Wind Could Replace Coal As US’ Primary Generation Source, New NREL Data Suggests

August 4th, 2015 by Guest Contributor

Originally published on The Handleman Post.

By Clayton Handleman

The National Renewable Energy Laboratory (NREL) recently released data showing that the Capacity Factor (CF) for wind power can reach 65% which is comparable to that of fossil fuel based generation. While the headlines aren’t as sexy as Tesla’s ‘Ludicrous mode‘, the transformative implications for climate change dwarf Elon Musk’s latest accomplishment. Increasing a generator’s CF can increase its value in a variety of ways including: reduced cost of energy, improved transmission line utilization, and often, reducing stress on the grid by providing more power at times of peak demand. It will also likely reduce the amount of storage and natural gas needed to manage the grid under scenarios of high renewables penetration. Implicitly, NREL’s new report positions wind to become a dominant and possibly the primary source of electricity in the US.

Figure 1: Graph showing the area of the US with various gross capacity factors for differing wind technologies. The curve to the left shows the historical data, the middle (red) curve shows the data for state-of-the-art turbines and the blue shows the anticipated performance of ‘near future’ turbines.

CF is the ratio of a generator’s average power output over a year period to its nameplate rating. A CF of 100% would indicate that it was always on and operating at its full rated power. Simply stated, higher capacity factor means a given size generator will produce more energy over the year. CF sets a lower bound on the amount of time a generator operates. If a generator is not operating at its full nameplate rating all of the time then it will produce power for a percentage of time that exceeds its CF. *

Implicitly, NREL’s new report positions wind to become a dominant and possibly the primary source of electricity in the US.

With little fanfare, NREL released updated data showing that, with current technology, wind turbines could generate more than enough energy at 55% CF to power the entire US. However the real stunner is that near future turbine technology (140 m towers) could boost that to 65% CF. With the current national average wind CF (pg 34) at about 33%, this represents a near doubling. According to NREL using current technology and siting it in prime locations, wind power CF already can exceed that of natural gas. Using ‘near future’ technology wind power’s CF will exceed the CF of both coal (61%) and natural gas (48%) achieved nationwide in recent years.

Taking CF into account, on an energy basis, there is enough land suitable for siting 65% CF turbines to supply the nations electrical energy needs. From NREL’s new data, figure 1 shows how our understanding of wind’s potential is rapidly evolving. Advances in turbine technology are leading to taller turbines which can access the steadier, higher average speed winds at higher altitudes. Using figure 1 and data from this study, one finds that there is enough land available to site turbines with 1.5 TW capacity and 65% CF. At 55% CF there is enough land with sufficient resource to site roughly 3 times that or about 4.5 TW. For perspective, the average US electrical net generation was less than 0.5 TW in 2012. Even with higher CF, wind power is not dispatchable and therefor will not eliminate the need for other sources. However it can dramatically reduce the percentage of generation from fossil fuels.

Coal and natural gas are currently the country’s primary generation sources. According to the EIA, in 2014, coal power plants produced 39% of the nation’s electrical energy with gas in second place at 27%. Some reputable authors argue that current economics (which do not consider load shifting) places a limit on the upper bound of renewables penetration equal to their capacity factor. That criteria sets an upper bound for wind power’s contribution to the nation’s electric supply at 65% of the total. However, in the near term the limiting factor may be technical. A DOE funded study by GE suggests that the grid can tolerate about 55% wind and solar with the total being raised to 61% if the grid were reinforced with commercially available improvements. Using either metric and technology available today, the only remaining barrier to wind becoming the nation’s largest energy source is transmission capacity.

TRANSMISSION LINE UTILIZATION

The best wind resource is in the Great Plains Region (GPR), but the largest loads are on the coasts. For the most part the economics of low CF wind power has required that it be built relatively close to existing transmission lines rather than building new lines, so only a small fraction of the country’s best wind sites have been developed to date. To get the power from source to load will require expanding the transmission grid. This will include adding dedicated transmission lines. In the past it has been argued that these lines would be underutilized and therefor comparatively costly on a dollars-per-MWhr basis. High CF wind improves the overall economics of dedicated transmission lines by using a higher percentage of their available capacity.

With high CF wind cost improvement for transmitting power is roughly proportional to the improvement in the CF, so doubling the capacity factor approximately doubles the transmission line utilization. This, in turn, halves the cost per unit of energy transferred. The GPR tends to have the lowest population density. Not surprisingly there is little transmission access since very little electricity is required by the residents.

Developing the high CF sites will require new transmission lines capable of shipping high volumes of electrical power long distances and / or connecting to larger existing transmission assets. In the past people have expressed concern about building transmission lines due to under-utilization. The higher CF wind now elevates wind to levels of transmission line utilization comparable to that of traditional sources. For perspective, with wind power being produced at 65% capacity factor it will utilize the transmission assets at about 65% while traditionally the transmission infrastructure is utilized at about 60%.

As icing on the cake, there is already some transmission line upgrade activity under way. This is particularly true in Texas, which has upgraded transmission capacity specifically to expand their use of wind power. The new data suggests that as the near future turbines are commercialized these transmission projects will have better economics than originally planned.

ECONOMICS

Remember that CF is the ratio of a turbine’s mean power production divided by its nameplate rating, and all other things equal, if a generator runs at a high capacity factor, the economics improve. For example, a 3 MW wind turbine running at 33% capacity factor and charging $30 / MWhr for electricity will generate about $260,000 in annual revenue. That same turbine running at 65% capacity factor would produce $512,000. Alternatively it could charge $15.5 / MWhr and end up with the same profits as the 33% CF unit making it more competitive. When transmission capacity is available, the economics of wind are already quite good in the GPR. Moving to ‘near future’ turbines will likely further improve those economics.

Figure 2: Levelized wind PPA prices by PPA execution date and region. The interior region is shown as purple circles.

Figure 2 shows that recent Power Purchase Agreements (PPAs) have been coming in under $30 / MWhr in that region. The industry trend has been larger turbines and lower costs. While it is the case that land based turbine size has stalled at about 3 MW that is due to the transportation challenges of getting the turbines to site. One aspect of the ‘near future‘ turbines is that they are designed to address the transportation issues. NREL apparently sees sufficient promise and progress to refer to the required technology as ‘near future’. No doubt Europe’s success at commercializing turbines with 140m hub heights adds to their confidence.

INCREASED CAPACITY CREDIT

The Capacity Credit (CC) is the metric used by utilities to account for a generator’s intermittency and its ability (or inability) to provide power at times of peak demand.** The topic of CC (often found by determining the Electric Load Carrying Capability (ELCC)) is very important in utility planning and valuing of generation assets. It is discussed in detail here. It has to do with the fraction of a generator’s rated capacity that can be relied upon as available by the utility when it is most needed. The best approaches for calculating it look not only at the availability of the generator throughout the year, CF, but how well that availability correlates with peak demand on the grid. It is a complex business and is best done with real data. However, in the absence of real data, analytical techniques are used by utilities to begin planning and then the CC is corrected with data as generation is brought online.

It is worth considering that the increase in CC from today’s value could be more than 100% if the increased generation occurs at times that the grid is in most need of power. This in turn will increase wind power’s value and the degree of penetration that can be readily accommodated by the grid. In the southern GPR, e.g. TX, OK and KS, the wind tends to blow more at night which is off peak. However, one benefit of taller turbines is that they reach regions of steadier wind. This will tend to increase wind speeds during the day which will contribute more to the CC than increases at night. When all is said and done, real data will be required to determine the degree to which the CC will increase. However it is very likely that it will increase substantially and that it will increase the amount of wind that can usefully and economically be added to the system.

SUMMARY

The new CF numbers from NREL dramatically shift the landscape for how to proceed with energy policy in the US. Older studies such as EWITS are now obsolete. They used numbers similar to the black line in figure 1 and ignored the storage and load shifting potential of electric vehicles. In those studies, wind penetration up to about 30% was examined and presented as reasonable to pursue. Using the blue line in figure 1 with 65% capacity factor indicates that higher, probably much higher, penetrations can be achieved and at lower cost than previously anticipated. The capacity credit that can be assigned to this resource will go a long way towards determining whether the ramifications of the new data are evolutionary or revolutionary. However, the new NREL data, whether evolutionary or revolutionary, significantly strengthens the case for increasing the rate of expansion of wind power in the US energy portfolio.

AUTHOR’S NOTE: While this article covers the extraordinary opportunity in the GPR, new turbine technology is also opening up opportunities in regions previously not considered suitable for wind power development. See more details here.

FOOTNOTES:

* In practice, intermittent renewables rarely operate at their full nameplate rating. That means that the percent of time they are operating is greater than the rated Capacity Factor (CF). To illustrate, lets consider two different examples of a generator operating at 50% capacity factor:

Case 1: The generator runs at full power for 50% of the time and generates nothing the rest of the time.

Case 2 The generator runs at 50% of rated power all of the time and is never off. In case 1 the percent of time that the generator runs is equal to the capacity factor. In case 2 the percent of time that the generator runs is twice the capacity factor.

** Let’s expand upon the discussion of CF above*, to gain insight into what Capacity Credits (CCs) are, and how they are useful in utility planning. CCs are a critical tool used by utilities to assess generation assets to assure reliability of the electric grid. Loosely speaking, CCs are a way of assessing a generator’s intermittency, dispatchability and reliability in one metric. To illustrate, we look at case 1 and 2 from above, but will split Case 1 into two sub-cases. In Case 1a the time that the generator is on perfectly corresponds to the time of the utility’s peak demand and in Case 1b the time the generator is on never coincides with the time of peak demand.

Case 1a: The generator runs at full power for 50% of the time and generates nothing the rest of the time. The time that the generator is running coincides with all periods of peak demand on the grid. This generator would receive a substantial CC possibly higher than 100%.

Case 1b: The generator runs at full power for 50% of the time and generates nothing the rest of the time. The times that the generator produces power are low demand. This generator probably would receive a CC of 0.

Case 2: The generator runs at 50% of rated power all of the time and is never off. This generator reliably produces power at times of peak need but only at 1/2 of the generator’s rating. It would probably receive a CC in the mid range.

About the author: Clayton founded Heliotronics, Inc. to develop educational products that integrate live renewable energy data into curricula to teach about Science, Technology, Engineering and Math (STEM) – See www.solarlearninglab.com and www.heliotronics.com. Mr. Handleman blogs on renewable energy, Super Grid and low carbon transportation at http://handlemanpost. wordpress.com/ and is a guest contributor on The Energy Collective and Clean Technica

His earlier work includes solar inverter design, related project management and program management to develop and integrate data acquisition systems, web platforms and related educational software. He holds two patents relating to inverter design. Mr. Handleman is particularly interested in the predictive capabilities of experience curves and has incorporated them in writing about macro trends. He wrote papers that looked ahead almost a decade closely predicted when PV would break the $1.00 / Watt barrier. Prior to his work in renewable energy he did defense research and development at MIT Lincoln Laboratories. He holds a B.A. in physics and minor in mathematics from Washington University and an M.S. Physics from Purdue university.

Reprinted with permission.









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