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Spreadsheet Analysis of Feed-in Tariff Program Costs

Article by: Paul Gipe


The movement for feed-in tariffs in North America has finally chalked up a few victories, and, as a result, more and more jurisdictions are turning to this policy mechanism.

As they do, the question of “what does it cost” is being raised more frequently. Of course these questions are seldom followed by the qualifier, “relative to” something else, such as the costs of Renewable Portfolio Standards, or the costs of doing nothing-though the costs of doing nothing are real too.

And the question itself is politically loaded. Why is it assumed that there are “costs” to such programs? Isn’t it just as likely that the monetary benefits are greater than the costs? Or that it is in fact cheaper to use feed-in tariffs than other policy mechanisms, or even that it is in fact cheaper to rapidly develop renewable energy with feed-in tariffs than maintain the status quo.

Nevertheless, feed-in tariff advocates must develop some understanding of the possible monetary costs associated with the policy simply because of the questions that are invariably raised–justified or not.


Monetary Costs

First, the political debate is nearly always about the monetary costs, that is, the costs in cents per kilowatt-hour or in millions of dollars per year over and above the cost of doing nothing. This is sometimes expressed as the “overcost” of developing renewable energy with feed-in tariffs relative to the status quo.

The argument is not about the total environmental and social balance sheet. That argument is effectively over: renewables make environmental, social, and economic sense today. The German government, for example, argues that their successful feed-in tariff program either provides a net societal benefit or at worse is a wash once the environmental and social costs associated with offsetting generation from conventional sources are included. See Electricity from Renewable Energy Sources: What Does it Cost Us? (March, 2008) by the German Ministry for the Environment.

The results of studies into the “overcost” are sometimes counterintuitive. As more and more renewables are added through feed-in tariffs, most assume that the overcost would increase. This is certainly true some of the times, but surprisingly, not all the times. In one evaluation of the overcost of the French program, the overcost declined as more and more renewables, nearly all wind energy, were added to the system. See Development of Renewable Energies in France: What Contribution from the Carbon Market by Cécile Bordier, Caisse des Dépôts, (December 2008).



What are the criteria we should use in evaluating spreadsheet analysis of feed-in tariff program costs? Here are a few items that should be included.


  • Estimates of the cost of renewables not just that of one technology
  • Ability to change assumptions
  • Variable or fixed growth rates by technology
  • Possible reduction or increase in tariffs over time
  • Cost of conventional resources
  • Transparency (Public Domain)


The Cost of Renewables

Far too often advocates of one particular technology, whether it’s wind or solar PV, only examine the costs of that technology.

To be effective at reducing greenhouse gasses and offsetting conventional generation at the scale required, public policy must be directed at “renewable energy” not just at wind or solar energy. As a consequence, our estimates of the costs of public policy must include the monetary costs and monetary benefits (and there are monetary benefits) of a suite of technologies.

Yes, solar PV is the most expensive technology and attention is naturally focused first on solar PV, but the bulk of new renewable generation will come from wind energy for many years to come. At a minimum, costs should be estimated for at least wind and solar energy.


Flexible Assumptions

For a spreadsheet to be broadly applicable across the entire continent all assumptions should be clearly visible and each should be easy to change. This will allow maximum flexibility for all potential users, whether in government, the private sector, or regulatory commissions.

Ideally, the spreadsheet would include simplifying assumptions or formulas, for example, annual degression rates, but also allow entry of specific year-by-year assumptions where needed.


Growth Rates

Similarly, the analysis should include estimates of the growth of each technology over time. The spreadsheet, again, could use simplifying assumptions, but should also allow variable growth rates between technologies and variable growth rates over time. For example, one could assume that a particular technology grows at a certain percentage per year or by an amount of megawatts per year. Or there could be table of year-by-year entries for the amount of each technology added.


Change in Tariffs over Time

Those new to renewable energy or new to feed-in tariffs often wrongly assume that costs for new projects always decrease over time. While generally true, this is not universal. For example, costs for wind energy increased substantially from 2006 to 2008. Likewise, the cost of solar PV did not decrease as rapidly during this period as analysts had predicted.

Thus, any spreadsheet should include the option for changing assumptions about degression rates or removing them entirely. This is especially true if there is a short cycle between program reviews.

For policy programs that have a two-year cycle, it’s probably not wise to include any degression. These review periods are so short that they can effectively evaluate then current costs and make more reliable estimates of program costs for the next tariff-setting cycle.

In programs with four or five-year reviews, estimates of degression may be more valuable.


Cost of Conventional Generation

Again, any estimate of the cost of a feed-in tariff policy cannot consider the cost of renewables in isolation. The cost of renewables must be compared to the cost of conventional generation to calculate the “overcost” of the feed-in tariff program.

What is the cost of conventional generation? It could the average wholesale rate, the average “avoided cost” of new generation, the embedded cost of “heritage resources”, or the average retail rate.

Opponents of renewables will invariable choose to compare the cost of feed-in tariffs to heritage resources. These are the existing plants that have all been paid for, some many times over. Thus, the comparison is effectively with the running costs of old plants. In Canada, for example, this would be existing hydro resources. New renewables will always look more expensive when compared to generation sources with low running costs that don’t include any costs for amortization.

In North America there is a consensus that at a bare minimum the cost of new renewables should be compared to the cost of generation “avoided”. In California this is called the “Market Price Referent”.

It may be more enlightening to consumers, however, to also consider comparing the cost of renewables to the retail rate. The public may not understand wholesale costs or avoided costs, but everyone understands the retail rate they pay for electricity.



For spreadsheet analysis of program costs to be most useful in the public policy debate about the future of renewable energy, the spreadsheets must be in the public domain and all assumptions and formulas must be revealed.

Proprietary software is of no value to a truly democratic debate about the costs and benefits of renewables.

Similarly, non-proprietary software should not have any cells that are not revealed or where the formulas used are hidden from view.


Spreadsheet Rating

Three spreadsheets were evaluated.


The most sophisticated of the three is Dr. Jim White’s REP Calculator. It is an extensive, multi-tab spreadsheet that includes pull-down windows for US electric-utility-specific data.

The simplest is Right Cycle’s REESA FIT with all tables on one tab intended only for California.

ELM Enterprises’ spreadsheet is considerably more sophisticated than Right Cycle’s but much less so than White’s REP Calculator. ELM’s spreadsheet was designed for use in Maryland.

Only White’s REP Calculator includes all renewable technologies. Right Cycle’s and ELM’s are devoted to solar PV, though ELM’s spreadsheet does include a tab for solar DHW.

None provide a clear and succinct summary of the overcost, if any, of a feed-in tariff program that would be comprehensible by a policy analyst or a political leader.

Analysis of the cost or benefits of a feed-in tariff policy involves the complex interplay of a number of different factors that result in very large numbers. Users can get lost in a sea of numbers. Designers should liberally use rounding of large sums while keeping with units the public is familiar with such as kilowatt-hours (kWh), billions of kWh (terawatt-hours, TWh), and millions or billions of dollars. There should be no place for megawatt-hours or gigawatt-hours.

Complex spreadsheets with their many tabs and colorful cells can impart a sense of false precision. In the end, these spreadsheets are a mix of many different assumptions about the future. This is inherently an imprecise art. The results page or tab should convey this imprecision while providing a simple and clear message about potential costs and benefits of using feed-in tariffs in comparison to the status quo.

The pubic and their political representatives are interested in the big picture and any analysis should provide a clear message in simple and readily understandable units, whether as a table or as chart in numbers that are liberally rounded.

There remains a pressing need for a sophisticated yet user-friendly spreadsheet for estimating the cost or benefits of a feed-in tariff program that is suitable for use in a public policy setting.