ANALYSIS OF FEED-IN TARIFF FOR
RENEWABLE ENERGY SOURCES IN
ARMENIA
ARMENIA SUSTAINABLE ENERGY FINANCE PROJECT
INTERNATIONAL FINANCE CORPORATION
1
Acknowledgments
The authors would like to thank the following organizations for offering suggestions and information
during preparation of this study: Ministry of Energy and Natural Resource of Armenia, Public Services
Regulating Commission, KfW/German-Armenian Fund, Armenian Renewable Energy and Energy
Efficiency Fund
2
List of abbreviations
AMD – Armenian Dram
CF –capacity factor
CJSC – Closed Joint Stock Company
CPI – Consumer Price Index
ENA – CJSC Electric Networks of Armenia
FIT – Feed-in Tariff
FS – Feasibility study
GHG – Greenhouse gases
GW – Gigawatt
GWH – Gigawatt hour
IEC – CJSC International Energy Corporation
IFC – International Finance Corporation
IRR – internal rate of return
KV – Kilovolt
MW – Megawatt
MWh – Megawatt hour
NPP – Nuclear Power Plant
PSRC – Public Services Regulating Commission
RES – Renewable energy sources
SHPP – Small Hydro power plant
TPP – Thermal power plant
USD – United States Dollar
VAT – Value Added Tax
WACC – Weighted average cost of capital
3
Contents
Acknowledgments......................................................................................................................................... 2
List of Tables ................................................................................................................................................. 6
Executive Summary....................................................................................................................................... 7
1.
Introduction .......................................................................................................................................... 9
1.2
RES targets .................................................................................................................................... 9
2.
Overview of Armenian power sector and legislation ......................................................................... 10
3.
Overview of international best practices in FIT structures and their applicability in Armenia .......... 15
4
3.1
RES SUPPORT POLICIES in other states............................................................................................. 15
3.2
An Analysis of FIT models ........................................................................................................... 16
3.3
Market Independent FIT Polices ................................................................................................. 16
3.4
Market Dependent FIT polices .................................................................................................... 19
The impacts of FITs ............................................................................................................................. 22
4.1
Investment Flows Worldwide and in Armenia............................................................................ 22
4.2
FIT-based RE Deployment ........................................................................................................... 24
4.3
Job creation ................................................................................................................................. 24
4.3.1
4.4
Reduction of the GHG emissions ................................................................................................ 26
4.5
Impact on the Retail Prices ......................................................................................................... 26
4.5.1
4.6
5
6.
Other benefits of the SHPPs.................................................................................................... 26
Merit Order Effect ................................................................................................................... 29
Impact on the retails prices in Armenia ...................................................................................... 30
Elaboration of the required FITs design for the SHPPs ....................................................................... 32
5.1
Analysis of hydro potential ......................................................................................................... 32
5.2
Investment analysis for selected SHPPs...................................................................................... 33
5.3
Elaboration of new FITs and structures ...................................................................................... 37
Potential impacts of the proposed FIT structure on the wholesale prices ......................................... 41
6.1
Scenario 1: 2% annual increase in generation ............................................................................ 42
6.2
Scenario 2: 5% annual increase in generation ............................................................................ 43
6.3
Scenario 3: 7% increase in generation ........................................................................................ 45
6.4
Tied to year FIT structure ............................................................................................................ 47
Conclusions and recommendations ............................................................................................................ 53
4
List of Figures
Figure 1 Total primary energy supply in Armenia, 2008 ............................................................................. 10
Figure 2 Electricity generation mix of Armenia .......................................................................................... 12
Figure 3 Tariffs for SHPPs in Armenia, 2007-2011 AMD/KWh ................................................................... 14
Figure 4 Tariffs for SHPPs in Armenia, 2007-2011 USC/KWh .................................................................... 15
Figure 5 Annual Worldwide Investment in New Renewable Energy Capacity (billion of US dollars) ......... 23
Figure 6 Jobs in the SHPPs in Armenia (2005-2010) ................................................................................... 26
Figure 7 Breakdown of Electricity Costs in Germany (2009)...................................................................... 27
Figure 8 Support received vs. production from renewable energy sources in Spain, 2010 ....................... 28
Figure 9 Estimated savings in Spain and Denmark Due to wind power ..................................................... 30
Figure 10 Low Case (CPI 102%) .................................................................................................................. 38
Figure 11 Base Case (CPI 105%) ................................................................................................................. 39
Figure 12 High Case (CPI 107%) .................................................................................................................. 40
Figure 13 CPI 105%, and increase of exchange rate by 2%......................................................................... 41
Figure 14 Impact on wholesale prices, 2% annual increase in generation, 102% CPI ................................ 42
Figure 15 Impact on wholesale prices, 2% annual increase in generation, 105% CPI ................................ 43
Figure 16 Impact on wholesale prices, 2% annual increase in generation, 107% CPI ................................ 43
Figure 17 Impact on wholesale prices, 5% annual increase in generation, 102% CPI ................................ 44
Figure 18 Impact on wholesale prices, 5% annual increase in generation, 105% CPI ............................... 45
Figure 19 Impact on wholesale prices, 5% annual increase in generation, 107% CPI ................................ 45
Figure 20 Impact on wholesale prices, 7% annual increase in generation, 102% CPI ................................ 46
Figure 21 Impact on wholesale prices, 7% annual increase in generation, 105% CPI ................................ 47
Figure 22 Impact on wholesale prices, 7% annual increase in generation, 107% CPI ................................ 47
Figure 23 Impact on wholesale prices, 2% annual increase in generation, 102% CPI ................................ 48
Figure 24 Impact on wholesale prices, 2% annual increase in generation, 105% CPI ................................ 49
Figure 25 Impact on wholesale prices, 2% annual increase in generation, 107% CPI ................................ 49
Figure 26 Impact on wholesale prices, 5% annual increase in generation, 102% CPI ................................ 50
Figure 27 Impact on wholesale prices, 5% annual increase in generation, 105% CPI ................................ 50
Figure 28 Impact on wholesale prices, 5% annual increase in generation, 107% CPI ................................ 51
Figure 29 Impact on wholesale prices, 7% annual increase in generation, 102% CPI ................................ 51
Figure 30 Impact on wholesale prices, 7% annual increase in generation, 105% CPI ................................ 52
Figure 31 Impact on wholesale prices, 7% annual increase in generation, 107% CPI ................................ 52
5
List of Tables
Table 1 RES in Armenia (2010) ...................................................................................................................... 9
Table 2 Generation plants in Armenia (2010) ............................................................................................. 11
Table 3 Tariffs in Armenia, 2011 ................................................................................................................. 12
Table 4 Retail tariffs in Armenia (2011) ...................................................................................................... 13
Table 5 Advantages and disadvantages of Fixed Models ........................................................................... 18
Table 6 Strengths and Advantages of Market Dependent Models............................................................. 21
Table 7 Renewable Energy Added and Existing Capacities (GW) ............................................................... 24
Table 8 Jobs From Renewable Energy ........................................................................................................ 24
Table 9 Jobs from Renewable Energy in Armenia (cumulative as of Jan 2010)*........................................ 25
Table 10 Electricity Sales in Armenia (2003-2010) ..................................................................................... 30
Table 11 Payments to generating plants, 2010 .......................................................................................... 31
Table 12 SHPPs FIT Impact on the wholesale prices ................................................................................... 31
Table 13 SHPPs selected for the project ..................................................................................................... 33
Table 14 Financing facilities in Armenia ..................................................................................................... 34
Table 15 Calculation of post tax IRR ........................................................................................................... 35
Table 16. Required FIT variations to reach WACC benchmark (11% IRR) ................................................... 35
Table 17 New Proposed Tariff Model ......................................................................................................... 37
Table 18 Impact on wholesale prices, 2% annual increase in generation (AMD)....................................... 42
Table 19 Impact on wholesale prices, 5% annual increase in generation (AMD)....................................... 44
Table 20 Impact on wholesale prices, 7% annual increase in generation (AMD)....................................... 46
Table 21 Impact on wholesale prices, 2% annual increase in generation (AMD)....................................... 48
Table 22 Impact on wholesale prices, 5% annual increase in generation (AMD)....................................... 48
Table 23 Impact on wholesale prices, 7% annual increase in generation (AMD)....................................... 48
Table 24 Proposed FIT structure ................................................................................................................. 53
6
Executive Summary
Armenia has a great potential for the development of renewable energy sources. The development of
SHPPs in recent years Armenia has demonstrated that deployment of RES is very responsive to support
programs, especially feed-in tariffs. The purpose of the report is to assess how to expand further
development of RES and achieve the state target of reaching meeting 260 MW capacities of SHPPs by
2025133 percent of statewide electric power supply with renewable energy by 2025 with limited impact
on the electricity prices.
This report consists of two sections: an overview of the existing RES support polices and elaboration of
the new FIT structure for Armenia.
First part of the report focuses on the historical development of RES promotion policies worldwide and
their comparison with the Armenia. This part explains how different FIT models work and describe the
advantages and weakness of each model. Further, this part reveals that declining or front-ended FIT
model is the most cost effective tariff structure applicable for Armenia. In addition, this section is also
focuses on the economic benefits of RES, including impact of the investments and jobs.
The purpose of the second part of the report is to elaborate FIT structure, which will
1)
2)
resources
3)
Insure reasonable rate of return for developers
Insure application of state-of-art technologies and efficient utilization of scarce water
Will have minimum negative impact on electricity prices on a sustainable basis.
The basis of this research is the target approved by the government for SHPPs to reach 260 MW
installed capacity and 800 GWh annual generation by 2025. For the elaboration of the new FIT structure
we have conducted financial analysis of the SHPPs in Armenia based on actual costs prevailing in the
market. After selection of the financial indicator (internal rate of return - IRR), which will insure
reasonable rate of return we have elaborated declining tariff structure which will allow reaching the
aforementioned national targets and issues.
The report demonstrates also estimations of the impacts of the proposed structure on the wholesale
prices for the period 2012-2025. The analysis demonstrates that the proposed FIT structure will have
1
Energy Sector Development Strategy in the Context of Economic Development in Armenia, Adopted by the
Government of Armenia at June 23, 2005 session
7
very limited impact on the wholesale prices during first 10 years after introduction and then will create
long term savings. In particular, in the most conservative Scenario (2% annual increase of generation
and 102% CPI) the impact of the proposed FIT structure will gradually increase up to 0.142 or 1.6% of
the wholesale prices, and beyond that it will gradually decrease.
8
1. Introduction
Armenia, which is highly dependent on imported energy resources, has stepped up its efforts to develop
renewable energy resources (RES). The development of renewable energy in Armenia has been
characterized by considerable growth rates throughout the past 10 years. This development is mainly
driven by the promotion policy which has been in place since 2001. These promotion polices include:
FITs, guaranteed purchase and guaranteed access to the grid. At the end of 2010 almost 100% of RES
was from hydropower. Technology specific figures are shown in the Table 1 and Figure 1 below.
Table 1 RES in Armenia (2010)2
Technology
Number of Plants
Small Hydro
Wind
Biomass/Biogas
Solar
102
1
1
0
1.2
Installed capacity,
GW
0.13
0.0264
0
Delivery, GWh
399
3.73
2.93
0
RES targets
According to “Energy Sector Development Strategies in the Context of Economic Development in
Armenia” adopted by the Government of Armenia in August 2005, modernizing and replacing the
generating capacity is essential since:
-
38% of Armenian installed capacity has been in operation for more than 30 years;
-
The primary equipment at TPPs has reached 200 thousand hours level and does not
correspond to international standards in terms of technical, economic and ecologic criteria;
-
70 % of the installed equipment at HPPs has been in operation for more than 30 years, and
50% for more than 40 years.
The same document indicates that the capacity additions planned for 2005-2010 will include a mix of
thermal power plants (capacity additions to the two existing Yerevan and Hrazdan plants) and new
hydro and wind plants. The following additions RES to the grid were planned:
-
small hydro plants 70 MW
-
Meghri hydro plant 140 MW
-
wind plants 100 MW
For the period 2010-16 the planned capacity additions are:
2
PSRC
9
-
Loriberd hydro plant 60 MW
-
Small hydro plants 65 MW
-
Wind plants 200 MW
In 2010 Armenia has reached its target only for SHPPs, moreover due to the barriers described in the
next sections Armenia is not expected to reach its targets for 2025. Therefore, there is an urgent need to
reduce legislative and financial barriers to development of RES in Armenia.
2. Overview of Armenian power sector and legislation
Armenia does not have any fossil fuel resources and is highly dependent on the Russian Federation for
imports of natural gas. The imported gas and oil together account for nearly 75% of Armenia’s total
primary energy supply3 (see Figure 1). Moreover, Armenia imports nuclear fuel to run its nuclear power
station. Armenia is currently importing also natural gas from Iran, which is used solely for electricity
generation from the Yerevan TPP, which later is exported back to Iran.
(
Figure 1 Total primary energy supply in Armenia, 20084
3
Primary energy supply means the use of primary energy sources before their transformation into electricity, heat,
transport fuels or any other products.
4
Source: IEA
10
Domestically produced electricity is generated by a nuclear power plant, two thermoelectric power
plants (fuelled by natural gas) and numerous large and small hydroelectric power plants. The total
installed capacity of power system of Armenia is 3,213.2 MW, of which 1.1-1.2 is used during peak
hours. The following table 2 provides breakdown information on the capacity and ownership of the
generation plants in Armenia.
Table 2 Generation plants in Armenia (2010)
Generation type
Installed
capacity, MW
Thermal
1,756
Hrazdan TPP
Ownership
NOTE
1,100
Russian Federation
Used for peak load
Yerevan TPP
550
GoA
New Unit in Yerevan
TPP
210
GoA
Used for export to
Iran
Vanadzor TPP
96
ZakneftgasstroyPromethey
Not operational
Hydropower
1,049.2
Sevan-Hrazdan cascade
556
RUS Hydro
Vorotan cascade
404.2
GoA
Small HPP (<10MW)
132
Private owners
Nuclear
408
Metsamor NPP
408
Lowest tariff
GoA, under financial
management of INTER
RAO UES
11
Figure 2 Electricity generation mix of Armenia
7% 0.05%
Nuclear
38%
33%
Thermal Power Plants
Biogas plants
Large Hydro Plants
Small Hydro Power Plants
0.06%
Wind Power Plants
22%
According to the Energy Law the electric energy generated from renewable energy sources by licensed
entities is subject to 100% purchase during 15 years starting from the operation beginning. The Power
Purchase Agreement (PPA) between a renewable energy generator and theElectric Networks of Armenia
CJSC (ENA) is signed after the construction of the power plant and obtaining the Operation License from
the Public Services Regulating Commission (PSRC). The PSRC sets the feed-in tariffs for all generators,
the transmission tariff for high voltage networks, and the service fees for the operator and the
settlements center, which the ENA should pay to all mentioned market participants according to the
Energy Law. The current tariffs for the generation, transmission and system services are shown in the
table 3.
Table 3 Tariffs in Armenia, 20115
NAME
Tariff (without VAT)
Capacity charge
Armenian NPP
4.230 AMD/kWh
2,619.75 AMD/kW
Hrazdan TPP
24.066 AMD/kWh
913.87 AMD/kW
Yerevan TPP
6.7 AMD/kWh
909.56 AMD/kW
Vorotan HPP cascade
3.708 AMD/kWh
179.17 AMD/kW
IEC, Sevan-Hrazdan HPP cascade
0.718 AMD/kWh
435.43 AMD/kW
Dzora HEK HPP
2.745 AMD/kWh
-
5
Source: PSRC
12
High Voltage Networks
0.827 AMD/kWh
-
System Operator
84,071,800 AMD/month
-
Settlement Center
9,242,900 AMD/month
-
All the aforementioned costs that ENA bears are passed through to the final distribution tariffs. The
average weighted generations price that ENA pays, including the run-off-river small hydropower plants
is around 10 AMD/kWh. Starting from April 1, 2009 the PSRC resolution №70N from Feb 27, 2009 set
new distribution tariffs, which are shown in the table 4.
Table 4 Retail tariffs in Armenia (2011)
#
Consumers group
Unit
Tariff (VAT included)
1
35 kV and above connection
AMD/kWh
21
1.1
night-time rate
AMD/kWh
17
2
6(10) kV direct connection
AMD/kWh
25
2.1
night-time rate
AMD/kWh
17
3
6(10) kV non-direct connection
AMD/kWh
30
3.1
night-time rate
AMD/kWh
17
4
0.38 kV networks
AMD/kWh
30
4.1
night-time rate
AMD/kWh
20
5
residential customers
AMD/kWh
30
5.1
night-time rate
AMD/kWh
20
The tariffs for the RESs are recalculated annually according to the following equation, which was
introduced in 2007 by the PSRC decision N207N.
 PI

ER1
T  T1  K1
 K2
 (1  K1  K 2 )
ER2
 100

where:



T - The new tariff, AMD;
T1 - Existing tariff, AMD;
k1 - Constituent coefficient of existing tariff which is taken equal to 0.35;
13




PI - The ratio of consumer price index in September of current year to the one in the same
month of previous year;
k2 - Constituent coefficient of existing tariff which is taken equal to 0.35;
ER1 - The arithmetic average of AMD/USD exchange rate during the period of JanuarySeptember of current year;
ER2 - The arithmetic average of AMD/USD exchange rate during the period of JanuarySeptember of previous year.
The current tariffs for RES are presented in the Figures 3-4below.
Figure 3 Tariffs for SHPPs in Armenia, 2007-2011 AMD/KWh
AMD/kWh
20.000
19.28
18.337
18.274
17.214
17.127
16.000
12.224
12.182
11.475
12.000
8.122
7.651
8.000
12.853
11.417
7.613
8.151
8.57
4.000
Run-of river
Irrigation
Drinking water
0.000
2007
2008
2009
2010
2011
14
Figure 4 Tariffs for SHPPs in Armenia, 2007-2011 USC/KWh
USC/KWh
7.00
6.06
5.63
6.00
4.69
5.00
4.04
4.00
4.91
5.21
Run-of river
3.75
3.13
3.27
3.48
Irrigation
Drinking water
3.00
2.00
1.00
0.00
2007
2008
2009
2010
2011
3. Overview of international best practices in FIT structures and their
applicability in Armenia
3.1
RES SUPPORT POLICIES in other states
To date at least, 83 (including 42 developing) countries6 have different types of policy to promote
renewable energy generation. The 10 most common policy types include:
1.
2.
3.
4.
5.
6.
7.
8.
6
Feed-in tariffs,
Renewable portfolio standards,
Capital subsidies or grants,
Investment tax credits,
Sales tax or VAT exemptions,
Green certificate trading,
Direct energy production payments or tax credits,
Net metering,
Source REN21
15
9. Direct public investment or financing,
10. Public competitive bidding.
Feed-in tariffs are increasingly considered as the most effective policy to promote renewable energy
sources and are currently enacted in more than 70 countries worldwide. They have consistently
delivered new renewable energy (RE) supply more effectively, and at lower cost, than alternative policy
mechanisms. Moreover, FITs have spurred innovation and increased interest and investment in many
countries. They have had the largest effect on wind power but have also influenced solar PV, biomass,
and small hydro development.
3.2
An Analysis of FIT models
As it was mentioned above recent experience from the countries which have implemented RES
promotion polices, FITs are the most effective policy instrument to encourage the rapid and costeffective deployment of RE. There are several designs of FITs each with its own strengths and
weaknesses. We have analyzed two groups of FITs: market dependent and market independent FIT
polices.
3.3
Market Independent FIT Polices
The market independent FIT structure is not tied to the fluctuations in the actual market price of
electricity. This model has its strengths and disadvantages (See Table 8), but most countries with FIT
polices choose this approach of tariff design7.
The fixed price model remains independent from the market prices and other factors, such as the price
of fossil fuels, thus creating stable conditions for investors, resulting in risks reduction and lower project
financing costs8. The fixed price models can vary also in terms of taking into consideration the inflation
rate or the CPI. However, a tariff structure which is not reflecting the inflation rate will result in
decreasing actual value of the revenues obtained over time. Moreover, for the developing countries
there is a high risk for projects financed in foreign currency (USD, Euro). Thus it is often better to choose
an option which helps to hedge inflation risk – the fixed model with full or partial inflation or CPI
adjustment. For example, in Spain the FIT is calculated by adjusting for inflation fully, minus a certain
number of basis points (e.g. Spanish Royal Decree 661/2007), while Ireland offers full, or 100%, inflation
7
Source: Klein, A., 2008. Feed-in Tariff Designs: Options to Support Electricity from Renewable Energy Sources.
Lightning Source Inc., Tennessee, USA.
8
Policy instrument design to reduce financing costs in renewable energy technology projects, David de Jager and
Max Rathmann, 2008
16
adjustment for renewable energy projects9. In Armenia On the other hand, only 35% of the FIT base
price is adjusted to CPI on an annualbasis10, whereas the Canadian province of Ontario offers an
inflation annual adjustment on a 20% portion of the base price of electricity for all eligible technology
types over the course of 20-year contract for all RES, with the exception of solar11. Each of these
jurisdictions represents a different approach to the calculation of inflation adjustments in feed-in tariff
rates, and provides a means of tracking changes in the broader economy.
The third feed-in tariff policy design option examined here is the front-end loaded model . In this model,
higher payments are offered in the early years than in the later years, effectively skewing the cash flows
in favor of the earlier years of the project’s life. An example of front-end loading is implemented under
the State of Minnesota’s Community-Based Energy Development (C-BED) policy12). This policy offers a
higher tariff for the first 10 years of the contract term, while offering a lower tariff for the remaining 10
years. In the wake of new changes to the State’s C-BED program, renewable energies other than wind
are accepted, and each will receive the higher initial payments under the front-end loading policy (State
of Minnesota, 2007). Shifting project revenues to the earlier years of a project’s life risks putting greater
near-term upward pressure on policy costs, as higher initial payments must be made in the initial years
of production. This can make the policy seem more costly in the early years. In addition, when used to
adjust for resource intensity (as in France, Germany, Cyprus, and Switzerland), this model ends up
offering higher average FIT payments to projects in less windy areas. This can put upward pressure on
the costs of RE development, while working against the principle of comparative advantage, which
would suggest that the most productive sites should be tapped first. On the other hand, there are a
number of advantages of the front-end loaded policy design. First, it enables project developers to
benefit from higher revenues streams when they need it the most (i.e. during the period in which the
loans and/or equity investors are being repaid), while leaving lower revenues, and therefore diminished
impact on retail electricity prices in the later years of a renewable energy project’s life. This approach
enables renewable energy developers to receive the same total revenue they would receive through a
9
France, 2006.Arrˆete´ du 10 juillet 2006. Ministere del’e´conomie, desfinancesetde l’industrie.
Ireland, 2006. Renewable Energy Feed-in Tariff: Terms and Conditions. Department of Communications, Energy
and Natural Resources, May 1, 2006. http://www.dcenr.gov.ie/NR/rdonlyres/ E260E316-B65A-4FDC-92F09F623BA18B55/0/REFITtermsandconditionsV2.doc
10
An analysis of feed-in tariff remuneration models: Implications for renewable energy investment Toby Couture,
Yves Gagnon, 2009
11
Ontario Power Authority (OPA), 2006. Renewable Energy Standard Offer Program, Final Program Rules, v.2.0,
Ontario.
12
State of Minnesota, 2007. C-BED Legislation Omnibus Energy Bill, SF1368 and HF1344.
17
fixed price policy over the project life time, while allowing for proportionally higher net profits through
higher cash flows when interest payments are highest. This practice enables developers to pay off loans
and/or equity investors more quickly, while retaining reliable revenue streams after debts are fully, or
largely, paid back. The front-end loaded tariff design model, when used in this way, also has the
advantage of offering predictable project revenues until the very end of the project’s useful life, adding
significant investment security by making the remuneration framework clear to all investors at the
outset. Alternatively, when used to allow FIT payments to be differentiated according to resource
intensity, as in the case of wind power in Germany, France, Cyprus, and Switzerland among others, this
strategy can reduce the risks of overcompensation at the windiest sites, while providing a number of
benefits for grid Market Dependent FIT Polices operators and project developers, in addition to
facilitating participation for local communities.
Table 5 Advantages and disadvantages of Fixed Models
Weaknesses
Name
Indifference to the market prices and trends
Indifference to the load14
Name
Stable and predetermined revenues
Description
Fixed price FITs in which the remuneration
remains independent from prevailing electricity
prices distort competitive electricity prices13.
This distortion arises because the purchase
prices offered under fixed-price FITs remain fixed
over time, regardless of electricity market price
trends. This means that even if conventional
prices decline dramatically, RE producers will
continue to receive the guaranteed prices,
leading to higher prices for electricity customers,
and thus to an alteration of what the ‘‘real’’
market price would be otherwise.
The fixed price FITs ignore prevailing electricity
demand, offering the same prices regardless of
the time of day at which electricity is supplied.
This indifference to the time of day can increase
costs for utilities and ratepayers, as electricity
maybe supplied from RE sources when demand
is low, which means lower marginal cost
generation option shave to be scaled back.
Strengths
Description
Fixed models offer greater investment security,
13
Source: Lesser and Su, 2008
Source: Langniss, O., Diekmann, J., Lehr, U., 2009. Advanced mechanisms for the promotion of renewable
energy: models for the future evolution of the German Renewable Energy Act. Energy Policy 37 (4), 1289–1297
14
18
Contribution to the electricity prices stabilization
3.4
which in its turn leads to higher deployment in
comparison with other models. Moreover,
greater security leads to the reduction of
interest rates and increase of debt financing
from banks and IFIs. Indeed, since introduction
of FITs four special purpose financing facilities
were established in Armenia with the
participation of local banks and IFIs (WB, EBRD,
IFC, DEG). Expressed in terms of provided loans
this represents around $80 million of debt
financing provided by the local banks since 2006.
Since Fixed FIT policies are independent from the
electricity volatilities they can help to stabilize
market prices if the cost of generation in
conventional plants will increase.
Market Dependent FIT polices
Three market dependent policy options were analyzed: premium price model, variable premium price
model and percentage of the retail price model, where the FIT payment is based on a percentage of the
retail rate (which could be lower or higher than 100%). This structure was abandoned by Germany and
Denmark in 2000 and by Spain in 2006; today, both Spain and Germany use the renewable energy costbased methodology.15 Although the market dependent policies generally apply in deregulated markets,
it was decided to analyze those to assess the applicability to Armenia.
In the premium price model the FIT is based on the bonus or premium above the average retail
price . With the premium price remuneration scheme, the price paid to the renewable energy
developers fluctuates according to the market price of electricity at the time. In this way, renewable
energy producers are remunerated more if market prices go up, and less if market prices go down, all
else being equal. Similar to fixed-price policies, the premium amounts can be differentiated according to
technology type, and project size, allowing a diversity of renewable energy projects and technologies to
be profitable16. The premium price model is currently offered as an option in the Czech Republic,
15 Jacobsson, S.; Lauber, V. (2005). “Germany: From a Modest Feed-in Law to a Framework for Transition.”
Switching to Renewable Power: A Framework for the 21st Century. Volkmar Lauber, editor, ISBN 1-902916-65-4,
Earthscan, London, pp. 122-158, Held, A.; Ragwitz, M.; Huber, C.; Resch, G.; Faber, T.; Vertin, K. (2007). “Feed-in
Systems in Germany, Spain and Slovenia: A Comparison.” Fraunhofer Institute Systems and Innovations Research
in Karlsruhe, Germany, October 2007. Accessed at http://www/feed-incooperation.org/images/files/ific_comparison_of_fit-systems_de_es_sl.pdf
16
Spanish Royal Decree 661/2007,Ministerio de Economı´a, REALDECRETO661/2007, de
26demayo.:/http://217.116.15.226/xml/disposiciones/min/disposicion. xml?id_disposicion=240846&desde=minS.
19
Slovenia, Estonia, Denmark, and Spain, though the latter has recently moved to a more sophisticated
variable premium price policy17.
In 2007 Spain enforced a variable premium FIT policy model that includes both caps and floors
into its FIT policy structure, effectively allowing the premium to vary as a function of the market price. In
this model, the premium amount declines gradually until the retail price reaches a cap, at which point
the premium declines to zero, and the producer receives the spot market price (Spanish Royal Decree
661/2007). In this representation, the higher line on the graph represents the development of the total
remuneration (premium + market price) that an electricity producer would receive(y axis), depending on
the current market price (x axis). As electricity prices increase (on the x axis), the premium amount
declines. Thus, the lower line represents the development of the premium amount awarded, as it acts to
keep the remuneration between the ‘‘bottom’’ and the ‘‘top’’ limit indicated. As shown in the graph, if
the market price approaches zero, the premium increases to make up the difference, until the premium
represents the entire remuneration offered. This is effectively the floor, or ‘‘bottom’’ limit that this
model guarantees for RE producers. With regards to the ‘‘top’’ limit, this is the upper limit on
remuneration that can be supported by the premium—when price goes higher than this the premium
falls to zero and the producer simply receives the spot market price. This variable premium model is
designed partly to minimize windfall profits in the event that retail prices rise unexpectedly, and partly
to introduce a greater degree of investment security in the event that market prices drop18. It does this
by introducing a ‘‘corridor’’ within which the premium amount fluctuates19 . This can help keep actual
remuneration more closely aligned with project costs20.
Under the percentage of retail price model, the total remuneration paid to renewable energy producers
is entirely dependent on changes in the market price for electricity. This means that if prices increase
suddenly, RE producers are likely to benefit from sudden windfall profits, while if they decrease
suddenly, they are likely to fall short of the revenues required to ensure profitability. This exposure to
market volatilities that have no immediate relationship to RE generation costs makes this policy option
Held, A., Ragwitz, M., Huber, C., Resch, G., Faber, T., Vertin, K., 2007. Feed-in Systems in Germany, Spain, Slovenia:
A Comparison. Fraunhofer Institute & Energy Economics Group, APE, Germany.
17
Klein, A., 2008. Feed-in Tariff Designs: Options to Support Electricity from Renewable Energy Sources. Lightning
Source Inc., Tennessee, USA.
18
Gonzalez, P., del, R., 2008. Ten years of renewable electricity policies in Spain: an analysis of successive feed-in
tariff reforms. Energy Policy 36 (8), 2917–2929.
19
Langniss, O., Diekmann, J., Lehr, U., 2009. Advanced mechanisms for the promotion of renewable energy:
models for the future evolution of the German Renewable Energy Act. Energy Policy 37 (4), 1289–1297
20
Source: T. Couture, Y. Gagnon / Energy Policy 38 (2010) 955–965
20
significantly more risky from a producer’s perspective, as cash flows are no longer primarily contingent
on efficient project operation, but instead on un- controllable factors in conventional energy markets.
The percentage of retail price model was used in Germany and in Denmark in the 1990s to drive wind
development, as well as in Spain between 2004 and 200621.
Table 6 Strengths and Advantages of Market Dependent Models22
Disadvantage
Name
Higher RES deployment costs
Greater uncertainly for investors
Name
Effectiveness for peak load
Description
Recent analysis have shown that marketdependent policies with premium payments over
the market price require a risk premium of 1–3
Euro cents/kWh and result in higher overall
renewable energy deployment costs than fixed
price policies based on the cost of generation23.
The absence of long term reliable projections for
retail price creates uncertainty about future
prices and revenues. The risk for the renewable
energy producers is larger in the case of the
premium [market-based] option, because the
total level of remuneration is not determined in
advance and there is no purchase obligation as is
typically the case with the fixed option.
Therefore the remuneration of the premium
option has to be higher than the one of the fixed
tariff option in order to compensate the higher
risk for [renewable energy] producers (if the
same investments in new installations are to be
achieved) (Mendonc- a, 2007, p.98).
Strengths
Description
Market-dependent FIT policies like the premium
price model could be employed to help meet
peak demand in jurisdictions where daily price
volatility is common, and where the spread
between peak and off-peak prices is significant.
Encouraging demand-sensitivity on the part of
RE generators could help alleviate some of this
price volatility by creating an incentive to supply
21
Jacobsson, Staffan,Lauber,Volkmar,2006.Thepoliticsandpolicyofenergysystem transformation—explaining
theGermandiffusionofrenewableenergy technology. EnergyPolicy34(2006),256–276.
22
23
T. Couture, Y. Gagnon / Energy Policy 38 (2010) 955–965
Ragwitz, M.; Held, A.; Resch, G.; Faber, T.; Haas, R.; Huber, C.; Coenraads, R.; Voogt, M.; Reece, G.; Morthorst,
P.E.; Jensen, S.G.; Konstantinaviciute, I.; Heyder, B., 2007. Assessment and optimization of renewable energy
support schemes in the European electricity market: Final Report. Optimization of Renewable Energy Support
(OPTRES), Karlsruhe, Germany.
21
Contribution to the electricity prices stabilization
power in times of high demand, which may
provide benefits to both grid operators and
society24
By letting the remuneration vary with market
demand, an incentive is created to supply
electricity to the grid in times of high demand,
when prices are highest. This can create a more
efficient electricity market, by encouraging
supply in times when electricity is needed most
As it is clear from the analysis above, market-independent policies are providing a stronger
and more cost-efficient policy option in the near-term than market-dependent options. Given the
lower-risk and greater revenue certainty they provide, fixed price models have thus far proved to be
more effective at encouraging broader participation in RE development, while providing a policy
structure more conducive to leveraging large amounts of capital toward renewable energy
development. So our further analysis of the applicable FIT designs for Armenia will be based on these
polices.
4 The impacts of FITs
Although advanced FIT polices like those in EU have not yet been implemented in Armenia, the
experience from Germany, Spain and other European Countries provides lessons that can be useful for
future adoption and implementation in Armenia.
4.1
Investment Flows Worldwide and in Armenia
Total investment in renewable energy capacity (excluding large hydro) was about $150 billion in 200925.
Investment in utility-scale renewable energy additions dropped 6 percent in 2009 from the 2008 level,
despite “green stimulus” efforts by many of the world’s major economies and increased investments
from development banks in Europe, Asia, and South America. The total worldwide RE investments in
new utility-scale renewable energy development (including biofuel refineries but excluding large hydro)
declined to $101 billion in 2009, compared with $108 billion in 2008. In addition, investments in smallscale projects (e.g. rooftop solar PV and SHW) reached $50 billion worldwide in 2009 and about $45
billion was invested in large hydropower.
24
Langniss, O., Diekmann, J., Lehr, U., 2009. Advanced mechanisms for the promotion of renewable energy:
models for the future evolution of the German Renewable Energy Act. Energy Policy 37 (4), 1289–1297.
25
REN21 Renewables 2010, Global Status Report
22
Figure 5 Annual Worldwide Investment in New Renewable Energy Capacity (billion of US dollars)26
bln $USD
180
160
140
120
100
80
60
40
20
0
2004
2005
2006
2007
2008
2009
The market monitoring reports for Armenia published by the PSRC demonstrate the growth trend in the
construction of the SHPPs. Although, the information about actual investments in RE in Armenia is
limited, the estimated actual investments were calculated based on the available feasibility studies,
estimations of investment costs per kW and study done by the KfW German-Armenian Fund (GAF). The
estimations done by PSRC27 show that total cumulative investment in SHPPs will reach $ 190 million in
2015. Investments in biogas, wind other RE technologies will be negligible, due to the barriers described
below.
Although FITs are enforced for wind energy a number of barrier currently hinder wind energy
development:
-
26
27
Low FITs
Lack of knowledge and experience
Complexity with the transportation of equipment to the installation areas due to narrow and
winding roads and mountainous terrain. This especially refers to wind power installations.
Complex terrain
Lack of actual measurements.
REN21 Renewables 2010, Global Status Report
Annual reports on SHPPs development (www.psrc.am)
23
4.2
FIT-based RE Deployment
Successful implementation of RE promotion policies has generated significant RE deployment.in the
European Union (EU). FIT policies resulted in deployment of more than 15,000 MW of solar photovoltaic
(PV) power and more than 55,000 MW of wind power between 2000 and the end of 2009. In total, FITs
have supported approximately 75% of global PV and 45% of global wind deployment28. Countries such as
Germany, in particular, have demonstrated that FITs can be used as a powerful policy tool to increase RE
deployment and help meet combined energy security and emissions reductions targets.
Table 7 Renewable Energy Added and Existing Capacities (GW)29
Power Generation
Wind Power
Small Hydropower <10MW
Biomass Power
Solar PV, grid-connected
Geothermal power
Concentrating solar thermal
power (CSP)
Ocean power
Hydropower (all sizes)
4.3
Added during 2009
38
2
2
7
0.4
0.2
Existing at 2009
159
60
54
21
11
0.6
0
31
0.3
980
Job creation
Experience in implementation demonstrates that FITs polices can be a significant driver of domestic job
creation, industry development and innovation. For example, at the end of 2007 the RE sector in
Germany supported 250,000 jobs, spurred by a combination of FIT polices, strong domestic demand,
and the growth of export capacities in the green technology sector. Moreover, it is projected that the
number of jobs in renewables will triple by 2020 and hit 900,000 by 203030. Similarly, the same
increasing trend is observed in all countries which have introduced RE promotion polices (table 8).
Table 8 Jobs From Renewable Energy31
Industry
Biofuels
Wind Power
Solar hot water
Solar PV
Biomass Power
Estimated Jobs Worldwide
>1,500,000
>500,000
~300,000
~300,000
-
28
REN21 Renewables 2010, Global Status Report
REN21 Renewables 2010, Global Status Report
30
BMU 2008
31
REN21 Renewables 2010, Global Status Report
29
24
Hydropower
Geothermal
Solar Thermal Power
Total
~2,000
>3,000,000
Data for Armenia on existing FIT policy impacts on green jobs is limited, however estimations32 show
that current policy supported about 5000 jobs, the breakdown of which is shown in the table 9.
Table 9 Jobs from Renewable Energy in Armenia (cumulative as of Jan 2010)*
Sector
Design /consulting
Equipment manufacturing
Construction
O&M
Total
Jobs
50-80
60-100
3000-4000
1000-1200
4110-5380
*Due to limited information we have not included the jobs in the financial sector and short term temporary jobs
(for example drivers for transportation of equipment and pipes).
It must be noted that the real impact of the SHPPs on the jobs is higher, because the renewable energy
industry is presently dominated by the SMEs, which are located in the remote rural areas with high
unemployment rate. Moreover, despite the economic crisis the sector showed the job growth even in
2008-2009 in contrast with other sectors of economy33.
32
The estimations are based on the surveys among sector experts, designers, construction companies, developers
and PSRC.
33
The official unemployment rate went up from 6.3 to 6.9 percent between 2008 and 2009.
25
Figure 6 Jobs in the SHPPs in Armenia (2005-2010)
900
800
700
600
500
400
300
200
100
0
2005
2006
2007
2008
2009
2010
4.3.1 Other benefits of the SHPPs
In addition to the benefits described above development of RES creates other benefits, including:
-
-
-
Increased tax payments – in 2010 SHPPs paid about $3.2 mln of VAT (about 0.5% of total VAT
payments) and about $1.3 mln of Profit tax payments (about 1% of total payments). Moreover,
in 2008-2009 the total VAT payments reduced in Armenia, while the SHPPs have demonstrated
about 50% growth in VAT payments.
Technology transfer and increased international trade – current trends show that developers
increased the import of state-of-the-art turbines and generators, which will have positive impact
on overall quality of the SHPPs.
Reduced dependence on the imported fuel and \eduction of the capital outflow from Armenia
for natural gas.
Participation in the CDM – Armenia has 5 registered CDM projects and about 20 under
development and currently almost all large international buyers are developing or expressed
their interest in the development of CDM projects in RES sector.
-
4.4
Reduction of the GHG emissions
4.5
Impact on the Retail Prices
We have examined these impacts of FiTs in EU countries. Figure 7 represents the breakdown of the of
the electricity costs in Germany in 2009. It reveals that the share of the RE electricity costs is about 6%
of the total average electricity costs.
26
Figure 7 Breakdown of Electricity Costs in Germany (2009)34
16%
Gen., T&D, etc
9%
8%
RE
61%
Heating Act
Concession Charge
Electricity Tax
1%
5%
VAT
Due to the marginally higher costs of RE, on average, over existing conventional generation,
implementing an aggressive FIT policy is likely to put near-term upward pressure on electricity rates. For
example, since implementing a FIT schemes several EU countries have experienced very high rate of
growth of solar PV, which resulted in increases in final electricity prices. This has led to public discontent
and in some cases even strong political opposition to renewable energy. To tackle this problem, the
governments cut the level of support for PV, and in the case of the Czech Republic, suspended approvals
of new PV projects.
Such uncontrolled growth, known as “PV bubbles” was mainly the result of inadequate design of policies
and the lack of monitoring and/or controlling the installed capacity. While investment costs for PV were
falling sharply, the support policies were unable to adapt quickly enough to changing conditions. As
Error! Reference source not found. demonstrates, in Spain the amount of financial support for PV is the
highest compared to other renewable energy sources, while the amount of electricity produced by these
systems is relatively small.
34
Source BMU 2009
27
.
Figure 8 Support received vs. production from renewable energy sources in Spain, 2010
Source: UNESA estimations (September 2010), referred to in Philibert, 2011
Error! Reference source not found.7 also demonstrates that the cost of support for other, more mature,
technologies like small hydro, wind and cogeneration was relatively small. BiH can learn from this
experience in three ways. First, it can focus on most cost-effective technologies for the use of which the
country has high potential, like small hydro, wind and biomass. Second, it can introduce transitory cap
mechanisms for each technology and put in place strict monitoring in order to keep under control the
amount of installed capacity, which benefits from support. Third, it can design its support taking into
account expected further reductions in the costs of PV technologies. The draft law proposals and FIT
methodologies in both FBiHa and RS take these lessons learned into account. Thus, they introduce caps
on the total amount of installed capacity by technology, as well as automatic annual reduction of the
tariff for solar PV by 7%.
28
However, recent researches demonstrate that development of RE helps to stabilize electricity prices35.
4.5.1 Merit Order Effect
The analysis carried out by the Fraunhofer Institute for Systems and Innovation in Germany revealed
positive impacts attributable to what is called the “merit order effect” (Sensfuss et al.2007). The merit
order effect is intended to capture the impact of adding new supply into existing system with an existing
supply curve. The effect is most prominent in systems during times of high demand, particularly in
systems with tight supply that rely on high-cost generation during those hours.
Based on stimulations conducted by the Fraunhofer Institute for Systems and Innovation, the reduction
in market prices due to the merit order effect in periods of high demand reached as high as $ 46.4/Mwh.
Total price savings in 2006 in Germany were $ 6.35 billion.
Similar to the effect described above development of wind power has contributed to reducing of
electricity tariffs in other countries (See Figure 9). The studies conducted in Spain demonstrate that due
to the aggressive wind power development over the past 10 years, electricity prices in 2005, 2006, and
the first half of 2007 were 11.7,%, 8.6% and 25.1% lower, respectively, than they would have been
without wind power36). The same trend was revealed in Denmark, where reduction of 12-14% occurred
in 2005 due to wind power alone.
35
Saenz de Miera, Gonzalo, Pablo d. R. Gonzalez, and Ignacio Vizcaino. 2008. “Analysing the impact of renewable
electricity support schemes on power prices: The case of wind electricity in Spain..” Energy Policy 34(9): 3345-3359
Germany FederalMinistryfortheEnvironment,NatureConservationandNuclear Safety
(BMU),April2009.Developmentofrenewableenergysourcesin Germany,
2008:graphicsandtables,Berlin,Germany.BasedonWorking Group onRenewableEnergiesStatistics.
36
Sáenz de Miera, G., P. del Río, I. Vizcaíno (2008) Analysing the impact of renewable electricity support schemes
on power prices: The case of wind electricity in Spain. Energy Policy36: 3345-3359.
29
Figure 9 Estimated savings in Spain and Denmark Due to wind power37
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Spain 2005
4.6
Spain 2006
Spain 2007
Denmark 2005
Impact on the retails prices in Armenia
In order to analyze the RE FIT impact on the end-user electricity price, an analysis of the historical
retail electricity sales, prices and bills was conducted, using the data from the PSRC. Table 10 shows
a snapshot of retail electricity prices for the period of 2003-2010.
Table 10 Electricity Sales in Armenia (2003-2010)38
Total Sales, mln AMD
Sales From SHPPs
Share of SHPPs in Total Sales
Total Consumer Payments
Share of SHPPs in Total
Consumer Payments
Residential Payments
2003
2004
2005
2006
46,377
41,548
64,970
60,114
1,205
2,050
2,338
3%
5%
78,050
1.54%
2007
2008
2009
2010
56,491
57,131
57,936
53,764
2,493
3,225
3,502
4,552
7,002
4%
4%
6%
6%
8%
13%
79,697
86,603
88,749
94,798
98,576
107,298
116,520
2.57%
2.70%
2.81%
3.40%
4%
4%
6%
78,050
82,890.8
86,602.58
88,748.92
94,797.78
98,576.28
107,719.52
116,519.79
33,385.5
35,540.12
36,974.68
37,543.30
38,583.71
38,836.82
42,822.94
46,759.70
As it is clear from the Table 11 the tariffs for SHPPs are substantially higher than those for large HPPs,
ANPP and TPPs. However, it is necessary also to take into consideration the capacity charge which ENA
pays to all thermal and nuclear power plants, which is used by those generators to cover up-front capital
37
38
Sources: de Niera et al. 2008, Motorhorst 2006
Source: PSRC
30
costs for modernization and construction (see table 3 on p. 12). The actual payments per kWh produced
by generators are shown in the table 11.
Table 11 Payments to generating plants, 2010
Power Plant
Energy delivered,
MWh
Hrazdan TPP
ANPP
IEC (Sevan Hrazdan
Cascade)
Yerevan TPP
New Unit in Yerevan
TPP
Vorotan Cascade
Dzora HPP
SHPPs
Payment by ENA, mln
AMD, without VAT
Payment per KWh
delivered to the grid,
AMD/KWh
11,925.9
37.2
17,228.5
7.5
2,679.6
3.7
320.6
2,286.5
716.2
61.26
335.98
1,726
2,513.1
28.2
7.5
1213.74
1,731.8
1.42
399.9
5834.9
14.6
There is no official data on the impact of the SHPP development on the retail electricity prices, so the
analysis was conducted in accordance with the following steps:
Step 1. Collected the data on the generation from all power sources in the grid.
Step 2. Collected the data on the wholesale payments for all power sources in the grid.
Step 3. Calculated the average wholesale electricity price with and without SHPP share in the generation
mix.
Step 4. Calculate the generation growth and price impact for the corresponding year.
The results of the calculations for 2004-2010 are shown in the table 12 below.
Table 12 SHPPs FIT Impact on the wholesale prices
Impact on the
prices
SHPP
generation
growth
2004
2005
2006
2007
2008
2009
2010
1.722%
1.012%
1.217%
1.983%
2.25%
3%
7%
30%
9%
8%
27%
9%
29%
34%
As it is clear from the table 12 above the increase of generation in SHPPs by 34% resulted only in 7%
increase in the wholesale prices, i.e. in total during past 8 years the generation from the SHPPs has been
31
increased by 270%, while their impact on the increase in wholesale prices was only by 8%. This increase
was significantly lower in retail prices, since currently the wholesale prices represent roughly half of
retail prices: about 3.3% of retail prices in 2009-2010).
5 Elaboration of the required FITs design for the SHPPs
5.1
Analysis of hydro potential
Small hydro power plants could play a significant role in the power generation sector of Armenia. The
technical potential for the development of new SHPP sites, as well as retrofits and upgrades of existing
sites is considerable, particularly in Syunik and Lori regions. The Feed-in Tariffs and guaranteed purchase
of energy produced by RES introduced in 2001 by the Energy Law supported development of SHPPs in
Armenian. However, currently hydropower developers and experts in the industry are pessimistic about
future of the industry and expect that it will become stagnant in the next decade. The development of
the new SHPPs will be severely constrained by the following reasons:
-
Most of the sites with high capacity factor (CF) and low investment costs are fully
developed
-
Most of the sites are close to fully developed and have little surplus, meaning that the
upgrades or replacement of the equipment will only increase the stability and reliability
of the operation with small impact on the capacity.
-
Current tariff structure and increased investment costs do not secure expected rate of
the return for the developers.
In 2008 ARMEHEP39 released The Update of the Existing Scheme For Small Hydro Power Plants40 in the
Republic of Armenia, which contains detailed cost estimates for additional 86 potential hydro project
sites across the country. The database includes costs estimates for each project including capital and
O&M and also has estimation of future generation for each project.
The database includes 86 projects with total capacity 74 GW, annual generation 275 GWh and total
investment cost about $90 mln. Average load factor of the proposed SHPPs is about 0.42.
39
40
ARMEHEP – Armhydroenergyproject CJSC
The study was financed by R2E2 within the WB project
32
The average capital cost calculated for these projects is about 890 USD/kW, which is considerably lower
than current investment costs in Armenia. Moreover, the cost estimations by ARMEHEP do not include
project development cost (design, license, EIA etc). These costs usually are fixed and account for
S30,000-50,000. These costs do not include the capacity deposit41, which all developers have to provide
before the issuance of the license for construction.
5.2
Investment analysis for selected SHPPs
8 SHPP were selected for detailed analysis. The selection criteria included:
Availability of well documented cost and generation estimations
Coverage of different regions
Coverage of the SHPPs with different capacity factor
Coverage of the SHPPs with the different development stage (feasibility study (FS), construction
and operation)
Coverage of the SHPPs with the different equipment sources
The basic information about SHPPs selected for the analysis is shown in the table 13.
Table 13 SHPPs selected for the project
Name
Location
Development
stage
Equipment
source
Capacity,
MW
Annual
generation
1
Jermuk-2
Vayots Dzor
10.24
Katnarat
Lori
2.07
6.6
2,355,000
3
Dzoraget-5
Lori
Under
construction
2.12
7.09
2,330,000
4
Dzoraget-6
Lori
Under
construction
1.48
4.6
1,550,000
5
Artavan
Vayots Dzor
Operational
2.87
7.8
2,460,000
6
Amassia
Shirak
1.2
4.16
1,580,000
7
8
Meghri
Getik-4
Syunik
Gegharkunik
Under
construction
Feasibility study
Feasibility study
Czech
republic
Local
(Vorotan
Turbo)
Local
(Vorotan
Turbo)
Local
(Vorotan
Turbo)
Local
(Khorda)
Czech
republic
China
Czech
republic
2.35
2
Construction
completed
Under
construction
Total
investment
required,
VAT
excluded,
USD
3,315,000
6.5
3
18.0
11.0
5,750,000
3,420,000
41
2,500 AMD/kW
33
A financial model was constructed to estimate the required level of a FIT to support financial viability of
the projects. The project IRR was used as a basis for calculation of the required FIT. Provided that the
projects are financed by both equity and debt sources, the appropriate benchmark is weighted average
cost of capital (WACC), which represents the weighted average of the costs of various sources of
financing in the financing structure. This benchmark represents the minimal required Project IRR of the
project to be economically attractive.
The following WACC equation is applied to estimate the required return on capital as a benchmark for
this project IRR:
Equation 1
Where:
Re : cost of equity
Rd : cost of debt
E : Amount of equity in the project
D : Amount of debt in the project
V : Total investment cost (=E + D)
Tc : average enterprise tax rate.
Determination of the cost of the debt financing
There are three financial facilities which provide targeted financing for SHPPs (See Table 14).
Table 14 Financing facilities in Armenia
Name
Total amount
APR
Dept/Equity
USD 14 mln
Currency of
financing
USD
Ameriabank
(successor of Cascade
Credit, source WB and
EBRD)
German – Armenian
11%
70/30
Euro 6 mln – first stage
AMD
10.5%
70/30
34
Fund
Ameriabank (Source
IFC and FMO)
Euro 18 mln – second stage
USD 25 mln
USD
12%
70/30
The applied cost of dept is 10.5%.
Determination of the cost of the equity financing
The official data on the cost of the equity financing is not available in Armenia, so a rate of 12.5% was
selected based on the a number of discussions with the WB economists and commercial bank’s prime
lending rates.
The WACC benchmark calculated based on the Equation 1 is 10%.
As it is clear from the table 15 the average post-tax project IRR is about 9.35%, which is lower than the
benchmark 11%.
Table 15 Calculation of post tax IRR
SHPP
Jermuk-2
Katnarat
Dzoraget-5
Dzoraget-6
Artavan
Amassia
Meghri
Getik-4
Average IRR
IRR
8.82%
7.15%
8.56%
8.12%
9.29%
8.92%
9.06%
9.03%
8.62%
Table 16. Required FIT variations to reach WACC benchmark (11% IRR)
Variations of FIT to reach benchmark
Jermuk-2
FIT
+15%
Variation
Tariff,
AMD/kWh
22.172
Katnarat
Dzoraget-5
Dzoraget-6
Artavan
Amassia
Meghri
Getik-4
+25%
+5%
+17%
+6%
+19%
+6%
+6%
24.1
20.244
22.56
20.43
22.94
20.43
20.43
35
As it is clear from the above table 16 the required variations of FIT to cross the selected benchmark are
between 20.244 and 24.1 AMD/KWh(VAT excluded). The lowest calculated tariffs are for Dzoraget-5,
Artavan-1,Meghri and Getik-4SHPPs which is explained by the following:
-
These SHPPs have high load factor (>40%)
-
The equipment selected for these SHPPs is local or Chinese, which is cheaper than
European. However, recent report published by GAF indicates that cheap Chinese and
local equipment is not efficient and do not secure stable operation of the SHPPs.
Based on the above conducted calculations the marginal feed-in tariff for further deployment of the
SHPPs in Armenia is proposed to be about 24-25 AMD/KWh.
This above proposed feed-in tariff estimation is in line with other estimations, in particular with the
analysis carried out by the KFW financed German-Armenian Fund (GAF) – “Small Hydro Power Plant
feed-in tariff analysis in the Republic of Armenia” (2010). This study was conducted within the
framework of German Financial Cooperation with Armenia and specifically the Program on Renewable
Energy development in Armenia. The main purpose of the report was to define the appropriate level of
feed-in tariffs for SHPP in RA, which would ensure construction of technically admissible power plants in
current crediting conditions in Armenia. The key findings and recommendations from the report are
provided below:
-
Despite a large number of private investments into renewable energy, major problems
still exist which hinder the overall construction quality and durability of newly built
SHPP.
-
The aforementioned problems have their root in the inadequacy of the current tariff
levels. There are also other objective and subjective issues at play, but the principal
issue lies with the low level of the feed-in tariff.
-
The study analyzes the available data to derive the “threshold” level of feed-in tariffs,
which would allow for a proper technical quality of SHPP construction. Furthermore, the
study also presents a “recommended” tariff level, which would provide for a sustainable
development of the small hydro sector. The data used in the analysis is derived from the
feasibility studies of ten run-of the-river projects, four irrigation systems based projects,
and one drinking water system based project.
36
-
The concept of a “threshold” feed-in tariff is the level, which ensures the loan
repayment within 12 years given 70/30 debt/equity structure and 10.5% annual interest
rates. “Recommended” tariff is the level, which ensures 12% IRR level over 30 years of
SHPP operations.
-
The study derives both “threshold” and “recommended” tariff levels using an
assumption that stations use modern aggregates with automated regulation systems
and the derivation is implemented using either new metallic pipes or GRP pipes.
Moreover, the study takes into account the fact that efficiency of these stations will
increase by 15% due to the quality of the equipment and derivation compared to the
basic implementation choice (i.e. old pipes and low quality generating equipment
without automated regulation systems). The “threshold” tariff calculated for run-ofriver SHPPs is 6.54 US cents or 23-24 AMD/kWh.
-
Finally, report recommends to introduce declining tariff structure which would resolve
several issues simultaneously. In the early years higher tariff level will ensure proper
loan repayments for the owners, and in the latter years it would stabilize at much lower
levels, which would decrease the overall price effect of the small hydro tariff on the
retail electricity price in RA.
5.3
Elaboration of new FITs and structures
As it was described above, the front –end loaded model of FIT is one of the most efficient models to
stimulate the deployment of RES. Based on that model and the carried out calculations of marginal FIT
we have elaborated the following FIT structure table 17.
Table 17 New Proposed Tariff Model
Year of
1
operation
Tariff,
25
AMD/KWh,
without
VAT
2
3
4
5
6
7
8
9
10
11
12
13
14
15
25
25
25
24
24
24
24
24
20
20
20
20
20
20
The proposed FIT structure can be applied in two approached: 1) starting from a certain year (i.e.
starting from 2012 the declining tariff is introduced) and 2) the declining tariff is provided to all new
37
SHPPs starting from commissioning date. As it is clear from the table 5 the proposed tariff structure
allows for all projects to have an IRR higher than the calculated WACC
We began our analysis with the comparison of the existing and proposed tariff structure. Three
reference scenarios were elaborated: a Base Scenario which assumes a 105 percent CPI growth
between the years 2012 and 2027; a Low CPI Scenario with a rate of growth 103 percent until 2027; and
a High-Growth Scenario with a 110 percent CPI growth rate until the year 2027.
Figures 10-12 below demonstrate that the proposed FIT structure will have less impact on wholesale
prices in comparison with current structure. The proposed tariffs will intersect in 2020, 2019 and 2021 in
Base, Low and High CPI scenarios, respectively.
Figure 10 Low Case (CPI 102%)
30
2021
25
AMD/kWh
20
15
Current FIT
Proposed FIT
10
5
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
YEAR
38
Figure 11 Base Case (CPI 105%)
30
2020
25
AMD/KWh
20
15
Current FIT
Proposed FIT
10
5
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
YEAR
39
Figure 12 High Case (CPI 107%)
30
2019
25
AMD/KWh
20
15
Current FIT
Proposed FIT
10
5
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
YEAR
It must be noted, that in all scenarios described above possible changes of AMD exchange rate were not
considered in the analysis, due to the lack of reliable projections for future exchange rate of AMD.
However, according to the statements of governmental representatives the exchange rate of AMD will
slightly go down in future and as an example we have calculated compared proposed structure with the
scenario with base CPI (105%) and annual increase of USD exchange rate 2% until 2020. The results are
presented in the figure 13 below.
40
Figure 13 CPI 105%, and increase of exchange rate by 2%
35
30
2017
AMDkWh
25
20
Current FIT
15
Proposed FIT
10
5
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
YEAR
6. Potential impacts of the proposed FIT structure on the
wholesale prices
The analysis of the cost impact of the proposed FIT structure is based on the National target of
reaching 260 MW capacity with annual generation 800 GWh by 2025 and annual projected deployment
of 25-30 Mw. Three Scenarios were developed for the projection of the generation and consumption in
Armenia: Scenario 1 – annual increase of generation 2%, Scenario 2 – annual increase of generation 5%
and Scenario 3 – annual increase of generation 7%. Here, the generation is equal to total consumption,
since the exported electric energy has no implication on end-user and wholesale tariffs. For each
Scenario we have compared the cost impact of the proposed FIT structure with the current one based
on three Sub-scenarios – 102%, 105% and 107% CPI respectively.
The potential cost impacts are calculated according to the Equation 2 below.
Equation 2
ΔP=P1-P2, where
ΔP – cost impact, AMD/kWh
P1 – average wholesale price per 1 KWh with proposed FIT structure, AMD/kWh
P2 – average wholesale price per 1 KWh with current FIT structure, AMD/kWh
41
6.1Scenario 1: 2% annual increase in generation
This Scenario assumes that the generation will increase by 2% annually and will reach 7365 GWh
in 2025. The share of the SHPPs will increase from 7% in 2010 to 11% in 2025.
Table 18 below presents the cost impact for this scenario. In all sub-scenarios (102, 105 and
107% CPI) the proposed structure will have very limited impact on the wholesale prices, in particular the
highest impact will be in 2020 – 0.142 AMD (102% CPI) or roughly 2% of the average wholesale price. In
105% and 107% CPI cases the proposed structure will increase the wholesale prices until 2022 and 2021
respectively and beyond that will produce long term savings. The results are presented graphically in
Figures 14-16.
Table 18 Impact on wholesale prices, 2% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.02
0.05
0.07
0.09
0.10
0.11
0.12
0.13
0.14
0.13
0.12
0.10
0.09
0.08
105%
0.03
0.05
0.06
0.07
0.08
0.08
0.08
0.08
0.07
0.04
0.01
-0.02
-0.05
-0.08
107%
0.02
0.04
0.06
0.07
0.07
0.06
0.05
0.04
0.02
-0.02
-0.06
-0.11
-0.16
-0.21
Figure 14 Impact on wholesale prices, 2% annual increase in generation, 102% CPI
0.160
0.140
ΔP AMD/kWh
0.120
0.100
0.080
0.060
0.040
0.020
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Year
42
Figure 15 Impact on wholesale prices, 2% annual increase in generation, 105% CPI
0.100
0.080
0.060
ΔP AMD/kWh
0.040
0.020
0.000
-0.020
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.040
-0.060
-0.080
-0.100
Year
Figure 16 Impact on wholesale prices, 2% annual increase in generation, 107% CPI
0.100
0.050
0.000
ΔP AMD/kWh
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.050
-0.100
-0.150
-0.200
-0.250
Year
6.2 Scenario 2: 5% annual increase in generation
This Scenario assumes that the generation will increase by 5% annually and will reach 11376 GWh in
2025. Comparing with 2010 the share of the SHPPs will remain on the same (7%) level.
43
Table 19 below presents the impact on wholesale prices for this scenario. In all sub-scenarios
(102, 105 and 107% CPI) the proposed structure will have very limited impact on the wholesale prices, in
particular the highest impact will be in 2020 – 0.08 AMD (102% CPI) or roughly 1.1% of the average
wholesale price. In 105% and 107% CPI cases the proposed structure will increase the wholesale prices
until 2023 and 2021 respectively and beyond that will produce long term savings. The results are
presented graphically in Figures 17-19.
Table 19 Impact on wholesale prices, 5% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.03
0.04
0.06
0.07
0.07
0.08
0.08
0.08
0.08
0.08
0.07
0.06
0.05
0.04
105%
0.02
0.04
0.05
0.06
0.06
0.06
0.06
0.05
0.04
0.02
0.008
-0.01
-0.03
-0.04
107%
0.02
0.04
0.05
0.05
0.05
0.04
0.04
0.02
0.01
-0.01
-0.04
-0.06
-0.08
-0.10
Figure 17 Impact on wholesale prices, 5% annual increase in generation, 102% CPI
0.100
0.090
0.080
0.070
AMD
0.060
0.050
0.040
0.030
0.020
0.010
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Year
44
Figure 18 Impact on wholesale prices, 5% annual increase in generation, 105% CPI
0.080
0.060
0.040
AMD
0.020
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.020
-0.040
-0.060
Year
Figure 19 Impact on wholesale prices, 5% annual increase in generation, 107% CPI
0.080
0.060
0.040
0.020
AMD
0.000
-0.020
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.040
-0.060
-0.080
-0.100
-0.120
Year
6.3 Scenario 3: 7% increase in generation
This Scenario assumes that the generation will increase by 7% annually and will reach 15097
GWh in 2025. The share of the SHPPs will decrease 7% in 2010 to 5.3% in 2025.
45
Table 20 below presents the impact on wholesale prices for this scenario. In all sub-scenarios
(102, 105 and 107% CPI) the proposed structure will have very limited impact on the wholesale prices, in
particular the highest impact will be in 2020 – 0.106 AMD (102% CPI) or roughly 1.2% of the average
wholesale price. In 105% and 107% CPI cases the proposed structure will increase the wholesale prices
until 2023 and 2021 respectively and beyond that will produce long term savings. The results are
presented graphically in Figures 20-22.
Table 20 Impact on wholesale prices, 7% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.02
0.04
0.06
0.07
0.08
0.08
0.09
0.09
0.09
0.08
0.07
0.06
0.05
0.040
105%
0.02
0.04
0.05
0.061
0.062
0.060
0.056
0.050
0.043
0.025
0.008
-0.010
-0.03
-0.04
107%
0.023
0.039
0.048
0.053
0.050
0.044
0.035
0.024
0.011
-0.013
-0.036
-0.059
-0.08
-0.10
Figure 20 Impact on wholesale prices, 7% annual increase in generation, 102% CPI
0.100
0.090
0.080
0.070
AMD
0.060
0.050
0.040
0.030
0.020
0.010
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Year
46
Figure 21 Impact on wholesale prices, 7% annual increase in generation, 105% CPI
0.080
0.060
0.040
AMD
0.020
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.020
-0.040
-0.060
Year
Figure 22 Impact on wholesale prices, 7% annual increase in generation, 107% CPI
0.080
0.060
0.040
0.020
AMD
0.000
-0.020
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.040
-0.060
-0.080
-0.100
-0.120
Year
6.4 Tied to year FIT structure
Table 21-23 below presents the impact on wholesale prices for this scenario. In all sub-scenarios
(102, 105 and 107% CPI) the proposed structure will have very limited impact on the wholesale prices, in
particular the highest impact will be in 2019 – 0.119 AMD (2% annual increase of generation and 102%
47
CPI) or roughly 1.4% of the average wholesale price. In all sub-scenarios the proposed structure will
increase the wholesale prices until 2020 and beyond that will produce long term savings. The results are
presented graphically in Figures 23-31.
Table 21 Impact on wholesale prices, 2% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.02
0.05
0.07
0.08
0.09
0.10
0.11
0.12
-0.02
-0.03
-0.03
-0.02
-0.02
-0.009
105%
0.03
0.05
0.06
0.06
0.06
0.07
0.07
0.06
-0.09
-0.12
-0.14
-0.15
-0.16
-0.18
107%
0.03
0.05
0.06
0.05
0.05
0.04
0.04
0.02
-0.14
-0.18
-0.22
-0.24
-0.27
-0.30
Table 22 Impact on wholesale prices, 5% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.03
0.05
0.07
0.07
0.07
0.08
0.09
0.09
-0.01
-0.02
-0.02
-0.02
-0.01
-0.006
105%
0.03
0.04
0.06
0.05
0.06
0.06
0.05
0.05
-0.07
-0.08
-0.10
-0.10
-0.11
-0.12
107%
0.02
0.04
0.05
0.04
0.04
0.04
0.03
0.02
-0.11
-0.13
-0.15
-0.17
-0.18
-0.20
Table 23 Impact on wholesale prices, 7% annual increase in generation (AMD)
CPI
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
102%
0.03
0.04
0.06
0.06
0.07
0.07
0.08
0.08
-0.01
-0.02
-0.02
-0.01
-0.01
-0.004
105%
0.02
0.041
0.05
0.047
0.05
0.05
0.05
0.04
-0.06
-0.07
-0.08
-0.08
-0.08
-0.08
107%
0.02
0.04
0.05
0.04
0.04
0.03
0.02
0.01
-0.09
-0.11
-0.12
-0.13
-0.14
-0.15
Figure 23 Impact on wholesale prices, 2% annual increase in generation, 102% CPI
0.140
0.120
0.100
0.080
AMD
0.060
0.040
0.020
0.000
-0.020
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.040
-0.060
Year
48
Figure 24 Impact on wholesale prices, 2% annual increase in generation, 105% CPI
0.100
0.050
0.000
AMD
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.050
-0.100
-0.150
-0.200
Year
Figure 25 Impact on wholesale prices, 2% annual increase in generation, 107% CPI
0.100
0.050
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.050
AMD
-0.100
-0.150
-0.200
-0.250
-0.300
-0.350
Year
49
Figure 26 Impact on wholesale prices, 5% annual increase in generation, 102% CPI
0.100
0.080
0.060
AMD
0.040
0.020
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.020
-0.040
Year
Figure 27 Impact on wholesale prices, 5% annual increase in generation, 105% CPI
0.080
0.060
0.040
0.020
0.000
AMD
-0.020
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.040
-0.060
-0.080
-0.100
-0.120
-0.140
Year
50
Figure 28 Impact on wholesale prices, 5% annual increase in generation, 107% CPI
0.100
0.050
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
AMD
-0.050
-0.100
-0.150
-0.200
-0.250
Year
Figure 29 Impact on wholesale prices, 7% annual increase in generation, 102% CPI
0.100
0.080
0.060
AMD
0.040
0.020
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.020
-0.040
Year
51
Figure 30 Impact on wholesale prices, 7% annual increase in generation, 105% CPI
0.080
0.060
0.040
0.020
AMD
0.000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.020
-0.040
-0.060
-0.080
-0.100
Year
Figure 31 Impact on wholesale prices, 7% annual increase in generation, 107% CPI
0.100
0.050
0.000
AMD
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
-0.050
-0.100
-0.150
-0.200
Year
52
Conclusions and recommendations
The key conclusions and recommendation of this report are provided below:
-
-
Current FIT structure does not secure reasonable profitability for Small Hydro Power
Plants in Armenia.
The necessary level of FIT for run-of-river SHPPs is around 24-25 AMD/kWh.
The proposed declining FIT structure (see table 24) will increase the financial
attractiveness of the investments in small hydro power plants and insure safe and
reliable operation of the SHPPs.
The analysis demonstrates that the proposed FIT structure will have practically no
impact on wholesale electricity costs. If the CPI will be more than 105% the costs would
be negligible starting from 2018-2020. The impact of the proposed FIT structure on the
retail electricity price is even more negligible since the retail price formation is only
partially depending on the wholesale price of electricity and take into account a whole
number of issues not related to the FIT.
Table 24 Proposed FIT structure
Year of
1
operation
Tariff,
25
AMD/KWh,
without
VAT
2
3
4
5
6
7
8
9
10
11
12
13
14
15
25
25
25
24
24
24
24
24
20
20
20
20
20
20
53
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54