World Bank’s experience with CDM methodologies for the power sector

The examples of Jepirachi Carbon Offset and Amoya Environmental Services projects

W. Vergara and A. Deeb, LCSES, World Bank

Power sector in Colombia

• Installed capacity: 13.000 Mw (65% Hidroenergy)

• Generation: 43.000 Gwh per year (75-80% Hidroenergy)

• GHG emissions: 6 millions tons C02 (e)/year • The system is managed by the National

Dispatch Center – CND, in a competitive dispatch regulation.

CDM projects in Colombia(not just energy generation)

• Jepirachi Carbon Offset (20 MW, ERPA for US$3.2M)

• Amoya Environmental Services (80 MW, ERPA for E$19M)

• Rio Frio Wastewater treatment and GHG abatement (in formulation; CH4, C02,N20)

• Furatena Agroindustry energy efficiency (in formulation;C02)

Jepirachi: Carbon Offset Project

• First aeolic facility in Colombia, owned and operated by EEPPM (largest local utility, well managed), with 19.5 MW. Inaugurated, January 23, 2004

• Project is seen as providing strategic information for assessment of wind option for power sector

• During 2004-2024 the facility is anticipated to displace 1.17 Million tons CO2e, that would otherwise be released from both natural gas and coal based

thermal power generating plants.

Local Wayuu Indigenous community: many unmet socialwelfare priorities.

Jepirachi Carbon Off-set project

Sustainable Development:--Desalinizationplant installed 6 m3/hr--Water storage--Health center (solar powered);--Education facilities

Rio Amoya Environmental Services Project

•Run of river unit (no reservoir)•80 MW 5 million t CO2 e E$80 m investment by ISAGEN & HIDROGER•Paramo ecosystem enables project for:

•power generation 568 Gwh/year; •and CO2 reduction in power sector (ca. 5 MMT by 2019)

•

Amoya environmental services project:Highly bio-diverse ecosystem would be protected:

--most civil works underground--access roads to be reverted to natural landscape

But also: 10% of carbon revenues go a Paramo ProtectionProgram.…and 10% to a social development program aimed at health, education and sustainable agriculture.

Sustainability cycle for Amoya Environmental Services Project

Conservation ofthe Paramo

SustainableWater Cycle

Long Term WaterSupply

Amoya ProjectElectricity Generation

Carbon EmissionsReduction

Carbon Revenues

+

+

+

+

+

+

Amoya PositiveSustainability Cycle

General guidance on methodologies relating to baselines and monitoring

• (i)     Elaborate the provisions relating to baseline and monitoring methodologies contained in decision 17/CP.7, …and relevant decisions of the COP/MOP;

• (ii)    Promote consistency, transparency and predictability;

• (iii)  Provide rigour to ensure that net reductions in anthropogenic emissions are real and measurable, and an accurate reflection of what has occurred within the project boundary;

• (iv)  Ensure applicability in different geographical regions and to those project categories which are eligible in accordance with decision 17/CP.7 and relevant decisions of the COP/MOP;

• (v)   Address the additionality requirement …

Baseline

The best tool to represent and estimate the emissions in a conservative and transparent way of the electrical system, is using BL methodologies based on dispatch models, because these tools take into account the real operation (CND uses dispatch model to manage the system).

The estimate of emission reductions was made running the model with and without the proposed project.

Additionality

• 1) Is the project additional to normal business (or government) practice?– These projects would not have taken place – Without carbon finance; IRRs not attractive, substantial

barriers; with cf, sustainable development components are financed (social IRR very high)

• 2) Assuming the project is additional, what is the quantity of net ER it causes relative to what would have occurred in the absence of the proposed project?.

Method Option Description Emission Factor

OM Operating Margin: project’s predominant effect is to modify the operation of existing or future power plants.

OM1 Generation weighted average emission factors for the entire grid. 0.131 OM2 Generation weighted average emission factor for all plants in the grid excluding

must-run and low running cost, such as hydro, geothermal, wind, low cost biomass, and solar.

0.571- 0.450

Dispatch decrement analysis: define the actual plant mix that operates at the margin. Op.1 Total energy block equals Amoya energy generation expected value 0.847 Op.2 Total energy block twice Amoya expected generation 0.689 Op.3 Total thermal energy generated by plants dispatched by merit 0.849

OM3

Op.4 Approved Consolidated Methodology ACM0002 0.116 Dispatch model simulations. Detailed, large, complex mathematical models simulate

the operation of the interconnected grid with and without the CDM project.

Op.1 First 7 years simulation period 0.662 Op.2 10 years simulation period 0.667

OM4

Op.3 Average for 21 years 0.696

BM Build margin methods assume that the net impact of new CDM plants is delaying the implementation of a series of (future) plants, the emissions reductions could then be estimated as the combination of them.

BM1 Uses the most recent 20% capacity addition to the network, or most recent 5 plants; or a combination of most recent capacity additions to the grid.

Op.1 The 5 most recent commissioned plants, including a large hydro plant, are averaged 0.082 Op.2 Large plants, with capacity greater than 250 MW, are excluded in the selection of

the 5 most recent commissioned plants 0.860

Op.3 Approved Consolidated Methodology ACM0002 0.023 BM2 Emission factor of planned future plant that might be displaced by the

commissioning of the CDM project. For Colombia the expected emission factor from planned power plants (2007 to 2012) was utilized.

0.452

CM Combined Margin methods propose a simple average of OM and BM estimates.

Min Average of the lowest found emission factors in both OM and BM methods 0.106 Min 2 As above excluding over-simplifications 0.464 Max Simple average of highest emission factors estimates by OM and BM 0.855

Estimated margins vs ACM002

• OM range: 0.450 to 0.849 tCO2/MWh.

• BM range: 0.452 to 0.860 tCO2/MWh.,

therefore• CM range: 0.451 to

0.855 tCO2/MWh.

• OM estimate:0.116 tCO2/MWh.

• BM estimate:0.023 tCO2/MWh.,

therefore;• CM estimate:

0.069 tCO2/MWh

The application of the approved consolidated methodology for the case of Colombia (its application is independent of the project under analysis) produces ER estimates that fall outside the range of the most pessimistic considerations.

OM 1 System average

OM 2 Average excluding low-cost/must-run

facilities

OM 3 Dispatch analysis

OM 4 Dispatch model

simulation

Accuracy / rigor Low /. Unknown; there is no basis to assess accuracy

Should produce acceptable accuracy if marginal analysis reflects existing dispatch rules.

Good accuracy if operating rules, system topology and technical restrictions are accounted for.

Practicality Easy to apply Easy to apply Requires access to grid operating data

Requires well established data system and expertise with models.

Cost Effectiveness1 1,284 421 -196 47 Applicability

High High High: Requires grid operating data

Country specific

Transparency Simple assumptions and straightforward calculations.

Simple assumptions and straightforward calculations.

Requires basic understanding of grid dispatch analysis and technical audits

Complex models with assumptions not always implicit. Demands modeling expertise.

Conservative Biased towards low estimates

Biased towards low estimates.

Unknown Emphasizes accuracy over bias.

Consistency Low Low Low to moderate High Reliable/predictable High High Moderate High

Emission factor range (tCO2/MWh)

0.131 0.450 to 0.571 0.689 to 0.849 0.662 to 0.696

General assessment Simple method to use. It is not accurate, does not represent real system behavior, it is biased towards low estimates. Penalizes CDM projects, reduces CDM incentives, and increases cost of compliance for Annex I countries.

Simple to use and requires limited data. It is biased, inaccurate and inconsistent. Does not capture system behavior.

Produces unreliable results; should provide good estimates for systems without technical restrictions and limited storage plants. Lacks rigor, practicality, and consistency. It was not possible to assess its bias.

Best method when conditions are met: a) established data collection system; b) experience with complex simulation models; c) third party (independent) simulation runs; and, d) technical audits.

1 Measured in terms of lost CERs income for a project size of 80MW, expressed in US$K/year

Conclusions

• High variability of results and the subjective nature of deciding which method to use.

• Usefulness of country wide approaches, or grid wide approaches.

• High cost, to the CDM project developers, of opting for simplified methods. As compared with the “recommended” estimate OM2 methods –those endorsed by OECD – IEA- results in a regret value of over US$420,000/year.