Objectives 18
© EMD International • www.emd.dk • windPRO 4.1 • September 2024
8a.4.2.1 Offshore Vs Onshore costs
The onshore and offshore cost setups share many cost components, and both include costs for the internal grid
connection, but with very different price constants. A main difference is that the onshore cost model includes the
cost of connecting roads where the offshore model includes the cost of a main grid connection (to the shore).
Also, the foundation and area are calculated differently. See the manual for the Cost tool for further details
(windPRO BASIS manual, chapter 2 section 18).
8a.4.3 LCOE
Levelized cost of energy (LCOE) is often preferred as an objective in optimizations, as on the one hand it includes
the effect of costs, in particular Capex, the cost of building the wind farm project. On the other hand, LCOE does
not require further assumptions of uncertain quantities such as the future development of the electricity price. In
addition, a discount rate must also be assumed for the costs to calculate LCOE, to discount future costs to
present day values. The discount rate is a baseline interest rate often representing the average interest rate for
secure investments such as bonds or set as the baseline return of investment in a company. windPRO uses the
so-called real discount rate, which is corrected for the general inflation (i.e., it excludes inflation). Hence, future
costs in the windPRO cost tool should not account for inflation.
In LCOE the future AEP is discounted similarly, which might seem counter-intuitive. However, the AEP of each
year through the lifetime will lead to a cash flow via the sale of the produced electricity. In this regard the AEP
produced next year is worth more than AEP produced in say 15 years (for positive discount rates), assuming a
constant electricity price. Thus, LCOE accounts for the discount effect of both future costs and future sale of
electricity and calculates the average cost of electricity from these, as expressed in the equation below (e.g. [2]
or [3]):
In the above expression, costs are condensed to a single sum for each year. In practical terms, the Capex will
occur in year zero, the installation year, and the Opex occurs in all the following years until end of lifetime,
possibly increasing over time if the user has set a cost index for the future years.
The LCOE has the drawback in that it tends to increase (i.e., deteriorate) with increasing size of a wind farm, as
the best resource positions get taken first and wake effects tend to increase with park size. The total profit of the
project on the other hand would typically increase with the size of the wind farm. Hence, LCOE will generally
lead to too small a wind farm if used as an objective to determine the wind farm size. When comparing wind
farms of equal capacity LCOE does not suffer from this shortcoming.
8a.4.4 NPV
The Net Present Value (NPV) for a wind farm project is simply put the total profit of the wind farm through its
lifetime converted to present day value. NPV bares many similarities with the LCOE, and addresses the main
drawback of LCOE, that it under evaluates the value of larger projects. Still NPV includes the penalizing effect
of Capex costs for spread-out layouts. The main difference between NPV and LCOE is the fact that the AEP in
future years is explicitly converted to a cash flow. This requires the assumption of a future electricity price (P) in
addition to the discount rate (d), which by default is constant, but may include future projections via a price index
similar to the cost components. The equation below summarizes the calculation of NPV (e.g. [3]).
NPV is the most flexible objective function of the three objectives supported in the Optimizer. In fact, NPV can
give similar results to both AEP optimizations and LCOE optimizations. The performance is controlled by the
trade-off between costs and AEP, which is defined by the assumed electricity price. For very high electricity
prices, costs lose importance and the NPV objective approaches the AEP objective. For very low electricity
prices AEP loses its importance and the optimization will be driven mainly by costs to minimize expenses. If the