1. Private Investment in Wind Power in Colombia*
David Robinson, Oxford Institute for Energy Studies
Alvaro Riascos, Quantil SAS
David Harbord, Market Analysis Ltd
SP 27
July 2012
* A report commissioned by the UK Foreign and Commonwealth Office's Latin America
Prosperity Fund
3.
Preface
This study was carried out under a grant from the UK Foreign and Commonwealth Office
Prosperity Fund for Latin America to the Oxford Institute for Energy Studies (OIES) for the
implementation of the project entitled: Colombia: Developing a Framework to Promote
Renewable Power. David Robinson (OIES) directed the project team. Alvaro Riascos
(Quantil SAS) led the financial modelling work and David Harbord (Market Analysis Ltd.)
advised on the Colombian firm energy market.
We thank Lucía Martínez for her research on Colombian land and indigenous peoples issues,
and Ivan Cadena and Mauricio Romero of Quantil for their research assistance with the
financial model. We also thank the following people for reading and commenting on parts of
the report: Malcolm Keay, Adam Mantzos, Michael Tennican and Charles Donovan.
Finally, we thank staff from the Colombian Regulatory Commission for Electricity and Gas
(CREG), the Colombian Ministry of Environment, Housing and Territorial Development, the
Colombian Ministry of Mines and Industry, Empresas Públicas de Medellín, Isagen,
EMGESA, the Global Wind Energy Council and other anonymous reviewers for meeting
with us and/or for helpful discussions.
The contents of this paper are the authors’ sole responsibility. They do not necessarily
represent the views of the Oxford Institute for Energy Studies or any of its Members.
iii
4.
Contents
1. Introduction and executive summary ............................................................................. 1
2. The Colombian power sector and the case for wind power .......................................... 6
2.1 Supply side of the Colombian power sector .............................................................. 6
2.2 Why consider additional non-conventional renewable power? ................................. 7
3. Two policy instruments: ENFICC and CER ................................................................ 12
3.1 Colombian firm energy market ...................................................................................... 12
3.2 CER payments ............................................................................................................... 23
4. Financial analysis of a wind project under the current regulatory regime ............... 25
4.1 What the model calculates ............................................................................................. 25
4.2 The logic of the model ................................................................................................... 26
4.3 Main elements of the financial modelling ..................................................................... 27
4.4 Benchmarking the model ............................................................................................... 28
4.5 Results of the main policy scenarios .............................................................................. 29
4.6 Sensitivities .................................................................................................................... 31
4.7 Risk Analysis ................................................................................................................. 35
4.8 Other financial considerations ....................................................................................... 36
4.9 Conclusions from modelling .......................................................................................... 38
5. Other risks and opportunities facing investors ............................................................ 40
5.1 Commercial opportunity and risk .................................................................................. 40
5.2 Political considerations .................................................................................................. 42
5.3 Regulatory risk ............................................................................................................... 43
5.4 What most investors look for from policy and regulation ............................................. 43
6. Conclusions ...................................................................................................................... 47
Annex 1: Information on Colombian Electricity System 2010-2011 .................................. 49
Annex 2: Colombian commitments on climate change mitigation ..................................... 51
Annex 3: Requirements to execute a wind project in Colombia, in Indigenous Territory53
Annex 4: Quantil Model ......................................................................................................... 58
iv
5.
Tables
Table 1: Installed capacity Colombian integrated electricity system, 31/12 2011. .................... 6
Table 2: Generation output 2010-2011 for the Colombian electricity system............................ 7
Table 3: Levelized costs of energy from different technologies................................................. 9
Table 4: ENFICC % for different technologies ........................................................................ 13
Table 5: Outcome of May and June 2008 auctions for firm energy in Colombia .................... 14
Table 6: Outcome of December 2011 auctions for firm energy in Colombia .......................... 15
Table 7: Calculation of Wind Capacity Credit Factors in USA................................................ 21
Table 8: CREG ENFICC methodology for wind applied to El Niño periods .......................... 22
Table 9: PJM methodology to determine ENFICC for wind in Colombia ............................... 23
Table 10: World Bank parameterization with Quantil model ................................................... 28
Table 11: Comparing results of WB and Quantil model – Project IRR ................................... 29
Table 12: Results of reference scenario and of changes in ENFICC ........................................ 30
Table 13: Results of reference scenario and of changes in ENFICC and CER ........................ 30
Table 14: Results of sensitivity case for changes in Benchmark Investment cost (BIC) ......... 32
Table 15: Results of sensitivity case for changes in wind speed .............................................. 32
Table 16: Results of sensitivity case for changes in energy price ............................................ 33
Table 17: Results of sensitivity case for changes in interest rate ............................................. 34
Table 18: Results of sensitivity case for changes in exchange rate .......................................... 35
Table 19: Results of Monte Carlo Analysis – Mean Reversion................................................ 35
Table 20: Results of Monte Carlo Analysis – Model 2 ............................................................ 36
Figures
Figure 1: LCOE for different generation technologies: Q2-2009 to Q4-2011 .......................... 9
Figure 2: Estimating the Effective Load Carrying Capability (ELCC) .................................... 19
Figure 3: Average surface wind speed en January in Colombia (m/s) ..................................... 41
v
6. 1. Introduction and executive summary
Colombia currently has a very low penetration of non-conventional (non large-scale hydro)
renewable energy sources. This reflects the dominance of large-scale hydro projects within
the generation sector. However, increasing concerns over the impact of periods of serious
drought in El Niño periods, most recently in 2009/10, have meant that Colombia is investing
heavily in new thermal power capacity, increasing the country’s carbon footprint.
A study by the World Bank suggests that onshore wind power may be a cleaner and
economically viable alternative, as a compliment to the substantial hydro resources in
Colombia1. We understand that the Colombian Government is also considering whether and
how to develop a range of alternative energy sources, including solar, biomass and
geothermal power. Many other large developing countries, including Brazil, Peru and
Mexico, are already encouraging private investment in a range of renewable energy sources,
especially wind power. However, under current regulatory arrangements in the Colombian
power sector, wind power appears not to be financially viable. Colombia is currently
considering changes in the regulatory framework that would extend the payment for ‘firm’
energy to wind power and other non-conventional renewable sources of generation. The
question addressed in this paper is whether the change in regulation will make private
investment in wind power attractive in Colombia.
This report is the main output of a project supported by the UK Foreign and Commonwealth
Office (FCO) Prosperity Fund for Latin America. It examines the feasibility of private
investments in wind power in Colombia within the current regulatory framework. By
reference to a specific wind power project, it estimates the financial gap between what
investors might require and what they can expect, and considers how this gap could be
reduced under the current regulations. Although our terms of reference do not include
detailed research on alternative regulations that might be introduced in Colombia, we have
drawn on international experience to identify some ideas that deserve further exploration if
the government wishes to encourage private investment in non-conventional sources of
renewable energy.
In their letters related to the Prosperity Fund, officials from the Colombian energy regulator
(CREG) and the Ministry of Environment both indicated a particular interest in research on
the firm energy payment (cargo de confiabilidad) for non-conventional renewable power
generation, including wind.2 Since then, the CREG has made a proposal that estimates the
1
Vergara, W., Alejandro Deeb, Natsuko Toba, Peter Cramton and Irene Leino Wind Energy in Colombia: A
Framework for Market Entry (World Bank, July 2010). In our paper, we refer to this as the “World Bank
report”, or simply “WB”.
2
In his letter, Javier Augusto Diaz of the CREG indicated an interest in the “desarrollo de una metodología para
medir la energía firme que pueden aportar proyectos de generación eléctrica con base en recursos renovables.”
In her letter of 2 June 2011, Andrea García Guerrero of The Ministry of the Environment, Housing and
Territorial Development wrote, “Nos gustaría se analizara el mecanismo para que proyectos de las energías
7.
firm energy factor (ENFICC) from wind power at between 6% and 7.3% of the wind plant’s
capacity. This compares to an ENFICC of over 90% for coal and gas-fired plants, and
between 30% and 50% for hydro plants. The central policy issues analysed in the report are:
(a) the methodology for determining the ENFICC for wind power; and (b) the financial
implications for private investors of the choice of methodology for setting the ENFICC. The
analysis is relevant for the regulation and remuneration of wind power and of other non-
conventional sources of renewable power, for instance including geothermal, biomass and
solar.
The report has five sections, in addition to the introduction and executive summary. Section
2 provides basic background to the Colombian electricity sector and the reasons to consider
the development of non-conventional renewable power, such as wind. The heavy reliance on
hydroelectricity explains Colombia’s relatively small carbon footprint in the sector by
comparison to most countries in Latin America. However, during extended periods of dry
weather (specifically those referred to as El Niño periods), hydro generation falls and the
system requires alternative sources of energy. In this context, there is potentially a role for
non-conventional renewable sources of power, including wind power, provided these
technologies are economically viable. Since reliability is a particularly important issue in the
Colombian electricity system due to the problems associated with El Niño, a central question
is what contribution these alternatives sources of power would make to the system’s
reliability, and what the payment for that reliability should be.
Section 3 considers two policy variables that are key determinants of profitability. The most
important of these is the methodology for determining the contribution of wind plant to
system reliability; in other words, for determining the firm energy factor (ENFICC) for a
wind power station. We draw on international experience to analyse different ways of
estimating ENFICC for wind power. In our view, the CREG’s methodology probably
undervalues the contribution of wind to system reliability and possibly by a significant
margin. Whereas the CREG estimates of ENFICC for the Jepírachi plant are between 6%
and 7.3% of the plant’s capacity, our estimates suggest that the ENFICC is at least 15% and
possibly in the range of 27% to 33%. The difference between our conclusions and the
CREG’s proposals are firstly related to the fact that the CREG’s methodology does not focus
on El Niño periods, when the system is under stress. Furthermore, its use of percentiles (i.e.
to estimate the probability that wind power will be available) probably understates the
contribution of wind power to system reliability and is conservative by international
standards.
In addition to the analysis of the ENFICC, Section 3 introduces an international policy
variable, the payment for Certified Emission Reductions (CERs) under the UNFCCC Clean
Development Mechanism (CDM). This variable influences the profitability of wind projects
in Colombia and illustrates the importance of valuing externalities – in this case, through
compensation for avoiding the negative global externality related to CO2 emissions.
anteriormente mencionadas [renovables aparte de hidroelectricidad] pueden ser consideradas dentro del
esquema actual de cargo por confiabilidad de la Comisión de Regulación de Energía y Gas (CREG)”.
2
8.
Although the CER is an international policy mechanism, it has domestic policy implications.
This is largely because eligibility under the CDM (and hence CER income) is conditional
upon demonstrating that the project’s economic viability requires these additional payments;
but economic viability depends in large part on the way that wind power is compensated
under Colombian regulations.
Section 4 uses a simplified financial model to analyse the viability of private investment in a
specific wind power in Colombia under the current regime, as well as the impact of changing
the ENFICC and the CER3. For different estimates of ENFICC, we estimate the real internal
rate of return (IRR) on equity, the payback period and the debt service cover ratio (DSCR)
for a 302 MW wind power project in the Guajíra region of Colombia, financed by a 70/30
ratio of non-recourse debt to equity. We carry out sensitivities on a number of non-policy
parameters (e.g. investment cost, interest rates, exchange rates, wind speed, and energy
prices) and use Monte Carlo simulations to examine specific risks related to energy prices
and wind speed. Under the current CREG proposal for ENFICC, the project fails by a
significant margin to meet the main financial tests for viability. The financial gap between
the required real return on equity (14%) and the expected return (3.5%) is substantial. When
we increase the ENFICC to reflect the higher end of our estimates of firm energy (30%), the
equity IRR increases to almost 8%, but the financial gap compared to an IRR of 14% is still
large.
In Section 4, we also analyse the impact of different CER prices to compensate wind power
for the avoidance of CO2 emissions through the displacement of more carbon intensive
generation at the margin. Under all reasonable scenarios (of ENFICC and CERs), additional
income from CERs is insufficient to reach the 14% IRR threshold for the project. We have
not attempted to model the impact of internalizing other environmental and social costs
related to the development of large hydro and coal projects, and we are not sure of the degree
to which these are already fully taken into account, but these costs could be very important.4
Section 5 identifies some risks and opportunities that investors, especially foreign investors,
are likely to take into account when they are considering whether to invest in wind power in
Colombia. These include the potential upside from entering the market early, as well as
political and regulatory risks and related costs. Some of these are specific to Colombia, for
instance the size of the potential wind resource and the costs of dealing with social and
environmental concerns in the Guajíra Indigenous Territory, where the project is located.
Some risks are common to most countries, in particular the volatility of revenue streams for
intermittent renewables like wind power, the availability of information about wind resources
3
The model is not intended as a basis for making financial decisions for a specific investment. It is rather a
simplified model that aims to explore the implications of changing two key policy assumptions as well as key
variables, assuming a given wind turbine technology and wind conditions that apply in the Guajíra region of
Colombia. It does not attempt to analyze all the relevant policy issues, technologies or locations for wind power
in Colombia. It draws on the data and the logic of a more detailed model developed in the World Bank Report to
study wind power in Colombia; that report analyzes a wider range of policy issues.
4
The recent controversy over the development of the Quimbo hydroelectric project illustrates the fact that
hydroelectricity projects have social costs.
3
9.
and the cost and speed of transmission connections. As background for possible future
research and consideration by the government, we identify some of the policies and
regulations that have been adopted in other countries to attract investment in wind power,
without advocating any one in particular.
Section 6 summarizes our conclusions, which we reproduce here. First, the result of the
financial modelling is that wind power does not appear to be an immediately attractive
investment in Colombia. Under the reference scenario that sets ENFICC at 6%, the financial
gap is substantial (target IRR of 14% v. expected IRR of 3.5%). Increasing the ENFICC to
30% from 6% narrows the financial gap substantially (14% v. 8%), and adding CER income
of $10/tCO2 reduces the gap even further (14% v. 9.4%). However, only with optimistic
assumptions, or subsidised finance, do we see a prospect of eliminating the gap entirely
within the current regulatory system and with our estimated electricity prices.
It is important to stress that our conclusion is based on the conditions that apply to a
particular power station, similar in turbine technology to the Jepírachi station, although much
larger (302 MW v. 19.9 MW). To the extent that new turbine technologies are more efficient,
for instance at lower wind speeds, the financial appraisal will be more positive, provided the
improved efficiency more than compensates for any additional costs. Furthermore, wind
conditions may be better at other sites. We have carried out a number of sensitivities,
including ones that are optimistic about the wind speed, investment costs, CER payments,
interest rates and energy prices. Even then, the financial gap is large when we use the
CREG’s proposed ENFICC. Our conclusion is that the regulatory treatment of the ENFICC
is a key barrier in Colombia, even for wind power projects that might face better conditions
than the project we have analysed.
Second, as the fixed costs of wind power decline and the need for additional capacity grows,
it becomes increasingly important to calculate the ENFICC properly for wind and other non-
conventional renewables sources of power. This is important to ensure that the system is
building the optimal mix of plant, which won’t happen if the ENFICC is measured
inaccurately. For investors, an increase in the ENFICC for alternative technologies such as
wind power increases the mean return on investment in wind power. In particular, it reduces
the uncertainty of cash flows because the firm energy payment will be guaranteed for a
period of 20 years for new plants that are successful in the firm energy auctions.
Third, the calculation of capacity credit factors (i.e. like ENFICC) for wind and other non-
conventional power is a recent development around the world and there is no universally
accepted method. However, our recommendation is that the CREG reconsider the
methodology for defining the ENFICC for wind power, and also consider using a similar
(revised) methodology for other sources of non-conventional power that have potential in
Colombia, including geothermal, biomass and solar energy. One well-recognized
methodology for measuring a plant’s contribution to system reliability is known as the
effective load carrying capability (ELCC). Alternatively, we would encourage the CREG to
consider the approach used by PJM, adapted to reflect relevant peak hours in El Niño periods
(as summarized in our Table 9). According to our provisional calculations, using this
4
10.
adapted PJM methodology would increase the ENFICC (for conditions at Jepírachi) from the
CREG’s value of 6% to our estimate of about 30%. This would make an important
contribution to project revenues and help to finance the projects at lower cost.
Fourth, the CERs are important for a number of reasons. One is that CER income contributes
to closing the financial gap faced by investors. Although the contribution is relatively small at
today’s CER prices, there is a reasonably good prospect that the CER price will rise, mainly
because the EU is determined to raise the price of CO2 emission allowances in the EU
Emission Trading Scheme (ETS), which effectively drives the price of CERs. Second, project
developers typically reach an agreement to sell CERs for a number of years; this makes CER
income predictable and therefore helps investors to raise finance for these projects. Third,
CERs are a way of recognizing the economic cost of externalities, in this case by
compensating the avoidance of CO2 emissions. Although the CER is an international policy
mechanism, it has domestic policy implications since eligibility under the CDM is
conditional upon demonstrating that the project’s economic viability requires these additional
payments. When projects are economically viable without CER payments, the Government
will need to decide whether to compensate new projects (i.e. those which are not eligible for
CER payments) for the avoidance of CO2 emissions. We would encourage the government to
support domestic and international mechanisms that penalize the negative externalities
associated with some power stations (e.g. coal power), and/or compensate power stations
(e.g. wind power) that are able to avoid these externalities.
Fifth, the risk assessment suggests that investors could decide that uncertainty about revenues
is so great that they require a higher expected return to compensate for that risk. The risk and
hence the required return will be lower if a larger share of the revenue stream can be
guaranteed. This conclusion underlines the importance of both policy variables (ENFICC and
CER) since they both offer revenue guarantees.
Finally, we have drawn on international experience to identify some of the issues that
investors will be considering, in addition to those studied in our financial analysis. Investors
stress the importance of having reliable sources of information about wind speeds at 60
meters and about the potential and geographical distribution of other renewable resources in
Colombia, in particular geothermal, biomass and solar. They also stress the importance of a
clear policy with respect to the development of renewable energy sources and of legislation
to carry it out. In addition, they emphasize the timing and cost of gaining access to
transmission networks. This international experience provides background for possible future
research and consideration by the Colombian Government should it decide that it wants more
actively to promote investment in these new technologies. If so, auctions, accompanied with
long-term contracts, are particularly useful mechanisms for promoting competition among
investors.
5
11.
2. The Colombian power sector and the case for wind power
When one speaks about the prospects for wind power in Colombia, the first question is: why
bother with wind power and other non-conventional renewables, when the country has so
much hydroelectric capacity? This is a very good question. Hydro normally accounts for
about 80% of Colombia’s electricity; this explains why Colombia is in the bottom four Latin
American countries in terms of carbon intensity in electricity5. In order to reply to the
question, we first introduce some essential features on the supply side of the Colombia power
sector.
2.1 Supply side of the Colombian power sector
The Colombian system relies very heavily on hydroelectric power, but thermal generation
also plays an important role6. Table 1 indicates that about 64% of generation capacity is large
hydro, with 32% thermal and especially gas-fired. Today, wind power, all from the Jepírachi
plant, accounts for only 0.1% of total generation capacity.
Table 1: Installed capacity Colombian integrated electricity system, 31/12 2011.7
In a normal year, the reliance on hydro generation is especially evident: it accounts for about
80% of electricity output. This poses problems for reliability during periods of el Niño.
Drought substantially reduces hydroelectric generation, with potentially serious economic
and political consequences. More generally, demand for water for all uses is growing and
this raises the value of water and reduces its availability for hydroelectric generation. This
underlines the importance of having backup generation to replace hydro during periods of el
Niño.
5
Colombia has a carbon intensity of 136 grams CO2/kWh from electricity and heat; only Paraguay, Brazil and Bolivia have
lower carbon intensity. See IEA CO2 Emissions from Fuel Combustion 2011 edition, page II-69.
6
Annex 1 summarizes the regulatory framework for renewable power and reproduces tables that provide more detail on the
Colombia electricity system, as reported by XM (the system operator) on its website.
7
XM (System Operator) website: http://www.xm.com.co/Pages/DescripciondelSistemaElectricoColombiano.aspx
6
12.
Table 2 illustrates both how el Niño affects the supply of hydroelectricity and the important
role of thermal power. In 2010, which included the effects of El Niño at the beginning of the
year, hydroelectric power accounted for 67% of output, and thermal plants for 27%. In 2011,
not an El Niño year, hydro generation increased by 20%, thermal generation fell by 40%, and
their respective shares of output were 78% and 16%.
Table 2: Generation output 2010-2011 for the Colombian electricity system8
2010 2010 2011 2011 CHANGE GROWTH
GWH % GWH % GWH %
Hydro 38,088.60 67 45,583.10 78 7,494.50 19.7
Thermal 15,590.70 27 9,383.70 16 -6,207.00 -39.8
Minor plants 2985.6 5 3,336.70 6 351 11.8
Cogeneration 222.7 1 316.9 1 94.1 42.3
TOTAL 56,887.60 100 58,620.40 100 1,732.80 3
The question now is what additional generation capacity should be built in order to
supplement hydro in normal years and to provide backup in very dry El Niño years. Should
the system rely more on thermal power or diversify to include non-conventional renewables,
or other sources of power and demand response? As explained more fully later in Section 3,
Colombia holds auctions to select the new plants that will be built to provide system
reliability, and to determine the price for firm energy. In the last auction (December, 2011),
thermal plants accounted for over 70% of the additional new capacity and 89% of firm
energy. We understand that most thermal plants will provide backup to hydro. However,
some new thermal plants could also operate base-load or mid merit, thereby increasing the
carbon footprint (i.e. absolute level of emissions) of Colombia’s electricity sector. Wind
power would partly displace thermal plants at the margin, as confirmed by an analysis of the
potential for Jepírachi plant to avoid CO2 emissions, and thereby limit growth of total
emissions9.
2.2 Why consider additional non-conventional renewable power?
There are a number of reasons why Colombia may wish to consider non-conventional
renewable sources of power, like wind and solar, as alternatives to coal and gas-fired plants.
Here are some of the arguments frequently offered in support of this view.
8
XM (System Operator) website: http://www.xm.com.co/Pages/DescripciondelSistemaElectricoColombiano.aspx
9
AENOR, “CDM Validation Report Renewal Of Crediting Period, International Bank For Reconstruction And
Development As The Trustee Of The Prototype Carbon Fund, Validation Of The Project Activity: Jepírachi Wind Power
Project”, 3 March, 2011, page 7.
7
13.
First, as we have just illustrated, Colombia risks an increasing carbon footprint, which is
difficult to limit in the sectors where it is most important, especially in transport10. Colombia
has a relatively low carbon footprint compared to other countries in Latin America 11 .
However, the absolute level of emissions has been growing and is expected to grow further,
along with economic growth.12 Colombia has made a unilateral commitment to the UNFCCC
to address the challenges of climate change, including that “at least 77 per cent of the total
energy capacity installed by 2020 will be generated from renewable sources”.13 International
experience shows that it is easier to reduce or at least manage growth in CO2 emissions
through the electricity sector than in other carbon-emitting sectors, such as transport. So,
even if the Colombian electricity sector has a relatively small carbon footprint today, the
government may want to reduce it further or at least ensure that it does not grow too much.
Second, there appears to be a natural complementarity (or hedge) between hydro on the one
hand, and wind generation on the other: during periods of el Niño, less rain appears to
coincide with stronger wind. Any assessment of the backup sources to hydro generation
should reflect this complementarity. In particular, the payment for firm energy from wind
and solar should reflect the contribution of these technologies to system reliability at times of
shortage, especially during el Niño periods. Of course, the complementarity has to be
demonstrated for the specific plant in question, and the ENFICC should be plant specific.
Third, including non-traditional renewable energies in its energy portfolio may make the
Colombian electricity sector and the economy less exposed to volatile hydrocarbon prices.
While this portfolio argument has stronger appeal in a country without its own hydrocarbon
resources, it may also apply in Colombia since domestic market prices for natural gas and
international prices for coal are volatile.
Fourth, the costs of renewable power are declining and the cost of fossil-based generation is
likely to rise. Bloomberg New Energy Finance (BNEF) has tracked the changes in the
levelized cost of energy (LCOE) for wind power, other renewables and fossil fuel based plant
over the past three years.14 The costs of onshore wind power and solar PV have been on a
10
Transport accounted for a third of Colombian CO2 emissions in 2009 and electricity for about one sixth. See IEA CO2
Emissions from Fuel Combustion 2011 edition, page II-173.
11
1.33 tonnes CO2/capita in 2009, compared to the Latin American average of 2.16. See IEA CO2 Emissions from Fuel
Combustion 2011 edition, page II-57.
12
CO2 emissions grew about 35% between 1990 and 2009. See IEA CO2 Emissions from Fuel Combustion 2011 edition,
page II-173.
13
Annex 2 reproduces the relevant pages from the “Compilation of information on nationally appropriate mitigation actions
to be implemented by Parties not included in Annex I to the Convention, UNFCCC, Note from the Secretariat, Ad Hoc
Working Group on Long-term Cooperative Action under the Convention”, FCCC/AWGLCA/2011/INF.1, 18 March 2011.
14
LCOE is a way of comparing the price of electricity that would enable an investor to achieve a required rate of return to
justify investment. It is helpful as a way of measuring changes in the relative cost of certain technologies. On the other
hand, it would be misleading to suggest that wind power and thermal power have the same value when they have the same
LCOE. This is because, in any given power system, the value will depend on when the generation occurs. Wind power may
have the same LCOE as gas-fired generation, but could be worth more or less than the CCGT depending on when the plant
was actually generating. If the average price is $60/MWh, and wind generation runs when prices are less than that, then wind
is worth less than the average. If wind runs mainly at times of system peak, it would earn more than the average system price
and could be more valuable than the CCGT. For a more detailed review of the difficulty of comparing the costs of
8
14.
downward trend that has accelerated recently. For wind power, the decline in costs reflects
increasing scale of wind turbines, more efficient turbines, less expensive manufacturing
methods and intense competition. Meanwhile, according to BNEF, the cost of generation
from coal and gas-fired plants has been rising. Figure 1 illustrates the trend (assuming a
target of 10% IRR on equity), with the dashed-yellow line representing wind power.
Figure 1: LCOE for different generation technologies: Q2-2009 to Q4-2011 15
Table 3 summarizes the LCOE from different technologies in Q4-2011, using BNEF figures
and assuming a 10% IRR on equity. It illustrates the wide range from high to low estimates
of LCOE for onshore and offshore wind, whereas the LCOE for coal and gas-fired plants do
not vary much between high and low estimates. It also suggests that onshore wind can
sometimes be competitive with gas and coal-fired plants.
Table 3: Levelized costs of energy from different technologies16
Technology Low ($/MWh) Base ($/MWh) High (S/MWh)
Coal 70.68 76.51 81.3
Gas CCGT 57.34 62.24 66.81
Wind onshore 59.02 80.04 117.22
Wind offshore 143.14 232.20 331
conventional plant and intermittent plants, see Paul Joskow, “Comparing The Costs Of Intermittent and Dispatchable
Electricity Generating Technologies”, September 27, 2010 (Revised February 9, 2011) DISCUSSION DRAFT.
15
Bloomberg, New Energy Finance model, assuming a 10% IRR on equity.
16
Ibid, assuming 10% IRR on equity.
9
15.
In Colombia, the comparison between wind power and other plants will reflect different
technical and cost conditions, but the relative decline in the costs of wind technologies will
nevertheless improve the competitive position of wind power17. So, even if these non-
conventional technologies do not seem competitive with coal and gas under today’s market
conditions, they are likely to become increasingly competitive.
Fifth, the negative externalities and the long lead times required for large hydro and coal
plants contrast with relatively limited externalities and flexibility offered by non conventional
renewable sources of power. Whenever a new hydroelectric project is under consideration, it
is likely to involve complicated and controversial impact assessments, especially when the
project involves deviating rivers and flooding valleys where people live. This can delay the
building of these large projects, which in any case can take many years. When new coal
based generation is built, the impact on the environment and on the people who live in the
area is also potentially significant, and the large scale of these plants means that they also
require a relatively long time to build. By comparison, wind power and other non-
conventional renewables have a relatively benign impact on local communities and
environment. Furthermore, they are quick to build and therefore have the advantage of
flexibility 18.
Sixth, non-conventional renewable power provides energy in areas that are isolated and
badly (if at all) connected with the national grid. Non-conventional renewables (like solar
panels) may be the preferred alternative (economically) compared to the use of diesel
generators or kerosene, wood or candles. Furthermore, these new technologies can be
combined with mobile communications, with the latter offering a means of paying for the
energy, while the electricity provides a way to recharge mobile phones.
Seventh, the small scale of non-conventional renewable energy facilitates entry and
competition. Large-scale hydro and large coal plant require very significant capital and this
limits the number of potential competitors. Renewable energy projects like wind and solar
energy are, by contrast, relatively small and require limited amounts of capital. The evidence
throughout the world, including in Latin America, is that competition among renewable
energy suppliers is very effective in driving down prices required by investors.
There are other reasons why governments consider non-conventional renewable power,
including: employment creation; industrial policy aimed at promoting innovation in
technologies with promising international markets; and regional policy to support economic
development in rural areas.
Faced with these and other considerations, a key question for policy makers is: should the
government actively promote non-conventional renewable power, if these technologies
17
In countries with a high penetration of wind power, the cost of wind energy could rise when the best locations have been
exploited.
18
However, wind power projects do have an environmental and social impact. Annex 3 of this report summarizes some of
the requirements and costs associated with developing a wind park in the Guajíra region.
10
16.
would not be chosen in a technology-neutral regulatory system? This requires a full cost-
benefit analysis that we have not undertaken. However, our study contributes to the decision
making process by answering a more limited set of questions related to the current regulatory
regime:
• Within the current regulatory framework for electricity, are all technologies treated in a
comparable way, especially with respect to the payment for firm energy (and the
measurement of ENFICC)? If not, what adjustments are warranted?
• Are the returns sufficient to attract private investors to wind power projects under the
current regime? If not, what is the financial gap between the expected return and the
required return? Can policy reduce this gap without actively promoting specific
technologies?
• What other risks or opportunities in Colombia will influence investment decisions,
especially by foreign investors, and what policies have been adopted in other countries to
attract investment in wind power and in other non conventional sources of renewable
energy?
The remainder of the report deals with these questions, before drawing conclusions.
11
17.
3. Two policy instruments: ENFICC and CER
This section analyses the two policy instruments that we have modelled in our report. One of
these policies is domestic: the payment for firm energy. The other is an international policy
instrument: the CER payment for avoided CO2 emissions.
3.1 Colombian firm energy market
In 2006, the CREG introduced a new scheme to ensure the long-term reliability of the
electricity supply in Colombia, and in particular to guarantee that there is always sufficient
capacity available to meet peak demand during El Niño periods, when hydro resources are
significantly reduced.19 The scheme allocates “firm energy obligations” (“OEFs”) to new
and existing generation plant at prices determined in competitive auctions. OEFs are “option
contracts” that commit generating companies to supply given amounts of energy at a
predetermined Scarcity Price whenever the spot price in the electricity market rises above the
Scarcity Price.20 They receive the spot price for any additional generation above their firm
energy obligation, and pay a penalty if they cannot meet their firm energy obligation, equal to
the difference between the spot price and the scarcity price on the OEF quantity not met in
any hour.
In return for agreeing to supply at the Scarcity Price, generators allocated OEFs in the
auctions receive a fixed annual option fee (the firm energy price, or Cargo por Confiabilidad)
for each capacity unit contracted. This option fee makes an important contribution to the
recovery of fixed costs for generating plants that sell very little in normal times, such as the
CCGT plants in central Colombia that generate infrequently outside of El Niño periods.
The maximum amount of firm energy that a generator may offer in a firm energy auction is
known as its ENFICC (Energía Firme para el Cargo por Confiabilidad). ENFICC refers to
the amount of energy a generator of a given type can reliably and continually produce during
periods when hydro generating capacity is at a minimum. 21 Table 4 shows the typical
ENFICCs for different generation technologies in Colombia as a percentage of a plant’s
CEN, or effective net capacity.
19
See Harbord, D. And M. Pagnozzi, “Review of Colombian Auctions for Firm Energy”, 25 November 2008. The paper
was commissioned by the CREG.
20
The Scarcity Price is established by the CREG and updated monthly based on the variation of the Fuel Price Index. In
March 2012 it was approximately US$248/MWh. The Scarcity Price has a double purpose. On the one hand, it indicates the
time when the different generation units or plants will be required to fulfill their firm energy obligations, which happens
when the Spot Price exceeds the Scarcity Price; on the other hand, it is the price at which this energy will be paid.
21
See CREG RESOLUCIÓN 071 DE 2006: “Energía Firme para el Cargo por Confiabilidad (ENFICC): Es la máxima
energía eléctrica que es capaz de entregar una planta de generación continuamente, en condiciones de baja hidrología, en un
período de un año.”
12
18.
Table 4: ENFICC % for different technologies
Technology Maximum ENFICC (%)
Hydro with storage 55
Hydro without storage 30
Coal 97
Natural Gas 93
Fuel Oil 88
Wind 6
If a coal plant, for example, has an ENFICC of 97%, the maximum annual OEF for a 100
MW plant would be 100 MW*0.97*8760 hours = 849,720 MWh. Hence the maximum firm
energy payment the plant could receive in a year is 849,720 MWh multiplied by the auction-
determined firm energy price (currently US$13.998/MWh), or $11,894,381 (US). In 2015
this will increase to $13,340,604 (US) when the option fee set by the 2011 firm energy
auction (US$15.7/MWh) will be applied.
The first firm energy auctions were held in May and June 2008 and allocated OEF’s for up to
twenty years beginning in December 2012. About 9,300 GWh per year of OEF’s were
allocated to new resources, including 1,117 GWh from new coal plant and 1,678 GWH from
new gas-fired generation plant at an auction-determined option fee of $13.998/MWh.
Existing plant will receive this option fee until December 2015, and the new plants are
guaranteed this fee for up to 20 years. Table 5 below shows the results of these auctions.
13
19.
Table 5: Outcome of May and June 2008 auctions for firm energy in Colombia22
Project Company Market Generation Capacity OEF
Entry Date Type (MW) Assigned
(Gwh/Año)
6 May Gecelca III Gecelca Dec 2012 Coal 150 1117
Auction
Amoyá ISAGEN Dec 2010 Hydro 78 214
Termocol Poliobras Dec 2012 Gas 200 1678
13 June Pescadero Pescadero Dec 2018 Hydro 1200 1085
Auction Ituango Ituango
Isagen Sogamoso Dec 2014 Hydro 800 2350
Emgesa Quimbo Dec 2014 Hydro 396 1650
EPM Porce IV Dec 2015 Hydro 400 961
Promotora Miel II Dec 2014 Hydro 135 184
EPSA Cucuana Dec 2014 Hydro 60 50
The second firm energy auctions were held in December 2011. Table 6 summarizes the
allocation of 3,700 GWh of OEFs to five new generation projects, with a total capacity of
575 MW. The option fee (firm energy price) resulting from this auction was $US15.7/MWh.
As noted earlier, over 70% of the selected capacity was thermal plant, and over 89% of the
OEFs assigned. The option fees will be paid to new plant for 20 years, starting on December
1, 2015.23
22
http://www.acolgen.org.co/jornadas3/expansion2012.pdf
23
http://www.creg.gov.co/html/i_portals/index.php?&p_origin=plugin&p_name=news&p_id=N-341&p_options
14
20.
Table 6: Outcome of December 2011 auctions for firm energy in Colombia24
Project Company Location Type of Capacity OEF
Generation MW Assigned
(Gwh/Año)
Proyecto Energía de los Tolima Hydro 45 75
Hidroeléctrico del Andes S.A.S. E.S.P.
Río Ambeima
Central Hidralpor SAS ESP Antioquia Hydro 78 200
Hidroeléctrica
Carlos Lleras
Restrepo
San Miguel La Cascada SAS ESP Antioquia Hydro 42 123
Gecelca 32 Generadora y Córdoba Thermal 250 1,971
Comercializadora de
Energía del Caribe
S.A.
Tasajero II Termotasajero S.A. Norte de Thermal 160 1,331
E.S.P. Santander
To pay the option fees for the OEFs allocated in the auctions, the regulatory system collects
revenue through the CERE. This is a tax per unit of electricity generated. Each generator
contributes in proportion to the energy generated. The generators (above 20 MW) treat this as
a cost, which raises the price of electricity.
The aim and the effect of the auction and the resulting option fees are to help finance plants,
especially those that run very little but are considered important for system reliability. In
particular, the ENFICC for CCGT plant corresponds to 93% of its rated capacity. In this way,
the system provides a revenue guarantee that justifies building plants that primarily provide
backup to hydro generation during El Niño periods. At the new firm energy price of
$15.7/MWh, a 100 MW CCGT would be guaranteed an annual revenue stream of $12.8 (US)
million per year, even if the plant never generates electricity. In the words of the CREG,
‘Así mismo, aseguró que para los generadores los beneficios se concretan en ingresos
fijos asociados a la obligación de energía firme hasta por 20 años, lo cual se traduce
en una estabilización de su flujo de caja y reducción de sus riesgos de inversión.’25
24
CREG website: http://www.creg.gov.co/html/i_portals/index.php?&p_origin=plugin&p_name=news&p_id=N-
341&p_options. See also, Obligaciones De Energía Firme Asignadas En La Subasta De OEf 2015-2016, XM
(htp://www.xm.com.co/Resultados %20Subasta/OEF_Asignada_Subasta.pdf)
15
21.
3.1.1. Calculating ENFICCs
CREG Resolution 071 of 2006 (Annex 3) describes in detail how the CREG calculates
ENFICCs (i.e. the maximum amount of “firm energy” a generator can sell in the auction) for
the hydro and thermal plants that receive firm energy payments. For thermal plant this is
essentially CEN*(1-IHF) where CEN = “effective net capacity” (the generation capacity of
the plant) and IHF = the historical probability of forced (i.e. unplanned) outages.26
The ENFICC of hydro plants is calculated using a computational model that maximizes the
minimum energy that a hydro generation plant can produce monthly during dry periods.27
The model incorporates historical data on average monthly water inflows; discharges and
restrictions in the water conduction systems; characteristics of the generation plants including
the average efficiency and their minimum and maximum generation; water reservoir data and
other uses of water like aqueduct or irrigation and environmental restrictions; historical
unavailability due to forced outages; and flow constraints.
The minimum production numbers are then ordered from least to greatest, and the lowest is
defined as the plant´s ENFICC BASE, or the amount of energy the plant can be relied upon
to produce with 100% probability. In other words, ENFICC BASE corresponds to the
minimum monthly energy supply obtained from the maximization model. The CREG also
defines the ENFICC 95% PSS - the amount of energy the plant can be relied upon to produce
with 95% probability. 28
As noted in Table 4 above, while thermal plant ENFICCs (expressed as percentages of
effective net capacity) tend to exceed 90%, hydro plant ENFICCs typically range between
30% and 55%.
Until recently, wind power was not eligible for a firm energy payment in Colombia. In July
2011 however, the CREG released a proposal for measuring ENFICCs for wind plants based
upon the historical experience of EPM´s Jepírachi plant. 29 Following a broadly similar
methodology to that applied to hydro plant, the CREG used historical generation data from
2004 to 2011 to estimate monthly capacity factors for the Jepírachi wind farm, and derived an
ENFICC BASE of 6% and an ENFICC 95% PSS of 7.3%.30
25
Ibid.
26
The CREG also takes account of fuel availability and constraints on the supply and transport of natural gas, for generators
that use natural gas for their energy generation.
27
See also CREG, Firm Energy for the Reliability Charge: Hydraulic Plants
(http://www.creg.gov.co/cxc/english/enficc/plantas_hidraulicas.htm)
28
Hydro plants which wish to bid their ENFICC 95% PSS into firm energy auctions in order to receive firm energy
payments on this larger amount must demonstrate that they have signed contracts with other generators to supply the
difference between these two amounts.
29
CREG Document 075, 7 July 2011, Energía Firme para Cargo por Confiabilidad de Plantas Eolícas.
30
Rather than measuring Jepírachi energy production during dry periods or “en condiciones de baja hidrología”, the CREG
appears to use the entire seven-year production history of the Jepírachi plant to arrive at the 6% figure. We show below that
this ENFICC changes if only El Niño periods are used for this calculation.
16
22.
In its July 2011 document and in its subsequent draft Resolution 148 of October 2011, the
CREG suggest two alternative methods for calculating ENFICCs for wind plants: one for
plants that have less than 10 years of information on wind resources; and another for plants
that have at least 10 years of information. In the first case, they use the operating experience
from Jepírachi as the basis for determining the ENFICCs for a new wind power plant, i.e. 6%
ENFICC BASE and 7.3% ENFICC 95% PSS.
For plants for which there is more than 10 years of wind data, they use the following formula.
E = min (24*1000*k*v3, 24*1000*CEN*(I-IHF))
Where:
E: energy (kWh/day)
k: conversion factor for wind plants, reflecting the number of turbines
[MW/(m3/s3)]
v: average monthly wind speed (m/s)
IHF: historic forced outage rate
CEN: effective net capacity (MW)
With this formula, the CREG constructs a probability distribution curve, from the lowest to
the highest level of firm energy, using monthly values. The lowest firm energy factor
corresponds to a 100% probability of it being exceeded and the highest value has a 0%
probability of being exceeded.
The World Bank study, on the other hand, suggested measuring ENFICCs for wind plants
using the following exponential smoothing formula under which the “firm energy rating” (the
ENFICC) is updated annually:
Firm energy rating in t+1 = ½ (firm energy rating in t)+ ½ (energy produced in year
t),
The firm energy rating for the initial year t could be based on recent data; for instance, plants
located on the northern coast could use the period of generation recorded by Jepírachi.
According to the World Bank, the firm energy rating will adjust quickly to the long run
average level of firm energy capability, even if the initial estimate is wrong.31
Applying their formula to a 24-year series of monthly wind and production data related to the
Jepírachi plant, the WB estimated an average annual firm energy rating of 38%, with a range
between 25% and 47%. They also estimated a firm energy rating for dry seasons of 40%,
with a range from 30% to 47%. Their summary financial results refer to a maximum 36%.
The difference of these two approaches (WB and CREG) is significant when measured in
terms of the financial consequences. Following the CREG approach to estimating ENFICCs
for wind energy, a 100 MW wind plant would earn approximately $735,734 per annum at the
31
WB, pages 33-34.
17
23.
current firm energy price ($13.998/MWh). Using the World Bank approach (at 36% firm
energy rating) the wind plant would earn approximately $4.4 million in annual firm energy
payments. As shown in Section 4 below, this makes a large difference to the potential
financial viability of wind farms in Colombia.
3.1.2. International practice in calculating firm
energy/capacity credits for wind energy
There is no universally accepted method for calculating the contribution of intermittent
generating technologies (such a wind) to system reliability. However, there are some basic
principles that guide the methodology to be used, as well as experience in the application of
this methodology.
The basic principle, as described by Cramton and Stoft (“A Capacity Market that Makes
Sense”, Electricity Journal, 18, 43-54, August/September 2005) is to only reward capacity
that contributes to system reliability as demonstrated by its ability to supply energy or
reserves during shortage hours. According to Cramton and Stoft, a major flaw in many US
electricity capacity markets has been that they pay for capacity based on average availability,
which may or may not contribute to energy reliability when it is actually required.32
Similarly, Cramton and Ockenfels (“Economics and design of capacity markets for the power
sector,” 30 May 2011) point out that capacity that qualifies for the capacity market needs to
be accurately defined, verified and rated. In particular, they suggest that the contribution of
wind and solar energies to system reliability is usually smaller than the contribution of
thermal units, and should attract reduced capacity credits.
The correct principle is that the capacity market should only elicit the construction of, and
pay for capacity that contributes to system reliability or firm energy when it is actually
needed, and on the appropriate time scales. The idea behind the CREG's concept of ENFICC
is consistent with that principle: it should measure the contribution of a plant to the system’s
reliability when it really matters.
One internationally recognized methodology for measuring a plant’s contribution to system
reliability is referred as the effective load carrying capability (ELCC), which is conceptually
the same as the term “capacity credit” as used in the UK.33 In the simplest terms, the ELCC
reflects the effect on system reliability of adding the new plant to the system, where
reliability is measured in terms of the loss of load probability (LOLP), or the expectation of
lost load (LOLE). In other words, when a new plant is added, the ELCC refers to the
additional load that can be sustained by the system without a change in reliability. This is the
32
Cramton and Stoft give an example of a “dog” plant which with a long start up time which would never be called upon to
relieve short-term energy shortages but which has a high average availability when running, as an example of a plant that
should receive very low, or even zero, capacity payments.
33
For a definition of the ELCC, See Milligan M, and K. Porter, “Determining the Capacity Value of Wind: An Updated
Survey of Methods and Implementation”, National Renewable Energy Laboratory Conference Paper NREL/CP-500-43433
June 2008.
18
24.
same as the following UK definition of capacity credits: “a measure of the amount of load
that can be served on an electricity system by intermittent plant with no increase in the loss-
of-load probability (LOLP), which is often expressed in terms of conventional thermal
capacity that an intermittent generator can replace."34
In Figure 2, the new plant adds 400 MW of load carrying capacity, while maintaining the
same LOLE (10%). If the information is available to calculate the ELCC of wind and other
types of generating plant it would make sense for the CREG use it to measure the ENFICC
for all plants on the system.
Figure 2: Estimating the Effective Load Carrying Capability (ELCC)35
While the formal methodology has been widely used for system planning, it has seldom been
used to define the capacity factors for wind power stations. This seems mainly to reflect the
absence of sufficient data, but there may be other reasons, including the complexity of the
ELCC calculation and a preference for simpler, established methodologies. The alternatives
are approximations to the ELCC and, consequently, have various drawbacks. Nevertheless,
they are widely used and serve as the basis for analysing the alternatives that the CREG
should consider.
There are two types of methodology: risk-based or time period-based. The risk-based
methods approximate the system’s LOLP, whereas the time period-based methods capture
risk indirectly by assuming a high correlation between hourly demand and LOLP. Although
there are drawbacks to time period-based approaches, they are simpler to calculate and
frequently used in the USA and elsewhere and we focus on them below.36
34
“The Costs and Impacts of Intermittency: An assessment of the evidence on the costs and impacts of intermittent
generation on the British electricity network,” A report of the Technology and Policy Assessment Function of the UK
Energy Research Centre, with financial support from the Carbon Trust, March 2006, page 6.
35
Milligan M, and K. Porter, “Determining the Capacity Value of Wind: An Updated Survey of Methods and
Implementation”, National Renewable Energy Laboratory Conference Paper NREL/CP-500-43433 June 2008, page 8.
36
M. R. Milligan “Modeling Utility-Scale Wind Power Plants Part 2: Capacity Credit,” March 2002, p. 18, finds that
although “capacity factor (i.e. risk-based or time period-based) methods are not as accurate as ELCC methods for calculating
19
25.
In time period-based estimates of the ELCC, the idea is to measure a plant’s contribution to
the system’s reliability when it matters. The World Bank ENFICC calculations for wind in
Colombia concentrate on annual average output. Arguably, these do not provide sufficient
guarantees that the wind energy will be there when it is needed, in particular when the system
is under stress during El Niño periods.37
The most straightforward approach to time period-based methods for approximating the
ELCC or capacity credit is to calculate the wind capacity factor (i.e. the ratio of the mean to
the maximum energy output) during times of high system demand. Many US regulators and
utilities use this method. Each system has different hours of shortage, and each wind power
station within a system will have output that coincides more or less with those shortage
hours. To the extent that wind generation is higher at times of shortage, the plant will have a
higher capacity credit factor. If generation occurs mainly during off-peak hours and little
during shortage hours, the capacity credit factor will be much lower.
There are different ways to use the time period-related data to approximate the wind capacity
credit factor (some of which have been adopted in the USA, as summarized in Table 7). A
common but arbitrary method is to order the wind generation output (or estimates based on
wind speed) that has been observed, and then to adopt a threshold percentile limit. For
instance, the regulator in the Southwest Power Pool in the US sets the capacity factor by
reference to the level of wind generation that has occurred at least 85% of the time. This
arguably ignores the statistical independence of outages and wrongly suggests that all
generation below the threshold makes a zero contribution to system reliability. Consequently,
it potentially underestimates the plant’s contribution to system reliability. 38 The CREG’s
approach (ENFICC Base) is especially conservative because it ignores the contribution of all
generation that cannot be guaranteed 100% of the time. While we would not recommend the
use of percentiles as a methodology, we would at least encourage the use of a lower
percentile figure than the CREG has adopted.
capacity credit,” time-period based methods provide “a reasonable trade-off between accuracy and effort, either early in
project assessment or if it is not possible to calculate the ELCC”.
37
Although as noted above, the World Bank did calculate firm energy factors for wind power during dry periods, which
more closely corresponds to the time-period based approach.
38
“The use of a percentile arbitrarily discounts reliability contributions that are achieved at levels below the percentile value.
These approaches are based on fallacious use of probability theory, and they ignore the statistical independence of outages
and the fact that system reliability can be achieved at a very high level (such as 1 day in 10 years LOLE) even though every
unit in the system is somewhat unreliable.” [Milligan, M. and K. Porter, Determining The Capacity Value Of Wind: A
Survey of Methods and Implementation, NREL, May 2005, page 20, http://www.nrel.gov/docs/fy05osti/38062.pdf.]
20
26.
Table 7: Calculation of Wind Capacity Credit Factors in USA
Based on average three year output in peak summer
PJM 13,00%
hours.
NYSO 10% - 30% Based on previous years output in peak hours
Based on 85% percentile of outputs in highest 10%
SWPP 10,00%
of load hours monthly
Minnesota 26,70% Based on sequential Monte Carlo study
Pacifcorp 20,00% Based on sequential Monte Carlo study
Based on wind generation during peak summer
ERCOT Texas 2,90%
hours
Nebraska 17,00% Unknown
Based on 79% percentile of outputs in peak
Idaho 5,00%
summer hours
Pacific
15,00% Unknown
Northwest
Three year average of monthly hourly peak
California 15% - 60%
production
Based on hourly wind eerngy production from
Colorado 12.5%
1996-2005
The alternative methodology (to the use of percentiles) is to average the wind-related
generation over the relevant shortage periods. The PJM, for instance, uses the following
methodology. 39
A. Sum all of the “hourly outputs” for each of the summer calculation hours (3PM -
7PM) in the year that is three years prior to the current year.
B. Then, for each of those same summer calculation hours, sum the Net Maximum
Capacity values (the manufacturer’s output rating less the Station Load).
C. The quotient of the summed summer calculation hour outputs (a) divided by the
summed summer calculation hour Net Maximum Capacities (b) will yield a single
year capacity [credit] factor for that year.
39
PJM is a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts
of 13 states and the District of Columbia. For information on capacity factors, see PJM Manual 21 Rules and Procedures for
Determination of Generating Capability Revision: 09 Effective Date: May 1, 2010
http://pjm.com/~/media/documents/manuals/m21.ashx )
21
