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Current wind turbine design and operations practices do not account for variation in project variables that affect profitability and reliability. This article describes a method of tailoring turbine operation to these project variables in order to maximize profitability, while considering the risks and costs of turbine unreliability.

Wind turbines are most often designed for a standard IEC wind regime, with an assumed mean wind speed, air density, turbulence intensity and wind shear. These assumptions are conservative for many projects, which means that turbine component life is consumed at a lower rate than the turbine was designed for. While this might sound like a good thing, it does not make the best use of the expensive components used to build a turbine.

Ideally, a turbine at the end of its design life should have consumed nearly all of the life of each component, with only a small remaining reserve for natural variability, and a much larger reserve for safety-
critical items such as the hub, blades and tower. While it is not always possible to reach this ideal state, it is possible to come much closer by adjusting the turbine maximum power level in such a way that considers factors such as the effect on component reliability, power purchase agreement (PPA) pricing, site wind regime, the age of the turbine, desired remaining life of the project, sub-component vendor, cost of component repairs and other factors. By increasing the turbine maximum power setting, more energy is produced and, therefore, more revenue generated.

Increasing the maximum power setting also increases the likelihood of a premature component failure, which could be costly, and such costs must be considered when determining the ideal maximum power level.

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To adjust the maximum power to achieve optimal profit, an owner/operator must develop a model of the increased revenue and increased maintenance cost versus maximum power level. Building the portion of the model that calculates the value of the increased energy is straightforward. A modified power curve for each increment of additional maximum power is created and combined with site wind speed data to calculate the amount of annual energy production (AEP). The value of the AEP is calculated by multiplying the amount of power produced by the PPA price for the site.

A net present value (NPV) analysis of the projected revenue stream for the remaining life of the project transforms the value of the revenue stream to a single number, incorporating the time value of money, meaning that revenue received sooner is more valuable than revenue received later. Clearly, as the maximum power rating of the turbine is increased, the turbine creates more power, resulting in higher yearly revenues. This is only half of the story, however, as the increased cost of unplanned maintenance due to the increased maximum power must be calculated as well. Performing this calculation is a three-step process, shown schematically in Figure 1.

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First, a representative loads spectrum for the site is created for each maximum power level. Next, a simulation model of each of the turbine components (blades, hub, tower) and subsystems (drivetrain, generator, power converter) affected by the change in maximum power is created, and the change in each component reliability is calculated.

Figure 2 shows an example simulation model of a common wind turbine drivetrain configuration. Using the calculated stresses and number of cycles for each maximum power level, the probability of failure of each gear and bearing in the drivetrain can be computed.

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Figure 3 shows the stress on the rolling element bearing for a typical load case as predicted by the simulation model. The stress analysis can determine the probability of failure. Similar calculations can be made for the balance of the affected turbine components. These probabilities are then entered into a turbine system level reliability block diagram. The reliability model can then be used to calculate the probability of failure of each turbine component, at each maximum power level, for the remaining life of the project.

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Figure 4 shows drivetrain system unreliability (likelihood of failure) curves versus time for a hypothetical drivetrain operated at several different maximum power levels. The reliability of the drivetrain decreases with increased maximum power operation. Armed with this information for each turbine sub-system, the probable cost of failure is then calculated, and an NPV (cost) of the component failures for the remaining life of the project is calculated for each maximum power level. The increase in NPV due to the increase in AEP and the decrease in NPV due to decrease in reliability are summed and plotted versus maximum power. The resulting curve shows the optimal maximum power rating for the turbine, calculated using the specific factors for the project.

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Figure 5 provides an example “NPV versus max power level” plot for a hypothetical project and turbine type, with a nominal maximum power of 2.3 MW. The optimal maximum power operation for the assumed project and turbine is 2.4 MW and results in an NPV increase of 6%. The actual optimal maximum power for any turbine and project will vary due to such factors as turbine design margins, PPA pricing, wind regime and the remaining life of the project. Higher PPA pricing would tend to increase the optimal maximum power level of the turbine, as the gains from doing so would be higher, while the costs would be unchanged. Older sites also tend to benefit more from running at increased maximum power levels, as they are closer to the end of their design lives, and if they have been operated conservatively, they have a relatively larger amount of life remaining.

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Although the focus of this article thus far has been on the benefits of running a turbine at increased maximum power levels, there are also cases where it can be advantageous to operate a turbine at less than its nominal maximum power level. One such case is when a turbine is known to have a load-related reliability issue, either for all such turbines or for those with sub-components manufactured by a particular vendor or using a particular manufacturing process. In such cases, reducing the turbine maximum power level can reduce the cost of failures by more than the cost of the lost power. This is particularly likely for a site with a relatively low PPA price.

Another case where it may be beneficial to operate a turbine below its nominal maximum power is when the project is owned by a rate-regulated utility. Power rates are approved by a regulatory board and are based on the cost of assets and assumed major maintenance costs. If major maintenance costs exceed the initial assumptions, the utility must either request approval to pass these costs on or, more likely, absorb the costs from its profits. Thus, for a utility, it can be more profitable to produce slightly less power from its wind turbines, in exchange for reducing the potential for uncompensated expenses from major maintenance.

To be able to change the maximum power level of the turbine, the turbine must have variable pitch technology. The actual implementation of the change is made through the turbine control software. Most turbine manufacturers provide owners a means of reducing the turbine maximum power. Increasing the maximum power is more challenging, has higher potential risks and often requires the support of the original equipment manufacturer (OEM). At least one major OEM has begun providing customers its own proprietary version of a methodology for adjusting turbine operation to maximize profitability.

This article has outlined a method for calculating an optimal maximum power level for a turbine from a profitability perspective, but other considerations remain. In order to perform this study, expertise with each of the engineering disciplines involved, as well as an expert-level understanding of the particular turbine technology, is essential. If properly implemented, however, the method can be used to increase the profitability of a wind project, and do so at very low cost. w

 

Rob Budny is president and principal engineer at RBB Engineering, a Petaluma, Calif.-based consultancy. He can be reached at (805) 280-9044 or rob@rbbengineering.com. Ashley Crowther is vice president of engineering at Romax Technology, an international rotating equipment consultancy. He can be reached at (303) 562-6064 or ashley.crowther@romaxtech.com.

Industry At Large: Operations & Maintenance

Maximizing Profit Without Impacting Turbine Operations

By Rob Budny & Ashley Crowther

Several considerations can help wind farm owners and operators balance output with maintenance.

 

 

 

 

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