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A comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources
in G20 countries for the period 2015-2030
Manish Ram*, Michael Child, Arman Aghahosseini, Dmitrii Bogdanov, Alena Lohrmann, Christian
Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland
*Corresponding Author. E-mail address:
Despite the positive momentum achieved by the renewable energy sector in recent years, there are
substantial challenges that need the attention of the global community, and one of the more pressing issues
is dealing with the deleterious external costs of power generation. One of the parameters to compare costs
of energy across various technologies is levelised cost of energy (LCOE), but it has been conventional
practice to neglect the external costs in estimating the LCOE of power generation technologies.
Furthermore, as LCOE is a critical indicator for policy and decision makers, there is a need to juxtapose
actual costs of renewable and conventional power generation technologies. This research paper attempts to
internalise some of these external and GHG emission costs across various power generation and storage
technologies in all the G20 countries, as they account for 85% of global power consumption. As future
investment decisions are largely influenced by costs, estimates in this research prove renewables and
storage to be far cheaper than fossil and nuclear sources by 2030, even without considering external costs.
The myth that renewables are ‘way too expensive’ has been debunked repeatedly, and the cost decline of
wind and solar photovoltaic (PV) technologies have outpaced most industry expectations. The results of
this research not only substantiate this trend, but also statistically display that all the G20 countries have
the opportunity to decrease their energy costs significantly, between now and 2030. Renewable energy
technologies offer the lowest LCOE ranges across G20 countries in 2030. Utility-scale solar PV generally
shows the lowest values ranging from16 to 117 €/MWhel and onshore wind LCOE range is from16 to 90
€/MWhel. Rooftop solar PV generally offers the next lowest LCOE ranging from 31 to 126 €/MWhel,
followed by LCOE of offshore wind power ranging from 64 to 135 €/MWhel. Solar PV and battery systems
are highly competitive on an LCOE basis at utility-scale ranging from 21 to 165 €/MWhel and at residential
scale from 40 to 204 €/MWhel. The G20, as well as other countries, can continue to develop their economies
in a sustainable manner, along with substantial co-benefits in adjacent policy fields such as higher national
welfare, better health of citizens, lower respective health costs and improved energy security.
Group of Twenty (G20), Renewable Energy, Levelised cost of energy (LCOE), External Costs
BECCS Bioenergy Carbon Capture and Storage
BEV Battery Electric Vehicle
BNEF Bloomberg New Energy Finance
CAES Compressed Air Energy Storage
CBM Coal Bed Methane
CCS Carbon Capture and Storage
CCGT Combined Cycle Gas Turbine
COP Conference of the Parties
CSP Concentrated Solar Thermal Power
DACCS Direct Air Carbon Capture and Storage
EU European Union
FLH Full Load Hours
GDP Gross Domestic Product
GHG Greenhouse Gases
GW Gigawatts
G20 Group of Twenty
IEA International Energy Agency
IMF International Monetary Fund
IPCC International Panel on Climate Change
IRENA International Renewable Energy Agency
LCOE Levelised Cost of Electricity
MW Megawatt
OCGT Open Cycle Gas Turbine
PHEV Plug-in Hybrid Electric Vehicle
PHS Pumped Hydroelectric Storage
PtL Power-to-Liquids
PtX Power-to-X
PV Photovoltaics
RE Renewable Energy, partly used in the sense of Renewable Electricity
SDGs Sustainable Development Goals
SNG Synthetic Natural Gas
TPED Total Primary Energy Demand
TW Terawatt
USD Unites States Dollar
WEO World Energy Outlook (flagship report of the IEA)
1. Introduction
The United Nations adopted two historically significant agreements in 2015: the Paris Agreement
(UNFCCC, 2015) and the 2030 Agenda for Sustainable Development (United Nations, 2015). Governments
agreed to a long-term target of limiting the increase in global average temperature to well below 2 °C above
pre-industrial levels and to pursue efforts to limit temperature increase to 1.5 °C (UNFCCC, 2015;
Roehrkasten et al, 2016). The agreement calls for global greenhouse gas (GHG) emissions to peak as soon
as possible, recognizing that this will take longer for developing countries, and for rapid emission reductions
thereafter. Moreover, the United Nations has for the first time included energy in its new Sustainable
Development Goals (SDG 7 - Ensure access to affordable, reliable, sustainable and modern energy for all),
calling for an increased acceleration of renewable energy (RE) deployment. Two-thirds of global GHG
emissions stem from energy production and consumption, which puts the energy sector at the core of efforts
to combat climate change and the successful outcome of these international agreements will depend on a
rapid transition of the global energy system (IPCC, 2014; Halsnæs and Garg, 2011).
Economies around the world face the complex challenge of tackling climate change whilst ensuring the
social and economic progress of their populations. In this context, the Group of Twenty (G20), which is a
critical forum for global economic governance, has the prerogative to set the agenda for a global energy
transition. It includes twenty of the world’s largest economies: Argentina, Australia, Brazil, Canada, China,
the European Union (EU), France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia,
South Africa, Republic of Korea, Turkey, the United Kingdom (UK) and the United States of America
(USA)(G20 Research Group, 2018). Member countries account for 86% of the global gross domestic
product (GDP), more than three quarters of global energy demand and 84% of global GHG emissions from
the energy sector as indicated in Figure 1. Given the sheer weight in the global energy system of the G20
countries with nearly 85% of the global power consumption, it is not surprising that 87% of global
renewable power capacity addition happened in the G20 nations as indicated in Figure 1. Hence, any
collective move by the group will have substantial effects on global energy markets.
Figure 1: G20 share of global GDP, Greenhouse Gas (GHG) emissions, power consumption and share of
renewable energy (RE) installed capacity (IEA, 2016; IMF, 2017; IRENA, 2017b).
A rapid transition of power systems in the G20 countries is taking shape, and in this context, costs will play
an important role in determining the required investment levels across the entire power system. The fall in
costs of wind turbines, solar photovoltaics (PV) and batteries, mainly due to their increasing deployment,
is well documented and demonstrated by overall investments in renewable sources remaining quite flat
between 2011 and 2015 despite annual capacity additions rising by 40% (Ram et al., 2017; Frankfurt
School-UNEP Centre/BNEF, 2017). An International Renewable Energy Agency (IRENA) analysis shows
that between the end of 2009 and 2016, solar PV module costs have fallen by around 80% and those of
wind turbines by 30-40% (IRENA, 2016). In many regions of the world, biomass for power, hydropower,
geothermal and onshore wind can all now provide electricity competitively compared to fossil fuel-fired
electricity generation (Ram et al., 2017). The levelised cost of energy (LCOE) of solar PV has fallen by
more than 60% between 2010 and 2016 based on preliminary data; moreover, solar PV achieved highly
competitive levels at the utility-scale across the world (IEA-PVPS, 2017).
The G20’s energy agenda has been evolving in recent years. The task of the G20 through successive
summits has been to seize the momentum of the Paris Agreement and the SDGs to foster collective action
towards a sustainable, decarbonised and affordable global energy system (Roehrkasten et al., 2016).
Investments in efficiency and renewable energy are expected to become the norm, as investments in fossil-
based power generation will be an exception with clearly defined timelines for an exit. One of the main
agendas for the global community is to move away from fossil fuel subsidies both in developed and in
developing countries that are beginning to show adverse economic impacts (Mills, 2017). A shift in
investments towards sustainable energy sources is already underway, as governments and financial
institutions want to avoid lock-in effects. This will be a challenging undertaking, as the G20 members are
highly diverse, often with very divergent interests in the energy spectrum. Figure 2 highlights the diverse
energy mix of the G20 countries and their corresponding shares of installed capacities. If the G20 members
agree on joint action, this will have important international signalling effects and considerable influence on
international policymaking. This could make the G20 an ideal forum to steer an energy transition by
complementing existing institutions and bringing greater coherence to the global energy architecture.
Figure 2: Shares of different power generation capacities across the G20, with Brazil at the top having
the highest share of renewables and Saudi Arabia at the bottom with the lowest share (IRENA, 2017b;
Coal Tracker, 2017; Knoema, 2016; Schneider and Froggatt, 2016).
Technology and finance are strong determinants of future societal paths. While society’s current systems
of allocating and distributing resources while prioritising efforts towards investments and innovations are
in many ways robust and dynamic, there are some fundamental tensions with the underlying objectives of
global sustainable development. Technological innovations and financial systems are highly responsive to
short-term motivations, and are sensitive to broader social and environmental costs and benefits only, to an
often limited extent that these costs and benefits are internalised by regulation, taxation, laws and social
norms (IPCC, 2014). In this context, as costs are a vital indicator for planning and decision making of
government’s around the world, this research paper analyses the costs of power generation in the G20
countries in the present context and from a future perspective for 2030. It involves estimating the LCOE
for different power generation and storage technologies for each of the G20 member countries in 2015 and
for the possible situation in 2030. It also considers the effects of externalities such as, additional costs of
GHG emissions, health related costs amongst other societal costs and subsidies on the levelised costs of
power generation in the G20 countries. Additionally, this is a first of its kind study aimed at estimating
LCOE (with and without external as well as GHG emission costs) of the most relevant power generation
and storage technologies for each of the G20 member countries from a future perspective. Furthermore,
providing a comparative analysis of the costs of renewable power generation combined with storage, and
the costs of fossil fuels and nuclear. These results will assist policy makers across the G20 and other
countries to make informed decisions in carving out their future energy pathways, and inform all the
stakeholders as well as civil society in general. This paper includes a literature review of most relevant cost
of energy estimations across the different parts of the world presented in section 2. The detailed
methodology along with the relevant assumptions and parameters adopted for estimating LCOE are
presented in section 3. Followed by section 4, which highlights the results for all G20 countries in 2015 and
2030, and section 5 presents the analyses of LCOE for renewables and storage in comparison to
conventional fossil fuel and nuclear power generation. Finally, section 6 draws conclusions and raises a
few policy implications of the results.
2. Literature review
In general terms, LCOE is the estimated amount of money that is incurred for a particular electricity
generation plant to produce a standard amout of electricity (either kWh or MWh) over its expected lifetime.
Despite some critiques of LCOE as a tool for comparing costs across power generation technologies such
as Hirth et al. (2015), Schmalensee (2016) and Synapse Energy Economics (2016) amongst others. LCOE
remains a robust tool, as it offers several advantages as a cost metric, such as its ability to normalise costs
into a consistent format across decades and technology types. Additionally, it provides ample flexibility to
incorporate many factors and parameters to provide comprehensive cost perspectives. Consequently, it has
become the de-facto standard for cost comparisons amongst the many stakeholders such as policymakers,
analysts, and advocacy groups (Rhodes et al., 2017). There are many organisations that estimate LCOE
values on an annual basis, a few of them are BNEF (Frankfurt School-UNEP Centre/BNEF, 2018; 2017)
that analyse LCOE for the different power generation technologies and Lazard ( 2016; 2017) that determine
the LCOE of all technologies in the power sector of the USA. Whereas, IRENA estimates the renewable
power genration costs across the world on a periodic basis (IRENA, 2018; 2015). Similarly, the IEA with
it’s annual flagship report world energy outlook (WEO) (IEA, 2018; 2016) have long term projections of
LCOE upto 2050 for the different power generation technologies. Furthermore, various government
organisations have developed LCOE models customised for their respective countries, such as US LCOE
model by the Department of Energy (DOE) (USDOE and NREL, 2018), UK Government’s electricity costs
model developed by Department of Energy and Climate Change (DECC) (DECC, 2013) and Australia
Energy Technology Assessment (AETA) model by Bureau of Resources and Energy Economics (BREE)
(Arif Syed (BREE), 2013), amongst many others. But, there is a huge variance in the consideration of
externalities and other papameters, as shown in the comparitive analysis amongst these along with a few
other LCOE models in Foster et al. (2014).
Although LCOE is a well developed and standard technique in evaluating energy sector economics, authors
approach model formulation in various ways, so as to ensure the model matches research objectives and
data availability (Foster et al., 2014). There have been previous efforts to comprehensively integrate social
and environmental costs of power generation as part of LCOE estimations, some of the interesting
approaches are (Rhodes et al., 2017), which applied a geographically-resolved method to calculate the
LCOE of new power plants on a county-by-county basis while including estimates of some environmental
externalities across the USA. Küchler and Meyer (2012) estimate the full cost of power generation and
systematically compare state subsidies for nuclear, hard coal, and lignite with those for renewables across
Germany. Also, Siemens Wind Power (2014) showcases LCOE including societal and economic benefits
for the different power generation technologies across UK and Germany.
While these studies have comprehensively addressed the aspects of externalities of power generation costs
from national perspectives, there is still a need to expand this to a broader global context to inform public
policy discourse. In this regard, the research paper is an effort to highlight LCOE of key power generation
technologies across the G20 countries with and without consideration of external and GHG emmission
costs. Moreover, most LCOE estimations lack in providing a long term purview of cost developments that
can aid in developing future plans and agendas. Therefore, this research estimates LCOE in 2015 to
represent the current trends and LCOE in 2030 to represent the likely development prospects of the various
technologies across the G20 countries. Besides, almost none of the studies include storage (mainly batteries)
as part of power source options. In recent years, apart from the increasing share of battery storage adoption
among prosumers (REN21, 2017), there have been a growing number of utility-scale battery storage
installations across countries such as USA, UK, Germany and Australia (Clover, 2018; Cook, 2017;
Mcconnell, 2018; Walton, 2018). The trend for solar PV with large-scale battery storage intsllations is
becoming more widespread as costs of batteries are declining rapidly (Kenning, 2018). Considering these
develoments, this research paper is the first of its kind to estimate levelised costs of batteries along with
solar PV (both utility-scale and rooftop) across the G20 countries for 2015 and 2030. Unlike most LCOE
estimates, this research juxtaposes the estimated levelised costs of renewable power and storage with those
of fossil fuel and nuclear power, considering external as well as GHG emission costs, in 2015 and 2030,
across all the G20 countries. With the growing relevance and significance of the G20 forum, the purpose
of this research is to inform global energy policy discourse with comprehensive, rigorous and impartial cost
analysis of the existing as well as emerging power technologies.
3. Materials and methods
In order to represent the comparative annualised costs of electricity generation for different technologies
on an equal footing, a LCOE calculation is often employed (Short et al., 1995). In general, LCOE
calculations include all the costs of building and operating a power plant in relation to the energy generation
over its lifetime. Costs of transmitting and distributing this energy are not usually included in such plant
level LCOE calculations. Importantly, socio-ecological externalities are also often excluded from LCOE
calculations beyond the market cost of CO2 emissions. However, this analysis will attempt to include the
full costs of energy generation by internalising them as fairly as possible. To this end, a wider range of costs
both upstream and downstream from power plants are included in order to give a more accurate
representation of the full costs of energy generation. Such costs will include those related to effects on
human health, the environment, global warming, long-term waste management, plant decommissioning,
financing and budget overruns.
Too often, LCOE calculations merely represent so-called overnight costs of power plants, which do not
fully represent the fact that the true costs may differ significantly from originally budgeted costs. As
financing of construction may be done over many years and there may be significant time and budget
overruns. For some technologies, these are exceptional. However, for others, they appear to be rather
normal due to inherent complexity and changing public expectations (Sovacool et al., 2014). For example,
a solar PV rooftop system on an individual home can be ordered from a service provider who can deliver a
turnkey product within weeks. In addition, as such projects can be paid for by homeowners, financing costs
are rather minimal. In contrast, a nuclear power plant will take many years to go through the long process
of availing permissions and construction. Moreover, a recent trend has been observed in time and cost
overruns that dramatically inflate the originally projected overnight cost. A case in point is the Olkiluoto 3
reactor in Finland. The first application for the project was made in 2000 to the Finnish cabinet, and
construction began in 2005. The project was originally estimated to be completed by 2010 for a cost of
approximately 2.8 b€. However, the reactor has not yet been commissioned by end of 2017, and recent cost
estimates exceed 8.5 b€ (Koistinen, 2012; World Nuclear Association, 2017b).
It is generally agreed that many values representing these components vary greatly on a global level. Hence,
low, median and high values of LCOE for each technology have been calculated for each of the G20
countries in 2015 and 2030. Accurate background data were available for all technologies and collected
using respected international and local sources. These include the following:
International Energy Agency (IEA, 2016)
International Energy Agency Photovoltaic Power Systems Programme (IEA-PVPS, 2016)
European Commission Joint Research Centre, 2014 (EC, 2014)
Danish Energy Agency, 2016 (Danish Energy Agency, 2016)
International Renewable Energy Agency, 2015 (IRENA, 2015)
European Technology & Innovation Platform Photovoltaic (ETIP-PV) 2017 (ETIP-PV, 2017)
Bongers, 2015 (Bongers, 2015)
Lazard, 2016 (Lazard, 2016)
Grausz, 2011 (Grausz, 2011)
Ahmad and Ramana, 2014 (Ahmad and Ramana, 2014)
World Nuclear Association, 2017 (World Nuclear Association, 2017c)
Rafaj and Kypreos, 2007 (Rafaj and Kypreos, 2007)
International Energy Agency and Nuclear Energy Agency, 2015 (IEA & NEA, 2015)
Mann et al., 2014 (Mann et al., 2014)
Schlissel, 2016 (Schlissel, 2016)
Government of India Ministry of New & Renewable Energy, 2017 (MNRE, 2017)
Central Electricity Regulatory Commission of India, 2015 (CERC, 2015)
UBS, 2017 (UBS, 2017)
Pöller, Obert, and Moodley, 2015 (Pöller et al., 2015)
World Nuclear Association, 2017 (World Nuclear Association, 2017a)
Schneider and Froggatt, 2016 (Schneider and Froggatt, 2016)
The current situation is represented by values from 2015, which are at this time the latest available on a
global scale. LCOE is also estimated for 2030 using recognised projections of cost components. In many
cases, when reliable data was unavailable for a particular G20 country, a value was substituted from a
source found from a regionally neighbouring country. Primarily, these assumptions were made based on
the similar economic and geographic conditions prevailing in some of the G20 countries. Such regional
groupings were most often related to geographic closeness, but could also represent political closeness in
the case of EU member states. Regional groupings were most often made for Argentina and Brazil;
Australia, Indonesia, India, Japan and the Republic of Korea; Canada, the USA and Mexico; the Kingdom
of Saudi Arabia and South Africa; the United Kingdom and the countries of the EU. These can be further
examined in the suplementary material.
The components of the LCOE calculations employed in this analysis includes real capital expenditures
(capex) instead of overnight costs. In addition, this analysis includes plant decommissioning costs, fixed
operational and maintenance expenditures (opex fixed), variable operational and maintenance expenditures
(opex variable), storage costs, fuel costs, GHG emission costs, waste disposal costs, and a full range of
additional socio-economic costs. Other important components of the LCOE calculations are plant lifetimes
and full load hours (FLH) of annual operation.
The calculation of LCOE (expressed as €/MWhel), representing a discounted cash flow approach for the
case of constant annual cash flows (Short et al., 1995), in this report is characterised by the following
equation (1):
   
       
     (1)
CapexReal is annual capital expenditures (€/MWel), which include a low and high estimate for investments
and budget overruns; Opexfixed are fixed operation and maintenance costs (percentage of capex/year);
Decommissioning costs are expressed as a percentage of capex for all technologies except nuclear power
plants, for which they are expressed as a value in €/MWel; N is the operational lifetime of the technology
(years); Opexvariable is the annual variable operation and maintenance costs (€/MWhel); LCOS is the levelised
cost of storage in €/MWhel (see below); Fuel costs are expressed in €/MWhel; Waste disposal costs are
expressed in €/MWhel; External costs (annual) include a range of socio-economic costs related to energy
generation (€/MWhel); GHG costs (annual) include the full socio-economic costs of GHG emissions
(€/MWhel). Importantly, there has been no discounting of decommissioning costs, i.e. a social discounting
rate of 0% has been applied for supporting real societal costs. Instead, they are applied to the time of energy
The capital recovery factor (crf) is calculated according to the following equation (2):
      
    
WACC is the weighted average cost of capital; N is the operational lifetime of the technology (years).
WACC is set at 7% per year for all technologies with the exception of coal and nuclear power, which are
set at 10%. In general, the WACC represents the weighted cost of both debt and equity based capital.
WACC is also a representation of the relative risk that various investors perceive in the development of a
project. For this reason, a higher WACC was used for coal and nuclear power. This is due to the fact that
we are currently seeing divestment from such assets and a higher risk of stranded investments (Baron and
Fischer, 2015). This risk is a result of accelerated phasing out of coal plants in many parts of the world due
to climate change mitigation, and shut downs of nuclear plants in a post-Fukushima world. In addition,
budget overruns in recent years of nuclear power projects have left investors sceptical (Koplow, 2011;
Moody’s Investors Service, 2008; Pearce, 2017; Schneider and Froggatt, 2016), making it more difficult to
raise capital.
The levelised cost of storage (LCOS) is calculated for the case of both rooftop and utility solar PV according
to the following equation (3):
        
 
 
Major components of LCOE are further described in turn below. Afterwards, a brief explanation of how
low, median and high values of LCOE were calculated.
3.1 Capex
Overnight capital expenditures were derived from a range of internationally recognised sources for each of
the G20 countries. In most cases, these sources supplied low and high ranges for many technologies. When
data was not available for a particular country, values from a neighbouring country were substituted in the
manner described above. For utility-scale solar PV, the most economical option is sometimes a fixed,
optimally tilted system. Such is the case for countries such as Canada, France, Germany, Japan, Russia and
the UK. However, at other times it is more advantageous to operate a single-axis tracking system, since the
higher yield of the system outweighs the additional capex. Therefore, for all other countries an additional
cost of 10% was added to capex values (Bolinger et al., 2017) to reflect the additional costs of the tracking
In all cases but three, a value for this overnight capex was the starting point of all calculations. The
exceptions will be discussed below in the section on CapexReal.
3.2 Investment and overruns
For solar PV and wind energy generation technologies, a low value of 1.5% of capex and high value of
3.5% of capex were added (IEA & NEA, 2015). These values reflect the fact that solar and wind
installations typically have very short construction times (1-2 years), but that some delays may occur due
to complex procedures related to permitting. Battery technologies did not have an investment and overrun
addition. It was assumed that coal and gas-based thermal power plants have low and high investment and
overrun additions of 5% and 15%, respectively. These capex additions are consistent with estimates made
by the IEA (IEA & NEA, 2015; IEA, 2016). For nuclear power, a low investment and overrun addition of
20% was assumed due to the longer construction times of nuclear power plants. This was also consistent
with high IEA estimates. However, another source was used to estimate the high investment and overrun
addition of 40% (Koomey and Hultman, 2007). This source was deemed to better account the reality of the
international trend towards longer construction times and budget overruns. It also showed that such
overruns have gotten progressively larger over time. Currently, nuclear power plants in Finland and France
are seven years beyond their scheduled construction time of 5 years, and cost overruns are approximately
300% (Koistinen, 2012; Le Monde, 2012). The applied range of 20% - 40% of cost overruns is rather
conservative, given the scientific analysis for 180 nuclear reactors which had a cost overrun of 117% on
average and no single reactor within the planned budget had been found (Sovacool et al., 2014).
3.3 CapexReal
A high and low value for CapexReal was calculated by adding the high and low investment and overrun
additions to the high and low values of capex. In some cases, only a single value for capex was available,
and so the variance in CapexReal represents only the variance in the investment and overruns addition.
In three cases, values for CapexReal were not the result of calculations, but were taken straight from the
literature. Thereby, the value of overnight capex could be derived in reverse for the high values of
Argentinian, Chinese and South Korean nuclear power plants. The high CapexReal value for nuclear power
in Argentina was based on a known cost of 5.8 bUSD for the 800 MW Atucha 3 reactor (Schneider and
Froggatt, 2016; World Nuclear Association, 2017a). Interestingly, the technology provider for the Atucha
3 reactor is Chinese (CANDU), and the cost of similar projects in China are generally reported at much
lower costs. This indicates a high level of domestic subsidy possibly incorporated in the reported overnight
costs that are commonly used in international publications. The same phenomenon is suspected for Korean
technology providers. Therefore, high CapexReal values for China and the Republic of Korea are derived
from known costs for the same technologies in other countries. The high CapexReal value for nuclear power
in China was based on a known cost of 9.6 bUSD for the 2028 MW Karachi 1&2 reactors in Pakistan built
by the Chinese National Nuclear Corporation (Schneider and Froggatt, 2016; World Nuclear Association,
2017a). Likewise, the high CapexReal value for nuclear power in South Korea is based on an estimated cost
of 32 bUSD for the 5380 MW Barakah 1-4 reactors in the United Arab Emirates, which are built by the
Korean Electric Power Corp. (Schneider and Froggatt, 2016).
3.4 Decommissioning
A decommissioning cost of 5% of capex was applied to solar PV, wind, coal and gas technologies. No
decommissioning costs were applied to batteries. For nuclear power plants, a decommissioning cost of 1100
€/kW was applied. However, the difficulty in accounting decommissioning costs accurately merits further
discussion. Globally, there is very little actual experience and information related to fully decommissioned
nuclear power plants. For this reason, estimates of future costs range from values as low as 200 €/kW for
reactors in Finland (219 mUSD for 2*440 MW VVER) to 1500 €/kW for reactors in Slovakia (1.3 b€ for
2*440 MW VVER) (EC, 2016; IAEA, 2002). In this research, it is assumed that decommissioning costs
globally will be 1100 €/kW in 2015 and 2030. The effect of varying this value by ± 50% has an effect on
LCOE of ±1 €/MWhel.
3.5 Opex
This category is divided into fixed and variable operational and maintenance expenditures. Opexfixed is
commonly expressed as a percentage of capex per year, and represents costs unrelated to how many hours
per year the plant operates. Such costs include material, personnel, administration and insurance costs, but
do not include fuel or emissions costs. Opexvariable represents costs that are directly related to the frequency
and duration of plant operations. Some operations and maintenance costs, such as those related to pumps,
fans and lubricating fluids, are incurred only when the plant operates. In the case of batteries, a similar
value to Opexvariable is calculated based on the costs related to storage losses. These losses are a function of
the energy throughput and battery efficiency.
3.6 Lifetime
Assumptions made related to plant lifetimes are consistent with those made by the International Energy
Agency and other international agencies. Wind energy plants are assumed to have a lifetime of 25 years.
Solar PV rooftop units and power plants are assumed to have a lifetime of 30 years (ETIP-PV, 2017). This
value was chosen even though some facilities may have physical lifetimes of up to 35 years. Increasing PV
lifetime by 10 years would mean that LCOE could be reduced by about 5 €/MWhel. The real lifetime of
solar PV modules and wind turbines installed today are, obviously, unknown. More relevant to LCOE
calculations, however, is the perceived economic lifetime by the international community, including
investors. Another unknown is the lifetime of batteries, which has been set at 10 years for 2015 and 15
years for 2030. The extended lifetime for 2030 is based on projected lifetimes of electric vehicle Li-ion
batteries (UBS, 2017). Complicating this matter is that batteries have both calendric and cycle lifetimes,
meaning batteries that are charged and discharged more frequently and deeply will have reduced lifetimes.
The lifetimes of coal and gas power plants is assumed to be 40 years. Nuclear power plant economic lifetime
is set at 50 years. It should be noted, however, that nuclear power plants are typically given operating
permits for 30-40 year periods, after which refurbishment or renovation is needed to extend the physical
lifetime to 60 years or beyond. And again, perceived economic lifetimes for investors are typically shorter,
making a 40 year economic lifetime perhaps more relevant for the purposes of LCOE calculations. The
same was done by Lazard (Lazard, 2016). The competition of low cost solar PV and wind plants already
led to earlier than possible shut down decisions (Nikolewski, 2016). However, the high risk profile of
nuclear power plants may lead to much shorter lifetimes, due to detracted societal willingness to accept the
risk, which seems to be also well covered by liberal western constituencies, as confirmed by the Federal
Constitutional Court of Germany in 2016 (The Federal Constitutional Court of Germany
(Bundesverfassungsgericht), 2016).
3.7 Full load hours
For nuclear plants, baseload operation is assumed. Therefore, FLH values reflect capacity factors of 80%
at a low end to 90% at the high end. For coal power plants, some of which have not witnessed such high
FLH in recent years due to competition with renewable energy and decarbonisation targets, capacity factors
range between 50% and 90%. Median values for coal and nuclear power plants are the average between the
lower and upper estimates. For open cycle gas turbines, low, median and high capacity factors are assumed
to be 10%, 45% and 80%, respectively, due to the more peak following profile of generation. Similarly,
these values are set at 40%, 60% and 80%, respectively, for combined cycle gas turbines. These values are
consistent with international agencies.
For solar PV and wind energy generation, FLH for each country in the G20 were calculated individually,
based on real weather data over the period of 1994 to 2005. The procedure for estimating FLH was complex,
but took into account both geographic and temporal variation of the resources. Data was derived from
(Stackhouse, 2016; Stetter, 2012), which gave irradiation and wind speed data on an hourly resolution for
the years indicated. The geographic resolution of the data is a 0.45° latitude by 0.45° longitude node
(approximately 50 km by 50 km at the equator). These nodes were ranked in terms of the quality of the
resource as percentiles, with the 100th percentile being the node with the highest average annual irradiation
or wind speed. Maximum FLH for solar PV and wind energy were determined as the highest value for the
100th percentile node over the time period (1994-2005). Minimum FLH were determined as the lowest value
for the 51st percentile node over the same time period. To determine the median FLH value, a weighted
average of nodes was used. It was assumed that not all capacity of solar PV and wind could be located in
only the best sites, and that most of the worst sites could be rejected as being infeasible. So, 10% of capacity
would be located in the areas ranked from the 51st to 60th percentile, 10% of capacity would be located in
the areas ranked 61st to 70th, 20% of capacity would be located in areas ranked 71st to 80th, 30% of capacity
would be located in areas ranked 81st to 90th, and the remaining 30% of capacity would be located in areas
ranked 91st to 100th. The weighted average value for FLH was calculated for each country and each year,
and the median was calculated as the average over the time period.
Exceptions to the above were made for several countries that have less than ideal wind conditions: Brazil,
Indonesia, India, Mexico, Kingdom of Saudi Arabia, and Turkey. It was assumed that there would be
limited locations of sufficient wind quality in some onshore and offshore locations, so the range of
acceptable nodes were limited between the 81st and 100th percentiles. For Italy, Mexico, Kingdom of Saudi
Arabia and Turkey, this limitation was applied only to offshore wind energy generation.
For single-axis tracking PV systems, FLH data was only available for a single year (2005). However, this
data was compared to the values for fixed optimally tilted systems for the same year, and values for other
years were extrapolated based on this comparison.
For LCOE calculations for solar PV + Batteries, FLH were assumed to be the same for solar PV rooftop.
However, the ratio of storage capacity to generation capacity was varied, with a ratio of 1 assigned for low
and median LCOE calculations, and a ratio of 2 assigned for high LCOE calculations. This takes into
account that larger battery capacity that would lead to higher LCOE. At the same time, this raises an
important point. The LCOE for the solar PV + Battery systems may not, therefore, be immediately
comparable to the LCOE of the other generation technologies, but should be compared to consumer’s costs
of electricity in order to determine if it is low or high.
3.8 Fuel
Fuel costs were taken from projections found in Bloomberg New Energy Finance’s New Energy Outlook
2015 (BNEF, 2015) and are summarised in Table 1.
Table 1: Fuel cost assumptions for coal (upper) and gas (lower) in €/MWhth.
Coal Europe
Coal China
Coal India
Gas Europe
Gas Japan
Gas China
A cost of 5.26 €/MWhel for nuclear fuel (IEA, 2016) was assumed for all countries for both 2015 and 2030
due to large stockpiles of nuclear fuel. This corresponds to an approximate cost of 7 USD/MWhel, and may
vary by ±1 €/MWhel globally.
3.9 Waste disposal
Waste disposal costs were considered only for nuclear power plants and were derived directly from the IEA
(IEA & NEA, 2015). This source reported values for each country in 2015 which included both fuel and
waste disposal costs. The waste disposal costs were determined after subtracting the above fuel costs.
Values reflect the economic difficulty that some countries have in safely disposing off nuclear waste (Japan,
the USA and the UK).
3.10 External costs
A comprehensive review by Climate Advisers (Grausz, 2011) of the total social cost of different forms of
electricity generation determined that the work of Rafaj and Kypreos (Rafaj and Kypreos, 2007) provided
the most comprehensive estimates of the external costs of electricity generation. Similarly, these same costs
have been used as the basis for LCOE calculations in this present study, and are summarised in Table 2
below. Note that values do not include external costs related to CO2 emissions, which will be explained in
the next section.
Table 2: External costs of electricity generation excluding CO2 costs used for LCOE calculations. From
(Rafaj and Kypreos, 2007). All values are in 2015/MWh of electricity produced and based on long-term
conversion of 1.33 EUR/USD and 57% inflation of the USD between June 1995 and June 2015. ASIA
includes all Asian countries. OECD includes Australia and all other countries not specified. NAME
includes all North American countries. EEFSU includes all Eastern European and Former Soviet Union
countries. LAFM includes countries of Latin America, Africa and the Middle East.In the Table, PP is Power
Plants, CCS is Carbon Capture and Storage and CCGT is Combined Cycle Gas Turbine.
Coal PP
Coal PP + CCS
Nuclear PP
Solar PV
Wind turbine
3.11 GHG emission costs
For CO2 emissions, a range of costs exist that represent the cost of a metric ton of emissions. Some of these
are market based, while others are politically determined. Carbon markets are percieved to be imperfect
mechanisms that often transfer and consolidate power and wealth, as concluded in (Sovacool, 2011) in
which the author reviewed more than 300 articles discussing the merits and drawbacks of global and
regional carbon markets over the past decade. In this research, a value of 7 €/ton of CO2eq was assumed
based on the market value of carbon in the EU for the year 2015. For 2030, a value of 74 €/ton of CO2eq
was assumed based on estimates of the social cost of carbon by the Stern Review (Stern, 2007). The recent
report of the High-Level Commission on carbon price confirms CO2eq emission costs of up to 74 €/ton of
CO2eq for the year 2030 (Carbon Pricing Leadership Coalition, 2017). However, it should be noted that
there are a range of estimates related to the actual costs of carbon from 30 to 165 €/ton of CO2eq (Moore
and Diaz, 2015). Some others (Jakob et al., 2016) arugue that emissions pricing could be utilised to promote
sustainable socio-economic development by providing public goods that are essential for human well-being
through public financing.
Determining a single, universally acceptable value for GHG emissions is an impossible task, which often
leads to confusion or objection. In truth, measuring the full socio-economic impacts of GHG emissions is
inherently inaccurate and thus open to debate. The range of impacts included or excluded play a major role.
The Stern Review (Stern, 2007) was amongst the first influential publications to place a social cost on GHG
emissions. This was set at 85 USD2007 per ton (74 2015/ton of CO2eq) for the case of a business as usual
scenario with global concentration exceeding 550 ppm in the atmosphere. However, the Stern Review
acknowledged that this cost could be up to a third lower if global concentration was around 450 ppm. This
shows that the cost of GHG emissions, even the social cost, is not static. Instead, we must accept that the
costs will be higher as global atmospheric concentrations increase. What is more, a recent study (Moore
and Diaz, 2015) suggests that higher concentrations of GHG emissions in the atmosphere will have a so far
inadequately accounted, negative effect on economic growth, which may lead to much higher impact on a
full socio-economic level. The article argues that to this point the focus has been on the environmental
impacts of GHG emissions on people. The authors remind that there will also be significant economic
impacts on people. If effects on global economic growth are also taken into account, the full cost of GHG
emissions could be much higher, up to 220 USD/ton (165 €/ton of CO2eq) (Moore and Diaz, 2015).
3.12 Other technical assumptions
The technology-wise assumptions are as listed below,
1. Wind onshore: Full Load Hours (FLH) are based on the power curve of a 3 MW onshore wind
turbine (Enercon E101with a hub height of 150 m).
2. Wind offshore: Full Load Hours (FLH) are based on the power curve of a 3.6 MW offshore wind
turbine ( Siemens SWT-3.6-120 with a hub height of 100 m).
3. PV rooftop: Performance characteristics are based on the scale of a 5 kWp system.
4. PV utility: Performance characteristics are based on the scale of a 50 MWp system.
5. Li-ion batteries: Charactersitics based on power capacity of 1-3 MW and storage capacity of 0.5-
1.2 MWh for utility-scale.
6. Coal PP: Characteristics based on supercritical, pulverised coal condensing power plant burning
black coal; plant efficiency based on lower heating value.
7. CCGT PP: Characteristics based on combined cycle gas turbine of up to 580 MW (net); plant
efficiency based on lower heating value of fuel.
8. OCGT PP: Characteristics based on advanced open cycle gas turbine of up to 250 MW (net); plant
efficiency based on lower heating value of fuel.
9. CCS: Characteristics based on post-combustion carbon capture; plant efficiency based on lower
heating value of fuel.
10. Nuclear PP: Characteristics based on advanced light water reactor technologies in the range of 1000
to 3300 MW; plant efficiency based on lower heating value of fuel.
3.13 Summary of calculations
Calculations for low, median and high LCOE were made to account for national differences in LCOE
components and variance in energy generation from different technologies, and these are available along
with all the Supplementaty Material. The variance may be due to geographic factors in the case of solar PV
and wind energy generation, but also due to how technologies are used in the energy system (peaking vs.
baseload plants). The main factors for the variance in LCOE are capex, investment and overruns, and FLH.
At the same time, fuel costs and assumptions about technology lifetimes could slightly increase the variance
as discussed above. Low LCOE values are calculated from a combination of low capex estimates, low
values for investment and overruns, and high FLH. Median LCOE values are calculated from a combination
of low capex estimates, low values for investment and overruns, and median FLH. High LCOE values are
derived from a combination of high capex estimates, high values for investment and overruns, and low
FLH. Importantly, high values for gas turbines should not immediately be seen as entirely negative. Such
high values are primarily the result of low FLH of peak-following gas turbines, which have an important
regulatory function in many energy systems.
While a full range of values were calculated for LCOE, only values below 250 €/MWhel are shown in
figures in the results section. Above this level, investments are highly unlikely to be profitable in all but the
most extreme, off-grid situations, or when technologies play an important regulatory function, such as
frequency control of grids.
4. Results
Results of LCOE calculations for all the G20 countries are presented in Figures 3 to 6, and all the applied
assumptions and data are shown in detail in the supplementary material. The range of LCOE values for the
years 2015 and 2030 are represented by bars that are coloured corresponding to the different technologies
as shown in the legend. The range of LCOE values for conventional technologies (coal, gas and nuclear)
also include CO2eq and external costs. The median values for LCOE across the different technologies are
represnted by the red dots (which do not include the CO2eq and external costs) and the white dots (which
include the CO2eq and external costs).
In general, onshore wind energy currently shows the lowest overall LCOE, especially in regions of high
latitudes (either north or south). Notable exceptions exist for some regions in Asia where wind resources
are less favourable as compared to the solar resource, which is more favourable. In 2030, solar PV utility
power plants represent the lowest LCOE of all technologies across all the G20 countries with the exception
of Northern European countries that are part of the European Union, where onshore wind continues to have
the lowest LCOE. On a global level represented by all the G20 countries, rooftop solar PV becomes more
competitive than conventional energy production (fossil fuels and nuclear) in 2030, especially when a more
complete range of costs are internalised for all technologies. Cost reductions projected for battery storage
in 2030 also increase the competitiveness of PV + Battery systems (rooftop and utility) across all the G20
countries. Conventional fuels become significantly less competitive in 2030 when the costs of CO2eq and
other externalities are fully considered. Gas-based technologies, important providers of flexibility to global
energy systems, have the potential to reduce overall LCOE through switching from natural gas to more
sustainable bio-based or synthetic methane. Carbon capture and storage offers an opportunity to reduce
costs associated with fossil fuel combustion, but remains significantly higher in costs than renewable energy
generation, even with the anticipated cost reductions due to development of CCS technology. It needs to be
noted that net zero emissions are almost impossible with fossil-fuel based CCS, and still incur higher costs
than renewable energy based energy systems. Nuclear power has already lost its competitiveness to wind
and solar PV in 2015 in most of the G20 countries and further worsens its relative competitiveness with
renewable energy in 2030 when high levels of social, environmental and economic risks are internalised in
the LCOE calculations.
As shown in Figure 3, the results for Argentina indicate that LCOE of wind onshore power (25 €/MWhel)
is already lower than fossil fuel based power generation (with coal having LCOE of 46 €/MWhel) in 2015,
and by 2030 LCOE of wind (22 €/MWhel) along with utility-scale PV (22 €/MWhel) will be much lower.
In the case of Australia, wind onshore power has lower LCOE (35 €/MWhel) as compared to fossil based
power (with coal having LCOE of 55 €/MWhel) in the present context, and by 2030 rooftop (36 €/MWhel)
and utility-scale PV (22 €/MWhel) along with wind onshore (30 €/MWhel) will be the cheapest sources of
electricty. In Brazil, LCOE of wind onshore power (44 €/MWhel) is competitive with respect to fossil fuel
based power generation (with coal having LCOE of 46 €/MWhel) in 2015 and remains competitive in 2030,
whereas utility-scale PV (24 €/MWhel) along with utility battery storage (32 €/MWhel) will have the lowest
LCOE in 2030. In Canada, fossil fuel based power generation (with coal and CCGT having LCOE of 52
€/MWhel) has lower LCOE in the present context, whereas wind onshore (40 €/MWhel) and utility-scale
PV (35 €/MWhel) will have lower LCOE by 2030. In China, wind onshore power has the lowest LCOE (29
€/MWhel) in 2015, and by 2030 wind onshore (27 €/MWhel) and utility-scale PV (23 €/MWhel) will have
lower LCOE than fossil and nuclear power (with coal having LCOE of 36 €/MWhel).
Figure 3: Results of LCOE calculations for the G20 countries Argentina, Australia, Brazil, Canada and
China in 2015 and 2030 (€/MWhel).
As shown in Figure 4, the results for France and Germany show that LCOE of wind onshore power (with
47 and 44 €/MWhel) is presently competitive with fossil fuel based power (with coal having LCOE of 43
and 42 €/MWhel), and by 2030 wind onshore (29 and 28 €/MWhel) and utility-scale PV (32 and 40 €/MWhel)
have much lower LCOE. In India, fossil fuel sources (with coal having LCOE of 34 €/MWhel) have lower
LCOE in 2015, whereas utility-scale PV (25 €/MWhel) has much lower LCOE in 2030. Similary in
Indonesia, fossil fuel sources (with coal having LCOE of 39 €/MWhel) have lower LCOE in 2015, whereas
utility-scale PV (25 €/MWhel) has much lower LCOE in 2030. In Italy, fossil fuel produces power (with
coal having LCOE of 43 €/MWhel) at a lower LCOE in 2015, whereas by 2030 wind onshore (29 €/MWhel)
and utility-scale PV (27 €/MWhel) will have much lower LCOE.
Figure 4: Results of LCOE calculations for the G20 countries France, Germany, India, Indonesia and
Italy in 2015 and 2030 (€/MWhel).
As shown in Figure 5, the results for Japan indicate that fossil fuel based power (with coal and CCGT
having LCOE of 57 and 55 €/MWhel) have lower LCOE in 2015, whereas by 2030 utility-scale PV (31
€/MWhel) and wind onshore (54 €/MWhel) will have lower LCOE. In the case of Republic of Korea, fossil
fuel and nuclear power (with coal and nuclear having LCOE of 37 and 40 €/MWhel) have lower LCOE in
2015, whereas utility-scale PV (29 €/MWhel) has much lower LCOE in 2030. In Mexico, utility-scale PV
(60 €/MWhel) is competitive with fossil fuel based power (with coal and CCGT having LCOE of 52
€/MWhel) in 2015, and by 2030 utility-scale PV (21 €/MWhel) and wind onshore (51 €/MWhel) have much
lower LCOE. Whereas, in Russia, wind onshore power (59 €/MWhel) is competitive with fossil fuel based
power (with coal and CCGT having LCOE of 52 and 51 €/MWhel) in 2015, and by 2030 utility-scale solar
PV (36 €/MWhel) and wind onshore (52 €/MWhel) have lower LCOE. In Saudi Arabia, fossil fuel sources
(with coal and CCGT having LCOE of 47 and 49 €/MWhel) have lower LCOE in 2015, whereas utility-
scale PV (21 €/MWhel) has much lower LCOE in 2030.
Figure 5: Results of LCOE calculations for the G20 countries Japan, Republic of Korea, Mexico, Russia
and Kingdom of Saudi Arabia in 2015 and 2030 (€/MWhel).
As shown in Figure 6, the results for South Africa indicate that fossil fuel produces power (with coal having
LCOE of 47 €/MWhel) at a lower LCOE in 2015, whereas by 2030 wind onshore (46 €/MWhel) and utility-
scale PV (21 €/MWhel) will have lower LCOE. Similary, in Turkey, fossil fuel sources (with coal having
LCOE of 43 €/MWhel) have lower LCOE in 2015, whereas utility-scale PV (25 €/MWhel) and wind onshore
(40 €/MWhel) have much lower LCOE in 2030. In the UK, wind onshore power (44 €/MWhel) is competitive
with fossil fuel based power (with coal having LCOE of 43 €/MWhel) in 2015, and by 2030 wind onshore
power has the lowest LCOE (23 €/MWhel). In the USA, wind onshore power (31 €/MWhel) has the lowest
LCOE in 2015, and by 2030 wind onshore (30 €/MWhel) and utility-scale PV (25 €/MWhel) have much
lower LCOE than fossil fuel based power (with coal having LCOE of 55 €/MWhel). Lastly, in the EU, wind
onshore power (40 €/MWhel) has lower LCOE in comparision to fossil fuel based power (with coal and
CCGT having LCOE of 43 and 51 €/MWhel) and by 2030, wind onshore (30 €/MWhel) and utility-scale PV
(30 €/MWhel) have lower LCOE.
Figure 6: Results of LCOE calculations for the G20 countries South Africa, Turkey, United Kingdom,
United States of America and European Union in 2015 and 2030 (€/MWhel).
5. Discussion
The LCOE of all technologies across the G20 countries are compiled into renewables and storage that
includes wind onshore, wind offshore, PV rooftop, PV utility, Li-ion batteries rooftop and Li-ion batteries
utility, and fossil fuels and nuclear that includes Coal PP, Coal with CCS, CCGT, CCGT with CCS, OCGT
and Nuclear PP. Further, the LCOE of renewables and storage are evaluated against the LCOE of fossil
fuels and nuclear, with and without the consideration of external and CO2eq costs for 2015 as well as 2030.
Figure 7 presents the comparative results for LCOE of renewables and storage with LCOE of fossil fuels
and nuclear in 2015, with and without external and CO2eq costs. Countries are shaded in green when the
LCOE of renewables and storage is lesser than the LCOE of fossil fuels and nuclear, shaded orange when
the LCOE of renewables and storage are the same as the LCOE of fossil fuels and nuclear, and shaded red
when the LCOE of fossil fuels and nuclear are lesser than the LCOE of renewables and storage.
On comparing the LCOE of all power generation technologies across the G20 countries in 2015, it can be
concluded that the LCOE of renewable energy sources are already on par with fossil and nuclear sources in
many of the G20 countries even without the inclusion of external and CO2eq costs. Whereas, when external
and CO2eq costs are included in the LCOE estimations, the LCOE of renewables and storage are lesser than
the LCOE of fossil fuels and nuclear in almost all the G20 countries. Apart from Republic of Korea, where
the LCOE of fossil fuels and nulear is still lesser than the LCOE of renewables and storage, and in Italy and
South Africa, where the LCOE of renewables and storage are the same as the LCOE of fossil fuels and
Figure 7: Results of LCOE calculations for the G20 countries in 2015 without external and CO2eq costs
(top) and with external and CO2eq costs (bottom).
Onshore wind is currently the least cost source of electricity in many of the G20 countries, ranging from18
to 121 €/MWhel (excluding Indonesia), and utility-scale PV, which is quite competitive in many of these
countries ranges from 36 to 140 €/MWhel (excluding Russia). These values are comparable to present
auction prices as shown in (Agora Energiewende, 2017). As indicated in Figure 7, if external and CO2eq
costs are taken into account, wind and solar PV along with batteries will be cheaper in almost all the G20
countries in terms of LCOE.
Fossil fuel based energy generation currently appears relatively low in cost due to low costs of GHG
emissions imposed by many global markets, which does not represent the real impacts of those emissions.
Coal-based generation appears to be the lowest cost of the fossil fuels due to the baseload nature of plant
operation when compared to gas based technologies. It should be noted, however, that gas based
technologies play important roles in grid stabilisation and balancing. Therefore, lower full load hours of
gas turbine plants are a major contributor to higher LCOE. CCS technologies appear very high in costs at
the moment and do not represent an economically competitive option in the near term. Nuclear power
appears relatively lower in costs in China and the Republic of Korea (likely due to high domestic subsidies),
but has significantly higher costs in other parts of the world, when the costs of financing, budget overruns,
waste management, decommissioning and associated risks are included.
Figure 8 presents the comparitive results for LCOE of renewables and storage with LCOE of fossil fuels
and nuclear in 2030 for all G20 countries without including external and CO2eq costs. It is quite evident that
renewables and storage prove to be much cheaper even without considering external and CO2eq costs on a
LCOE basis. This is primarily due to the rapid decline in costs expected for solar PV and battery systems,
along with a steady decline in the costs of wind tubines up to 2030 (Breyer et al., 2017).
Figure 8: Results of LCOE calculations for the G20 countries in 2030 without external and CO2eq costs
Renewable energy technologies offer the lowest LCOE ranges across G20 countries in 2030. Utility-scale
solar PV generally shows the lowest values ranging from 16 to 117 €/MWhel, although there are notable
exceptions for regions where the solar resource is more variable or the onshore wind resource is particularly
good. The onshore wind LCOE range is from 16 to 90 €/MWhel (excluding Indonesia). This is the case for
several countries at higher northern latitudes. Rooftop solar PV generally offers the next lowest LCOE
ranging from 31 to 126 €/MWhel, followed by LCOE of offshore wind power ranging from 64 to 135
€/MWhel. However, similar exceptions exist for higher northern latitudes and in areas that typically have
higher quality offshore wind resources (e.g. Canada, USA, UK). Solar PV and battery systems are highly
competitive on an LCOE basis at utility-scale (21 to 165 €/MWhel) with overall market costs of electricity
depending on local costs, and at residential scale (40 to 204 €/MWhel) depending on consumer costs of
electricity including taxes, transmission costs, and distribution costs. As shown by Lazard (Lazard, 2017)
and IRENA (IRENA, 2018), these costs are attainable even before 2030 with the current market trends
indicating substainal drops in the costs of renewable technologies. This is futher substantiated with the
recent bids for solar PV in Chile and Mexico reaching 21.48 USD/MWh and 20.57 USD/MWh,
respectively. Also, bids in Saudi Arabia for solar PV were below 20 USD/MWh (Bellini, 2017a; 2017b;
Kenning, 2017). Interestingly, the lowest LCOE values seen for renewable energy technologies in the G20
are in Argentina, where both solar and wind resources are exceptional.
On the contrary, fossil fuel and nuclear power generation represents higher LCOE ranges across the G20
countries in 2030. Firstly, gas based energy generation represents the highest LCOE values with 107 to 124
€/MWhel for CCGT and 142 to 162 €/MWhel for OCGT. However, it must be reiterated that many of the
higher range values are the result of operational conditions for gas turbines, especially OCGT. These
operational conditions include the provision of essential control and stability for electricity grids, which
may significantly limit the FLH of operation. In addition, gas-based technologies have the great potential
to reduce costs associated with GHG emissions and external costs by switching to more sustainable fuels,
such as biomethane and synthetic methane. Secondly, coal based power represents amongst the highest
LCOE values ranging from 115 to 186 €/MWhel when CO2eq and external costs are accounted. This trend
is seen across the G20 countries. Thirdly, nuclear power shows a wide range of LCOE values from 62 to
152 €/MWhel. Low values for 2030 are observed in China and the Republic of Korea as it is unclear if the
reported overnight costs represent subsidised values. The technologies provided in these countries
domestically differs significantly in cost to the same technologies installed internationally by the same
technology providers. Conservative cost assumptions were used to specify the upper limit in relation to
financing and overruns (40% of overnight capex). However, several projects worldwide have shown that
such costs can exceed 300% of capex (Koistinen, 2012; Le Monde, 2012; Schneider and Froggatt, 2016)
and the averaged cost overrun for 180 reactors has been found to be 117% (Sovacool et al., 2014). Lastly,
CCS offers little hope for positive business cases in the Americas through to at least 2030. The range of
LCOE for coal CCS is 89 to 205 €/MWhel, and the range for CCGT CCS is 102 to 179 €/MWhel.
In comparision to other LCOE estimates, such as Lazard (Lazard, 2017), IRENA (IRENA, 2017a, 2018)
and Agora Energiewende (Agora Energiewende, 2017), these estimates seem rather on the conservative
side with respect to LCOE values of renewable technologies, specifically utility-scale PV and onshore wind.
Also, recent bids for large scale solar PV projects across Saudi Arabia, Chile and Mexico (around 20-22
USD/MWh) have demonstrated the rapid cost decline potential of solar PV power (Bellini, 2017, 2017;
Kenning, 2017). Lazard’s LCOE estimates show utility-scale solar PV ranging from 43 to 48 USD/MWh
and wind onshore ranging from 30 to 60 USD/MWh, whereas coal ranges from 60 to 143 USD/MWh and
nuclear ranges from 112 to 183 USD/MWh. Agora Energiewende estimates the average LCOE for onshore
wind in the context of Germany to be in the range of 5-9.5 cents €/kWh. As the global energy transition
increasingly shifts towards renewables and away from fossil and nuclear sources, the costs of energy are
expected to decline futher (Breyer, et al., 2017). These estimates futher substantiate the results of this
research in the context of 2030, as LCOE of renewables and storage are continuting to decline.
6. Conclusions
From the LCOE results presented in Figures 7 and 8, it is clear that renewable electricity generation in
several of the G20 countries is already lower in cost than conventional alternatives. These include the USA,
Argentina, Brazil, the EU, Turkey, China and Australia. This is the case when external and CO2eq costs are
not considered, but with clear socio-economic and enviornmental impacts of power generation along with
increasing adverse direct health impacts of fossil fuel and nuclear power generation being evident (Health
Care Without Harm, 2015; Markandya and Wilkinson, 2007), the need to represent the real costs of power
generation is incontrovertible. When the external and CO2eq costs of the various power generation
technologies are considered, LCOE of renewables and storage are seen to be much lesser than the LCOE
of fossil fuels and nuclear across most of the G20 countries. This suggests that there are clear socio-
economic benefits in making the right energy choices for governments of the G20 countries as well as rest
of the world. At the same time, as indicated earlier it is expected that all G20 countries will demonstrate
full cost competitiveness of renewable sources by 2030 on a LCOE basis. Even without the consideration
of external and CO2eq costs, renewables and storage make a fully viable economic case for all the G20
countries by 2030.
However, it should be stressed that all countries should begin to invest in renewable energy sources well
ahead of 2030 in order to take full advantage of this opportunity and minimise adverse impacts. Firstly,
waiting too long will mean that expanding intermittent renewable capacities may be unnecessarily
disruptive to power systems if growth is too rapid. More gradual increases in capacities over the coming
decade or so can mitigate such technical disruptions. Furthermore, existing industries and companies may
need to adapt to the energy transition , and a steadier transition towards 2030 may help prepare them for
the task ahead. Secondly, eliminating external costs as soon as possible will result in improved health and
well-being, particularly in countries such as India and China (Jakob et al., 2016). As stated previously, these
external costs are often felt disproportionately by the most vulnerable members of society. Therefore, each
country must find its own unique transition towards greater sustainability based on their levels of
population, affluence and technology (Shuai et al., 2017), and it would be unwise for any to lack an
appropriate sense of urgency. Finally, renewable based power generation seems to be the reasonable option,
as not only is it lower in costs and more efficient, but it also generates jobs and sustains economic growth
as indicated in (Ram et al., 2017). Governments and institutions that most aggressively adopt the energy
transition and create an enabling environment to facilitate faster flow of capital investments into their
regions for renewable energy development will witness far more economic growth and benefit from it (Binz
et al., 2017). It appears to be logical from an economic perspective, an environmental perspective, a health
perspective and a moral perspective.
The authors would like to thank GREENPEACE e.V., Hamburg, for funding the research report
‘Comparing electricity production costs of renewables to fossil and nuclear power plants in G20 countries’,
the findings of which are the basis for this independently conducted research paper.
Supplementary Material
The supplementary data associated with this article can be found in the online version at:
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... Equation (1) estimates the profit of the utility, which combines the cost of procuring energy from discharging EVs, solar, wind, microturbine, upstream grid, as well as the revenue earned from charging EVs and load demand tariff. The profit of the prosumers is given by Equation (2), which is equivalent to the difference between the revenue earned at the set bid price for solar, wind, and EV discharging, and the levelized cost of energy (LCOE) [31] for solar, wind, and EV charging. ...
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To reduce global greenhouse gas emissions, the world must find intelligent solutions to maximise the utilisation of carbon-free renewable energy sources. In this paper, multi-agent reinforcement learning is used to control a microgrid in a mixed cooperative and competitive setting. The agents observe fluctuating energy demand, dynamic wholesale energy prices, and intermittent renewable energy sources to control a hybrid energy storage system to maximise the utilisation of the renewables to reduce the energy costs of the grid. In addition, an aggregator agent trades with external microgrids competing against one another and the aggregator to reduce their own energy bills. For this, the algorithm deep deterministic policy gradients (DDPG) and multi-agent DDPG (MADDPG) are used to compare the use of a single global controller versus multiple distributed agents, along with the single and multi-agent variants of distributional DDPG (D3PG) and twin delayed DDPG (TD3). The research found it is significantly more profitable for the primary microgrid to sell energy on its own terms rather than selling back to the utility grid, and is also beneficial for the external microgrids as they also reduce their own energy bills. The methods that produced the greatest profits were the multi-agent approaches where each agent has its own reward function based on the principle of marginal contribution from game theory. The multi-agent approaches were better able to evaluate their performance controlling individual components of the environment which allowed them to develop their own unique policies for the different types of energy storage system.
H2O/CO2 splitting via two-step solar thermochemical cycles performed with metal oxides is a promising path for fuel production. However, currently the reduction temperature of two-step thermochemical cycles is still too high and the fuel productivity is relatively low, which significantly hinders the improvement of solar-to-fuel efficiency. In this work, a two-step electro-thermochemical cycle integrated with a solid oxide electrolysis cell (SOEC) is proposed to overcome the challenges faced by two-step thermochemical cycle. Meanwhile, the definition, classification and operating mode of this cycle are expounded in detail. Additionally, a thermodynamic model of electro-assisted reduction thermochemical cycle (ERTC) for CO2 splitting is established and applied to investigate the effect of voltage (E), reduction temperature (Tred), oxygen pressure (pO2), heat recovery of the solid and gas phases on solar-to-fuel efficiency. The efficiency increases from 0.29 to 0.40 as E increases from 0 V to 0.6 V due to the required Tred of ceria could be reduced from 1500℃ to 1000℃, with pO2 = 10⁻⁶ bar. Moreover, the oxygen carrier in SOEC could even be reduced in air instead of a low-pO2 atmosphere while maintaining high efficiency. The technological challenges of high Tred and the oxygen carriers’ high sensitivity to pO2 can be mitigated in this innovative path.
The EU decarbonization policy was and is the most serious challenge for those countries where coal was used in the energy industry to quite a large extent. The transformation of energy mixes is a time consuming and capital intensive task. Therefore, the aim of this article is to identify the directions of transformation of the energy mixes of selected European Union countries (those with an above average share of hard coal in meeting energy needs) conducive to reducing carbon dioxide and making the adaptation to EU climate requirements possible. Multidimensional analysis of the emissivity and composition of energy mixes used in the research allowed to distinguish 4 transformation paths. The most effective of them – in the context of achieving environmental goals – proved to be a quick and consistent abolition of coal and its replacement with renewable and nuclear energy and hydroelectricity. The lack or low level of reduction of the share of coal in the mix brought highly unsatisfactory effects in terms of emission reduction (Poland, Bulgaria, Turkey). The conclusions fill the research gap in the area of: (1) long term analysis of the relationships between decisions concerning shaping the energy mix and their climate effects, and (2) identification of effective pathways for reducing carbon dioxide in economies where hard coal was the key raw material.
In the context of climate change, developing countries with sizeable coal capacities; such as South Africa, Chile and India; are exploring coal plants retirements by repurposing them for productive uses. However, a framework to establish the economic rationale for repurposing does not exist. We develop a detailed cost-benefit framework for the same; for three applications – solar energy, battery energy storage and synchronous condenser; and apply it to a representative coal plant in India. Our findings reveal a strong economic rationale for repurposing. First, the present value of repurposing benefits outweigh corresponding decommissioning costs – the direct, indirect and total decommissioning costs are $58.11 million, $45.80 million and $103.91 million, respectively; the corresponding gross benefits from repurposing are $122.79 million, $468.03 million and $590.82 million, respectively. Second, even after excluding social benefits, net benefits of repurposing cover 10%–32% of capital expenditure of the repurposing options. Third, decommissioning costs are about 50% for coal plants in India (at $58 million) vis-à-vis plants in the US (at $117 million). Finally, we provide a methodology for selecting plants for repurposing; based on age, energy charges and qualitative factors. Our methods will serve as a template for coal plant repurposing in developing countries.
Technical Report
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The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2016, the utility-scale sector installed more than 2.5 times as much new capacity as did the residential and commercial sectors combined, and is expected to maintain its dominant position for at least another five years. This report—the fifth edition in an ongoing annual series—provides data-driven analysis of the utility-scale solar project fleet in the United States. We analyze not just installed project prices, but also operating costs, capacity factors, and power purchase agreement ("PPA") prices from a large sample of utility-scale PV and CSP projects throughout the United States. Highlights from this year's edition include the following: Installation Trends: The use of solar tracking devices dominated 2016 installations, at nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the Southwest, the median long-term average insolation level at newly built project sites declined again in 2016. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio has stabilized in 2016 at 1.3 for both tracking and fixed-tilt projects. Installed Prices: Median installed PV project prices within a sizable sample have fallen by two-thirds since the 2007-2009 period, to $2.2/WAC (or $1.7/WDC) for projects completed in 2016. The lowest 20th percentile of projects within our 2016 sample were priced at or below $2.0/WAC, with the lowest-priced projects around $1.5/WAC. Overall price dispersion across the entire sample and across geographic regions decreased significantly in 2016. Operation and Maintenance (“O&M”) Costs: What limited empirical O&M cost data are publicly available suggest that PV O&M costs were in the neighborhood of $18/kWAC-year, or $8/MWh, in 2016. These numbers include only those costs incurred to directly operate and maintain the generating plant. Capacity Factors: The cumulative net AC capacity factors of individual PV projects range widely, from 15.4% to 35.5%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed-tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010- to 2013-vintage projects, where they’ve remained fairly steady among both 2014- and 2015-vintage projects as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. Meanwhile, several of the newer CSP projects in the United States are struggling to match long-term performance expectations. PPA Prices: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time. Most recent PPAs in our sample are priced at or below $50/MWh levelized, with a few priced as aggressively as ~$30/MWh. Though impressive in pace and scale, these falling PPA prices have been offset to some degree by declining wholesale market value within high penetration markets like California, where in 2016 a MWh of solar generation was worth just 83% of a MWh of flat, round-the-clock generation. At the end of 2016, there were at least 121.4 GW of utility-scale solar power capacity within the interconnection queues across the nation. The growth within these queues is widely distributed across all regions of the country: California and the Southeast each account for 23% of the 83.3 GW of solar that first entered the queues in 2016, followed by the Northeast (17%), the Southwest (16%), the Central region (12%), Texas (6%) and the Northwest (3%). The widening geographic distribution of solar projects is a clear sign that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.
Technical Report
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Technical Report "Global Energy System based on 100% Renewable Energy – Power Sector", published at the Global Renewable Energy Solutions Showcase event (GRESS), a side event of the COP23, Bonn, November 8, 2017 A global transition to 100% renewable electricity is feasible at every hour throughout the year and more cost effective than the existing system, which is largely based on fossil fuels and nuclear energy. Energy transition is no longer a question of technical feasibility or economic viability, but of political will. Existing renewable energy potential and technologies, including storage can generate sufficient and secure power to cover the entire global electricity demand by 2050 . The world population is expected to grow from 7.3 to 9.7 billion. The global electricity demand for the power sector is set to increase from 24,310 TWh in 2015 to around 48,800 TWh by 2050. Total levelised cost of electricity (LCOE) on a global average for 100% renewable electricity in 2050 is 52 €/MWh (including curtailment, storage and some grid costs), compared to 70 €/MWh in 2015. Solar PV and battery storage drive most of the 100% renewable electricity supply due to a significant decline in costs during the transition. Due to rapidly falling costs, solar PV and battery storage increasingly drive most of the electricity system, with solar PV reaching some 69%, wind energy 18%, hydropower 8% and bioenergy 2% of the total electricity mix in 2050 globally. Wind energy increases to 32% by 2030. Beyond 2030 solar PV becomes more competitive. Solar PV supply share increases from 37% in 2030 to about 69% in 2050. Batteries are the key supporting technology for solar PV. Storage output covers 31% of the total demand in 2050, 95% of which is covered by batteries alone. Battery storage provides mainly short-term (diurnal) storage, and renewable energy based gas provides seasonal storage. 100% renewables bring GHG emissions in the electricity sector down to zero, drastically reduce total losses in power generation and create 36 million jobs by 2050. Global greenhouse gas emissions significantly reduce from about 11 GtCO2eq in 2015 to zero emissions by 2050 or earlier, as the total LCOE of the power system declines. The global energy transition to a 100% renewable electricity system creates 36 million jobs by 2050 in comparison to 19 million jobs in the 2015 electricity system. Operation and maintenance jobs increase from 20% of the total direct energy jobs in 2015 to 48% of the total jobs in 2050 that implies more stable employment chances and economic growth globally. The total losses in a 100% renewable electricity system are around 26% of the total electricity demand, compared to the current system in which about 58% of the primary energy input is lost.
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It is widely agreed that energy subsidies impede the efficient functioning of markets. The resulting distortions in prices work at odds with policies to improve energy efficiency and reduce the cost of energy services and associated externalities such as health and environmental damages. The analysis developed in this article finds that kerosene is used in 173 countries, at a cost to consumers of $43.4B/y, $60.3B/y including direct economic subsidies, and $77.2B/y including certain externalities. Despite low world oil prices, direct economic subsidies for kerosene were $18.4B in 2013, and $34.7B including environmental externalities. These values correspond to 72% and 56% of total kerosene costs being passed through to consumers, respectively. When excluding advanced economies, the pass-through values fall to 40% and 35%. Approximately 52% of the global kerosene supply receives direct subsidy, or 63% when externality costs are considered. The cooking end use receives $2.0B/y in direct kerosene subsidies, lighting $7.1B/y, and heating and other residual uses $9.3B/y, or $76 per over all households each year. Defining subsidies at this level of granularity is useful for pinpointing policy issues and opportunities. Promoting a transition to energy efficient off-grid energy services is one of the most cost-effective ways of reducing dependency on subsidies. However, the very presence of subsidies undercuts this process by diluting market price signals and rendering energy efficiency investments less cost-effective, while competing with other social and development-focused budgetary needs. Kerosene subsidies are additionally counterproductive because the emerging technologies they impede (e.g., improved lighting and cook stoves) also improve productivity, safety, and quality of life. Forty-five countries—many in the developing world—have priced kerosene such that there are no direct subsidies, and twenty-two have done so even when accounting for environmental externalities, suggesting the practice is economically and politically feasible.
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In this analysis we developed and applied a geographically-resolved method to calculate the Levelized Cost of Electricity (LCOE) of new power plants on a county-by-county basis while including estimates of some environmental externalities. We calculated the LCOE for each county of the contiguous United States for 12 power plant technologies. The minimum LCOE option for each county varies based on local conditions, capital and fuel costs, environmental externalities, and resource availability. We considered ten scenarios that vary input assumptions. We present the results in a map format to facilitate comparisons by fuel, technology, and location. For our reference analysis, which includes a cost of $62/tCO2 for CO2 emissions natural gas combined cycle, wind, and nuclear are most often the lowest-LCOE option. While the average cost increases when internalizing the environmental externalities (carbon and air pollutants) is small for some technologies, the local cost differences are as high as $0.62/kWh for coal (under our reference analysis). These results display format, and online tools could serve as an educational tool for stakeholders when considering which technologies might or might not be a good fit for a given locality subject to system integration considerations.
The power sector is faced with strict requirements in reducing harmful emissions and substantially increasing the level of sustainability. Renewable energy (RE) in general and solar photovoltaic (PV) in particular can offer societally beneficial solutions. The LUT energy system transition model is used to simulate a cost-optimised transition pathway towards 100% RE in the power sector by 2050. The model is based on hourly resolution for an entire year, the world structured in 145 regions, high spatial resolution of the input RE resource data, and transition steps of 5-year periods. The global average solar PV electricity generation contribution is found to be about 69% in 2050, the highest ever reported. Detailed energy transition results are presented for representative countries in the world, namely, Poland, Britain and Ireland, Turkey, Saudi Arabia, Brazil, Ethiopia, and Indonesia. The global average energy system levelised cost of electricity gradually declines from 70 €/MWh in 2015 to 52 €/MWh in 2050 throughout the transition period, while deep decarbonisation of more than 95% around 2040, referenced to 2015, would be possible. The targets of the Paris Agreement can be well achieved in the power sector, while increasing societal welfare, given strong policy leadership.
The voluminous literature on industrial catching-up in Southeast Asian countries has regularly argued that successful catching-up largely depended on a committed state, which orchestrated industry development with a relatively uniform set of policies, including R&D support, subsidies, trade restrictions, and local content requirements. In contrast, recent contributions from the technology lifecycle literature have argued that policies should be tailored to differing technological characteristics in industries for mass-produced standardized goods, complex engineered products, and—as we argue—complex product systems (CoPS). In this paper, we extend this argument by introducing a set of separate policy mixes for each industry type, which appears most capable of providing the key resources required for catching-up: knowledge, market access, financial investment and technology legitimacy. This framework is used to analyze catching-up patterns in China’s wind, solar PV, and biomass power plant industries, drawing mainly on policy documents and 106 interviews with key industry actors.
The global energy system has to be transformed towards high levels of sustainability in order to comply with the COP21 agreement. Solar photovoltaic (PV) offers excellent characteristics to play a major role in this energy transition. The key objective of this work is to investigate the role of PV in the global energy transition based on respective scenarios and a newly introduced energy transition model developed by the authors. A progressive group of energy transition scenarios present results of a fast growth of installed PV capacities and a high energy supply share of solar energy to the total primary energy demand in the world in the decades to come. These progressive energy transition scenarios can be confirmed. For the very first time, a full hourly modelling for an entire year is performed for the world, subdivided in 145 sub-regions, which is required to reflect the intermittent character of the future energy system. The model derives total installed solar PV capacity requirements of 7.1–9.1 TWp for the electricity sector (as of the year 2015) and 27.4 TWp for the entire energy system in the mid-term. The long-term capacity is expected to be 42 TWp and, because of the ongoing cost reduction of PV and battery technologies, this value is found to be the lower limit for the installed capacities. Solar PV electricity is expected to be the largest, least cost and most relevant source of energy in the mid-term to long-term for the global energy supply.
As electricity generation based on volatile renewable resources is subject to fluctuations, data with high temporal and spatial resolution on their availability is indispensable for integrating large shares of renewable capacities into energy infrastructures. The scope of the present doctoral thesis is to enhance the existing energy modelling environment REMix in terms of (i.) extending the geographic coverage of the potential assessment tool REMix-EnDaT from a European to a global scale, (ii.) adding a new plant siting optimization module REMix-PlaSMo, capable of assessing siting effects of renewable power plants on the portfolio output and (iii.) adding a new alternating current power transmission model between 30 European countries and CSP electricity imports from power plants located in North Africa and the Middle East via high voltage direct current links into the module REMix-OptiMo. With respect to the global potential assessment tool, a thorough investigation is carried out creating an hourly global inventory of the theoretical potentials of the major renewable resources solar irradiance, wind speed and river discharge at a spatial resolution of 0.45°x0.45°. A detailed global land use analysis determines eligible sites for the installation of renewable power plants. Detailed power plant models for PV, CSP, wind and hydro power allow for the assessment of power output, cost per kWh and respective full load hours taking into account the theoretical potentials, technological as well as economic data. The so-obtined tool REMix-EnDaT can be used as follows: First, as an assessment tool for arbitrary geographic locations, countries or world regions, deriving either site-specific or aggregated installable capacities, cost as well as full load hour potentials. Second, as a tool providing input data such as installable capacities and hourly renewable electricity generation for further assessments using the modules REMix-PlasMo and OptiMo. The plant siting tool REMix-PlaSMo yields results as to where the volatile power technologies photovoltaics and wind are to be located within a country in order to gain distinct effects on their aggregated power output. Three different modes are implemented: (a.) Optimized plant siting in order to obtain the cheapest generation cost, (b.) a minimization of the photovoltaic and wind portfolio output variance and (c.) a minimization of the residual load variance. The third fundamental addition to the REMix model is the amendment of the module REMix-OptiMo with a new power transmission model based on the DC load flow approximation. Moreover, electricity imports originating from concentrating solar power plants located in North Africa and the Middle East are now feasible. All of the new capabilities and extensions of REMix are employed in three case studies: In case study 1, using the module REMix-EnDaT, a global potential assessment is carried out for 10 OECD world regions, deriving installable capacities, cost and full load hours for PV, CSP, wind and hydro power. According to the latter, photovoltaics will represent the cheapest technology in 2050, an average of 1634 full load hours could lead to an electricity generation potential of some 5500 PWh. Although CSP also taps solar irradiance, restrictions in terms of suitable sites for erecting power plants are more severe. For that reason, the maximum potential amounts to some 1500 PWh. However, thermal energy storage can be used, which, according to this assessment, could lead to 5400 hours of full load operation. Onshore wind power could tap a potential of 717 PWh by 2050 with an average of 2200 full load hours while offshore, wind power plants could achieve a total power generation of 224 PWh with an average of 3000 full load hours. The electricity generation potential of hydro power exceeds 3 PWh, 4600 full load hours of operation are reached on average. In case study 2, using the module REMix-PlaSMo, an assessment for Morocco is carried out as to determine limits of volatile power generation in portfolios approaching full supply based on renewable power. The volatile generation technologies are strategically sited at specific locations to take advantage of available resources conditions. It could be shown that the cost optimal share of volatile power generation without considering storage or transmission grid extensions is one third. Moreover, the average power generation cost using a portfolio consisting of PV, CSP, wind and hydro power can be stabilized at about 10 €ct/kWh by the year 2050. In case study 3, using the module REMix-OptiMo, a validation of a TRANS-CSP scenario based upon high shares of renewable power generation is carried out. The optimization is conducted on an hourly basis using a least cost approach, thereby investigating if and how demand is met during each hour of the investigated year. It could be shown, that the assumed load can safely be met in all countries for each hour using the scenario's power plant portfolio. Furthermore, it was proven that dispatchable renewable power generation, in particular CSP imports to Europe, have a system stabilizing effect. Using the suggested concept, the utilization of the transfer capacities between countries would decrease until 2050.