings located in the south of Germany. The results show a general rise of grid feed-in rates between 21% and 27% due to increased photovoltaic production. Climate change is expected to raise battery utilization during the winter months, whereas decreasing effects from efficiency enhancement dominate in the summer. The self-consumption rate declines between 4% and 12%, whereas self-sufficiency rises up to 6%. Consequently, in the assessment of battery dimensioning approaches maximizing self-consumption or profitability, we recommend including the shifts in battery utilization and residual loads arising from future changes in climate and consumption loads.
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How do changes in climate and consumption loads affect residential
PV coupled battery energy systems?
Andrea Reimuth
*
, Veronika Locherer, Martin Danner, Wolfram Mauser
Department of Geography, LMU Munich, Luisenstr. 37, 80333, Munich, Germany
article info
Article history:
Received 7 November 2019
Received in revised form
3 March 2020
Accepted 6 March 2020
Available online 7 March 2020
Keywords:
Residential battery storage system
Energy ow modeling
Climate change
Consumption loads
PV self-Consumption
Residual load
abstract
Weather conditions and domestic consumption belong to the essential boundary conditions in the
optimal dimensioning of residential battery storage systems. In future, both factors will undergo tran-
sitions due to climate change and efciency enhancement of domestic appliances. This study seeks to
assess potential developments in climate and consumption loads on the battery ows and residual loads
for the near-time future. For this purpose, a land surface processes model with an integrated domestic
energy system component is applied. Three scenarios project changes in consumption loads and
meteorological conditions for the year 2040. The study area includes 4906 buildings located in the south
of Germany. The results show a general rise of grid feed-in rates between 21% and 27% due to increased
photovoltaic production. Climate change is expected to raise battery utilization during the winter
months, whereas decreasing effects from efciency enhancement dominate in the summer. The self-
consumption rate declines between 4% and 12%, whereas self-sufciency rises up to 6%. Consequently,
in the assessment of battery dimensioning approaches maximizing self-consumption or protability, we
recommend including the shifts in battery utilization and residual loads arising from future changes in
climate and consumption loads.
©2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
In recent years, residential battery storage systems have gained
growing popularity among the owners of rooftop mounted
photovoltaic (PV) systems. Taking Germany as an example, already
every second newly installed PV systemwith less than 30 kWp was
coupled to a storage system in 2017 [1]. Consequently, residential
batteries are expected to inuence the ows in the energy systems
signicantly.
Apart from the ability of increasing the PV self-consumption
rates, battery storages have the potential of reducing harmful
feed-in peaks into the local grids [2]. Therefore, much research has
been undertaken in the dimensioning of residential battery storage
capacities for cost-effective and grid-friendly operation modes. A
variety of studies presents optimization techniques for the battery
size aiming at different goals like the maximization of economic
benets for the PV owners under different tariffs [3e6], the
reduction curtailment losses under feed-in restrictions [7] or the
additional utilization for frequency regulation apart from self-
consumption [8]. In these elds of application, the protability of
a battery system is driven by the excess energy production of the
domestic PV plants and the residential consumption loads apart
from economic parameters. Apart from the future technological
development of the PV and battery systems, these two factors will
undergo severe transitions in the next decades.
Climate change will inuence the operation temperature of PV
systems, but also the availabilityof the bottom-of-atmosphere solar
irradiation due to shifts in cloud cover and humidity [9]. Several
studies have examined how far the PV production rates will be
affected by the changing meteorological conditions. In a rst
approach, the potential changes in PV energy are quantied from
daily temperature and solar radiation means of global climate
projections for 2080 [10]. This approach has been adopted in sub-
sequent studies, which used improved climate projection data in
order to determine the future annual changes in PV production and
their variance for different spatial extents and scales [11e13]. Their
spatial resolutions of PV production scenarios originate from the
grid sizes of the underlying climatic projections. The results provide
a general estimate of potential changes in the production of
*Corresponding author.
E-mail addresses: a.reimuth@iggf.geo.uni-muenchen.de (A. Reimuth), v.
locherer@iggf.geo.uni-muenchen.de (V. Locherer), martin.danner@iggf.geo.uni-
muenchen.de (M. Danner), w.mauser@lmu.de (W. Mauser).
Contents lists available at ScienceDirect
Energy
journal homepage: www.elsevier.com/locate/energy
https://doi.org/10.1016/j.energy.2020.117339
0360-5442/©2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Energy 198 (2020) 117339
residential PV systems up monthly temporal scale [14]. In order to
increase the temporal resolution, the meteorological parameters of
the climate projections have been temporally downscaled from
monthly means to daytime values assuming sinusoidal curves of
the daily courses for temperature in Ref. [10] or irradiance in
Refs. [14]. However, the impacts of changing PV production rates
resulting from the future shifts in the meteorological conditions on
the charging and discharging quantities of battery storage systems
have not yet been investigated closer.
The mitigation of global warming implies a transformation of
the residential energy sector, which belongs to the considerable
contributors of greenhouse gas emissions [15]. Apart from the
conversion to renewable energy resources, potential measures also
include energy savings by changes in behavior or improvements in
energy efciency [16]. Signicant reductions in the electrical en-
ergy demand can be achieved if appropriate regulative instruments
are implemented [17]. Recent research has shown that the resi-
dential consumption prole has a strong inuence on the self-
consumption of PV systems coupled to battery storages [18].
Thus, a decrease of the energy demand arising from increasing
efciency rates of domestic appliances will induce changes in the
residual loads and battery utilization.
These future developments of climate and efciency enhance-
ment will affect PV production and consumption loads in adverse
ways. To the authorsknowledge, the effects of changes in con-
sumption loads and climatic conditions on the residential storage
systems have not yet been analyzed.
In this context, the study sets out to assess their impacts on the
charging and discharging quantities of batteries. The paper seeks to
answer the following questions: Firstly, in which way will the
battery ows and utilizations be inuenced by changes in climate
and consumption loads in the course of the year? Secondly, how far
will the local grid ows be affected that have to be managed bygrid
suppliers? Third, to which degree will self-consumption, self-sup-
ply, and battery cycles be inuenced? The parameters are analyzed
as changes in the battery utilization and grid ows potentially shift
the optimal dimensioning.
In order to answer these questions, a spatially distributed land
surface model with an integrated residential energy system
component is applied. This model enables the assessment of mul-
tiple residential systems on regional scale with building-specicPV
inclination angels and orientations, energy demands and battery
sizes. As currently available climate projections provide insufcient
temporal resolutions needed for an assessment of battery ows, a
statistical climate generator is applied, which downscales the
meteorological drivers to an hourly time step. Three different sce-
narios for the future development of climate change and efciency
enhancement are assessed to gain a comprehensive understanding
of the impacts on the energy ows in the residential systems in the
near-term future.
2. Materials and methods
2.1. Model environment
The simulation environment of the domestic energy systems is
embedded into the land surface processes model PROcesses of
Radiation, Mass, and Energy Transfer (PROMET) [19]. The PROMET
model was originally developed for analyzing hydrological and
agricultural processes. It uses a raster-based approach, which
means that each domestic energy system is referenced to a grid
point in the simulation domain. It is spatially explicit and strictly
conserves mass and energy in all components. The presented
simulation results were carried out with a temporal and spatial
resolution of 1 h and 100 m. The basic input includes hourly
meteorological point data for air temperature, air humidity, wind
speed, cloud cover, and precipitation [19]. These parameters are
spatially interpolated with the inverse squared distance weighting
method considering topographical conditions. The incoming direct
and diffuse radiation relevant for the hourly PV generation is
calculated according to the approaches of [20], and [21]. A detailed
description of the general model and application is given in Mauser
and Bach [19], and Mauser, Bach et al. [22].
The domestic energy system simulating the residential energy
ows consists of three components (see Fig. 1).
The PV model calculates the energy production rate P
PV
from the
solar direct and diffuse irradiation E
Dir
and E
Dif
and background
reection E
Ref
on the inclined PV panel area A
PV
following the
method of [28] (see Eq. (1)). Temperature effects T, and aging aare
considered by the efciency parameters
h
T
and
h
A
. Snow coverage
exceeding 2 cm impedes the PV production.
P
PV
¼E
Dir
þE
Dif
þE
Ref
.1000 ,A
PV
,
h
T
ðTÞ,
h
A
ðaÞ(1)
The temporal course of the energy consumption E
D
is calculated
from the annual consumption E
Da
, which is downscaled by hourly
load proles h
f
depending on season Sand day of the week doW
(see Eq. (2)).
E
D
¼E
Da
,h
f
ðS;doWÞ(2)
The PV model and the consumption component were validated
with measurement data on hourly resolution obtaining determi-
nation coefcients of 0.58 respectively 0.56.
The battery model includes the simulation of the ows within
the accumulator as well as the grid conversion. The battery is
assumed as rechargeable, always connected to the grid, and directly
coupled to the PV module. The performance of the battery P
B
is
determined from the available energy excess or decit
D
E, the state
of charge SOC, the useable capacity C
N
, and the maximum charging
or discharging power P
B,max
(see Eq. (3)). The inuences of tem-
perature and current are considered bya constant efciency rate
h
B
.
The battery model includes self-discharge and aging effects.
P
B
¼minP
B;max
;SOC ,C
N
;
D
E,
h
B
(3)
The battery management is based on daily dynamic feed-in
limits (DFLs) as shown in Fig. 1. The battery starts to charge as
soon as the PV production rate, which is not immediately
consumed by the household, exceeds the DFL, and stops, when the
residual load falls below this threshold. In this way, the selected
battery charging strategy both optimizes the self-consumption rate
and decreases the feed-in peaks. The DFL thresholds have been
previously determined assuming a perfect forecast. The battery is
discharged when the hourly energy consumption exceeds the
production.
The residual load of a domestic energy system RSL
B
, which is
supplied by or fed into the grid, is dened as the difference between
the consumption rate and the power ows of PV panel and battery.
The efciencies of the MPP-Tracker
h
MPP
and inverter
h
Inv
are
considered as shown in Eq. (4).
RSL
B
¼E
D
ðP
PV
,
h
MPP
P
B
Þ
h
Inv
(4)
The degree of self-consumption (DSC) assesses the percentage
of production used by the domestic energy system (see Eq. (5)). The
degree of self-sufciency (DSS) quanties the annual percentage of
the total consumption that is supplied by the domestic PV pro-
duction (see Eq. (6)). The degree of autarky (DA) represents the
changes in the grid ows in comparison to the state without a
residential energy generation and storage system (see Eq. (7)). RSL
þ
A. Reimuth et al. / Energy 198 (2020) 1173392
denotes the hourly energy ow supplied by the grid.
DSC
B
¼1XRSL
þ
.XE
D
(5)
DSS
B
¼XE
D
XRSL
þ
.XP
PV
(6)
DA
B
¼XjRSLj.XE
D
(7)
A detailed description of the model setup and structural
embedding into the PROMET model is given in Reimuth et al. [23]
and in its associated technical notes [24e27].
2.2. Generation of the meteorological scenarios
The effects of anthropogenic emissions on the future climate are
studied extensively under the umbrella of the Intergovernmental
Panel on Climate Change (IPCC) [29]. The IPCC established four
Representative Concentration Pathways (RCPs), which project
different future developments of the greenhouse gas concentra-
tions. The impacts of these RCP emission scenarios on the future
climatic conditions are assessed using global climate models. The
generated output data sets of the global climate models as well as
those of derived regional models often have a grid size of several
kilometers and low temporal resolutions [30]. Since local climatic
conditions can considerably deviate from the coarse-grid model
representations, we downscale the meteorological drivers to a
resolution of 100 m and 1 h. In this way, the small-scale variation of
the weather conditions, which induce relevant differences in the
energy ows within a regional system [23], can be adequately
considered in this study.
From the variety of downscaling processes [31] we chose the
statistical climate generator of Mauser [32], which uses the general
regional future climate change trends to rearrange measured his-
torical data sets in a way that they represent future climatic con-
ditions. This method relies on climate parameters measured by a
station network and therefore produces spatially and physically
consistent meteorological data sets with a temporal and spatial
resolution identical to the measured data. Thus, the full compara-
bility of the results for the current and future states of the domestic
energy systems is ensured and local peculiarities are preserved.
Precondition for this method is an hourly data set with a suf-
ciently high number of observation years. The approach further
assumes a continuance of the current climate regime, which can be
presumed in Central Europe for the near-time future [33]. In this
manner, robust correlations representing the local weather char-
acteristics can be derived. Therefore, the historical weekly sums of
precipitation and weekly averages of temperature measurements
are analyzed with respect to their annual variation and covariance.
In the next step, the annual future trends in temperature and
precipitation are extrapolated for the study region from the
selected IPCC climate projection. A two-dimensional statistical
random generator is applied to mimic the natural variability of the
future climate. The nal projection is obtained by rearranging the
historical data set according to the future synthetic weeks. In this
way, the projected meteorological scenario follows the future
temperature trend of the IPCC scenario but maintains the measured
consistency of the region. A detailed description of the method and
its restrictions is presented in Ref. [33] and the supplementary
material.
3. Case study
3.1. Description of the study area
The research area Bavarian Oberlandis located in the south of
Germany and covers the administrative districts Miesbach, Bad
T
olz-Wolfratshausen, and Weilheim-Schongau (see Fig. 2). The re-
gion consumes 2161 GWh of electrical energy in the annual average
(2013e2016), of which 21.6% can be attributed to the private sector
[34e40]. This corresponds to a mean consumption of 5127 kWh per
building for an average household with 4.3 persons.
Between 1994 and 2016, a total of 13,940 PV systems feeding
into the local grids have been registered within the study area [43,
44]. 7522 plants mounted on residential houses could be identied
as domestic systems by using an airborne laser scanning data set
containing the building outlines [45]. With an annual incoming
solar irradiation of 1167 kWh/m
2
, the study area belongs to the
regions of Germany with the highest PV potentials under the cur-
rent climate [46].
3.2. Basic input for the domestic energy model
To consider the variability of the energy system setups, 4906
residential buildings with existing, rooftop mounted PV systems
are selected having a nominal power between 3.0 kWp and
10.0 kWp. The inclinations and orientations of the panels are
derived from the corresponding roof pitches of the building out-
lines provided by [45]. On average, the panels have a nominal po-
wer of 6.3 kWp, a size of 44.17m
2
at an inclination of 27.36
aligned
towards the southeast. The efciencies are estimated from the date
Fig. 1. Structure of the domestic energy module including the energy production of the PV panels, the battery with its environment and the grid power ows (left) and selected
charging and discharging strategy (right). RSL denotes the residual loads, DFL the dynamic feed-in limit.
A. Reimuth et al. / Energy 198 (2020) 117339 3
of installation and an efciency curve [47] and range between 10.7%
and 16.4%. Further parameters for the PV model are taken from
Quaschning [28] (see also Table A.1).
The load curves used to determine the hourly domestic energy
consumption rates are based on standardized consumptionproles
[48,49]. Three types of season and day are distinguished (see
Fig. A1).
The domestic energy storage devices are assumed as lithium-ion
accumulators, which have become the common type for domestic
applications [1]. The battery systems are limited to a useable ca-
pacity of 60% and a maximum power of 0.3 kW/kWh at an hourly
loss rate of 6.25 10
8
of the nominal capacity [50,51]. The con-
verter efciency is set to 94% and the losses of dis-/charging to 1% of
the power ow (see also Table A1).
The useable capacities of the battery systems are dimensioned
according to the nominal power of the corresponding PV systems
following Weniger et al. [52]. Thus, the nominal battery capacities
of the domestic energy systems range between 5.0 kWh and
16.7 kWh at an average value of 10.5 kWh.
These parameters are kept constant for all scenarios to quantify
the inuence of climate change and efciency enhancement of
domestic appliances. The basic input needed to drive the land
surface model PROMET is described in Mauser and Bach [19].
3.3. Input required for the climate generator
The hourly measurement data used for the statistical analysis
and the reassembling process are taken from 377 weather stations
German and the Austrian Weather Service network covering the
years 1960e2006.
The future local climate trend is developed from bias-corrected
projections for precipitation and temperature of ve global climate
models from the ISIMIP Fast Track input-data catalogue for each
RCP scenario [41e43].
The mean decadal temperature increases serving as input for
the climate generator were determined from the ve projections of
the grid cell representing the study area. The annual temperature
trend of each RCP scenario relevant for the study region was nally
found by tting a polynomial curve of third order to the ensemble
average of the temperature trends (see Table A.2 and Fig. A.2).
The changes of the weekly temperature averages and precipi-
tation sums were calculated from the ve data sets for each RCP
scenario in the following way: First, the long-term average for
precipitation sums and temperature means were obtained from the
time spans 1961 to 1990 and 2021 to 2050. The differences between
past and future time span were smoothened by taking the moving
average over 10 weeks. The differences in the weekly temperature
means and ratio of the precipitation sums were nally determined
as the ensemble average of the smoothened values from the ve
projections (see Fig. A3).
3.4. Scenario generation
The baseline scenario simulates the current state of the energy
system as reference using the year 2016. This year is characterized
by average annual energy consumption rates [34e40] and a global
irradiation sum deviating only marginally from the long-term
average [46]. The domestic consumption is obtained in a top-
down approach from the number of buildings and the annual
consumption of the municipalities [34e40]. The simulation is car-
ried out with hourly measurement data from 79 weather stations of
the German and the Austrian Weather Service network.
Three future scenarios are applied considering potential de-
velopments of the annual domestic energy consumption and cli-
matic conditions. The year 2040 is chosen as projected year, since a
time interval of 25 years corresponds to the performance guaran-
tees of the PV manufacturers [47].
The rst varied component is the underlying global climate
trend, which is used to project the meteorological conditions in
2040. Three out of four available RCP pathways of IPCC are selected
[53]:
RCP 2.6 represents the lower bound of a warming climate. It
assumes that the radiative forcing undergoes the lowest total
increase of 1.27 W/m
2
until 2050.
RCP 4.5 includes ambitious efforts in reducing temperature in-
crease. The scenario projects a rise of the radiative forcing by
2.04 W/m
2
.
Fig. 2. Location of the study area (left) and distribution of the 4906 selected households (right) (Data source: [41,42]).
A. Reimuth et al. / Energy 198 (2020) 1173394
The third scenario is based on RCP 8.5 assuming the highest rise
of global temperature. For the rst half of the 21st century the
radiative forcing is projected to rise by 3.04 W/m
2
.
The time span from 2038 to 2042 is simulated assuming con-
stant climatic conditions within ve years. The year with median
PV production is assumed to represent average meteorological
conditions for the respective climate scenario (see Fig. A4-A.6).
In terms of efciency enhancement, scenario Strongand
Mediumfollow the story lines developed in [54], who outlined
two scenarios for the energy consumption rates in Germany. These
scenarios already include the additional prospective energy con-
sumption arising from the growing use of air conditioning and
changes in behavior, which counteract the decrease in energy
consumption from efciency enhancement. The differences be-
tween the residential consumption of 2040 and 2016 (obtained by
linear interpolation between 2011 and 2020) represent the future,
potential increases in energy efciency used in this study. Scenario
Strongsupposes that the goals of the ofcial energy concept are
realized. This means a reduction of 17.1%. Scenario Mediumpro-
jects the German trend of the recent years leading to 15.5% in 2040.
Scenario Noassumes that the domestic energy consumption is
not reduced.
As the assumptions in the RCP scenarios already include explicit
projections of the global energy use, a consistent development of
energy enhancements and the greenhouse gas emissions is
assumed in this study (see Table 1). Scenario A is characterized by
strong efforts in climate change mitigation, which is consistent to
strong improvements in energy efciency. Scenario B projects a
medium range future with major emission reductions and medium
success in raising energy efciency. Scenario C assumes a business-
as-usual, non-sustainable development, which projects the current
emission path and consumption into the future.
4. Results
4.1. Temporal course of the energy ows
Fig. 3 shows the average annual course of the cumulated energy
ows and the cumulated differences of the future scenarios in
relation to the baseline.
The energy consumption rates do not show signicant seasonal
variations in all four scenarios, as they accumulate almost linearly
during the year. The reductions of the annual consumption result in
a decline of 868.83 kWh in scenario A and 786.97 kWh in scenario B
compared to the baseline and scenario C with 5110.48 kWh.
In contrast, the courses of the PV production rates are charac-
terized by sigmoid shapes caused by the steeper solar inclinations
angles in the summer months. Under conditions of scenario 0, the
average PV production rate exceeds the total energy consumption
with an annual yield of 6419.26 kWh. Due to the changing climate
conditions, the electrical energy generation is further raised be-
tween 12.5% and 17.5%. The deviations to scenario 0 have different
seasonal courses. In spring, the changes in scenario A and B un-
derlie a higher variability than the scenario C. In the further course
of the year, scenario B and C are characterized by a linear daily
increases in contrast to A, which continues to uctuate.
The differences, which can be observed in the production and
consumption rates, consequently lead to varying changes of the
battery ows. Scenario A is characterized by an annual, average
reduction of 105.96 kWh compared to the battery ows of the
baseline scenario 0 with 2832.31 kWh. In contrast, the battery
ows of the two future scenarios B and C increase by 46.81 kWh
and 280.61 kWh. In the spring and summer months, the deviations
in the battery ows follow the form similar to the linear variation of
the projected consumption rates. Scenario A decreases by 6.7%,
scenario B by 4.5%, and scenario C shows more or less no deviation.
However, at the beginning and end of the year, the changes in the
battery ows between scenarios and baseline converge to those of
the PV production rates. This time span is characterized by higher
uctuations as the batteries cannot always be fully charged.
The average ows between the domestic energy systems and
grids are collectively raised between 717.97 kWh in scenario C and
952.11 kWh in A, compared to 4121.80 kWh in the baseline year.
The courses of the grid ows are also subject to seasonality. During
the rst and last months of the year, all three scenarios are similar
to the baseline. However, the varied boundary conditions lead to
signicant changes between the scenarios in spring and summer.
Scenario A and B show the strongest increase of grid ows with a
daily average of 17.05 kWh and 17.95 kWh. This is caused by the
increased PV excesses and the lower battery utilization. In contrast,
the differences in the grid ows of scenario C show a more or less
constant daily increase of 15.93 kWh.
4.2. Variance of the energy ows
Fig. 4 represents statistical parameters of the annual energy
ows for the PV production, consumption, battery, and grid ows
referenced to their PV peak performance (see Table A.3).
Scaled by the PV power, the annual consumption rates show the
largest deviations among the 4906 domestic energy systems
varying by 2.93 MWh. The high number of outliers is caused by
households with small PV plants and high annual energy con-
sumption rates. According to the efciency increases of 17.1% and
15.5%, the medians of the consumption rates decline by 140.07 kWh
for scenario A and 127.10 kWh for scenario B related to the baseline
with 812.40 kWh per kWp PV. The distribution of scenarios C is
identical to 0, as no shifts in the consumptions are assumed.
The scaled PV production shows a more or less constant increase
between 12.8% and 15.3% of median and quartiles for all future
scenarios. Climate change increases the PV production but not the
variance among the domestic energy systems.
The scaled battery ows are generally characterized by lowest
variance of the analyzed parameters, as the interquartile range
varies only by 63.77 kWh per kWp in the baseline year for instance.
However, in scenario A and B the spread between the annual bat-
tery ows is increased under future conditions by up to
110.96 kWh. For smaller battery systems, the battery ows increase,
whereas for larger systems they decline.
This is different for the residual loads: The median increases
Table 1
Boundary conditions for three future scenarios concerning the IPCC emission scenarios and the progresses in energy efciency.
Greenhouse gas emission path
RCP 2.6 RCP 4.5 RCP 8.5
Efciency improvements 17.1% A
15.5% B
0% C
A. Reimuth et al. / Energy 198 (2020) 117339 5
between 26.7% in A, and 20.8% in C compared to the baseline sce-
nario. However, the future interquartile ranges are decreased. Thus,
the total grid ows of the domestic energy systems rise but the
variances between the energy systems decreases under the future
scenario conditions.
4.3. Development of the residual loads
The residual loads presented in Fig. 5 are obtained from a subset
of 2505 simulated domestic systems, which exclusively occupy a
raster grid point so that the energy ows can be directly linked to a
single building.
The maximum and minimum residual loads represent the range
of hourly power ows per kW-peak, which are supplied by or fed
into the grid. All four scenarios show that the feed-in peaks exceed
maximum consumption. The power excesses are also raised in all
three future scenarios in their quantities. In addition, the maximum
positive residual loads decrease in scenarios A and B due to the
reductions of the consumption rates. In all scenarios, the maximum
power ows of the households are dened by the PV excesses.
The extreme grid ows ank the average loads of the four
classes of storage capacities (<8 kWh, 8e11 kWh, 11e14 kWh,
Fig. 3. Cumulated energy ows of consumption, production, battery and grid ows for an average domestic energy system (left), and cumulated differences between the future
energy ows of Scenario A, B, and C and the baseline scenario (right).
Fig. 4. Quantiles of the domestic annual energy ows for consumption, production, battery, and grid per kWp-PV power for Scenario 0, A, B, and C.
A. Reimuth et al. / Energy 198 (2020) 1173396
>14kWh). All three future scenarios are characterized by an in-
crease of hours with medium and maximum excesses. This can also
be observed when analyzing the number of hours without grid
ows. In the baseline scenario, the domestic energy systems with
less than 8 kWh capacity have 47 h without grid interaction in
contrast to 1640h with systems of more than 14kWh. Under the
future scenario conditions, the hours of autarky decrease for all
sizes of the battery systems. Scenario C is characterized by the
highest decline for all battery sizes showing only 12 h without grid
interaction for the smallest and 1071 h for the largest category of
battery capacities.
4.4. Self-consumption and self-supply
Fig. 6 shows the degrees of self-consumption (DSC), self-supply
(DSS), autarky (DA), and the number of cycles (NoC) as a function of
the domestic battery capacities (see also Table A.4-A.5). The curves
are interpolated from the subset of 2505 domestic energy systems.
In the baseline scenario 0, the degree of self-consumption
ranges between 82.2% at a storage capacity of 5.0 kWh and 39.4%
at the maximum capacity of 16.7kWh. In the future scenarios, the
DSC decreases constantly by 11.9% in scenario A, 11.1%in B, and 4.1%
in C.
Whereas the degree of self-supply varies between 49.2% and
87.5% in the baseline scenario, the DSS increases under the future
scenarios. With 79.0%, scenario B is characterized by the highest
DSS rates for an average storage size, which means an increase of
6.2% compared to the baseline scenario. However, the sensitivity of
this parameter to the capacities declines. The range of the DSS
between the smallest and largest battery sizes decreases from
38.3% for the baseline to a 29.4% in scenario A and B.
Analyzing the degree of autarky shows a high dependency of the
battery size. Whereas the inuence of climate change and efciency
enhancement is low for small battery systems, the DA is very
sensitive to the future developments of efciency improvements
and climate change for larger systems. At the smallest battery size
of 5.0 kWh, the DA increases only up to 12.0% compared to scenario
0. For the largest analyzed battery storage size of 16.7 kWh, the DA
rises from the baseline result of 127.8%e222.3% in A, 220.4% in B,
and 163.5% in C.
The threshold of 100% is the point at which the annual grid ows
of the building are equivalent to grid supply. In the baseline sce-
nario, an energy system with a peak power of 8.1 kW and storage
capacity of 13.5 kWh has the same magnitude of grid ows as
without a production and storage component. Scenario A reaches
this threshold already at 9.2 kWh, Scenario B at 9.5 kWh and Sce-
nario C at 11.4 kWh.
While the DA increases under futures conditions, the cycle
numbers decline. In the baseline scenario, the NoC shows almost no
dependency on the battery size with 340.0 battery starts. In sce-
nario C in contrast, the NoC has the highest sensitivity to the battery
capacity with annual cycle numbers ranging from 321.2 to 341.9.
Scenario C is also characterized by the lowest decline of the NoC
from the future scenarios.
5. Discussion
5.1. Battery utilization
The results obtained in this study show that the inuence of
efciency enhancement and climate change on the battery ows is
both signicantly and strongly dependent on the season.
The summer months are characterized by the rising PV pro-
duction of up to 13.7% under RCP 4.5 (see Fig. 3). This leads to an
increased availability of excess energy in a time span, which is
already in the baseline year characterized by higher back ows and
fully charged batteries. Consequently, the discharging quantities
enabling the intake of surpluses gain more inuence. Since a
reduction of the consumption impedes the full discharging, ef-
ciency enhancement will lead to the decrease of the battery ows in
the summer months.
This is different in the winter times, when the changes in the
production rates determine the battery ows. The availability of
excess PV power will continue to play the decisive role on the
battery ows in winter. On the one hand, climate change will lead
to increasing precipitation events and therefore a reduced avail-
ability of solar irradiation. On the other hand, the days with snow
coverage and therefore the blocking of radiation absorption will
decrease under rising temperatures. Under a high climate change
scenario the positive effects of decreasing snow days exceed the
reduced availability of solar irradiation in the study region, which
leads to increasing PV production and battery ow rates.
However, the potential development of the battery ows is also
dependent the capacity. The battery ows will rise under the
conditions of high efciency improvements and low climate change
only for small capacities. This is also reected by the decrease of the
cycle numbers, which is caused by fewer and longer lasting energy
surpluses of the small systems.
Fig. 5. Distribution of the hourly residual loads of 2505 selected domestic energy systems with the minimum and maximum extrema enclosing the average ows for the battery
systems having capacities of less than 8 kWh, 8e10.99 kWh, 11e14.99 kWh, and larger than 14 kWh.
A. Reimuth et al. / Energy 198 (2020) 117339 7
The projected developments of the battery ows suggest that
the analysis of the future utilization requires scenarios containing
both the changing climate conditions and efciency improvements
as their inuences are subject to opposing temporal courses: The
effects of climate change dominate in the winter months, whereas
those of efciency enhancement prevail in the summer.
The spatial patterns of the meteorological drivers are less rele-
vant, as the annual ows of battery systems are characterized by
the smallest regional variance. The results indicate that a sufcient
temporal resolution of the energy efciency and climate pro-
jections plays a more important role in the regional assessment of
the changes in the future battery utilizations than the choice of the
grid size.
5.2. Residual loads
Similar to the battery ows, the projected deviations in the grid
ows underlie seasonal effects. In the winter months, all three
future scenarios are characterized by only small differences.
Despite the reductions in energy demand under scenario A and B,
the grid ows do not decline at the same magnitude as the energy
consumption. This indicates that also in winter the grid suppliers
have to deal with rising power excesses.
In the summer months, the feed-in rates are signicantly raised
when compared to the baseline scenario 0. These shifts arise from
several factors: Despite adverse effects of rising temperatures, the
PV production rates will increase due to more stable high-pressure
systems, which lead to reduced cloudiness and intensied short-
wave irradiance. The buffering function of the batteries balancing
PV production and consumption remains more or less constant or
even declines if the consumption is reduced. Therefore, the
increasing energy excesses have to be fully balanced by the grid
suppliers. Consequently, scenario A with the strongest reduction of
the battery ows is characterized by the highest increase of the grid
power ows. Scenario C assuming no efciency improvements and
the climatic conditions of RCP 8.5 shows the lowest rise of grid
ows, as the increased PV production can be better balanced under
the higher consumption rates.
The distributions of the residual loads, which shift to more
extreme PV excesses, also reect this development. Stresses in the
local grids caused by high PV production rates will further intensify,
if no countermeasures are taken. The annual grid ows scaled by
kWp PV show a high spread between the domestic energy systems,
which is caused by the variance in the consumption rates (see
Fig. 4). This can be explained by the divergence of PV sizes, which
were primarily dimensioned to maximize the grid-feed in recent
years.
The average increase of the PV production obtained in this study
Fig. 6. Degree of Self-Consumption (DSC), Degree of Self-Supply (DSS), Degree of Autarky (DA), and Number of Cycles (NoC) in dependency of the battery capacity. The lines
represent the tted curves, which are in logarithmic form for DSC and DA, and exponential form for DSS and NoC.
A. Reimuth et al. / Energy 198 (2020) 1173398
exceeds the projected rates of the discussed literature ndings
[11,12,14]. One reason for this divergence could be that the selected
study area belongs to a part in Germany with high PV potential but
also large sensitivity to climate change. Lying at the fringe of the
Temperate and Mediterranean climate, it will increasingly come
under the inuence of Mediterranean climate with milder winters
and dry, hot summers. This effect may be generally underrepre-
sented in climate models with larger grid sizes.
The deviations are further reasoned in the lower temporal res-
olutions of the climatic projections used in previous studies (1h in
our study vs. 3h in Ref. [12] or 1d in Ref. [11 ]). Temperature rise and
irradiation conditions, but also the inuence of the continuously
changing inclination angles between panels and sun are not
distributed linearly during daytime. Temporally coarser meteoro-
logical drivers using daily or even monthly values for assessing
changes in PV production rates cannot capture these effects due to
their coarse simulation of atmospheric processes. Consequently, we
recommend the utilization of climate data with a sufciently high
temporal resolution or downscaling methods when analyzing po-
tential effects on residual loads.
5.3. Limitations of the study
The study is subjected to several limitations concerning the
temporal and spatial variability of the consumption loads. As the
annual energy consumption is averaged on municipal scale and
temporally downscaled by standard load proles, the variance be-
tween the individual buildings cannot be represented with high
precision. That means that the obtained results for battery utiliza-
tion and grid ows are valid for residential buildings constructed in
recent decades but not new buildings like zero-energy homes.
The validity of the study is further restricted to buildings
without electrically based heating or cooling systems. Their elec-
trical consumption is additionally dependent on the supply with
thermal energy, which is driven by the heat demands, insulating
properties of the building materials and outside temperatures.
Consequently, the hourly consumption rates of buildings with heat
pumps strongly vary from the load proles used in this study.
Apart from this, the modeling of the efciency enhancement
applied to scenario A and B leads to further shortcomings. The
hourly decline of the consumption rate is depending on the single
efciency improvements of the running devices. However, the
progresses in efciency enhancement will differ between the
electrical goods. The varying developments in the improvements
will possibly induce unsteady changes of the hourly load proles.
The assumption of a temporally constant decrease insufciently
reects these shifts. Nevertheless, the approach applied in this
study offers a concise assessment on the battery and grid ows.
5.4. Implications for the battery dimensioning
The obtained results indicate that the current assumptions in
terms of battery utilization and grid ows will have to be adjusted
to the future developments when investigating optimal storage
sizes.
Climate change and efciency enhancement will reduce the
self-consumption rates between 4% and 12% depending on the
scenario conditions. The independency from the battery size is
reasoned in the point that climate change rises the PV production
constantly for all PV sizes as described in chapter 4.2. In contrast,
the development of the self-supply is strongly inuenced by the
scenario assumptions, which thus have to be carefully selected.
When applying dimensioning approaches with the goal of a high
self-consumption or self-supply in the future, we recommend
considering these future changes of the boundary conditions in the
optimization methods.
Another important factor for the system sizing under economic
constraints is the magnitude and time of the residual loads as the
cost savings are also indirectly dependent on the charging and
discharging amounts of the batteries. The results of this study
indicate that the balancing effect of the batteries will be weakened
for the majority of the systems. Especially during the summer time
with high PV excesses, the battery ows of the systems will be
generally reduced if consumption declines. At these times, a sig-
nicant increase of excessive grid feed-in rates has to be expected.
This will further raise curtailment losses if feed-in limits are
imposed by the government. These changes can have crucial im-
pacts on the protability apart from the future development of
economic parameters and technological improvement of the PV
and battery systems.
6. Conclusion
In recent years, small-scale battery storage systems have been
increasingly installed in households with rooftop mounted PV
systems. In the development of appropriate dimensioning ap-
proaches with different optimization goals, weather conditions and
energy consumption belong to the essential boundary conditions.
However, climate change and efciency enhancement of domestic
appliances affect PV production and consumption rates, which
consequently induces changes in battery and grid ows. We con-
ducted a regional model simulation study of three future scenarios
for the year 2040, which combines projected changes in climate
and consumption loads to assess the annual course of their impact
on 4906 spatially distributed households with PV systems and
battery storages.
The results of the study show a rising PV production and a
reduction of the charging cycles but rising battery ows for small
battery systems. However, a decline in the utilization of larger
residential batteries has to be expected with increasing sustain-
ability of the boundary conditions. The changes in the battery ows
are subject to a strong seasonal inuence: In summer with higher
PV production, they are driven by the reduction of the energy
consumption. In winter, they are induced by the changes of the PV
production rates, due to the lower availability of energy excesses. A
decrease of the self-consumption between 4% and 12% indepen-
dently from the battery and PV size has been found in this study,
which is reasoned in the constant increases of the PV production
rates.
The reduced buffering effect of the batteries and the increasing
PV-production also affect the grid ows. The residual loads are
shifted to more and higher energy excesses under future climate
change and efciency scenarios. Especially the summer months are
characterized by high PV excesses, which cannot be stored by the
residential energy systems. This will increase the probabilities of
bottlenecks in the grids and therefore the need for grid adjustment.
The projected changes should be considered in the application
and development of dimensioning approaches optimizing self-
consumption and cost-efciency. For a robust sizing, we recom-
mend the usage of scenarios, which include the potential de-
velopments of both weather conditions and consumption apart
from economic parameters.
Apart from the impact of climate and energy efciency analyzed
in this study, residential energy ows will also be affected by rising
rates of electrically based heating and cooling systems and the
launch of electric vehicles. These future developments highlight the
need for further research in the assessment of challenges and op-
tions for the grid integration of residential PV systems under a
changing climate.
A. Reimuth et al. / Energy 198 (2020) 117339 9
Declaration of competing interest
The authors declare that they have no conict of interests.
Acknowledgements
This study was carried out in the framework of the project
INOLA eInnovationen für ein nachhaltiges Land- und Ener-
giemanagement auf regionaler Ebene(grant code 033L155AN),
sponsored by the German Federal Ministry of Education and
Research (BMBF).
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.energy.2020.117339.
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A. Reimuth et al. / Energy 198 (2020) 117339 11
... It also utilizes excess power for water pumping [13]. The change in climate and the load are studied for a residential PV-Battery system in [14]. Solar radiation, environmental, and climate data greatly influence solar PV systems [15]. ...
... The schematic of the methodology adopted for the study of the PV/Battery system. mation of the PV panel size considering all the losses [14]. The design of the system starts with the calculation of daily average load demand in kWh/day. ...
... In conventional PV designing, the solar radiation is selected as the yearly average solar radiation and operating factor of 0.75 [14]. However, in this present work, for an agricultural load, the yearly average solar radiation except for rainy season months (5.14 kWh/m 2 /day) and operating factor of 0.7 is proposed for the desired LLP range of 0.01-0.05. ...
Article
A photovoltaic Battery (PV-Battery) system is considered one of the most promising renewable energy off-grid systems. In a conventional design of PV systems generally, an annual average solar radiation of a site and a constant operating factor of the PV module (0.75) are considered. This research aims to find the effect of the two design parameters (solar radiation and operating factor (OPV) of the PV panel) on the Loss of Load Probability (LLP) of the PV-Battery system and to find out the optimum PV array size which is adequate to supply the energy requirement of an agricultural farm, under the climatic conditions of Haldia, India. Four different global solar irradiation have been considered for the design of the PV-Battery systems based on worst month radiation (4.05 kWh/m²/day), yearly average radiation (4.84 kWh/m²/day), yearly average radiation without rainy season months (5.14 kWh/m²/day), and best month radiation (6.28 kWh/m²/day) along with three different OPV of the PV panel (0.7, 0.75 and 0.8). The monthly LLP, yearly LLP variations, PV panel size, and the area required for the installation of PV panels are studied. The results of the study find the LLP as 0 of the system at the radiation of 4.84 kWh/m²/day with the OPV as 0.75 while LLP is in the range of 0.01–0.05 for the radiation of 5.14 kWh/m²/day with the OPV as 0.7. The model developed should be able to design PV systems for any agricultural load designed with the desired LLP.
... Kim et al. [8], considering the Northeast and Midwest regions, concluded that the capacities of on-site PV systems required for nearly zero energy buildings can increase significantly up to 40 kWp (i.e., up to more than 10% of total PV installation capacities), as the different climate change scenarios were applied. Yang et al. [9] found that in the Mediterranean region, the average heating demands decrease by 5.4-19.2% in a mediumterm scenario and by [6][7][8][9][10][11][12][13][14][15][16][17][18][19].7% for long term projections. The increment in the average cooling demand varies by 3.5% and between 3.6 and 8% with medium-term and longterm projections, respectively. ...
... Implementing different energy efficiency measures, for a typical Swedish multi-story residential building constructed in the 1970s, Tettey et al. [15] asserted that cooling demand and overheating risk will increase with thermal envelope improvement. Considering 4906 buildings located in the south of Germany, Reimuth et al. [16] found a general rise of grid feed-in rates between 21% and 27% due to increased photovoltaic production. ...
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One of the strategies of the European Green Deal is the increment of renewable integration in the civil sector and the mitigation of the impact of climate change. With a statistical and critical approach, the paper analyzes these aspects by means of a case study simulated in a cooling dominated climate. It consists of a single-family house representative of the 1980s Italian building stock. Starting from data monitored between 2015 and 2020, a weather file was built with different methodologies. The first objective was the evaluation of how the method for selecting the solar radiation influences the prevision of photovoltaic productivity. Then, a sensitivity analysis was developed, by means of modified weather files according to representative pathways defined by the Intergovernmental Panel on Climate Change Fifth Assessment Report. The results indicate that the climate changes will bring an increment of photovoltaic productivity while the heating energy need will be reduced until 45% (e.g., in March) and the cooling energy need will be more than double compared with the current conditions. The traditional efficiency measures are not resilient because the increase of the cooling demand could be not balanced. The maximization of installed photovoltaic power is a solution for increasing the resilience. Indeed, going from 3.3 kWp to 6.9 kWp for the worst emission scenario, in a typical summer month (e.g., August), the self-consumption increases until 33% meanwhile the imported electricity passes from 28% to 17%.
... In this way the presented results might contribute to a quicker transition to a renewable energy system and fulfilment of climate goals. The problem of curtailment is expected to be even more prominent in the near future since, according to [20], changes in climate and consumption are expected to decrease the self-consumption by 4-12 %. ...
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The rise of photovoltaic (PV) penetration is contributing to the increasing incidence of overvoltage detection in the electrical grid during times of high-power generation. Overvoltage can cause disturbances or (partial) failures in the electrical supply network, since the components used are designed for a certain voltage band. One option to counteract too high voltage levels and thus ensure power quality, grid stability and resilience is the absorption of active power by means of a battery energy storage system (BESS). In this paper, we first built a suitable simulation setup for a typical European network section, including a large-scale PV system connected to the 10 kV level and a BESS model. A suitable charging and discharging algorithm for the BESS with the aim to realize peak shaving for the grid voltage was developed and implemented. Simulations, performed in MATLAB/Simulink®, show the dependence of the battery capacity and power on the grid-serving effect of BESS. By determining appropriate values for these two factors a significant reduction of the voltage level could be achieved.
... Aynı zamanda su pompaları, güneş ev sistemleri, şebekeden uzak binalar gibi birçok alanlarda yaygın olarak kullanılmaya başlanması FV enerjiye olan talebi her geçen gün arttırmaktadır. Fakat, FV panel veriminin düşük olması, atmosferik şartların gün içerisinde değişiklik göstermesi ve buna bağlı olarak FV panellerinden elde edilen güç değerlerinin de sürekli olarak değişmesi FV enerji sistemlerinin en önemli dezavantajlarını oluşturmaktadır [5][6]. ...
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z Gerilimin yüksek değerde olması istenilen fotovoltaik (FV) sistemlerde paneller seri olarak bağlanır. Seri bağlanan FV sistemlerde en önemli sorunlardan biri bina, ağaç, direk vb. nedenlerden dolayı panel üzerinde kısmi gölgenin oluşmasıdır. Kısmi gölgeleme, panel üzerinde sıcak noktaların oluşmasını neden olarak hem panele zarar verir hem de sistemin verimini düşürür. Bu olumsuz durumların engellenebilmesi için panellere paralel olarak bypass diyotları bağlanır. Bu çalışmada, kısmi gölgelenme durumundaki seri bağlı FV panel sayısının sırasıyla 2, 4 ve 6 olacak şekilde arttırılarak sistemde bypass diyotunun kullanılıp kullanılmamasına göre meydana gelen kayıp oranı incelenmiştir. Panel sayısı arttırılırken sistemin toplam gölgelenme oranı da azaltılmıştır. Çalışma, PSIM benzetim programı kullanılarak gerçekleştirilmiştir. Yapılan inceleme sonucunda kısmi gölgelenmeye maruz kalmış seri bağlı FV panellerde panel sayısı arttırılıp toplam gölgelenme oranı düştükçe bypass diyotunun kullanıldığı sistemlerde kayıp oranı azalmaktadır. Bypass diyotunun kullanılmadığı sistemlerde ise panel sayısı arttırılıp toplam gölgelenme oranının düşürülmesi kayıp oranını azaltmamakta aksine arttırmaktadır. Yani; bypass diyotsuz sistemlerde seri bağlı FV panel sayısı arttırıldığında düşük gölgelenme oranlarının bile verimliliği önemli ölçüde düşürdüğü görülmüştür. Abstract Panels are connected in series in photovoltaic (PV) systems where high voltage is desired. One of the most important problems in PV systems connected in series is the formation of partial shading due to the factors as building, tree, pole etc. Partial shading causes the formation of hot spots on the panel, damaging the panel and reducing the efficiency of the system. In order to prevent these negative situations, bypass diodes are connected parallel to the panels. In this study, the number of PV panels connected in series in partial shading is increased in a way to be 2, 4 and 6, respectively, and therefore the rate of losses when the bypass diode is used and is not used not in the system are
... technological projections, such as energy efficiency variations, different technologies market distribution, heating/cooling periods variations due to climate change [82], should be taken into account to accurately estimate primary energy consumption forecast in a scenario analysis [52]; geographic Information System can be integrated to support the geographical dimension of the analysis, to support infrastructure planning as well as the assessment of demand-side [83,84]; energy-related behavioural changes in households should be integrated into the socio-demographic perspective, to investigate the effects on primary energy consumption [53,54]; the ISTAT micro-data employed can be exploited to model the whole household sector, including the transport sector; indeed, a broader application of MOIRAE may be integrated to analyse the energy flows in a cross-sectoral perspective; MOIRAE can be applied to other countries and integrated to existing models; for instance, a hybrid approach (bottom-up and top-down approach integration) may be implemented to tackle both end-use demand side (at bottom" layer) and macroeconomic or market-level factors (at "top" layer), e.g. through soft-linking [73,85,86]; energy efficiency strategies, including top-bottom policies, should be considered to improve the economic-based scenario and to evaluate the outcome of energy investment decisions ...
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What are the required costs to sustain the electrification of the residential sector? What are the achievable primary energy savings? This paper tries to answer these questions, for the Italian residential sector, by providing coupled energetical and economic evaluations of the electrification pathways. To this end, this paper extends MOIRAE, a bottom-up modelling approach previously proposed by the authors. First, the input data have been upgraded by coupling, using ad-hoc statistical methods, different datasets provided by the Italian Institute of Statistics. Second, to estimate households’ time-variation, a socio-demographic model has been developed, validated, and implemented. Third, an economic model of fixed and variable costs for electrical and thermal appliances has been implemented. Subsequently, the modelling approach has been calibrated against detailed consumption data for the different Italian regions and validated against historical data. Finally, MOIRAE has been employed to unveil the electrification pathways with and without household budget constraints, aiming at replacing natural gas, LPG, diesel, and fuel oil energy carriers with electrical energy. For the different scenarios investigated, the changes in primary energy consumptions and the variation of variable and fixed costs have been included to consider both the energetic and the economic point of view.
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The use of a battery energy-stored quasi-Z-source inverter (BES-qZSI) for large-scale PV power plants exhibits promising features due to the combination of qZSI and battery as energy storage system, such as single‐stage power conversion (without additional DC/DC boost converter), improvements in the output waveform quality (due to the elimination of switching dead time), and continuous and smooth delivery of energy to the grid (through the battery energy storage system). This paper presents a new simplified model of a BES-qZSI to represent the converter dynamics with sufficient accuracy while using a less complex model than the detailed model (including the modelling of all switches and switching pulses). It is based on averaged values of the variables, voltage/current sources, and the same control circuit than the detailed model, except for the switching pulses generation. The simplified model enables faster time-domain simulation and is useful for control design and dynamic analysis purposes. Additionally, an energy management system has been developed to govern the power supply to grid under two possible scenarios: 1) System operator command following; or 2) economic dispatch of the stored energy. The results obtained from simulations and experimental hardware-in-the-loop (HIL) setup for different operating conditions of the grid-connected large-scale PV power plant with battery energy storage under study demonstrate the validity of the proposed simplified model to represent the dynamics of the converter and PV power plant for steady-state stability studies, long-term simulations, or large electric power systems.
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The building sector currently accounts for approximately 40% of final energy consumption. In the near future, renewable energy driven heat pump systems will play an important role in the thermal management of buildings. The work described here deals with the effect of weather conditions as well as operational characteristics on the energy and economic performances of a solar PV powered air conditioning unit (ACU) with a battery system (PBAS) based on numerical and experimental investigations. The experiments were carried out to test the developed TRNSYS model. In the assessment, various performance indicators were used while a novel performance indicator, the so-called annual grid independence ratio, was also proposed. The simulated studies revealed that annual energy performance indicators (self-consumption, self-sufficiency, grid independence and energy conversion ratios) are greatly affected by climatic conditions and operational characteristics. Besides, the economic analysis has shown that the net present value and the energy consumption profiles or diverging values for state of charges are directly proportional for the hottest (Seville) climate. However, they are inversely proportional for the coldest (Stockholm) climate for the considered PBAS configuration (PV-2.5 kWp, ACU-2.52 kW cooling and 2.84 kW heating, batteries-9.6 kW h) under the selected economic status of the sites.
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The African continent faces several challenges and threats: high vulnerability to climate change, the fastest population increase, the lowest degree of electrification and the need for an energy transition towards renewable energies. Solar energy constitutes a viable option for addressing these issues. In a changing climate the efficient implementation of solar capacity should rely on comprehensive information about the solar resource. Here, the newest and highest resolution regional climate simulation results are used to project the future photovoltaic and concentrated solar power potentials for Africa. We show that the high potentials for solar energy will not be reduced much throughout Africa with climate change. However, the PV solar potential is projected to decrease up to about −10% in limited areas of eastern central Africa; increases are also projected to the northwest and southern Africa (up to about +5%). These changes are mostly determined by changes in solar irradiance but in certain areas the warming is a critical factor limiting PV potential.
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Whilst a net zero energy (NZE) home produces the same amount of energy as it consumes it still exchanges significant amount of energy with the grid due to mismatch between the generation and load patterns. Consequently, the homeowner has to pay an annual electric bill because the cost of imported energy is usually higher than that of exported energy. Installing a local battery energy storage system (BESS) can reduce the electric bill by exchanging less energy with the grid. This paper proposes a method of determining the optimal size of a BESS for a typical NZE home with rooftop solar photovoltaic (PV) system to minimize the annual net payment for electricity and battery cost. The optimal battery size is determined through solving an optimization problem which is formulated using hourly load and PV generation data for a South Australian home, battery annual payment rate, retail price (RP), and feed-in tariff (FIT). The effects of interest rate, RP and FIT on the annual net payment are investigated. The results obtained are thoroughly analysed and clearly indicate that, with current installation cost of BESS and South Australian RP and FIT, the use of a local BESS is economically beneficial for the homeowner.
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Battery storage systems can help to integrate excess Photovoltaic (PV) energy into the local energy systems but also increase the request for higher self-consumption rates of the households. This study uses a spatially resolved approach with hourly time steps to analyze the influence of batteries on the domestic residual loads on a regional scale. A domestic energy component is developed consisting of a PV-system model, the demand component, and a battery storage device. The study area is located in the south of Bavaria and 4906 households with PV-systems between 3 and 10 kWp power were selected assuming a battery capacity of 6.2 kWh in average. Three charging strategies for domestic battery storage systems are assessed: (1) Maximization of self-consumption, (2) Fixed feed-in limit of 70% of the PV-peak power, and (3) Daily dynamic feed-in limit based on ideal forecasts. The best result is obtained through the third strategy with a self-consumption of 78.5% on average and the highest reduction of the grid flows by 20% by damping grid excesses. The influence of the charging strategy rises with increasing size of PV-and battery storage systems and therefore residual loads. Regional variations are further caused by the meteorological conditions, different PV-and battery sizes and parameters and demand profiles on municipal scale. Consequently, a sufficient sample size with different setups is recommended for a full evaluation of battery charging strategies.
Technical Report
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This is the annual report 2018 of the scientific monitoring, which accompanies the governmental funding program for pv home storage systems in Germany. It covers: 1. Market and technology development of pv home storage systems - Number of installations - Designs choices - Prices - … 2. Self-consumption, self-sufficiency and their effects on taxes and tariffs 3. High-resolution measurements of 20 privately operated pv home storage systems Please find more information on www.speichermonitoring.de
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It results widely common for distribution network operators to impose restrictions on delivered solar photovoltaic generated power when the power plant rated power is greater than the maximum allowed due to the distribution network capacity. Thus, a feasible solution to maximize the performance of the solar power plant is the integration of battery energy storage systems. Although this configuration has been extensively studied in the existing literature, an optimal design method to determine the proper size and operation of the energy storage system needs to be developed. In this paper, a novel method to help power plants designers to determine the optimal battery energy storage capacity to integrate into any solar photovoltaic power plant is provided. The proposed algorithm minimizes the potential power curtailment and optimizes the utilization rate of the batteries storage system. The algorithm can be applied to any grid connected solar photovoltaic power plant under delivery power restrictions, regardless of power capacity and location. The algorithm has been implemented to a simulated power plant with delivery limitations based in a real case, and results with the optimal battery capacity show that the system would be able to recover up to the 83% of the curtailed energy and a yearly average capacity utilization of 56%. Moreover, the BESS operation has been validated with a scaled model run in Simulink and laboratory measurements, achieving 98% of curtailed energy recovery rate and a 57% of average capacity utilization.
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This paper presents optimal sizing algorithms of grid-connected photovoltaic-battery system for residential houses. The objective is to minimize the total annual cost of electricity. The proposed methodology is based on a genetic algorithm involving a time series simulation of the entire system and is validated using data collected through one year. Genetic algorithm jointly optimises the sizes of the photovoltaic and the battery systems by adjusting the battery charge and discharge cycles according to the availability of solar resource and a time-of-use tariff structure for electricity. Houses without pre-existing solar systems are considered. The results show that jointly optimizing the sizing of battery and photovoltaic systems can significantly reduce electricity imports and the cost of electricity for the household. However, the optimal capacity of such photovoltaic battery varies strongly with the electricity consumption profile of the household, and is also affected by electricity and battery prices. Besides individual PV generation and battery storage for each house, this paper also investigates group battery optimizations for communities with different consumption levels or with different energy demand diversity to see their effects on optimal sizing and peak demands for aggregated PV-battery system.
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This paper builds upon previous research to develop a new mixed integer linear program (MILP) for optimal PV-battery sizing and energy scheduling. Unlike previous formulations, the MILP optimises under both time-of-use (TOU) and demand tariff structures. Optimisation is based on the highest system net present value (NPV). One residential and one commercial customer are used as case studies to contrast optimisation under TOU and demand tariff structures. Optimal PV-battery sizing is not found to be affected by the tariff structures analysed. Optimal solutions under both tariffs prefer larger PV systems coupled with small battery systems. Energy consumption from the grid under TOU tariff optimisation reflects a scaled profile of the consumer's energy demand curve. Peak consumption from the grid is heavily reduced under demand tariff optimisation to decrease the associated demand charge. In the residential case study, peak grid consumption over one year is reduced from 5.98 kWh to 2.25 kWh under demand tariff optimisation. In the commercial case study, peak grid consumption over one year is reduced from 450.3 kWh to 348.6 kWh. The reduction of peak grid consumption is achieved by using the stored energy in the battery.