Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-018-0105-4
© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
1The Energy Institute at the Johannes Kepler University Linz, Linz, Austria. 2Center for Secure Energy Informatics, Salzburg University of Applied Sciences,
Puch/Salzburg, Austria. *e-mail: reichl@energieinstitut-linz.at
R
esidential electricity prices are made up of a number of compo-
nents, including network tariffs, taxes and surcharges, such as a
renewables surcharge, a usage surcharge and an energy charge.
In this Article, we focus on network tariffs, which are the source
for recovering the capital and operational expenditures of provid-
ing transmission and distribution of electricity, as well as the grid
operators’ rate of return on grid investments, and are thereby the
main source for financing grid infrastructure.
As power grids are considered natural monopolies, the distribu-
tion of these costs between consumers is not achieved autonomously
through market forces1. In many modern economies, defining the
cost shares for providing transmission and distribution of electricity
between the consumers is assigned to an authorized entity, for which
most European Union nations have established a dedicated regula-
tory authority since the electricity market liberalization in 2003.
Commonly used and currently discussed network tariffs represent a
combination of volumetric energy charges (charging customers for
the amount of consumed electricity; € per kWh), fixed charges per
customer (independent of their energy consumption but possibly
varying over households based on their contracted capacity, but not
relying on actual load measurements; € per household per year) and
peak-demand charges (based on the actual measured capacity; € per
kW peak load).
The costs of electricity networks are mainly determined by their
capacity, the maximum amount of energy that the grid is dimen-
sioned to stand at any given point in time. Despite this, volumetric
tariffs, which do not directly reflect the nature of these costs, are
still widely applied. In the past, when load profiles of residential
users were approximately homothetic, the application of tariffs with
a dominant volumetric charge was well justified. Nowadays, there
is increasing diversity in daily load profiles, and part of this devel-
opment is due to the increase of distributed generation, the advent
of low-capacity storage (for example, in-home batteries for stor-
ing photovoltaic-produced electricity), the arising prospect of an
increasing number of electric vehicles, and the vision of house-to-
house electricity trading to balance the overproduction from own-
generation without the need (of higher levels) of the power grid2–5.
The deployment of renewable energy in the residential sector has
increasingly severe repercussions on electricity grids; for a growing
share of consumers, the connection to the public grid will largely
serve as a backup option, rather than being the primary source for
their electricity acquisition6,7. For such consumers, the volumes of
electricity consumed from the grid will be subordinate and likewise
will their contribution to the financing of the grid be low in the case
of volumetric tariffs8–11. These trends will inevitably lead to a real-
location of the burdens of grid-cost recovery12–14.
Considering that these innovations are more likely to happen
first among a subgroup of the population owning single-family
dwellings (as most of these innovations require property rights for
installation), a significant social imbalance induced from shifting
the burdens of financing the grid towards lower-income classes may
arise15,16. This can hamper the public acceptance of these innova-
tions. Moreover, some even envision a possible ‘death spiral sce-
nario’17–20, where higher network tariffs will be charged to poorer
customers, which eventually threatens to collapse the whole elec-
tricity supply system. Other recent studies (see for example refs 21,22)
consider such worries overblown, but still call for a timely and care-
ful revision of tariffs to avoid free-riding behaviour.
Several options have been proposed to deal with this issue,
among them minimum network charges per household irrespective
of actual grid utilization, increased fixed charges and peak-demand
charges23–26. These latter charges have long been used in commercial
and industrial network tariffs27, but are a novel development in the
residential electricity market.
Policymakers have to strike a proper balance between different
objectives when designing network tariffs, for example, they should
be easy to understand, fair, cost reflective, encourage energy effi-
ciency and send the right signals to maximize the economic effi-
ciency of power grids28. Therefore, difficult trade-offs have to be
made that will directly influence the monthly bills of the consum-
Exploring the impact of network tariffs on
household electricity expenditures using load
profiles and socio-economic characteristics
Valeriya Azarova   1, Dominik Engel   2, Cornelia Ferner2, Andrea Kollmann1 and Johannes Reichl1*
Growing self-generation and storage are expected to cause significant changes in residential electricity utilization patterns.
Commonly applied volumetric network tariffs may induce imbalance between different groups of households and their respec-
tive contribution to recovering the operating costs of the grid. Understanding consumer behaviour and appliance usage together
with socio-economic factors can help regulatory authorities to adapt network tariffs to new circumstances in a fair way. Here,
we assess the effects of 11 network tariff scenarios on household budgets using real load profiles from 765 households. Thus
we explore the possibly disruptive impact of applying peak-load-based tariffs on the budgets of households when they have
been mainly charged for consumed volumes before. Our analysis estimates the change in household network expenditure for
different combinations of energy, peak and fixed charges, and can help to design tariffs that recover the costs needed for the
sustainable operation of the grid.
Nature Energy | VOL 3 | APRIL 2018 | 317–325 | www.nature.com/natureenergy
317

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Articles
NaTUre EnerGy
ers and network tariff decisions should be informed by empirical
research that defines who these consumers are and how their bills
will change.
With the advent of smart metering, recent literature has recon-
sidered volumetric network tariffs and discussed measured,
capacity-oriented schemes to address the issues outlined above
(for Australia see, for example, ref. 29, for the United Kingdom
see, for example, ref. 30 and for the United States see, for example,
refs 27,31–34). The goal of this Article is to assess different network tariff
schemes with respect to their effects on the budgets of individual
households conditional on their socio-economic backgrounds. Our
analysis aims at contributing to the ongoing tariff debate by pre-
senting the effects of alternative tariff schemes. We use a unique
real-world dataset of 765 Austrian households, whose electricity
consumption was metered for a one-year period, and for whom we
have detailed socio-demographic data at the individual level. Our
analysis shows that alternative tariff schemes, in particular changing
from a volume-based scheme to a scheme that recovers a substantial
portion of network costs through measured peak-demand charges,
may induce substantially increased electricity expenditures to a cer-
tain share of households. Further analyses provide evidence that
these increasing expenditures cannot sufficiently be explained by
the possession of electric appliances, but are due to noticeable and
systematic differences in the electricity consumption patterns along
the politically critical dimensions of, among others, the households’
income situation and number of children.
Household survey data
Household-level data was gathered in two surveys among residen-
tial electricity customers, conducted between April 2010 and March
2011 in the region of Upper Austria, Austria. In the first survey, we
contacted more than 10,000 households via mail and asked them
to allow us to collect their 15-minute electricity load profiles. We
recruited 973 households for participation, all of which gave their
distribution grid operator written permission to send us the house-
holds’ individual 15-minute electricity load profiles measured by
smart meter, for the full period of our survey. In total, we collected
35,040 electricity load values for every household. Throughout the
registration process, households provided information about the
number of people in the household, the type (apartment, single-
family house, semi-detached house) and size (in m2) of their dwell-
ing, the technologies used for warm water and heat preparation
(electricity, gas, district heating, heat pumps, biomass, oil), as well as
their endowment with specific electric appliances that have a high
power demand (swimming pool, fish tank, water bed, sauna, home
cinema).
In the second survey, the same households were offered €10 if
they provided us with additional information about their socio-
economic characteristics (income and composition of the house-
hold) and further information about their electric appliances. The
final dataset includes 765 observations (which we refer to as ‘full
sample’), for 406 of which we have additional information about
the household’s income (henceforth called ‘subsample’). Details are
provided in Supplementary Note 1 as well as Supplementary Tables
1 and 2.
Scenarios of network tariffs
Ever since Bonbright examined the principles of designing tariffs
for recovering the costs of natural monopolies35 in 1961, the topic
has been intensely researched for public utilities in general, as well
as for electricity networks specifically (see, for example, refs 29,36–38).
As these sources establish, among others, a comprehensive devel-
opment of network tariffs has to consider the dimensions ‘system
sustainability’, ‘economic efficiency’ and ‘distributive justice’. In our
study, tariff scenarios are exclusively designed to cover the range
of potential network tariffs based on the candidate tariff compo-
nents discussed above, to allow comparisons of the effects of these
schemes on household expenditures. Technically, we follow the ulti-
mate quantitative paradigm for designing network tariffs, which is
to first determine the overall quantity of costs that shall be recov-
ered and then to define a distribution key by putting weights on
the candidate tariff components. According to this practice, and
by treating our sample as if it was a tariff zone on its own, we first
assess the sum of network charges to be paid by our full sample in
the Austrian tariff scheme as it was in force in 2016, which we refer
to as ‘reference scenario’ henceforth, resulting in total charges of €
136,209.10.
Tariff scenarios in our study recover this sum by putting differ-
ent weights on the three components: energy charges in € per kWh,
fixed charges in € per year and peak charges in € per kW peak load.
These different weights are given in Table 1. Details on the calcula-
tion of the respective tariffs are provided in Methods. Among the
selected 11 alternative tariff schemes, 8 scenarios include a peak-
demand charge. The three scenarios not relying on measured loads
(f100, f50/e50 and e100) are similar to the network tariffs currently
applied in Europe. As an example, scenario f100 is similar to the
scheme applied in the Netherlands in 2016, where all customers
faced the same network charges irrespective of their actual con-
sumption patterns39. An overview of the volumetric share of tariffs
as applied in some European countries in 2016 is given in ref. 40.
For our analysis, we quantify the annual network expenditures of
each household under the tariff scenarios. We define our quantity of
interest as the percentage by which the network costs differ between
the reference scenario and the respective alternative scenario. This
allows interpretation of the results in direct relation to the house-
holds’ expenditures in the status quo, that is, whether they would
face increasing or decreasing costs under a certain alternative tariff
scheme.
Tariff impact on household network expenses
We first assess the impact of the tariff scenarios from Table 1 on
household annual network expenditures (see Methods for details).
For the interpretation of our results, we point to the ex-post nature
of our investigation: each of the tariff scenarios sends a specific sig-
nal to households, for example, pa100 and pm100 provide incentives
for avoiding peak loads while they do not penalize high electricity
consumption. Ideally, households would analyse their consumption
patterns with respect to the applied tariff scheme and adapt their
behaviour to minimize costs under certain boundary conditions41,42.
As the data exploited in our study were already collected in 2010–
2011, households were not provided with such signals.
Figure 1 shows box plots of the change (in %) of the annual net-
work expenditures under the 11 tariff scenarios compared with
the reference scenario for the 765 households (for analysis of the
subsample see Supplementary Fig. 1). Several outcomes are evi-
dent: first, the most significant change in households’ network
expenditures are calculated for scenarios with a dominant share of
fixed or peak-load charges and thereby deviate significantly from
the reference scenario. The interpretation works vice versa, that is,
one would observe similar changes in household network costs if,
say, f100 was currently applied and was substituted for the reference
scenario.
Second, the box plots reveal that for some households the
increased costs in certain tariff scenarios are very high compared
with reference levels. For illustration, we marked the two house-
holds that experience the highest increase in network expenditures
under peak-based scenarios by open and filled triangles. These are
single-person households that consume moderate volumes of elec-
tricity (that is, 1,805 and 1,604 kWh in total during the observation
period) and their current network costs in the reference scenario
are low compared with the mean in our sample. At the same time,
these households produce massive peak loads (see Supplementary
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NaTUre EnerGy
Figs. 2 and 3). Consequently, under tariff schemes charging exclu-
sively for measured peak demand, such as pa100, they have to pay
up to €435.40 and €471.60 more, translating to +422% and +499%,
respectively. In contrast to their sensitivity to tariffs, which empha-
size measured peak demand, the same two households face only
around 80% higher charges in scenario f100. When neglecting peak
demand and applying scenario e100, which is determined by the
volume of consumed energy only, the opposite effect occurs and
these households would actually pay around 10% less than in the
reference scenario.
Even when the households marked by open and filled trian-
gles are considered as outliers in our sample, these cases are still
observed in a relatively small sample of 765 households, suggest-
ing that a relevant number of households may face significant addi-
tional burdens when/if household peak-load-based charges are
introduced. We check how many households in the sample also tend
to have increasing costs under some of the scenarios while experi-
ence cost savings in others. We find that 321 households face lower
costs in scenario e100 and higher costs in scenario pa100, which
means that nearly 40% of the households consume relatively mod-
erate volumes of energy in total, but at the same time frequently
produce significant peak loads. This example demonstrates that a
different weighting of the volumetric, peak and fixed components
can have strongly diametrical effects on the network expenditures
of individual households.
While we do not think that this result shall prevent the appli-
cation of such innovative network tariffs, before their introduction
a careful impact assessment appears necessary, which may be fol-
lowed by mechanisms balancing hardship cases during the transi-
tion period.
Network expenditures and socio-economic characteristics
In this section, we assess which household characteristics aid in
explaining the impact the tariff scenarios have on households’ net-
work expenditures. Regression results of the full sample with seven
explanatory variables and a constant are shown in Table 2 (for main
results excluding outliers see Supplementary Table 4), while results
–50
Scenario
0
50
100
150
200
250
300
350
400
450
500
Difference to currently applied tariff (%)
f100
f50/e50
e100
pa100
pa50/e50
f50/pa50
f/pa/e
pm100
pm50/e50
f50/pm50
f/pm/e
Fig. 1 | Change in annual network expenditure under different tariff
scenarios. The full sample of 765 households is analysed in each of the
scenarios.The bottom of each box is the 25th percentile, the horizontal
line in the middle of each box represents the median and the top of each
box is the 75th percentile. Vertical lines outside of the box (whiskers)
end at the 10th and 90th percentiles. The dots are considered as outliers.
For illustration, the two households with the highest increase in network
expenditures under scenarios including a peak component are marked by
open and filled triangles.
Table 1 | Residential network tariff scenarios applied to the full
sample of 765 households
Scenario
Description
(overall
network costs
are recovered
through)
Fixed charge
(€ per
household
per year)
Energy
charge
(€ per
kWh)
Peak/
capacity
charge
(€ per kW
peak load)
Reference
Tariff as applied in
Austria in 2016
24.60
0.043
–
f100
100% flat tariff
178.05
–
–
pa100
100% peak charge,
based on the
average of the
12 monthly
measured
peak loads
–
–
39.07
pm100
100% peak charge,
based on the one
maximum load
–
–
29.59
e100
100% energy
charge, only based
on consumed
volume
–
0.050
–
f50/e50
50% from fixed
charges and 50%
from consumed
volume
89.02
0.025
–
f50/pa50
50% from fixed
charges and 50%
from peak charges
(average of the
12 monthly peaks)
89.02
–
19.53
f50/pm50
50% from fixed
charges and 50%
from peak charges
(one maximum
load)
89.02
–
14.79
pa50/e50
50% from peak
charges (average
of the 12 monthly
peaks) charge
and 50% from
consumed volume
–
0.025
19.53
pm50/e50
50% from peak
charges (one
maximum load)
and 50% from
consumed volume
–
0.025
14.79
f/pa/ea
14% from fixed
charges, 43%
from consumed
volume and 43%
from peak charges
(average of the 12
monthly peaks)
24.60
0.022
16.83
f/pm/ea
14% from fixed
charge, 43%
from consumed
volume and
43% from peak
charges (one
maximum load)
24.60
0.022
12.75
aThe fixed charge is taken from the reference scenario, while the remaining portion is split into
equal quantities, see Methods for details.
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of the regression exploiting the subsample, including informa-
tion about the households’ income, household type and electricity
consuming amenities, are presented in Table 3. The results of both
regressions show similar impact on household expenditures for
network tariffs, with only minor changes in the magnitude of the
coefficients and their significance, which we interpret as evidence
for the robustness of our results. The applied statistical model is pre-
sented in Methods.
The regression results shown in Table 2 suggest that the number
of residents of a household (Nr_persons), the living space (Square),
whether the dwelling is in a rural or an urban environment (Rural)
or whether the dwelling is a single-family house (House), are associ-
ated with lower network costs under scenarios with a fixed charge
compared with the reference scenario, and higher costs in the fully
energy-based scenario e100. This is explained by the fact that these
households (ceteris paribus) consume higher volumes of electricity
and thereby benefit from tariffs, which put only subordinate weight
on the number of consumed units. Interestingly, almost the same
household characteristics (with exception of a rural versus urban
environment and living space) are related to lower network costs
in scenarios emphasizing measured peak demand (pa100, pm100,
pm50/e50, and pa50/e50), providing evidence that households with
more residents and households situated in a single-family house
tend to produce fewer peak loads, ceteris paribus.
The inclusion of the household amenities in the regressions is
essential even though it is not the focus of our analysis, as they would
likely suffer from omitted variable bias otherwise, as these are corre-
lated with the policy-relevant variables. The signs of their effects are
as expected conditional on which tariff component the respective
scenario emphasizes. Tumble dryer and PC increase households’
costs in the fully energy-based scenario e100, while flow heaters
become increasingly costly in the scenarios charging for peak loads.
Pool owners benefit from peak or fixed tariffs compared with the
status quo, and households with a sauna have a significant disadvan-
tage in tariffs charging for measured peak demand. Further analysis
of whether the amenities are related to high energy volumes or peak
loads are given in Supplementary Note 3 and Supplementary Tables
5 and 6.
From a policy perspective, it is important to notice that most
of the parameters that significantly contribute to lower network
charges under the respective alternative scenario (that is, house-
holds living in single-family houses, having larger living spaces
and swimming pool ownership) are usually associated with higher
income levels. As the sum of collected revenues from all households
together is required to remain unchanged under any new tariff
scheme, a reduction of the financial contribution of higher-income
households would automatically mean an increase of burdens for
lower-income households compared with the situation under the
reference scenario. As such distributional effects are problematic
from a public choice perspective, it is important to identify whether
certain tariff scenarios are actually associated with shifting burdens
towards households with lower incomes.
Most importantly, the regression output in Table 3 provides evi-
dence for the distributional effects when the tariff scheme is changed
from emphasizing the volumetric component as in the reference
scenario, towards a stronger weighting of peak demand. A house-
Table 2 | Effects of household characteristics and amenities on the relative difference of their network costs in our full sample
f100
f50/e50
e100
pa100
pa50/
e50
f50/pa50
f/pa/e
pm100
pm50/
e50
f50/pm50
f/pm/e
Nr_persons
−
15.643***
−6.564*** 2.515***
−6.275***
−
1.880***
−
10.959***
−3.782***
−7.351***
−2.418***
−11.497***
−4.245***
(1.354)
(0.568)
(0.218)
(1.312)
(0.626)
(1.096)
(0.605)
(1.383)
(0.656)
(1.143)
(0.639)
Square
−0.173***
−0.073***
0.028***
−0.085**
−0.029
−0.129***
−
0.049***
−0.050
−0.011
−0.112***
−0.034*
(0.042)
(0.018)
(0.007)
(0.041)
(0.019)
(0.034)
(0.019)
(0.043)
(0.020)
(0.035)
(0.020)
Dummy_
square
−8.391**
−3.521**
1.349**
−3.664
−1.158
−6.028**
−2.157
−3.384
−1.018
−5.888*
−2.036
(3.641)
(1.528)
(0.585)
(3.528)
(1.683)
(2.948)
(1.626)
(3.720)
(1.765)
(3.074)
(1.718)
Pool
−
23.022***
−9.661***
3.701***
−12.790**
−4.545*
−17.906*** −7.098***
−15.572***
−5.935**
−19.297*** −8.296***
(5.860)
(2.459)
(0.942)
(5.678)
(2.709)
(4.745)
(2.617)
(5.987)
(2.841)
(4.947)
(2.765)
Sauna
−2.238
−0.939
0.360
9.864**
5.112**
3.813
4.096**
16.102***
8.231***
6.932*
6.784***
(4.430)
(1.859)
(0.712)
(4.292)
(2.048)
(3.587)
(1.978)
(4.526)
(2.148)
(3.740)
(2.090)
Solarium
−12.620
−5.295
2.029
9.739
5.884
−1.440
3.327
9.265
5.647
−1.677
3.123
(10.398)
(4.363)
(1.672)
(10.075)
(4.806)
(8.420)
(4.643)
(10.624)
(5.041)
(8.779)
(4.905)
Rural
−18.723*** −7.856***
3.010***
−7.644
−2.317
−13.183***
−4.584**
−6.262
−1.626
−12.493*** −3.988*
(4.827)
(2.025)
(0.776)
(4.677)
(2.231)
(3.909)
(2.155)
(4.932)
(2.340)
(4.075)
(2.277)
House
−15.212***
−6.383***
2.445***
−
18.694***
−8.125***
−16.953*** −9.104***
−19.303***
−8.429***
−17.258***
−9.366***
(3.994)
(1.676)
(0.642)
(3.870)
(1.846)
(3.235)
(1.784)
(4.081)
(1.936)
(3.372)
(1.884)
Constant
108.497*** 45.665***
−
17.168***
52.072***
17.452***
80.285***
30.031***
49.352***
16.092***
78.925***
28.859***
(5.875)
(2.465)
(0.944)
(5.692)
(2.715)
(4.757)
(2.623)
(6.002)
(2.848)
(4.960)
(2.771)
Observations 763
R2
0.373
0.373
0.373
0.164
0.106
0.347
0.217
0.152
0.102
0.326
0.199
All values are given as percentage changes between the reference scenario and the stated alternative scenario. Standard errors are given in parentheses. * <
P
0.10, ** <
P
0.05, *** <
P
0.01.
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Table 3 | Effects of household characteristics and amenities on the relative difference of their network costs in our subsample
f100
f50/e50
e100
pa100
pa50/e50 f50/pa50
f/pa/e
pm100
pm50/e50 f50/pm50
f/pm/e
Logincome
−7.526
−3.183
1.160
−12.400** −5.620**
−9.963**
−5.875**
−12.187**
−5.514**
−9.857**
−5.782**
(5.014)
(2.121)
(0.773)
(5.124)
(2.435)
(4.235)
(2.373)
(5.551)
(2.646)
(4.417)
(2.556)
Dummy_income 6.012
2.543
−0.927
−2.266
−1.596
1.873
−0.580
−2.719
−1.823
1.646
−0.777
(4.418)
(1.868)
(0.681)
(4.514)
(2.145)
(3.731)
(2.090)
(4.890)
(2.331)
(3.891)
(2.252)
Square
−0.223***
−0.094*** 0.034***
−0.087
−0.026
−0.155***
−0.052** −0.050
−0.008
−0.136***
−0.036
(0.055)
(0.023)
(0.008)
(0.056)
(0.027)
(0.046)
(0.026)
(0.061)
(0.029)
(0.048)
(0.028)
Dummy_square −3.457
−1.462
0.533
−4.063
−1.765
−3.760
−1.991
−2.447
−0.957
−2.952
−1.291
(4.229)
(1.788)
(0.652)
(4.321)
(2.053)
(3.572)
(2.001)
(4.681)
(2.232)
(3.724)
(2.156)
Rural
−17.282***
−7.309*** 2.663***
−6.530
−1.933
−11.906**
−3.983
−5.243
−1.290
−11.263**
−3.425
(5.934)
(2.510)
(0.915)
(6.064)
(2.882)
(5.012)
(2.808)
(6.569)
(3.132)
(5.227)
(3.025)
House
−18.469*** −7.811***
2.846***
−10.319*
−3.736
−14.394*** −5.704** −11.259*
−4.206
−14.864*** −6.111**
(5.257)
(2.223)
(0.810)
(5.372)
(2.553)
(4.440)
(2.487)
(5.819)
(2.774)
(4.630)
(2.680)
Nr_persons
−10.824*** −4.578*** 1.668***
−4.959*
−1.645
−7.892***
−2.871**
−6.442**
−2.387
−8.633***
−3.514**
(2.816)
(1.191)
(0.434)
(2.878)
(1.368)
(2.379)
(1.332)
(3.118)
(1.486)
(2.480)
(1.436)
Dryer
−10.605*** −4.485*** 1.634***
−0.213
0.710
−5.409
−0.801
−2.266
−0.316
−6.435*
−1.690
(3.933)
(1.663)
(0.606)
(4.019)
(1.910)
(3.322)
(1.861)
(4.354)
(2.076)
(3.464)
(2.005)
Dishwasher
−5.062
−2.141
0.780
2.821
1.801
−1.120
0.884
−0.302
0.239
−2.682
−0.469
(5.754)
(2.433)
(0.887)
(5.879)
(2.794)
(4.860)
(2.722)
(6.369)
(3.036)
(5.068)
(2.933)
Pool
−6.495
−2.747
1.001
−15.068** −7.034**
−10.781**
−6.962**
−15.695**
−7.347**
−11.095**
−7.233**
(6.230)
(2.635)
(0.960)
(6.367)
(3.025)
(5.262)
(2.948)
(6.897)
(3.288)
(5.488)
(3.176)
Sauna
3.280
1.387
−0.506
14.893***
7.193***
9.086**
6.671***
24.390***
11.942***
13.835***
10.786***
(5.184)
(2.193)
(0.799)
(5.297)
(2.517)
(4.379)
(2.453)
(5.739)
(2.736)
(4.566)
(2.643)
Flowheater
0.539
0.228
−0.083
9.293*
4.605*
4.916
4.062*
11.837**
5.877**
6.188
5.164**
(4.842)
(2.048)
(0.746)
(4.948)
(2.351)
(4.090)
(2.291)
(5.361)
(2.555)
(4.265)
(2.468)
Boiler
−1.252
−0.529
0.193
0.238
0.215
−0.507
0.019
−1.092
−0.450
−1.172
−0.557
(4.011)
(1.696)
(0.618)
(4.099)
(1.948)
(3.388)
(1.898)
(4.440)
(2.117)
(3.533)
(2.045)
PC
−18.551***
−7.846*** 2.859***
−6.555
−1.848
−12.553*** −4.078*
−7.860
−2.500
−13.205*** −4.644*
(4.876)
(2.062)
(0.752)
(4.983)
(2.368)
(4.119)
(2.307)
(5.398)
(2.573)
(4.295)
(2.486)
Householdtype2 −7.891
−3.337
1.216
−4.129
−1.456
−6.010
−2.316
−4.803
−1.793
−6.347
−2.608
(13.331)
(5.638)
(2.055)
(13.623)
(6.474)
(11.260)
(6.308)
(14.758)
(7.035)
(11.742)
(6.796)
Householdtype3 −27.576*** −11.663*** 4.250***
−14.860** −5.305
−21.218***
−8.279**
−13.619*
−4.684
−20.597*** −7.741**
(7.120)
(3.012)
(1.097)
(7.276)
(3.458)
(6.014)
(3.369)
(7.882)
(3.758)
(6.272)
(3.630)
Householdtype4 −24.570*** −10.392*** 3.787***
−0.861
1.463
−12.716**
−2.014
−1.216
1.285
−12.893**
−2.167
(6.719)
(2.842)
(1.036)
(6.866)
(3.263)
(5.675)
(3.179)
(7.438)
(3.546)
(5.918)
(3.425)
Householdtype5 −11.895
−5.031
1.833
−0.521
0.656
−6.208
−1.020
1.329
1.581
−5.283
−0.218
(10.098)
(4.271)
(1.556)
(10.319)
(4.904)
(8.529)
(4.778)
(11.179)
(5.329)
(8.894)
(5.148)
Householdtype6 −20.376**
−8.618**
3.140**
−5.744
−1.302
−13.061*
−3.849
−3.351
−0.106
−11.864
−2.812
(9.071)
(3.837)
(1.398)
(9.270)
(4.405)
(7.662)
(4.292)
(10.042)
(4.787)
(7.990)
(4.624)
Householdtype7 −20.485
−8.664
3.157
−5.315
−1.079
−12.900
−3.670
−9.601
−3.222
−15.043
−5.527
(20.805)
(8.799)
(3.206)
(21.260)
(10.103)
(17.572)
(9.843)
(23.031)
(10.979)
(18.325)
(10.606)
Householdtype8 16.867
7.134
−2.599
2.876
0.138
9.871
2.372
5.545
1.473
11.206
3.528
(16.390)
(6.932)
(2.526)
(16.748)
(7.959)
(13.843)
(7.755)
(18.144)
(8.649)
(14.436)
(8.355)
Householdtype9 −29.454*** −12.457*** 4.539***
−15.589
−5.525
−22.522*** −8.720*
−14.585
−5.023
−22.020**
−8.285
(10.246)
(4.333)
(1.579)
(10.470)
(4.975)
(8.654)
(4.848)
(11.342)
(5.407)
(9.024)
(5.223)
Constant
198.240*** 83.846*** −30.552*** 143.948*** 56.698*** 171.096***
75.599*** 143.489*** 56.468***
170.866***
75.400***
(36.683)
(15.515)
(5.653)
(37.485)
(17.813)
(30.983)
(17.356)
(40.609)
(19.359)
(32.310)
(18.700)
Observations
404
R2
0.534
0.534
0.534
0.204
0.138
0.452
0.270
0.203
0.147
0.437
0.260
All values are given as percentage changes between the reference scenario and the stated alternative scenario. See Supplementary Table 2 for definitions of household types. Standard errors are given in
parentheses. * <
P
0.10, ** <
P
0.05, *** <
P
0.01.
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NaTUre EnerGy
hold’s income level is expressed in two ways in our sample, that is,
directly through the income variable and indirectly through the
household’s amenities. The regression output provides evidence that
the log of the monthly net income (Logincome) of a household is
significant (at different levels) for all scenarios introducing a charge
for measured peak demand, but it is not significant for the remain-
ing three scenarios. A respective negative coefficient of income
means that households with a higher income are associated with
lower peak loads compared with households with the same charac-
teristics but less income, ceteris paribus. As our regression controls
for a number of household characteristics and appliances, this effect
cannot stem from the disproportionate equipment of wealthier
households with these observed amenities per se, but instead could
be from (1) households with lower incomes having load-intensive
amenities not observed in our sample, which higher-income house-
holds do not have, (2) among those households having the same
amenities, wealthier households having more modern and thereby
less load-intensive amenities, or (3) wealthier households utiliz-
ing their amenities in a less load-intensive way, for example, using
their sauna differently. The question which of these options plays a
role in the frequency and extent of peak loads is important from a
policy perspective, considering that tariffs with a charge for mea-
sured peaks seem to favour higher income levels. In the case that
option (1) and/or (2) are relevant, policy could aid lower-income
households in identifying these load-intensive amenities and pro-
vide support for substituting them, where possible. To investigate
such a potential relationship, in Supplementary Table 3 we regress
the frequency of peak loads and the annual energy consumption on
household characteristics and amenities again, and extend the set of
explanatory variables by an interaction term for households with an
income below the median and the respective amenities. We find evi-
dence that electricity consumption patterns of two amenities differ
between the income groups: in below-median-income households,
pools produce fewer peak loads, while flow heaters are respon-
sible for about 1,000 additional kWh in this group. The higher
energy consumption of flow heaters in lower-income households
may actually point to the need for respective policy measures (see
Supplementary Note 2 for more details).
With respect to the composition of the households, we observe
that compared with single households, households made up of a
couple with children (Householdtype5/6) or a couple without chil-
dren (Householdtype3/4) have an advantage when tariffs with a
substantial fixed charge are introduced, and experience disadvan-
tages under a 100% energy-based tariff. Further evidence on the
systematically different consumption patterns of different house-
hold types are provided in Figs. 2 and 3. Figure 2 compares the load
profiles of these household groups on winter Saturdays and sum-
mer workdays: households with children (Householdtype2/5/6/7)
have substantially higher loads than those without children, and
higher-income households consume more electricity. While the
difference between households with and without children may, to
some extent, be rooted in the higher average number of residents,
the additional consumption during almost all times of the day sup-
ports the regression findings of energy-based tariffs being in favour
of childless households in absolute terms. Figure 3 compares the
percentage of households exceeding certain load thresholds at dif-
ferent times of a day at least once during the observation period:
again households with children are more likely to produce high
peak loads than those without children, and higher-income house-
holds are also more likely to produce significant peaks. However,
despite higher-income households being above the lower-income
households in both the average energy consumption (Fig. 2) and
the likelihood of exceeding a certain load threshold (Fig. 3), the
joint estimation suggests that peak-load-based tariffs are more
favourable for high-income households than tariffs based mainly
on energy charges (ceteris paribus).
Summarizing the results of our statistical analysis, we find that
the living situation of a household and its electricity consuming
amenities as well as its income level and the number of children
seem to play a role in whether a change from the reference scenario
to a tariff scheme charging for peak demand is associated with ben-
efits or additional burdens. Our results indicate that, ceteris paribus,
1.0
0.8
a
b
d
c
Average demand (kW)
Average demand (kW)
Average demand (kW)
Average demand (kW)
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
00:00 03:00 06:00 09:00
Time of day
Time of day
Time of day
Time of day
12:00 15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
Full sample
Higher-income
households
Lower-income
households
H0
Full sample
Households with
children
Households without
children
H0
Fig. 2 | Comparison of the load profiles of different types of households in our subsample with respective standardized load profile H0. Load profile H0
is used by Austrian utilities for forecasting and accounting household electricity consumption when no data from load metering is available43. a,b, Average
load on winter Saturdays (a) and summer workdays (b) for high-income households (above median monthly net household income of €2,043; n = 209),
low-income households (below-median income; n = 197), all households in the respective sample and the corresponding standard load profile H0.
c,d, Yearly average load on Saturdays during winter (c) and summer workdays (d) of households with (n = 91) and without children (n = 315),
compared with all households in the sample households and the corresponding standard load profile H0.
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households with higher income are better off when tariffs charging
for measured peak demand are introduced. Among others, ref. 44
showed that photovoltaic panels in combination with home stor-
age appliances reduce the peak loads of households significantly,
while at the same time also reduce the volumes of electricity pur-
chased via the grid. Higher-income households are likely to install
photovoltaic panels and related peak-load-reducing equipment at
higher rates than lower-income households45,46. Shifting the burdens
of recovering network costs towards lower-income households may
therefore become an even more pressing issue in the future.
Discussion
The ongoing transformation of the electricity system calls for the
reconsideration of the current network tariff schemes. Our analysis
aims to provide evidence on the potential magnitude of such tar-
iff changes, and identify whether potentially increasing costs are
equally distributed among the population or are more pronounced
for specific groups. Considering that the availability of residential
load profiles is relatively new to policymakers and scientists, and
that the lack of respective socio-economic background information
associated with these load profiles is hampering comprehensive
investigations, the knowledge gained in this study provides substan-
tial input for the ongoing debate about developing and implement-
ing new tariff schemes.
Investigating data on 765 households in Austria, we find that the
change in network charges, depending on the scenarios applied, can
(in extreme cases) reach a decrease of 50% or an increase of 500%
compared with the status quo. This demonstrates that some of the
tested tariff scenarios may have a disruptive impact on some house-
holds’ budgets if implemented from one accounting period to the
next.
We find it important to highlight the potentially low predict-
ability for households of their annual network costs under tariffs
emphasizing peak charges (per measured kW). Considering, for
example, tariff scenario pm100, where household network charges
are defined by the highest load during one out of 35,040 quarters of
an hour, significantly increased network costs from one year to the
other can arise from one unusually coincidental use of appliances.
The high level of sensitivity in household electricity costs to small
lapses on the part of the household when measured peak-demand
charges are used makes it necessary to apply such tariffs thought-
fully, following careful research. The need for a delicate handling of
the roll-out of measured peak-demand tariff schemes is particularly
urgent when such schemes are not accompanied by support mecha-
nisms, such as extended transition periods allowing households to
adapt to the new price signals.
Under volumetric network tariffs, every reduction of the units
of consumed energy results in an under-recovery of network costs.
This can either be compensated by increasing the price for the unit
of consumed energy, or by decoupling the revenues from network
tariffs from consumed volumes. Looking at the socio-demographic
characteristics of the households in the sample, we see that tariffs
combining measured peak-demand and volumetric components
could provide a new balance for the distribution of network costs—
as these tariffs are cost reflective and, due to the peak-load charge,
they signal the consumer to decrease overall consumption while not
penalizing any specific group of consumers. An additional fixed
component can account for costs invariant to consumption pat-
terns, such as charges for metering itself. We find that such tariffs
could provide a solid response to the increase of prosumers, while
avoiding the shifting of burdens towards households not yet ready
for taking this step.
This analysis is limited to data on Austrian consumers, so further
research with data on more households, including data on photo-
voltaic ownership and home storage installations could improve our
understanding of the tariffs’ effects on both consumers and prosumers.
Methods
Statistical methods. The quantity of interest in our analyses is defined as the
percentage by which the network costs differ between the reference scenario and
the alternative scenarios, and we refer to this quantity by:
Δ =
−
×
C
C
C
100
(1)
i j
i j
i r
i r
,
,
,
,
where Ci,r is the annual network costs of household i in the reference
scenario, while Ci,j stands for i′s costs in the jth alternative scenario, and
∈
∕
∕
∕
∕
∕
j
(f100, e100, f50 pa50, f50 e50, f50 pm50, pa100, pm100, f pa e,
∕
∕
∕
∕
pa50 e50, pm50 e50, f pm e). A negative sign of Δi,j therefore indicates a cost
Households exceeding
4 kW at least once (%)
Households exceeding
4 kW at least once (%)
60
a
c
d
b
40
20
0
Households exceeding
7 kW at least once (%)
Households exceeding
7 kW at least once (%)
10
8
6
4
2
0
10
8
6
4
2
0
60
40
20
0
00:00 03:00 06:00 09:00 12:00
Time of day
Time of day
Time of day
Time of day
15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
All households
Higher-income
households
Lower-income
households
All households
Households with
children
Households without
children
Fig. 3 | Percentage of households exceeding certain load thresholds at a certain time of the day at least once during the observation period. a,b,
Percentage of high-income households (above median monthly net household income of €2,043; n = 209), low-income households (below-median
income; n = 197) and all households in the respective sample exceeding 4 kW (a) and 7 kW (b) during the respective time of the day. c,d, Percentage of
households with (n = 91) and without children (n = 315), compared with all households, exceeding 4 kW (c) and 7 kW (d) during the respective time of the
day. Thresholds correspond to the available capacity contracts in the tariff zone of our sample; the minimum contract for households is for 4 kW, the next
higher contract is for 7 kW. In practice, actual demand limitations of household connections are significantly higher than contracted.
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reduction under the alternative scenario j compared with today’s regulatory
practice, while a positive sign points to increased costs for household i.
To investigate which household characteristics are associated with cost savings
or incremental costs under the different alternative scenarios, we estimate Δi,j with
a linear regression model. Thereby we consider that Δi,j is a function of household-
level characteristics
…
… …
x
x
x
,
,
,
,
,
i
k i
k i
1,
,
, , such that:
Δ
β
β
ε
=
+ … +
+
x
x
(2)
i j
i
j
k i
k j
i j
,
1,
1,
,
,
,
where βk,j holds the incremental average percentage points by which the network
costs change when alternative scenario j is applied instead of the reference scenario,
when household characteristic k increases by one unit. εi,j references the error term.
All regressions presented in this study estimate equation (2).
Considering that the alternative scenarios as used in our study are correlated,
for example, all ‘f’ scenarios rely on fixed charges, simultaneous estimation of all
11 alternative scenarios suggests itself. We pool the 11 resulting equations (one per
alternative scenario) and estimate the resulting system by the seemingly unrelated
regression model47. As we rely on exactly the same set of household characteristics
for explaining the deterministic portion in equation (2), results are identical to
the ordinary least squares model. Whenever a variable is included in one of the
regressions for which missing values have been imputed, a respective dummy
variable is included to test whether the imputation has significantly affected the
analyses. None of these dummies’ coefficients has a significant opposing effect
compared with the main variable’s coefficient, so we conclude that our analyses do
not suffer from bias caused by the imputations.
Scenarios. To cover the range of potential network tariff schemes, we designed a
total number of 11 scenarios.
First, we designed one respective tariff scenario recovering the network costs
through only one of the three components—volume, fixed charge and measured
peak load (average and maximum).
Scenario f100 is a 100% fixed charge. Thereby, f100 represents a flat charge for
all households, which is €136,209.10 / 765 households = €178.05 per household per
year for the full sample in our study.
Scenario pa100 represents a scheme charging for measured peak load only. In
this scenario, the definition of kW peak load follows the Austrian tariff structure
in 201648, where a so-called smart meter tariff was included for testing only (in the
residential sector). There, kW peak load as relevant for billing is not defined as
the one maximum load out of the 35,040 metered load values during one year
per Austrian meter. Peak load as relevant for setting a household’s peak charge
is defined as the average of the 12 monthly peak loads during the respective
year. Scenario pa100 for the full sample analysis therefore sets
€136,209.10 / 3,485.59 kW total = €39.07 per kW of billing relevant peak load,
where kW total is the sum over all 765 corresponding peak-load values.
Scenario pm100 also represents a scheme charging for peak demand only, but
instead of averaging, the highest of the 12 monthly peaks is applied, such that it sets
€136,209.10 / 4,603.3 kW total = €29.59 per kW of billing relevant peak load, where
kW total is the sum over all 765 corresponding peak-load values.
Scenario e100 is a fully volumetric tariff and includes only a payment
per unit of consumed energy. Thereby it is €136,209.10 / 2,691,272 kWh total =
€0.0506 per kWh consumed during the respective year, where kWh total is
the aggregated electricity consumption of all 765 households.
Next, we define five scenarios each representing a hybrid of two of the
candidate tariff components, fixed, peak-load and volumetric charges:
Scenario f50/e50 puts 50% of the weight on the fixed charge and 50% on
the consumed volume. Thereby, €136,209.10 × 0.5 = €68,104.55 are recovered
by the fixed component, and exactly the same amount comes from the
volumetric component. The fixed component of f50/e50 is therefore given by
€68,104.55 / 765 households = €89.02 per household per year. The volumetric
component of scenario f50/e50 results from distributing the respective
quantity of €68,104.55 among the total number of consumed kWh, that is,
€68,104.55 / 2,691,272 kWh total = €0.025 per kWh consumed.
Scenario f50/pa50 represents a scheme with 50% of the costs recovered
from a fixed charge and 50% from a measured peak charge. The fixed charge
per household is the same as in the f50/e50, the peak charge is defined as
€68,104.55 / 3,485.59 kW total = €19.53 per kW of billing relevant peak load,
where kW total is the sum over all 765 corresponding peak-load values
(average definition).
Scenario f50/pm50 the peak charges are calculated as €
68,104.55 / 4.603.3 kW total = €14.79 per kW of billing relevant peak load (based on
the one maximum peak definition).
Scenarios pa50/e50 and pm50/e50 are each a combination where 50% of weight
is put on the consumed volume of energy and 50% of the measured peak demand,
average or maximum definition, respectively.
In addition, we also test two scenarios combining all three candidate tariff
components:
Scenario f/pa/e imposes a fixed charged of €24.60 as found in the Austrian
tariff as applied in 2016, and splits the remaining quantity in equal portions.
Revenues collected from the fixed charge therefore result in a total of
€24.60 × 765 = €18,819 for our full sample, which we subtract from the €136,209.10
to calculate the shares that have to be recovered by peak and energy components:
(€136,209.10 − €18,819) × 0.5 = €58,695.05. The peak component is calculated as €
58,695.05 / 3,485.59 kW total = €16.83 per kW peak and the energy component is
equal to €58,695.05 / 2,691,272 kWh total = €0.022 for each kWh consumed.
Scenario f/pm/e fixed and energy charges are the same as in the previous
scenario and the peak charge is calculated with the maximum peak definition
instead of the averaged definition.
We compute the network charges for every household under each of the
scenarios for the full sample as well as for the subsample of 406 households, for
which the sum of network charges accumulates to €74,794.28 in the reference
scenario.
Ethics statement. The survey data were collected by Energy Institute at the
Johannes Kepler University Linz, following high European Union standards of data
protection and voluntary study participation. The methodology used in this paper
does not require institutional ethical approval according to the guidelines set out
by the Energy Institute at the Johannes Kepler University Linz. Confidentiality and
anonymity of participants was ensured and informed written consent was obtained
from all the interviewees.
Data availability. The data that support the plots within this paper and other
findings of this study are available from the corresponding author upon reasonable
request.
Received: 5 June 2017; Accepted: 26 January 2018;
Published online: 12 March 2018
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Acknowledgements
This research was done as a part of PEAKapp project that has received funding under the
European Union’s Horizon 2020 research and innovation programme, grant agreement
No. 695945 (http://www.peakapp.eu/). A.K. received additional funding from the
Austrian Ministry for Transport, Innovation and Technology (No. 848114). D.E. and C.F.
gratefully acknowledge the financial support by the Austrian Federal Ministry of Science,
Research and Economy, the Austrian National Foundation for Research, Technology
and Development and the Federal State of Salzburg. This research was enriched through
discussions about international network regulation with B. Mountain, R. Muruais,
J. Cohen and D. Pezenka.
Author contributions
A.K. and J.R. were primarily responsible for the creation and implementation of the
survey instrument. D.E. and C.F. were primarily responsible for the creation and
management of the dataset, including load profiles. V.A. and J.R. mainly contributed
to data analysis. All authors contributed to the writing of the paper with V.A., A.K. and
J.R. as the primary authors.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-018-0105-4.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to J.R.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Nature Energy | VOL 3 | APRIL 2018 | 317–325 | www.nature.com/natureenergy
325

    Scientific Research Study Methods:

Answer: Our study explored the impact of applying peak-load-based tariffs on the budgets of households that had mainly been charged based on consumed volumes before. We recruited households from Upper Austria, who gave us permission to collect their 15-minute electricity load profiles from their smart meter between April 2010 and March 2011. They also provided socioeconomic information and details of their electric appliances. We used this data to estimate the change in household network expenditures for 11 different combinations of energy, peak and fixed charges that try to recover network charges in different ways. Under each scenario, the household was sent specific signals about prices.


Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41560-018-0256-3
1Laboratory for Energy and Nanoscience, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. 2Dubai Electricity and Water
Authority, Dubai, United Arab Emirates. 3Arctic Renewable Energy Center (ARC), Department of Physics and Technology, University of Tromsø,
Tromsø, Norway. *e-mail: matteo.chiesa@ku.ac.ae
A
fter decades on the fringes, solar energy has arrived as a
major player in the electricity generation sector. While just
five years ago, conventional wisdom grouped solar with the
impractical and subsidy-dependent ‘alternative’ energy technolo-
gies, today every few months there are announcements of new large-
scale solar projects around the globe promising electricity prices
equal to or lower than what can be achieved with fossil fuels1,2, as
shown in Fig. 1a. While the well-publicized steep learning curve of
photovoltaic (PV) modules3 has played a large role in bringing us to
this point, other factors are now beginning to drive price reductions
as modules have dropped to just over a third of total system prices,
with the remainder represented by inverters, sun trackers (if used)
and other components for balance of system, as well as the cost of
labour and various ‘soft costs’ related to regulation, taxes, developer
expenses and grid connection, accounted for in Fig. 1b based on data
from the United States4,5. Particularly noteworthy and illustrative of
this trend have been a number of announcements of low-priced
utility-scale PV projects from the Middle East, especially the United
Arab Emirates and Saudi Arabia6–9, where power purchase agree-
ment (PPA) auction bids below 3¢ kWh−1 have become the norm.
If sustainable, these cheap projects would represent the achieve-
ment of the US SunShot pricing goals for 203010 more than a decade
ahead of schedule. As these targets represent the expectation that
unsubsidized solar overtakes fossil fuels on cost, this development
would have significant implications for the world’s energy systems. It
would make solar the economic favourite for new-generation capac-
ity, accelerating the transition to a renewable-based energy future.
However, the speed at which prices have fallen has prompted
suspicion that these prices are attained only via hidden government
subsidies; that they are not intended to make money at all but are
a consequence of a ‘race to the bottom’ driven by the PPA auction
model; or are ‘loss leaders’, strategic moves by large forward-look-
ing companies seeking to establish a foothold in a young industry
poised to grow11–13. Despite these concerns, low-priced PV projects
have spread across the world in the intervening two years14–16.
Understanding the extent to which these prices are real, sus-
tainable and reproducible is critical for setting energy strategies to
encourage the continued rapid PV deployment that will be needed
over the next decade and beyond to achieve energy system decar-
bonization at the rate required by climate agreement targets17. In this
analysis, we draw on a range of publicly available data on PV system
costs and performance, along with local knowledge of the region,
with the aim of answering the questions: how did electricity from
PV get so cheap so fast? Can these ultralow prices be sustained and
extended to other markets? And how much lower could they get?
Reasons for the decline in PV electricity prices
The recent trend in solar electricity prices has been much more
aggressive than that of hardware prices due to a number of factors.
One part of the answer is better exploitation of the solar resource—
the world’s sunniest areas are increasingly being developed as PV
projects become less dependent on state subsidies. Other plant opti-
mizations to maximize energy yield have become widely adopted,
including single-axis tracking and high inverter-loading ratios (d.c.
array capacity to a.c. inverter capacity) of ~1.3 for both tracking and
non-tracking systems. These implementations, coupled with fall-
ing hardware prices and tax incentives, were credited with bringing
the levelized cost of solar electricity in sunny regions of the United
States to around 5¢ kWh−1 in 201518.
Even in this context of rapid pricing declines, the arrival of the
United Arab Emirates, and Saudi Arabia soon afterwards, as leaders
in cheap solar electricity surprised observers with the sheer scale of
the price decrease in a region with little historical interest in renew-
able energy. A closer look at the details of these projects begins to
reveal the interplay of global and local conditions that lead to the
observed prices. The projects appear to have realized large reduc-
tions in both capital and operating expenses by leveraging on a
number of factors. These include forward-bidding of expected lower
future hardware prices, which up to this point have been borne
out; low construction and operation and maintenance (O&M)
labour costs that are typical of the Gulf region; scaling up the plants
from the 10–100 MW level that was prevalent until recently to the
gigawatt scale; extended PPA term to 25 years (relative to the then-
standard 20 years18); and favourable financial terms. Moreover,
Evaluating the factors that led to low-priced solar
electricity projects in the Middle East
Harry Apostoleris1, Sgouris Sgouridis   1, Marco Stefancich2 and Matteo Chiesa1,3*
The past few years have seen the rise of large-scale, low-priced solar energy projects around the world. Oil-producing coun-
tries in the Middle East, in particularly the United Arab Emirates and Saudi Arabia, have become unexpected leaders in this
movement with record-low power purchase agreement prices, below 3¢ kWh−1, for a number of new photovoltaic installations,
beating the cost of fossil fuel generation. In this Analysis, we bring together technical, economic and financial information from
global and local sources to study whether these prices can be replicated elsewhere and further reduced. We find that hardware
costs, cost of labour, favourable cost of capital, low taxes and low, but positive, profit margins contribute to the reduction in
costs. Reduced hardware prices contributed the most and also led to further reduction in cost of capital. We demonstrate how
similar costs can be and have been achieved in other markets.
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1109

Analysis
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it appears that developers did not have to account for land costs that
could be around US$5,000 per acre in the United States19. The verti-
cally integrated state-owned utilities are also involved as partners,
expected to take majority ownership of the venture on completion.
As such, they are believed to take on the costs for grid interconnec-
tion and access roads as infrastructure development projects13,20,21.
Project financing packages can be fairly complex but can be
outlined as featuring high debt-to-equity ratios (70–80%), low
interest rates (reported starting values for interest range between
120 and 180 basis points over the London Interbank rate (LIBOR))
and significant opportunities for refinancing over the project life-
time21. The establishment of these terms by the consortium of local
banks that financed Dubai’s 200 MW project in 2015 certainly
provided encouragement for international consortia to offer com-
parable packages for the subsequent, larger projects. In contrast,
utility-scale PV projects being developed at the same time in the
United States could expect debt ratios in the range of 40–60%, with
interest rates of 4–5%, and return on equity expectations around
10%22,23. In Table 1 we list the key financial parameters for the four
record-setting projects in the United Arab Emirates and Saudi
Arabia, as thoroughly as they can be reconstructed from a collec-
tion of public announcements.
The path to 3¢ kWh−1
To investigate the system configurations and cost structures
required to reach 3¢ kWh−1, we first establish a baseline system price
and LCOE breakdown for 2016, just before Dubai’s Mohammed bin
Rashid Al Maktoum (MBR) phase III announcement, based on a
number of sources5,23–26, shown in Fig. 2.
CapEx depends heavily on the price of hardware, which accounts
for the majority of the total installed cost, with construction labour
being the second-largest contributor. CapEx represents about 50%
of the LCOE in the baseline model, with operation and maintenance
(O&M) and financing costs representing the balance. The baseline
financial model assumes a debt fraction of 60% with an interest
rate of 5% and a return on equity of 10%, for a total weighted aver-
age cost of capital of 7%. The CapEx includes a profit margin for
the project developer, while the construction contractor’s profit is
implicitly included in the contractor markup. The LCOE represents
the cost to the project developer of building and operating the sys-
tem, and of satisfying the debt and equity financers of the project
with their required returns, and hence represents a minimum sus-
tainable price for the sale of electricity. The baseline assumptions
are listed in the first column of Table 2.
We subsequently chart a reasonable path to a sub-3¢ kWh−1
LCOE by applying a series of successive cost reductions, each
justified on the basis of a collection of publicly available industry
statistics and local knowledge. The full set of model inputs is listed
in Table 2. The key assumptions are listed below.
Continued future declines in the cost of hardware
Since in this analysis we are actually looking backwards to projec-
tions that were made in 2016, we consider what the project’s hard-
ware cost would be, assuming that the developers guessed future
pricing trends more or less correctly. At the time of the current
writing, both the Dubai and Abu Dhabi projects are in the middle
of their construction timeline, with hardware procurement under-
way. Therefore, we use current prices as a reasonable proxy for the
average cost of hardware for the systems. Additional discussion of
hardware pricing projects can be found in Supplementary Note 1,
Supplementary Table 1 and Supplementary Fig. 1.
Cost of labour is very low due to the connection between labour
markets in the Gulf and the low-cost Indian subcontinent. With
local contractors assuming most construction duties, and reported
wages for construction work and even some skilled trades reported
as less than US$5 h−1 by local sources27,28, we believe that a reduction
in labour costs of 50% relative to the US benchmark in refs 4,25 is a
reasonable and perhaps even a conservative estimate (further details
provided in the Supplementary Note 2 and Supplementary Table 2).
This low cost of labour is also the primary driver of reductions in
a
b
2.5
0
0.5
1.0
1.5
3.5
4.5
2.0
3.0
4.0
5.0
2010
2011
2012
2013
Year
Date
2014
2015
2016
2017
System or component price
(US$ Wdc–1)
Installed system price
Hard cost
Module
South Africa
France
Peru
India
Jordan
Zambia
Brazil
USA
Dubai
Germany
Mexico
Chile
Argentina
Morocco
200
China
Jan. 2010
50
100
150
250
300
350
Jan. 2011
Jan. 2012
Jan. 2013
Jan. 2014
Jan. 2015
Jan. 2016
Jan. 2017
Electricity price (US$ MWh–1)
0
Jan. 2018
Saudi Arabia
Abu Dhabi
Fig. 1 | The reduction of PV system and electricity prices around the world. a, Solar auction history of PPA prices (source: International Renewable Energy
Agency1). b, Module price evolution, hardware price and utility-scale system price—as a bar chart based on ref. 4; numbers for components and total
system price are from ref. 5 (except 2017).
Nature Energy | VOL 3 | DECEMBER 2018 | 1109–1114 | www.nature.com/natureenergy
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Analysis
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operation and maintenance costs, which we reduce to US$10,000
MW−1 yr−1, based on local assessments by industry insiders29, esca-
lating over the plant lifetime to US$15,000 to account for increased
service requirements and component replacements.
Financial parameters are based on the public reports summa-
rized in Table 1 and include a debt fraction of 80% and an inter-
est rate of 3.5%, with a return on equity of 10%. Some observers
have speculated that money was essentially given out for free to
the project developers, to achieve headline-grabbing pricing mile-
stones13. Our analysis shows that such extreme assumptions are not
necessary to achieve the reported prices under today’s conditions.
Furthermore, while early projects were financed principally by local
banks, encouraging some doubt as to the model’s global sustainabil-
ity, the growing role of international financial institutions suggests
that confidence in solar energy in the broader financial sector may
be growing to the point where similarly favourable financial terms
may be reached in other markets. In addition, the return on equity
can be maintained at a globally competitive 10%.
Tax treatment
While the baseline includes a 5% sales tax on hardware, the low-
cost model removes this in keeping with the tax-free conditions of
the United Arab Emirates at the time these bids were offered. The
2018 introduction of a 5% value-added tax may add this cost com-
ponent back in future projects. The LCOE model does not include
income taxes, which often will primarily be felt (due to the narrow
profit margins of these projects) through the effective reduction of
the cost of debt, in countries where debt interest payments are tax-
deductible. Hence, the net impact of income taxes is often to reduce
the effective cost of the project.
Overhead and profit margins
The CapEx includes two profit margins: one for the developers,
and one included in the contractor overhead. To estimate the over-
head + profit component for the large plant in the low-cost model,
we extrapolate the assumption that contractor overhead can be
reduced to some degree (in percentage terms) with plant size25.
Table 1 | Financial parameters for low-priced PV projects
MBR phase II
MBR phase III
Sweihan
Sakaka
Developers
ACWA Power
(Saudi Arabia) + TSK (Spain)
Masdar (United Arab Emirates) +
ALJ (Saudi Arabia) + EDF (France)
Marubeni (Japan) + Jinko
Solar (China)
ACWA Power
Date of close
July 2015
June 2017
May 2017
February 2018
Total debt in US$ million
34433,a
65521
65035
260
Debt fraction
86%
70%
75%
80% (estimated)
Debt interest rate (basis
points over LIBOR)
180 (average)
175 (starting)
120 (starting)
Unknown
Debt interest rate
(calculated)
2.6%b
3.6%
2.9%
N/A
PPA price (¢ kWh−1)
5.84
2.99
2.94
2.34
From left: 200 MW phase 2 of Dubai’s MBR solar park; 800 MW phase 3; Abu Dhabi’s 1,177 MW Sweihan solar park; and the inaugural plant of the Sakaka solar park in northern Saudi Arabia. Values are
based on reporting by IJGlobal except where otherwise noted. aSources differ, giving a range of total project costs between US$326 million34 and US$400 million33, with some specifying a 4% debt
interest rate33. The tabulated parameters are those that we judge most likely to be accurate on the basis of some educated guesswork and back-calculation. bFinancing for MBR phase II was provided
by a consortium of local banks. Subsequent projects were funded by international consortia, dominated by institutions from the developing partners’ home countries but including institutions from
other countries.
EPC hard
Construction labour
Permitting
Sales tax
Land and prep.
Contingency
EPC soft
Grid tie
1.0
0
0.2
0.4
0.6
1.4
0.8
1.2
1.6
2.0
a
b
System price (US$ W–1)
LCOE (×10–2 US$ kWh–1)
1.8
Hardware (via EPC contractor)
Construction labour
(via EPC contractor)
Construction, developer-borne
Debt service
Sales tax
EPC (markup)
Operation
Return on equity
5
0
1
2
3
7
4
6
8
Developer profit
Fig. 2 | Bottom-up model for PV costs. a,b, Capital expenditures in
−
S
U $ Wa c. .
1  (a) and levelized cost of electricity in US$ kWh−1 (b) in the baseline model
of a 100 MW plant with a 20-year PPA, based on 2016 market data for component costs, free of income tax and incentives. Cost components are
summarized and attributed in Table 1.
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The developer profit margin is typically low4, and can be reduced
further for a large project. As a further justification of our scaling
assumptions, we note that in several of these projects, the developer
either scaled up the plant to achieve the target price, or offered to
build a larger plant at a lower PPA price, giving some indication of
the gains that developers see in economies of scale. The assumption
of fairly slim profit margins can be justified by their large size as
well as by the fact that these are ‘trophy’ projects in a hot emerging
market that will form a cornerstone of their developers’ reputation.
However it is worth noting that we assume profit margins that are
slim, not negative—we do not see a reason to consider these proj-
ects as ‘loss leaders’ that sell below cost purely for the sake of estab-
lishing their developer in the market.
Implementing the full set of assumptions laid out in the second
column of Table 2, we find that LCOE below 3¢ kWh−1 is achieved
naturally without further manipulation as shown in Fig. 3. This
reduction is driven by the combined impact of low cost of financ-
ing and reduced capital expenditures, shown in Fig. 4. We note that
there is the possibility of relaxing some of the assumptions while
still achieving the price target. For example, we could remove the
assumption that the utility bears the costs of interconnection, which
has never been publicly confirmed, and still reach the target price;
or, we could account for the fact that PV hardware may in general be
subjected to some form of import duties (say 5% applicable to half
of the CapEx, which would boost the LCOE to 2.91¢ kWh−1 in our
model). If, on the other hand, we are to tighten these assumptions,
the 2.34¢ kWh−1 price of the Sakaka project can be reproduced, as
described in Supplementary Note 3 and Supplementary Fig. 3.
Discussion, outlook and lessons
We have shown here that, at least up to the level of about 3¢ kWh−1,
recently observed low solar electricity prices can be profitably real-
ized under reasonable assumptions for the region. The key ques-
tions remaining are whether these conditions are sustainable, and
how these low prices may be replicated in other markets.
The contribution of declining hardware prices, while not
solely responsible for the record bids, is nonetheless important. It is
therefore worthwhile to comment briefly on how sustainable the
current situation of declining module prices specifically seems to
be, although a deep study is beyond the scope of this analysis. We
point, by way of reassurance, to the recent decision in China to roll
back some support for its solar industry, which appears to have actu-
ally caused module prices to decline further due to falling domestic
demand (see more in Supplementary Discussion). Therefore, fears
of a sudden spike in module prices that would threaten near-term
investment in PV capacity seem, for now, unlikely to materialize.
There seem to be two areas where direct actions by government
have made a significant difference. The first is in the removal of
some indirect costs, which may be criticized as not applicable to
other markets. As a counterargument, we note the recent spate of
sub-3¢ kWh−1 PPAs in the southwestern United States, which seem
to have achieved many of the same savings by building along the
transmission infrastructure of soon-to-be decommissioned coal-
fired plants. Solar projects in the United States do not claim to be
unsubsidized, as they benefit from the 30% income tax credit; how-
ever, at least one project, outside of Las Vegas, NV, appears to come
in just below the 3¢ kWh−1 line even when the impact of the tax
credit is removed16. It is also worth noting that these projects will
be subject to the recent 30% tariff on imported modules, suggest-
ing that this move (probably mitigated by the continually falling
module prices) is having limited impact on PV development in the
United States. The second area is in the encouragement of favourable
financing deals for developers, which involves both the involvement
of state-connected companies and banks in the projects, and (more
widely applicable) the clear commitment of the government to
long-term renewable energy development, which certainly creates
a sense of stability in the industry. The low PPAs in the United
States suggest that the financing environment in the United States
has reached a similar level of favourability to solar that is found in
the Gulf, since—as we have shown—low-cost financing with high
debt fractions is an essential ingredient of low PPA prices. However,
another hotspot for low PPA prices has been Chile14, where PV proj-
ects can benefit first of all from the world’s best solar resource in
the Atacama Desert30,31, but where the financial environment is seen
to be less stable. This may have been mitigated in the latest round
of bidding by the prominence of the Saudi project developer ALJ,
which has won a number of projects across South America, and
can reasonably be speculated to have eased the financial aspect via
access to more freely flowing Gulf capital.
Conclusions
The rapid pace of development of solar energy, especially with
regard to pricing, can make it difficult to keep up with the true state
of the industry. Separating reality from hype requires a hard look
at the most aggressive claims in terms of costs, risks and returns—
the factors that really matter to developers, banks, and providers
and consumers of electricity. Solar costs vary widely by region,
and fully explaining this variation requires an understanding of
local conditions, which we have sought to provide as context for
the recent record-low solar electricity prices and bids in the United
Arab Emirates and Saudi Arabia, and their subsequent extensions to
Table 2 | Model inputs for low-cost PV project reconstruction
Baseline
3 ¢ kWh−1 in the
United Arab Emirates
Module cost (
−
S
W
U $
d c. .
1 )
0.6424
0.28a
Inverter cost (
−
S
U $ Wa c. .
1 )
0.12
0.084
Tracker + BOS cost
(
−
S
W
U $
d c. .
1 )
0.26
0.22 (−15% from
201836)
Sales tax (%)
5
0
Construction labour cost
(
−
S
W
U $
d c. .
1 )
0.16
0.07 (50% of
benchmark 20174)
EPC markup (%)
825
5b
Land (US$ per acre)
5,00019
0
Land prep. (US$ per acre)
10,00019
10,000
Environmental permitting
(US$)
250,00025
0
Interconnection cost
(
−
S
W
U $
d c. .
1 )
0.0325
0
Transmission line (US$)
6,000,00025
0 (borne by utility)
Contingency (%)
3
3
Developer margin (%)
1
0.5
Operation cost
(US$ MW−1 yr−1)
20,000, linear
escalation to 25,000
over 20 years18
10,000−15,000 over
25 years
Debt fraction (%)
6023
80
Debt interest (%)
523
3.5
Return on equity (%)
1023
10
PPA term (years)
2018,37
25
Y1 yield (
−
Wh MW
G
a c. .
1 )
2.836
2.836
CapEx (
−
S
W
U $
a c. .
1 )
1.93
0.95
LCOE (¢ kWh−1)
7.38
2.85
Cost figures from ref. 24 unless otherwise noted. BoS, balance of system. EPC, engineering–
procurement–construction. aPVInsights module spot price, 25 June 2018. bMarked down by
extrapolating the model of ref. 25.
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Analysis
Nature Energy
other regions. We have demonstrated here that unsubsidized prices
below 3¢ kWh−1 are attainable when the right combination of local
conditions is realized, and that the minimum for solar prices should
be expected to continue dropping (with some slowing expected as
interest rates rise) with hardware costs, barring some significant
change in the financial sector’s perception of solar’s profitability.
While certain costs do seem to have been mitigated in the case of the
Gulf projects, which could be argued to be a subsidy, the success of
projects in other regions at achieving comparable savings indicates
that these costs can be significantly reduced by skilful engineer-
ing, without state intervention. We wish to emphasize that govern-
ment policy remains an important element to remove barriers to
PV deployment, chief among which is the need for access to cheap
financing. As global interest rates are expected to rise, it becomes
more critical for solar projects to be able to prove themselves as low
risk to achieve favourable financing packages. The clear commit-
ment to renewable energy as state policy (as well as the involvement
of large public or semi-public companies in the development of PV
projects) was certainly a factor in providing the necessary degree
of confidence that allowed banks to overcome their historical aver-
sion to renewable energy projects32 and begin lending at low rates
in the Gulf. Such a mentality of strategic energy planning, whereby
governments intervene not to subsidize but merely to enable and
encourage investment in technologies that are believed to be of
value to the society, is an essential element of low electricity prices
and may provide guidance regarding how to continue the spread of
low solar prices into other markets.
Methods
Bottom-up cost and performance modelling. In the investigation of recent low-
priced PV bids and PPAs, public announcements regarding, for example, financing
terms, provide a window into some of the mechanisms by which these projects
probably achieved their low prices. However, significant uncertainty remains, not
the least of which surrounds the actual costs of these projects, for which there
are conflicting reports21,33,34. Furthermore this ‘top-down’ look gives little insight
into the relative impact of different cost-cutting mechanisms, and therefore
provides little grounding for assessing whether these prices can be transferred to
other markets; and as many of the details are proprietary, it would in fact not be
feasible to attempt an exact reconstruction of any particular project. To circumvent
this challenge, we adopted a bottom-up modelling approach, quantifying cost
components based on a range of industry reports and analysis to build up a model
of how these prices could be achieved under realistic global and local conditions.
5
0
1
2
3
7
4
LCOE (×10–2 US$ kWh–1)
6
8
Hardware
(via EPC contractor)
Construction labor
(via EPC contractor)
Construction,
developer-borne
Debt service
Sales tax
EPC (markup)
Operation
Return on equity
Baseline
7.38 ¢ kWh–1
Scale up
7.02 ¢ kWh–1
Extend PPA
term 6.51 ¢ kWh–1
Reduce hardware
prices 4.50 ¢ kWh–1
Reduce construction
labour cost 4.10 ¢ kWh–1
Reduce O&M
3.72 ¢ kWh–1
Lower cost of
financing 3.12 ¢ kWh–1
Land, tax, fees
2.85 ¢ kWh–1
Fig. 3 | Modelled LCOE for a sample plant under Abu Dhabi’s weather conditions. Sales tax of 5% applies to hardware cost; EPC markup covers overhead
and profit for the construction firm; developer-borne construction expenses include permitting and land costs.
1.0
0
0.2
0.4
0.6
1.4
0.8
1.2
1.6
2.0
1.8
Baseline
US$1.93 W
a.c.–1
100-->800MW
US$1.82 W
a.c.–1
Reduce
hardware cost
US$1.18 W
a.c.–1
Construction labour
cost reduction
US$1.05 W
a.c.–1
Remove
sales tax
US$1.01 W
a.c.–1
Free land
US$0.99 W
a.c.–1
Permitting fees
US$0.94 W
a.c.–1
EPC hard
Permitting
Construction labour
Contingency
Land and prep.
EPC soft
Sales tax
Grid tie
Developer profit
Module tariff
System price (US$ W–1)
Fig. 4 | Modelled CapEx for a sample plant based on assumed costs for UAE large-scale solar. For large-scale projects, hardware represents the bulk of
CapEx, followed by labour.
Nature Energy | VOL 3 | DECEMBER 2018 | 1109–1114 | www.nature.com/natureenergy
1113

Analysis
Nature Energy
This allows us to look at the relative impact of the many factors that contribute to
the ultimate cost of producing electricity. Performance modelling was performed
in System Advisor Model (SAM) for a plant designed to match the known
specifications of Dubai’s MBR solar park, phase 3, the first of the sub-3¢ kWh−1
projects. The ‘baseline’ model scaled down the MBR model to 100MWac while
retaining the same assumptions about hardware costs. LCOE was calculated in a
spreadsheet (available on request) as:
 =
+ ∑
∑
=
+
=
+
C
LCOE
i
N
O
r
i
N
E
r
1 (1
)
1 (1
)
i
i
i
i
using the annual energy yield calculated by SAM and discounting by the weighted
average cost of capital. Performance modelling of the UAE plant (3¢ kWh−1)
uses International Weather for Energy Calculations weather data for Abu Dhabi;
modelling for Sakaka (see Supplementary Note 1) used Photovoltaic Geographical
Information System weather data (http://re.jrc.ec.europa.eu/pvgis/, retrieved June
2018) for Al Jouf airport (coordinates 23.694° N, 40.088° E, a few kilometres from
the solar park site). The model was implemented in SAM 2014.1.14, based on
the known or presumed specifications of Dubai’s MBR phase 3, including large
(310 Wp) CS6X modules from Canadian Solar, 5000 W inverters from GE, an
inverter loading ratio of 1.3 and single-axis tracking.
Data availability
The data that support the plots within this paper and other findings of this study
are available from the corresponding author upon reasonable request.
Received: 6 March 2018; Accepted: 29 August 2018;
Published online: 8 October 2018
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Acknowledgements
We note the valuable contribution of our colleagues with whom we have discussed this
work, in particular I. Almansouri and A. Tuomiranta. We thank M. Kissamitaki for
designing the figures.
Author contributions
M.C. conceived and proposed this analysis, which was further elaborated in collaboration
with S.S. and H.A. H.A. carried out the analysis and drafted the manuscript in consultation
with M.C. and M.S. S.S. provided insights into the financial aspects of the projects and
M.S. provided a utility company perspective on the project development process.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-018-0256-3.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to M.C.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2018
Nature Energy | VOL 3 | DECEMBER 2018 | 1109–1114 | www.nature.com/natureenergy
1114

    Scientific Research Study Methods:

Answer: We developed a model to break down the levelized cost of electricity (that is, the total cost to the plant operator of producing one kilowatthour of electricity) into many different contributions related to hardware costs, installation labour, financing, site preparation and maintenance. This allowed us to estimate costs from the bottom up and evaluate the impact of different assumptions about cost components. We considered specific trends independently, such as recent reductions in the price of solar panels, or regional variations in labour costs and debt interest rates. We compiled data from various public sources, including annual analyses by the US National Renewable Energy Laboratory and other national laboratories, industry reports, local studies of installation, operation and maintenance costs and practices in the region, and press releases describing the financial packages offered to several low-priced photovoltaic projects in the United Arab Emirates and Saudi Arabia.