revenue-based prospecting: finding the best wind projects ......albany, new york | barcelona, spain...

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AWEA Windpower 2013 06 May 2013

Whitney Wilson Senior Engineer, Custom Services Team Lead

Revenue-based Prospecting: Finding the Best Wind Projects for the Competitive Energy Market

Colin Tareila Engineer


• Current analyses

• What are we missing and how can further definition benefit

• Revenue-based enhancements to current analysis

• Additional benefits

Overview


General process • Define wind resource, IEC class, and

appropriate technology

• Estimate infrastructure costs

• Model production and calculate COE

• Select and rank sites

Map-based Prospecting – Current Practices

The Map-based approach to prospecting is designed to minimize cost of energy (COE), while identifying areas with enough contiguous land for development of a specific project size.


• Wind Resource – standard wind map, annual average climatologic parameters

• Turbine Selection – composite turbine specifications and power curve for appropriate IEC Class

• Production – composite curve is applied and a regionally based standard set of losses are typically applied

• Infrastructure – costs are associated with distance to nearest primary or secondary highway and to the nearest POI (based on voltage, assume no loading on current transmission system)

• Cost of Energy (COE) – base assumptions are made for capital cost, O&M costs, fixed charges

Map-based Prospecting – Assumptions

A standard set of assumptions are applied to the sites. The assumptions are sometimes limited in their depth.


Case Study – Map-based Screening Site Ranking

0

0.2

0.4

0.6

0.8

1

0.0 5.0 10.0 15.0 20.0 25.0

Nor

mal

ized

Pow

er

Wind Speed (m/s)

Composite Power Curve

IEC Class 1 IEC Class 2 IEC Class 3

Site 4 87 113 140 155

Rank 1 2 3 4 5

CF (%) 36.3 33.6 33.5 31.8 30.3

COE ($/KWh) 0.090 0.097 0.097 0.102 0.106


The presence of a transmission line does not always mean capacity is available for interconnection of a wind farm.

• Current analyses assume no loading on the current circuits.

• Lines continue to become more loaded; thus, assumption could be misleading

• Upfront transmission-based analyses can eliminate transmission challenges and help design a creative and financially beneficial interconnection strategy

Current Electrical System Loading


A low cost of energy does not always mean the project will have a high rate of return

• Locational Marginal Pricing (LMP) drives the

market rates

• Benefits can be seen by employing trade-offs for additional transmission lines to interconnect to a location with a higher nodal price

• Normalize costs to understand trade-offs of additional debt to gain additional revenue

• Re-ranking prospecting sites may occur or new sites previously not considered viable may open up due to revenue potential

Understanding of Revenue for Prospecting

Image from MISO


Locational Marginal Pricing (LMP)

Locational Marginal Prices describe the value of energy based on the cost to generate and the cost to deliver to a load.

• LMP = Cost of Energy + Marginal Cost of Congestion + Marginal Cost of Losses

• Cost of Energy is set by the highest priced unit required to support the load

• Marginal Cost of Congestion is essentially a premium paid to use energy sources that avoid congestion areas and maintain system stability

• Marginal Cost of losses covers the price of the extra energy required due to losses


Locational Marginal Pricing (LMP) – Congestion Analogy

Low Congestion = Low LMP

High Congestion = High LMP


Lines that have high congestion can be beneficial

• Don’t avoid congestion, understand congestion

• Marginal congestion components to

LMP prices can be beneficial to a project

• Completing an analysis of congestion zones and transmission cost trade-offs can allow a more profitable project

Congestion: Barrier or Opportunity?

Images from MISO


Example: West Texas

Congestion is the key to prospecting for the most profitable project.

• Load center in North

• High wind penetration in West zone

• Competition of wind driving down bid price

• High congestion on lines between West and North

• Wind projects in North zone realize higher LMPs

Image from Texas OPUC


General process • Define wind resource, IEC class, and

appropriate technology

• Model production

• Estimate infrastructure capabilities, costs and potential

• Calculate revenue and cost trade-offs

• Calculate profitability

• Rank sites

Revenue-based Prospecting – Process

Revenue-based Prospecting begins with some of the same concepts as traditional site screenings. However, more sophisticated tools are

employed, which allow a more complete picture.


• Wind Resource – Hourly time-series climatologic approach for individual sites

• Turbine Selection – Run IEC class appropriate specific power curves

• Production – Hourly time-series net production approach

• Infrastructure – Transmission-based analysis is performed to determine viability of multiple potential POI.

• Revenue – a Security Constrained Unit Commitment (SCUC) model is run to determine the price point and rank sites by revenue potential

• Additional Metrics for Ranking – Profitability, Net Present Value (NPV), Internal Rate of Return (IRR)

Revenue-based Prospecting – Enhanced Assumptions

Since the methodology is more computational and iterative, the assumptions applied can be more in-depth and give a better overview of the site.


Site 4 87 113 140 155

Rank 2/3 5 1 4 2/3

Net Production (GWh)

229 217 216 204 194

Average LMP Price (CAD)

29.62

-136.38 35.62 30.71 34.5

Estimated Revenue (CAD)

6.7 M -28 M 7.6 M 5.8 M 6.6 M

Estimated Transmission Cost

per Year (CAD)

0.3 M 0.4 M 0.4 M 0.4 M 0.2 M

Adjusted Revenue (CAD)

6.4 M -28.4 M 7.2 M 5.4 M 6.4 M

Case Study – Revenue-based Screening Results – Site Selection

Adjusted Revenue for the case study only includes transmission costs, which are based on realizing the cost across a 10-year payback period. Analysis can include additional pro forma inputs for ranking Revenue is based on market price (hourly LMP) The analysis was completed for the current IESO electrical case files based on 2011 date and released in Sept 2012.


Case Study – Revenue-based Screening Results – POI Selection

Site 113 Option 1 Option 2

Rank 2 1

Net Production (GWh)

216 216

Average LMP Price (CAD)

34.54 35.62

Estimated Revenue (CAD)

7.3 M 7.6 M

Estimated Transmission

Cost (CAD)

0.2 M 0.4 M

Adjusted Revenue (CAD)

7.1 M 7.2 M

Difference $1.0 Mill ion/Year

Adjusted Revenue for the case study only includes transmission costs, which are based on realizing the cost across a 10-year payback period. Analysis can include additional pro forma inputs for ranking Revenue is based on market price (hourly LMP) The analysis was completed for the current IESO electrical case files based on 2011 date and released in Sept 2012.


Once we understand the revenue potential, it can be used to select projects with high profit potential

Case Study – Understand the Results

PROSPECTING – Site Selection • The Map-based Case Study shows that Site 87, would have been ranked high and

considered one of the most viable

• Due to transmission constraints and revenue potential, the Revenue-based Case Study considers Site 87 to be the lowest potential site, due to negative market price. PPAs of fair market price at this location will also be low.

PROSPECTING – POI Selection • Approximately $1.0 Million/year of additional revenue (at market price) can be realized

by incorporating transmission cost trade-offs and selecting a more distant POI.

• As many projects are PPA driven, this can be used to negotiate a more fair PPA rate at the POI


Understand revenue potential can benefit a project when negotiating contracts in the competitive energy market

• Evaluation of nodal prices can help gain an understanding of the value of the energy early in the development process

• PPA negotiations – use the analysis to set a fair price.

• Mid-life (10-15 year old projects) can make more informed decisions about renegotiation of PPAs or moving to a merchant structure

Additional Applications


Sources

[1] Midwest Independent System Operator (MISO). www.midwestiso.org/MarketsOperations/RealTimeMarketData/Pages/LMPContourMap.aspx

[2] Texas Office of Public Utility Counsel. www.opuc.texas.gov/ERCOT.html


Whitney Wilson Senior Engineer, Custom Services Team Lead [email protected] www.awstruepower.com

revenue-based prospecting: finding the best wind projects ......albany, new york | barcelona, spain...

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