Wind Resource & Economics Modeling
KEMA’s wind resource uncertainty analysis and economic modeling services help participants in wind energy development establish viable wind projects based on reliable economics
Uncertainty in the annual energy production of a wind farm complicates the evaluation of project economics. The risks are typically much higher for smaller community wind projects or where wind speeds are lower and closer to the cut-in speed of the wind turbine.
Uncertainty in annual energy production is expressed in percentiles, representing the confidence that a certain level of production will be achieved.
If a project can be shown to be financially viable at the P90 (90th percentile) mark for annual power production it can significantly reduce the financial risk associated with that project and increase the likelihood of securing public and/or private funding.
KEMA’s uncertainty analysis and economic modeling services provide participants in wind energy development with expertise drawn from experience in wind resource analysis and project economics. We understand project risks and controls and drivers of value as well as drivers of cost.
Wind resource uncertainty modeling
Our uncertainty modeling services address the full scope of project financers’ needs. Risk analysis requires that all parties understand the potential economic consequences of their business decisions. KEMA’s uncertainty modeling incorporates a variety of possible sources of error including:
topographical uncertainty
anemometer calibration
correlation of short-term site data with nearby long-term data source.
long term mean annual wind speed uncertainty
application of a wind turbine power curve.
These sources of error are used to construct a final total error in wind speed and annual energy production. This uncertainty in annual KWh production is then fed directly into our economic models providing actionable financial information to:
municipalities
financial investors (both private and public)
state funded economic development agencies (i.e. technology collaboratives)
investors/traders in renewable energy markets (i.e. Renewable Energy Credits).
Wind economics modeling
Typically the P90 (90th percentile) and P50 (50th percentile) of annual energy production are fed directly into our financial models (note that other percentiles could be used).
KEMA’s financial models then incorporate a variety of economic costs and benefits which may also have some variance (i.e. price of energy) to create a “risk matrix” of results based on those assumptions allowing for informed decision making by the client. Our financial model utilizes a variety of cost-benefit variables such as:
energy price escalation
net metering
Renewable Energy Credits (REC’s)
equipment and construction costs
operations and maintenance costs
public\private hybridized ownership schemes (i.e. time-swapped ownership)
tax shelters, depreciation and other benefits of renewable investment.
The different scenarios are then used to calculate a variety of payback scenarios to the client and illustrate the fluctuation in net present value based on these assumptions. The clients in turn can use this information to make better-informed decisions regarding the overall risk of the project.