Vienna, Austria -- The profitability of wind generation is dependent upon the design. If your wind farm is not designed to maximise available generation and minimise operating costs, chances are your wind farm will hemorrhage cash and profits for as long as you own it. However, the weak link in most designs is often not the turbine but the transformer.
Choosing the right transformer can increase sales by as much as 17 MWh annually for a 1.5-MW turbine, depending upon wind conditions. Operating costs can be reduced by as much as $2,800 per turbine annually in avoided backup power costs for periods when there is no wind. Losses can be substantially more if low first cost is the dominant consideration. In regions such as California, for example, where the generation from wind is 22 percent of rated capacity, a turbine will be idle 50 percent of the time.
When turbines are idling generation is not zero, it is negative. This is because there is a transformer connected to each turbine to step up the voltage to the wind farm's substation level, and that transformer must be kept powered. Since a transformer is always powered from the higher voltage side, it will draw from the substation and ultimately the system grid. Utility customers of wind farm merchant generators are aware of this and manage the matter contractually. Typically, there is a backup power charge whenever the wind farm draws power from the system. This backup power charge may be as much as three times the price at which the wind farm sells electricity to the utility.
Increasing the transformer efficiency will boost the generation available for sale, but this is only a partial solution, and does not address the underlying problem. Although load losses increase as the square of the load, this coil efficiency does not address the problem that the transformer may have no load for a relatively large proportion of the time, and operate at a reduced load for the majority of the time that the turbine is generating power. Profitability comes down to minimising the core (or iron) losses. This is a constant 24x7x365 loss – the power required to actually operate the transformer – of the power required to keep the core excited.
Assessing Optimised Transformers
To demonstrate the importance of properly optimised low-core-loss transformers, optimally designed 1750 kVA steel core (M3 grade steel) and low-core-loss amorphous core (SA1 grade material) step-up transformers have been considered in this analysis. A range of wind energy selling prices was assumed, from a low of $40/MWh to a high of $170/MWh, and a medium price of $100/MWh. A renewable energy production incentive (REPI) or production tax credit (PTC) of $0.021/kWh, as appropriate, was included, and a backup power price of three times the selling price was assumed. Finally, a variety of wind conditions was considered.
Both the steel core and the low-core-loss amorphous metal step-up transformer were optimised for the same specific combination of energy price (including backup price) and wind condition. Both transformers were optimised to minimise total owning costs (TOC) including the cost of the transformer as well as the cost of both load and no-load losses capitalised over the life of the project (assumed to be 25 years). The incremental results, on a per turbine basis, for a wind energy selling price of $40/MWh show that, for example, under light wind conditions use of a low-core-loss amorphous metal transformer will result in an additional 14 MWh of power available for sale, resulting in $567 more in sales revenue and $297 in PTC or REPI revenue. The greatest change in the total increase in cash flow of $1,613 is a reduction in purchased power of $749 at the backup power rate. Even without the savings in backup power, the increase in cash flow is $864, and the increase in profits is $553 ($1,302 with savings in backup power).
The results are even more striking in areas where carbon charges would apply. The reduction in the carbon footprint is between nine and 10 tonnes per turbine per year. Assuming a carbon charge of $25/tonne, the additional renewable energy available to displace fossil generation would be worth $214 in carbon credits.
Similar benefits are available at higher energy prices and under moderate and heavy wind conditions. The results under medium and high energy prices show that as wind conditions improve from light to heavy, the amount of additional generation available for sale increases and the relative amount of time the turbine is idle decreases. However, the benefits of a low-core-loss step-up transformer are still significant, even as the importance of purchased power savings declines. That is, the additional sales revenue from the lower operating cost of the low-core-loss amorphous transformer alone substantially increases profitability. Indeed, the increase in the subsidy alone exceeds the additional cost of the transformer, providing the increased benefits at no additional cost.
As a check on the robustness and reliability of these analytical results, their sensitivity to estimates of future prices was calculated. What is the cost of being wrong? What if energy prices materialise that are higher or lower than anticipated? Is a conservative strategy one that errs on the side of being "too efficient" or one that avoids spending too much? As a good rule of thumb: when in doubt, err on the side of greater efficiency.
Sensitivity analyses considered the consequences of three sub-optimal decisions:
- Low energy prices for the design when medium energy prices actually emerge;
- Medium energy prices for the design when high energy prices actually emerge; and
- High energy prices for the design when medium energy prices actually emerge.
The sensitivity analysis does not alter the conclusion that there is no combination of energy price and wind condition under which the additional cost of the low-core-loss amorphous transformer does not substantially improve profitability. The results support the need to err on the side of greater efficiency, especially when interest rates are in the low to moderate range, but they do not necessarily support a recommendation to always buy the most efficient transformer that can be constructed, except at very low interest rates.
The risk associated with under- or over-estimating energy prices is not symmetric. You are better off if your assumption of future energy prices is too high rather than too low, as this assumption will result in being more efficient rather than "optimal," giving rise to greater sales and increased avoidance of backup power costs. Energy prices that are conservatively low lead to lower sales and reduced protection against backup power costs. Specifically, assuming an energy price too low results in lower profitability, and the negative impact is greater the lower the energy price assumed. Although the cost of the transformer is less, the under-optimisation leads to lower sales. When the energy price is overstated and the transformer is over-optimised, the loss due to the higher cost transformer is offset by higher revenues for greater kWh sales.
Low Price Design in a Medium Price World
The consequences of using low energy prices to optimise the transformer are firstly fewer kWh sales and secondly a greater need for backup power. Offsetting that lost opportunity is a lower cost transformer.
Analysis shows that the low-price design will result in a transformer that is $2,293 to $2,999 less expensive, depending upon wind conditions. However, the transformer will be less efficient. The loss in annual sales revenue alone of $346 to $413 is greater than the annual savings in principle and interest of $179 to $235. The loss in subsidy payments of $73 to $87 and increase in backup power costs of $88 to $108 further lowers profitability.
Medium Price Design in a High Price World
Similar to the first case, the consequences of using medium energy prices to optimise the transformer are fewer kWh sales and a greater need for backup power, offset by a lower cost transformer.
This analysis shows that the medium-price design will result in a transformer that is only $1,702 to $2,275 less expensive, resulting in a savings in capital charges of $133 to $178, depending upon wind conditions.
However, the less efficient transformer will result in greater losses in annual sales revenue alone of $242 to $299, which more than offsets any savings in transformer cost. Moreover, the loss in subsidy payments of $31 to $38 and the increase in backup power costs of $20 to $87 further reduce profitability.
High Price Design in a Medium Price World
By contrast, the absolute impact on profitability is much less for an over-optimised design – that of designing for high energy prices when energy prices remain at the medium level. The consequences of using high-energy prices to optimise the transformer are a higher cost transformer offset by greater kWh sales and a reduced need for backup power, both evaluated at the actual, medium prices.
These results show that the high-price design will result in a transformer that is $1,702 to $2,275 more expensive, leading to additional capital charges of $133 to $178 depending upon wind conditions. However, the resulting more efficient transformer will produce greater annual sales revenue of $148 to $219, which offsets the transformer cost. The additional savings in backup power costs is $12 to $53 so the savings and the costs of the overly efficient transformer approximately balance. At relatively low interest rates, the impact is slightly positive. At higher interest rates, resulting in higher capital charges, the results could be somewhat negative. In any case, the differential risk favours erring on the side of efficiency in resolving uncertainty.
The Costs of Improving Efficiency
There is no combination of energy price and wind condition under which the additional cost of the low-core-loss amorphous transformer does not substantially improve profitability. Indeed, the annual incremental capital charge is always less than the additional PTC/REPI revenue alone. It's as if the increase in the subsidy exceeds the additional cost of the increased availability of power and associated sales revenues.
The preceding analysis is based on transformers designed using a commercial transformer design model, and on operation simulations under various wind conditions. Several wind farm developer/operators have changed their design practice to include low-core-loss amorphous step-up transformers and have commented on the additional, significant increase in profitability.
The models presented here, however, have not been "groundproofed" by calibrating the theoretical results with actual field data. Also, due to the absence of detailed operational experience data from wind farms of different sizes, no attempt was made to model the diversity inherent in large wind farms. That is, during some period of time some turbines may be idle while others are generating power, rendering access to backup power unnecessary. The analysis and the increased profitability do not, however, depend upon the savings in backup power costs, which are simply additional enhancements to the profitability.
Robert A. Berman is a principal of Berman Economics