Impact of Battery Cost on the Economics of Hybrid

Available online at www.sciencedirect.com
ScienceDirect
Energy Procedia 99 (2016) 157 – 173
10th International Renewable Energy Storage Conference, IRES 2016, 15-17 March 2016,
Düsseldorf, Germany
Impact of battery cost on the economics of hybrid photovoltaic
power plants
Svetlana Afanasyevaa,*, Christian Breyera, Manfred Engelhardb
a
LUT School of Energy Systems, Lappeenranta University of Technology, Skinnarilankatu 34, Lappeenranta 53850, Finland
b
M+W Group GmbH, Lotterbergstraȕe 30, Stuttgart 70499, Germany
Abstract
In recent years, photovoltaic (PV) technology has experienced a rapid cost reduction. This trend is expected to continue, which in
many countries drives interest in utility-scale PV power plants. The main disadvantage of such plants is that they operate only
when the sun is shining. The installation of PV modules together with energy storage and/or fossil fuel backup is a way to solve
that issue, but consequently increases the costs. In the last few years, however, lithium-ion batteries as well have shown a
promising price reduction. This paper studies the competitiveness of a hybrid power plant that combines a PV system, lithiumion battery and gas turbine (GT) compared to conventional fossil-fuel power plants (coal and natural gas-fired) with focus on the
battery cost. To fulfil the demand an auxiliary GT is used in the hybrid PV plant, but its annual generation is limited to 20% of
the total output. The metric for the comparison of the different technologies is the levelized cost of energy (LCOE). The
installation of the plants is showcased in Morocco, a country with excellent solar resources. Future market scenarios for 2020 and
2030 are considered. A sensitivity analysis is performed to identify the key parameters that influence LCOE.
2016The
TheAuthors.
Authors.
Published
Elsevier
© 2016
Published
by by
Elsevier
Ltd.Ltd.
This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of EUROSOLAR - The European Association for Renewable Energy.
Peer-review under responsibility of EUROSOLAR - The European Association for Renewable Energy
Keywords: photovoltaic; hybrid PV plant; lithium-ion battery; coal power plant; gas power plant; power supply security; dispatchable generation
1. Introduction
Solar energy is one of the primary sources of energy among renewable energy (RE) options. To convert sunlight
into electricity one of the most popular utility-scale options is the photovoltaic (PV) power plant [1]. Prices of PV
* Corresponding author. Fax: +358-5-621-2350.
E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of EUROSOLAR - The European Association for Renewable Energy
doi:10.1016/j.egypro.2016.10.107
158
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
systems have been dropping dramatically with an average learning rate of 20.9% [2], [3]. In the last 5 years the
global annual installation rate of PV systems averaged around 40 GW, resulting in more than 234 GW being in
operation by the end of 2015 [3], [4]. Among newly installed PV capacities about 50% are utility-scale projects.
And in many countries the interest continues to grow [3]. With higher penetration of RE, dispatchability and flexible
power generation is becoming increasingly important. The main disadvantage of PV plants is the intermittency of
power production. This can be solved by installing PV in tandem with batteries, which is more costly but better
fulfils the load requirements. Lithium-ion (Li-ion) batteries showed a great reduction in costs in recent years and
further experience curve driven cost reductions are expected [5]–[8], which makes hybrid PV-Battery plants more
competitive for utility-scale applications.
In this paper a feasibility analysis and a benchmarking based on future market scenarios are presented to identify
whether PV is economically feasible for a utility-scale application. A hybrid PV-Battery-Gas Turbine (GT) plant is
benchmarked with several utility-scale fossil-fuel power plants: (1) coal-fired, (2) open cycle gas turbine (OCGT)
and (3) combined cycle gas turbine (CCGT) plants. Wet and dry cooling systems are considered for coal-fired and
CCGT power plants.
For a comprehensive and objective comparison, the functionality of the hybrid PV plant has to match the
functionality of fossil-fuel power plants, i.e. the production profiles have to follow a given demand. For the hybrid
PV plant, a PV single-axis tracking system, Li-ion batteries and gas turbines for balancing are chosen.
Power plants connected to the grid must operate under varying environmental conditions and under changing
load, start-ups and shut-downs, etc. The efficiency of the power plants is strongly affected by the actual operation
conditions. Many studies use a single “design point” efficiency value when comparing different technologies [9],
which can easily cause misleading conclusions. In this paper the operation of the plants is modeled on an hourly
resolution taking into account variations in component performance.
The resource and demand profile are considered as fixed scenarios for the site of Ouarzazate in Morocco
(30.9167° N, 6.9167° W) and are related to the load profile of Morocco, respectively. The components of the hybrid
PV plant are dimensioned to limit the electricity generation by the natural gas-fired GT to 20%. Ultimately, the
LCOE of each plant is compared.
2. Methodology
This section focuses on the power plants’ design and discusses the aspects affecting the production efficiency.
The method for the economic analysis of the technologies is described.
2.1. Power plant components
Fig. 1-4 show the schematic of the considered power plants’ layouts, connected to the grid. The main components
are listed below.
Hybrid Photovoltaic (PV) power plant:
x
x
x
x
PV modules
Single-axis tracking system
Lithium-ion batteries
OCGT
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 1. Schematic view of hybrid PV power plant.
Coal-fired power plant:
x boiler
x steam turbine
Fig. 2. Schematic view of coal-fired power plant.
Open cycle gas turbine power plant:
x gas turbine
Fig. 3. Schematic view of OCGT power plant.
Combined cycle gas turbine power plant:
x gas turbine
x steam turbine
159
160
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 4. Schematic view of CCGT power plant.
2.2. Modeling of hybrid PV-Battery-GT plant
The nominal capacity of the considered power plant is 100 MW AC. A standard PV system layout is assumed as
shown in Fig. 5. A modular design approach is considered. It has shown to be a successful industrial solution, since
it gives greater flexibility in designing the power plant. The plant is divided into ten independent medium voltage
sectors. Each sector has five 2000 kVA low voltage to medium voltage (LV/MV) transformers. To every
transformer two 1000 kW inverters with a corresponding array of PV modules are connected. To decrease the LCOE
of the plant, a DC overdimensioning factor of around 140% is chosen. This means a PV unit of 1408 kWp is
installed for every 1000 kW inverter unit, or 2816 kWp for every standard block. PV modules and batteries are
coupled on the AC side. It is assumed that two battery blocks of 3000 kWh with a C-rate (which is the rate at which
the battery is discharged with a specified continuous current) of 0.33 are installed to each inverter, resulting in a
discharge time of six hours. Additionally, two 50 MW GT are chosen for backup.
Fig. 5. Hybrid PV-Battery-GT power plant schematic layout.
161
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
A substation is installed for the connection to the grid. It includes a transformer to change the voltage level from
medium to high (MV/HV transformer), switchgear, protection, measuring and control equipment.
2.2.1. PV system modeling
A single axis tracking system is considered, which can typically increase annual energy yield up to 25-35% [3],
[10]. PV modules are installed in a single-row with portrait orientation and row spacing of 2.5 m for easy access.
Backtracking is assumed for single-axis tracking, to reduce shading losses.
The production of the PV single axis tracking system is calculated using the software PVsyst [11], which is
widely used in the industry. The components considered for simulation are PV modules JAP6-72/300-320/3BB [12],
with a rated efficiency of 16.51%, and inverters Sunny Central 1000CP XT by SMA [13]. Overall 44,000 modules
are installed with a combined area of 85,289 m2, resulting in 2122 full load hours (FLh) for the considered PV
system.
2.2.2. Battery storage system modeling
In this paper a simple battery model is used. It accounts for self-discharge, degradation and charge/discharge
losses. Temperature effects are not considered and it is assumed that the batteries are installed in an environment
with recommended operating temperature. Cooling demand is taken into account as additional 1% power loss. The
maximum capacity fade is parameterized as guaranteed by some producers by the end of the operational lifetime of
the battery. The degradation per full cycle is modelled as a ratio of the maximum capacity fade to the total number
of cycles.
2.2.3. OCGT modeling
The net efficiency Knet and power output Pnet are calculated as
Knet Kgross ISO ˜ fload ˜ fT ˜ f P
amb
amb
(1)
where Kgross ISO is the efficiency of the gas turbine at the standard ISO conditions, fload, fTamb and fPamb are the
correction factors due to variation in the load, ambient temperature and pressure, respectively.
Pnet
Pgross ISO ˜Knet ˜ 1 r Pparasitic
(2)
where Pgross ISO is the GT power output at ISO conditions, Knet is the efficiency for changed operation conditions,
r(Pparasitic ) is the ratio of parasitic consumption because of the losses, e.g. due to bearing friction, which is assumed
0.1% of the nominal capacity.
In this paper, approximate equations are used for correction factors that were obtained from analysis of data from
different measurement campaigns. Variations in efficiency due to load and temperature are shown in Fig. 6. A
change of the ambient pressure affects power output of the GT proportionally.
162
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 6. Efficiency of the OCGT with varying ambient temperature and load.
2.2.4. Operation rules for hybrid PV plant
Operation rules for the hybrid PV power plant are set to yield the annual energy. To simulate the propagation of
the state of charge (SoC) of the battery, the Euler method with an integration step size of one hour is used.
ESoC
i 1
ESoC i 't ˜ f ti , ESoC
i
(3)
where ESoC i and ESoC i +1 is the SoC at time ti and ti+1, respectively, 't is the time step from time ti to ti+1, and
f(ti,ESoC i) is the ordinary differential equation, which is integrated numerically
f ti , ESoC
i
Pchargei ˜Kcharge Pdischargei Kdischarge 't
(4)
where Pcharge i and Kcharge are the power and efficiency when the battery is charged, Pdischarge i and Kdischarge are the
power and efficiency when the battery is discharged.
For every hour the power plant production must follow the demand. An example of a typical operation day is
shown in Fig. 7.
163
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 7. Example of a daily operation cycle: Eload is the demand energy, EPV is the energy supplied to the net from PV modules directly, EGT is the
production from auxiliary GT, Ebattery discharge is the energy supplied from batteries, Ebattery charge is the energy during charging mode of the battery,
Eproduced by PV is the overall available energy and Edumped PV is the dumped energy that is produced by solar panels, Efrom grid is the energy required by
the plant from the grid during shut-downs.
x t1-t2: standby operation, energy is consumed by the grid due to parasitic electrical consumption of inverter,
isolation transformer, control, protection systems, etc.
Eload 0, EPV
0 o Efrom grid ! 0
(5)
x t2-t3: load covered partly by battery and partly by GT
Eload ! 0, EPV 0, SoC ! 0 o Eload
Ebattery discharge EGT
(6)
x t3-t4: load covered by GT
Eload ! 0, EPV
0, SoC 0 o Eload
EGT
(7)
x t4-t5: load covered partly by PV and partly by GT
Eload ! 0, EPV ! 0, SoC 0 o Eload
EPV EGT
(8)
x t5-t6: battery is in charge mode, as battery charge is limited, a part of the energy is dumped
Eload ! 0, E PV! Eload o Eload
Eproduced by PV Ebattery charge Edumped PV
(9)
164
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
x t6-t7: battery is in discharge mode, part of load covered by PV, part by battery and rest by GT
Eload ! 0, Eload ! EPV , SoC ! 0 o Eload
EPV Ebattery discharge EGT
(10)
x t7-t8: equivalent to t2-t3
x t8-t1: equivalent to t1-t2
2.3. Modeling of fossil- fired power plant
Three types of conventional fossil-fired power plants are studied: coal-fired, OCGT and CCGT. Additionally, wet
and dry-cooling systems are considered for coal-fired and CCGT plants.
The same conditions as for the hybrid PV plant are set: the fossil-fired power plant’s output must follow the load.
At part-load operation, the power plant output efficiency decreases. For OCGT the efficiency curve shown in Fig. 6
is used. For coal-fired and CCGT plant efficiency, the curve is based on [14] and is shown in Fig. 8 and Fig. 9,
respectively. Power output and efficiency are calculated with Eq. (1) and Eq. (2).
Fig. 8. Efficiency of the CCGT plant with varying ambient temperature and load.
Fig. 9. Efficiency of the coal-fired power plant with varying ambient temperature and load.
165
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
The start-up energy required for the OCGT is negligible. For the CCGT it is assumed that start-up takes 40 min
and it requires half of the thermal energy needed for a full load hour. Parasitic losses for the steam turbine are 2.5%
of maximum power. For coal-fired and CCGT power plants with dry cooling systems the efficiency drops 3%
absolutely [15] and investments are 10% higher [16], [17].
2.4. Economic analysis method
Levelized cost of electricity (LCOE) is one of the most popular metrics to compare power plants with a different
type of energy resource, operating lifetime or/and cost structure [18]. Fundamentally LCOE is the ratio of the
accumulated costs, discounted to the present, and the electricity produced over the project lifetime, i.e. it is a cost of
generating electricity. In this work the capital investment (Capex), operation and maintenance (Opex), fuel and CO2
greenhouse gas (GHG) emission costs are considered. The general formula for LCOE calculation is
LCOE
Capex ˜ crf Opex fuel cost emission costCO2
AEPel
(11)
where AEPel is the power plant annual electrical energy production
AEPel FLh˜ PR˜ installed capacity
(12)
The degradation of power plant components is included in the so-called performance ratio (PR). For the PV
system the PR assumed for the FLh includes an annual degradation of 0.3% [19]–[21] for crystalline silicon PV
modules over the entire lifetime averaged for the lifetime. Degradation of the batteries is considered as a function of
NFC and degradation rate per cycle. For turbines to quantify performance loss is challenging, as it is a result of wear
of different turbine components. This depends on climate, type of fuel, operation mode, number of start-ups and
shutdowns, etc. Typically, during the first 24,000 hours of operation degradation is 2-6% [22]. When degraded parts
are replaced it decreases to 1-1.5% [22]. Thus, an optimally developed maintenance strategy is essential to keep
degradation to a minimum. It is assumed that at the end of the project lifetime expected performance degradation is
approximately 5% [23].
crf is a uniform capital recovery factor, which is equal to
crf
WACC ˜ 1 WACC
N
1 WACC 1
N
(13)
Weighted Average Cost of Capital (WACC) is the rate at which the company is expected to earn in order to
return to different investors and is commonly used as the discount rate. Usually a company is financed using equity
and debt, therefore, in this paper the basic formula for WACC calculation is used:
WACC
E
D
˜ kE ˜ kD
ED
ED
(14)
where E is equity and D is debt, kE and kD are rates of return on equity and interest, respectively, N is the project
lifetime.
Fuel cost is found from:
fuel cost
AEP ˜ hfuel ˜ cfuel
(15)
166
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
where hfuel is the heat value and cfuel is the price of fuel.
Eventually, the annual emission cost is the product of the specific emission cost cCO2 (€ per ton of CO2) and the
specific mass qCO2 and thermal annual production AEPth from burning fuel.
emission costCO2
AEPth, fuel ˜ qCO2 ˜ cCO2
(16)
CO2 emission calculation
To calculate the CO2 emission costs, firstly, the resulting mass of CO2 after burning the fuel is calculated. The
combustion of natural gas (CH4) is an exothermic reaction, as shown in the chemical equation:
CH4 2 ˜ O2 oCO2 2 ˜ H2O heat
(17)
The lower heating value (LHV) of natural gas is 47.1 MJ/kg [24], i.e. burning one kilogram of fuel gives 47.1 MJ
or 0.0131 MWh of heat energy. Thus, to produce 1 MWh of heat 76.43 kg of methane is used and 210 kg of CO2 are
emitted:
qCO2
qCH4 ˜ M CO2 M CH4
where qCO2 and
respectively.
qCH4
76.43 kg MWhth ˜ 44 16 210 kg MWhth
are specific masses,
M CH4
MC 4 ˜ MH
>12 4 ˜1@ g
M CO2
MC 2 ˜ MO
>12 2 ˜16@ g
M CO2 and MCH4
(18)
are molar masses in g/mol, of CO2 and CH4,
mol 16 g mol
mol 44 g mol
(19)
(20)
Steam (thermal) coal is often used for power generation. Its grade is between bituminous coal and anthracite.
Coal varies widely in chemical composition and energy content. In this paper, coal LHV is assumed with 25.83
MJ/kg [25]. Using information about the main components proportions, the specific CO2 emissions can be
determined:
C p H l Ok p l 4 k 2 ˜ O2 o p ˜ CO2 l 2 ˜ H 2O heat
(21)
Mcoal p ˜ MC l ˜ MH k ˜ MO
(22)
M CO2
qCO2
p ˜12 l ˜1 k ˜16
p ˜ >M C 2 ˜ M O @ p ˜ 44 g mol
qcoal ˜ M CO2 M coal
mcoal ˜
p ˜ 44
p ˜ 12 l ˜ 1 k ˜ 16
(23)
(24)
where p, l and k are the proportions of carbon, hydrogen and oxygen, respectively.
In this paper, it is assumed that combustion of 1 ton of coal results in 2.36 tons of CO2 emissions [25], i.e. nearly
340 kg of CO2 are emitted to produce 1 MWhth.
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
3. Studied case
The installation of the plant is considered in Ouarzazate, Morocco (30.9167° N, 6.9167° W), with an annual
global horizontal irradiation of 2117 kWh/(m2·a) equal to 2703 kWh/(m2·a) incident on module pane for a singleaxis tracking PV system, based on PVsyst results. A map of the solar resource in Morocco [26] is shown in Fig. 10.
Fig. 10. Map of global horizontal irradiation (GHI) in Morocco.
The nominal capacity of the plant is 100 MW AC. The total energy of an isolated system is constant. The
derivative of energy inside the system equals the sum of powers passing the system bounds, where power going into
the system is positive and out of the system is negative. Therefore, the conservation of energy is expressed in
powers for the plants. The annual full load hours are more than 5000, according to the assumed load profile (Fig.
11).
Fig. 11. Daily load profile.
167
168
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Economic assumptions for the years 2020 and 2030 for the hybrid PV plant and the fossil-fuel plants are given in
Table 1 and Table 2, respectively. Assumptions for the PV system are derived from [27], [28], taking into account a
tracking system cost. For OCGT and CCGT plants values for Capex, Opex, lifetime and efficiency are based on
[29]. Coal-fired power plant parameters are based on [30]. For power plants with dry-cooling systems Capex is 10%
higher [16], [17]. Based on current cost numbers and cost development estimates according to [5]–[8] the numbers
for batteries are derived. The weighted average cost of capital (WACC) is 7%. Prices of gas and coal are given in
Table 2 and the price per ton of CO2 emissions is 27.4/59.8 €/tCO2 for the cost years 2020 and 2030, respectively.
The CO2 emission and fuel prices are based on [31] assumptions. Cost for the heavy metal emissions of coal-fired
power plants [32]–[35] are not taken into account and remain as further risk and cost factors for coal plants.
Table 1. Economic parameters for the hybrid PV-Battery-GT plant.
System component
Dimension
Capex 2020/2030
PV single-axis tracking
282 MWel
Battery
448 MWhel,
6.2 hr,
Power price per MW (power
electronics, transformer,
switchgear, control)
Energy price per MWh (BMS,
Battery, Control)
Open cycle gas turbine
Opex fix 2020/2030
Lifetime 2020/2030
900/650 €/kWp
13.5/9.75 €/(kWp·a)
30/35
150/100 €/kWel
8.1/4.2 €/(kWel·a)
15/20
14.25 €/(kWel·a)
30
6000/10,000
max cycles
300/150 €/kWhel
2x50 MWel
475 €/kWel
Table 2: Economic parameters for coal and natural gas-fired power plants.
Power plant
Capex
Opex fix
Fuel cost 2020/2030
52.5/64.5 €/ton
Coal-fired steam turbine
1500 €/kWel
20 €/(kWel·a)
Gas-fired OCGT
475 €/kWel
14.25 €/(kWel·a)
Gas-fired CCGT
775 €/kWel
19.4 €/(kWel·a)
21.8/32.1 €/MWhth
Lifetime
Efficiency
40
43%
30
43%
30
58%
4. Results and discussion
Dimensions of the hybrid PV power plant components are chosen for a minimum LCOE with a constraint of gas
share of up to 20% in the annual production. As a result the total energy produced by the hybrid PV plant to cover
the load is 562.8 GWh, from which 61% is delivered by PV modules directly, 21% from batteries and the rest by
auxiliary GT. The annual number of full cycles (NFC) of the battery is 275. An example of the power plant
operation cycle and summary on monthly and annual energy production are presented in Fig. 12.
The power plant’s AEP, consumption of fossil-fuel, CO2 emissions and LCOE values are summarized in Table 3.
Table 3: Overview of the key power plants’ operational parameters.
Power plant
AEP, [GWh]
Annual fossil-fuel
consumption ·103, [ton/a]
Annual CO2 emissions
·103, [ton/a]
LCOE 2020/2030,
[€/MWh]
Hybrid PV-Battery-GT
562.8
19.6
53.9
97.7/74.3
Coal-fired steam turbine, wet cooling
518.4
201.0
474.2
72.8/108.0
Coal-fired steam turbine, dry cooling
518.4
207.2
488.9
76.4/112.8
Gas-fired OCGT
523.7
96.6
265.8
78.5/120.9
Gas-fired CCGT, wet cooling
518.4
71.6
197.0
67.3/99.0
Gas-fired CCGT, dry-cooling
518.4
73.9
203.1
70.1/102.8
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 12. Monthly and annual production values of the hybrid PV plant. Example of operational cycle for the days 90-92 of the sample year is
given.
In Fig. 13 power plants’ LCOE for given market scenarios are shown, where each influencing component is
defined. Capex for PV system drops by almost 30% and battery cost is reduced by 50% in the assumed scenarios.
Prices of fuel and CO2 emissions are increased for 2030 (Table 2). Results indicate an advantage for the hybrid PVBattery-GT plant in Morocco in 2030. The OCGT plant is the least favorable compared to the other options.
Fig. 13. Power plants’ LCOE for 2020 and 2030 market scenarios.
169
170
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
The future battery cost development is the major unknown factor. Sensitivity analyses are performed for the
hybrid PV plant, coal fired power plant and CCGT plant for 2020, shown in Fig. 14, and for 2030, shown in Fig. 15.
It identifies the relative importance of the parameters. The parameters are changed by +50% (orange color) and –
50% (blue color) and the impact is referenced to the LCOE of the plants in Table 3.
Key parameters for the hybrid PV plant in 2020 are WACC and Capex in PV modules and batteries. These
results show that improving bankability of PV projects and reducing the overall risk for respective investments are
key priorities, which was also emphasized in [27]. The effect of the CO2 emission prices on the LCOE plays only a
minor role. However, LCOE of the coal-fired plant is most sensitive to CO2 emission price in 2030. It clearly shows
the effect that governmental policies on mitigation of GHG emissions have on the competitiveness of the two
technologies.
There seems to be a very recent trend in accordance to the COP21 agreement [36] that countries decide by
regulation that newly built coal-fired power plants are not allowed anymore at all, as recently announced by
Vietnam [37] and the UK [38]. An alternative may be carbon capture and storage (CCS) technology, however, the
costs are estimated to result in about 110 – 130 €/MWh [39] for coal-based electricity and the technology may be
not available before the year 2030 [40]. Therefore, the uncertainty is not only about the appropriate CO2 price, but
also whether coal-fired power is allowed anymore at all, and whether there may be a coal-based technology
available to the market for a reasonable price.
For the CCGT power plant the greatest risk lies with the price of natural gas. CO2 emission costs have less impact
compared to coal-fired plants, as CCGTs emit less CO2. For a coal-fired plant the fuel price contributes to a lesser
part of the total LCOE, as coal is a comparably cheap resource.
Fig. 14. Tornado diagrams for sensitivity analysis of LCOE to + and – 50 % economic parameters for hybrid PV-Battery-GT plant and fossil-fuel
power plants in 2020.
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
Fig. 15. Tornado diagrams for sensitivity analysis of LCOE to + and – 50 % economic parameters for hybrid PV-Battery-GT plant and fossil-fuel
power plants in 2030.
5. Conclusion
PV technology is one of the solutions for reducing GHG emissions. The aim of this paper is to check if it is
economically viable for a utility-scale application. A comparison of utility-scale power production technologies
between the hybrid PV-Battery-GT and conventional fossil-fired power plants is presented based on the LCOE.
Market scenarios for 2020 and 2030 are considered.
The results clearly indicate that coal and natural gas fired power plants are not competitive anymore to hybrid
PV-Battery-GT plants in very sunny regions such as Morocco from 2030 onwards for the assumed scenarios when
CO2 costs are considered.
Sensitivity analysis shows the importance of WACC to the competitiveness of the hybrid PV project. The top
priority for industry is to increase confidence of the investors in the PV projects as a promising investment, but also
for governments to reduce respective investment risks. The progress in cost and performance for big stationary
battery installations are also key enablers for such hybrid power plants, as well as CO2 emission cost. The
responsibility for this incentive lies on the policy-makers of the countries.
Acknowledgments
The authors gratefully acknowledge the public financing of Bundesministerium für Umwelt, Naturschutz und
Reaktorsicherheit, Berlin, for the ‘THERMVOLT’ project under the number 41V6706. The authors would like to
thank Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Solarforschung and Institut für
Technische Thermodynamik, Stuttgart, for solar resource data, Fichtner GmbH for providing efficiency curves of
gas turbines and combined cycles, M+W Central Europe GmbH for description of standard PV system configuration
including battery integration. The authors would like to thank Michael Child for proofreading.
171
172
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
References
[1] International Energy Agency (IEA), “Technology Roadmap: Solar Photovoltaic Energy 2014 Edition,” Paris, 2014.
[2] J. N. Mayer, S. Philipps, N. S. Hussein, T. Schlegl, and C. Senkpiel, “Current and future cost of photovoltaics: Long-term scenarios for
market development, system prices and LCOE of utility-scale PV systems,” study on behalf of Agora Energiewende, Fraunhofer ISE,
Freiburg, Berlin, 2015.
[3] International Energy Agency Photovoltaic Power Systems (IEA PVPS) Programme, “Trends 2015 - In Photovoltaic Applications Survey
Report of Selected IEA Countries between 1992 and 2014,” St. Ursen, Switzerland, 2013.
[4] J. Shankleman, “As Oil Crashed, Renewables Attract Record $329 Billion,” Bloomberg Business, New York, 2016. [Online]. Available:
http://www.bloomberg.com/news/articles/2016-01-14/renewables-drew-record-329-billion-in-year-oil-prices-crashed.
[Accessed:
05.02.2016].
[5] W. Hoffmann, “Importance and evidence for cost effective electricity storage,” 29th Eur. PV Sol. Energy Conf. Exhib., pp. 3981–3988, 2014.
[6] UBS, “Will solar, batteries and electric cars re-shape the electricity system?,” Q-Series® Glob. Util. Autos Chem., no. August, pp. 1–58,
2014.
[7] S. Lacey, “Storage Is the New Solarௗ: Will Batteries and PV Create an Unstoppable Hybrid Forceௗ?,” Grintech Media, 2014.
[8] B. Nykvist and M. Nilsson, “Rapidly falling costs of battery packs for electric vehicles,” Nat. Clim. Chang., vol. 5, no. 4, pp. 329–332, Mar.
2015.
[9] F. Zickfeld and A. Wieland, “Desert Power 2050: Perspectives on a Sustainable Power System for EUMENA,” Dii GmbH, Munich, 2012.
[10] C. Breyer and J. Schmid, “Population Density and Area weighted Solar Irradiation: global Overview on Solar Resource Conditions for fixed
tilted, 1-axis and 2-axes PV Systems,” Proc. 25th EU PVSEC, pp. 4692–4709, 2010.
[11] PVsyst SA, “PVsyst website,” Satigny, Switzerland. [Online]. Available: http://www.pvsyst.com/en/. [Accessed: 05.02.2016].
[12] JA Solar Holdings Co. Ltd., “Data sheet: JAP6 72 300-320 3BB,” Shanghai, China. [Online]. Available:
http://www.jasolar.com/uploads/JAP6 72 300-320 3BB (1).pdf. [Accessed: 05.02.2016].
[13] SMA Solar Technology AG, “Data sheet: Sunny Central 1000CP-XT,” Kassel, Germany. [Online]. Available:
http://files.sma.de/dl/25585/SC1000CP-DEN1529-V21web.pdf. [Accessed: 05.02.2016].
[14] Gas Turbine World, 2010 Gas Turbine World Handbook, Volume 28. Pequot Publishing, 2010.
[15] U.S. Department of Energy (DoE), “Energy Demands on Water Resources,” Washington, 2006.
[16] B. J. Hannegan, “Water and Electricity: Living at the Energy - Water Nexus,” Environment and Renewable Energy Electric Power Research
Institute (EPRI), Palo Alto, 2013.
[17] States United Environmental Protection Agency (EPA), “Technical Development Document for the Final Regulations Addressing Cooling
Water Intake Structures for New Facilities,” Washington, 2001.
[18] W. Short, D. J. Packey, and T. Holt, “A Manual for the Economic Evaluation of Energy Efficiency and Renewable Energy Technologies,”
National Renewable Energy Laboratory (NREL), Golden, 1995.
[19] D. Polverini, M. Field, E. Dunlop, and W. Zaaiman, “Polycrystalline silicon PV modules performance and degradation over 20 years,” Prog.
Photovoltaics, vol. 21, pp. 1004–1015, 2013.
[20] D. C. Jordan and S. R. Kurtz, “Photovoltaic degradation rates - An Analytical Review,” Prog. Photovoltaics Res. Appl., vol. 21, no. 1, pp.
12–29, 2013.
[21] K. Kiefer, D. Dirnberger, B. Müller, W. Heydenreich, and A. Kröger-Vodde, “A Degradation Analysis of PV Power Plants,” Proc. 25th Eur.
Photovolt. Sol. Energy Conf., no. September 2015, pp. 5032–5037, 2010.
[22] F. J. Brooks, “GE gas turbine performance characteristics,” General Electric (GE) Power Systems, Schenectady, NY, 2000.
[23] A. Leyzerovich, Steam Turbines for Fossil Fuel Power Plants. The Fairmont Press, Inc., Lilburn, GA, 2008.
[24] R. G. Boundy, S. W. Diegel, L. L. Wright, and S. C. Davis, Biomass Energy Data Book: Edition 4. U.S. Department of Energy (DoE), Oak
Ridge National Laboratory, Tennessee, 2011.
[25] L. Schernikau, Economics of the international coal trade: The renaissance of steam coal, no. 1. Springer Science & Business Media, Berlin,
Heidelberg, 2010.
[26] GeoSun Africa, “Solar Maps,” Stellenbosch, South Africa. [Online]. Available: http://geosun.co.za/solar-maps/. [Accessed: 31.01.2016].
[27] G. Masson, C. Breyer, and E. Vartiainen, “PV LCOE in Europe 2015-2050,” in 31st EU PVSEC September 14-18, 2015.
[28] S. A. Mann, M. J. De Wild-Scholten, V. M. Fthenakis, W. G. J. H. M. Van Sark, and W. C. Sinke, “The energy payback time of advanced
crystalline silicon PV modules in 2020: A prospective study,” Prog. Photovoltaics Res. Appl., vol. 22, no. 11, pp. 1180–1194, 2014.
[29] D. Bogdanov and C. Breyer, “North-East Asian Super Grid for 100 % renewable energy supplyௗ: Optimal mix of energy technologies for
electricity , gas and heat supply options,” Energy Convers. Manag., vol. 112, pp. 176–190, 2016.
[30] European Climate Foundation, “Roadmap 2050,” The Hague, 2010, [Online]. Available: www.roadmap2050.eu. [Accessed: 12.02.2016].
[31] Bloomberg New Energy Finance (BNEF), “New Energy Outlook 2015,” London, 2015.
[32] Duguid B. (ed.), “Ontario’s Long-Term Energy Plan,” Ontario Ministry of Energy, Toronto, 2010.
[33] P. Epstein and J. Buonocore, “Full cost accounting for the life cycle of coal,” Ann. N.Y. Acad. Sci., no. Ecological Economics Reviews, pp.
73–98, 2011.
[34] J. J. Buonocore, P. Luckow, G. Norris, J. D. Spengler, B. Biewald, J. Fisher, and J. I. Levy, “Health and climate benefits of different energyefficiency and renewable energy choices,” Nat. Clim. Chang., no. 6, pp. 100–105, 2016.
[35] D. Coady, I. Parry, L. Sears, and B. Shang, “How large are global energy subsidies?,” International Monetary Fund, Washington, 2015.
Svetlana Afanasyeva et al. / Energy Procedia 99 (2016) 157 – 173
[36] United Nations Framework Convention on Climate Change (UNFCCC), “Adoption of the Paris Agreement. Proposal by the President,” in
Conference of the Parties, Twenty-first session, 30 Nov. to 11 Dec., 2015.
[37] The Socialist Republic of Viet Nam Online Newspaper of the Government, “Plans on developing power, coal sectors adjusted,” 2016.
[Online].
Available:
http://news.chinhphu.vn/Home/Plans-on-developing-power-coal-sectors-adjusted/20161/26495.vgp.
[Accessed:
12.02.2016].
[38] J. Moylan, “UK’s Coal Plants to be Phased Out Within 10 Years,” BBC Business News, 2015. [Online]. Available:
http://www.bbc.co.uk/news/business-34851718. [Accessed: 12.02.2016].
[39] Agora Energiewende, “Comparing the cost of low-carbon technologies: What is the cheapest option?,” study on behalf of Agora
Energiewende prepared by Prognos AG, Berlin, 2014.
[40] European Comission, “Integration of Renewable Energy in Europe,” study prepared by KEMA Consulting GmbH, DNV GL - Energy,
Imperial College and NERA Economic Consulting on behalf of DG Energy, Brussels, 2014.
173