NYSERDA RGGI-Funded Program Results

The New York State Energy Research and Development Authority (NYSERDA) report New York’s RGGI-Funded Programs Status Report – Semiannual Report through December 31, 2018 (“Status Report”) describes how New York invested the proceeds from the RGGI auctions.  I previously described the report by summarizing the results by sector.  This post provides results by program.

Background

The Regional Greenhouse Gas Initiative (RGGI) is ten years old and has been touted as a successful example of a “cap and dividend” pollution control program.  New York State has been involved in the program since its inception and touts its success.  I have written extensively on the results and have shown that in fact its successes have been limited.   For example, the fundamental assumption for any carbon pricing program is that the proceeds can be invested effectively.  However, the observed results for New York’s experience in RGGI suggests that this may not be the case.

The Social Cost of Carbon (SCC) is supposed to represent the future cost impact to society of a ton of CO2 emitted today.  It is a policy tool that attaches a price tag to the long-term economic damage caused by one ton of carbon dioxide, hence the cost to society.  It was extensively by the Obama Administration to justify the Clean Power Plan, has been proposed for use in the New York Independent System Operator carbon pricing initiative and is included in New York’s Climate Leadership and Community Protection Act.  In that law § 75-0113. Value of carbon, states that

      1. No later than one year after the effective date of this article, the department, in consultation with the New York state energy research and development authority, shall establish a social cost of carbon for use by state agencies, expressed in terms of dollars per ton of carbon dioxide equivalent.
      2. The social cost of carbon shall serve as a monetary estimate of the value of not emitting a ton of greenhouse gas emissions. As determined by the department, the social cost of carbon may be based on marginal greenhouse gas abatement costs or on the global economic, environmental, and social impacts of emitting a marginal ton of greenhouse gas emissions into the atmosphere, utilizing a range of appropriate discount rates, including a rate of zero.
      3. In developing the social cost of carbon, the department shall consider prior or existing estimates of the social cost of carbon issued or adopted by the federal government, appropriate international bodies, or other appropriate and reputable scientific organizations.

Therefore, it is entirely fair to use it as a metric to determine if the investments made from carbon pricing income are cost effectively reducing CO2.  I believe New York will base their carbon pricing on a $50 global social cost of carbon at a 3% discount rate so that is the cost benefit effectiveness threshold metric I will use.

NYSERDA RGGI Program Status

The key table in the Status Report is Table 2 Summary of Expected Cumulative Annualized Program Benefits through 31 December 2018.  It provides costs, energy savings, electricity savings or renewable energy production, greenhouse gas emission savings and the calculated cost benefit ratio.  The $/ton reduced metric is presented on an annual basis and as expected lifetime savings.  For the purpose of this post I use the annual numbers because all the reduction targets are based on an historic annual level (usually 1990).  In order to have an appropriate comparison it has to be annual to annual.

The NYSERDA RGGI Status Report Table 2 – Ranked Cost Benefit Ratio Datatable lists all the programs in the NYSERDA report ranked by the annual cost benefit ratio with just that parameter.  It lists 19 programs with associated CO2 reduction benefits and another 18 programs with no claimed CO2 reductions.  None of the 19 programs with CO2 reduction benefits meets the $50 SCC metric for cost effective investments.  Clearly the 18 programs with no claimed reductions would not be able to meet the metric either.

I prepared a brief summary overview of each of the programs in NYSERDA RGGI Program Cost Effectiveness  After reading the report and summarizing them for the overview I am not impressed and in fact I question the results.  The most cost-effective program, Multifamily Performance Program Assessments in the Green Jobs Green New York sector, had a cost effectiveness value of $58/Ton CO2e.  The program provides financing and co-funding for comprehensive energy assessments and the development of an Energy Reduction Plan, serving market-rate and low- to moderate-income residential buildings with five or more units to increase adoption of clean energy in NYS. Accomplishments.  According to the summary table, they managed to do a total of 316 assessments through December 2018 that resulted in 61,795 residential units served with installed measures for a cost of $3.3 million in “total incentives” and another $1.4 million in “total associated costs”.  Summing the incentives and associated costs and dividing by the 61,795 residential units yields $76.06 per unit.  The summary indicates that this is the cost the comprehensive energy assessment and development of a reduction plan and that rate per unit is reasonable.  But this also means that the actual costs to implement the energy reduction are not included.  So how did NYSERDA claim any CO2 reduction benefits and what are the chances that the actual CO2 reductions were double-counted?

There is another concern. A quick perusal of the programs listed with no reduction benefits demonstrates justifiable cynicism of yet another government program controlled by politicians.  The programs range from practical to clear pork barrel.  New York wants to be able to track emissions from generation sources within the State and from imported sources to create “tradable generation attribute certificates”.  Rather than fund this through the general fund it is easy to justify this as a necessary expense for these funds.  The research projects are another segment of funding where there is a justifiable rationale for funding projects that have no reduction benefits short-term because they could lead to long-term reductions.  At the extreme of clearly unjustified funding is the Brookhaven National Laboratory Ion Collider.  I have no idea the tortured logic that was used to justify spending any RGGI funds on this.

Conclusion

Advocates for carbon pricing schemes assume that the investments from the proceeds are worthwhile.  I think these results and the results from New York’s Clean Energy Dashboard demonstrate that is not the case.  The Regulatory Analysis Project (RAP) study: Economic Benefits and Energy Savings through Low-Cost Carbon Management notes that “Many advocates of carbon pricing begin with the proposition that the main point is to charge for carbon emissions “appropriately” and that carbon reductions will surely follow in the most efficient manner. While carbon pricing is a useful tool in the fight against climate change, there is now substantial experience to suggest that wise use of the resulting carbon revenues is equally important, or even more important, if the goal is to actually reduce emissions at the lowest reasonable cost.”

The other concern is the cost of the New York program relative to the social cost of carbon. The NYSERDA RGGI Status Report does not include a single program that reduces carbon dioxide more cost efficiently than $50 per ton.  Because I have shown that eliminating New York CO2 emissions that would provide a reduction, or a “savings,” of approximately 0.0026°C by the year 2050 and 0.0054°C by the year 2100.  Because you cannot measure that small a temperature difference there will be no tangible benefit of the CLCPA.

My primary concern with New York’s clean energy mandates is the cost.  If the cost is small then signaling New York’s virtue might have value. According to the Clean Energy Dashboard, New York has invested $1,051,359,837 through the third quarter of 2019 but can only claim 3,057,131 tons of reductions giving a CO2 invesment efficiency of $343.90 per ton of CO2 equivalent reduced.

It is not clear how advocates of these programs can justify the costs given these results.  The cost efficiency does not even approach the supposed appropriate cost of carbon dioxide and the there is no tangible expected change to global warming.

CLCPA Implementation Part 1

In the summer of 2019 the Governor Cuomo and the New York State Legislature passed the Climate Leadership and Community Protection Act (CLCPA) which was described as the most ambitious and comprehensive climate and clean energy legislation in the country when Cuomo signed the legislation.  In one of the first implementation tasks the New York State Department of Environmental Conservation (DEC) has started the process to calculate a statewide Greenhouse Gas emissions (GHG) limit for this law.  This post addresses that rulemaking.

DEC Rulemaking

DEC recently released a stakeholder draft for their statewide greenhouse gas emission limit rule.  This barebone draft lists six gases that will be considered in the inventory and lists potential economic sectors that might be covered.  As part of this process DEC announced a webinar for February 14 and again on February 28 to provide the public the opportunity to learn and ask questions about the rulemaking.  During the first webinar DEC said that they would post a recording of the webinar.  If you are unfamiliar with New York’s rulemaking process the webinar provided a good summary.

Existing New York GHG Inventory

The New York State Energy Research & Development Authority (NYSERDA) works with DEC to prepare an annual inventory of GHG emissions.  The most recent report, Greenhouse Gas Inventory 1990-2016 contains a detailed inventory of historical greenhouse gas emission data from 1990-2016 for New York State’s energy and non-energy sectors.  I have been involved with all aspects of emissions inventories from developing emission factors, using inventories for air quality modeling, to preparing reports for regulatory agencies over my entire career.  I can assure you that this is a non-trivial task.  Although I have some quibbles with the NYSERDA numbers I believe that it represents New York’s emissions adequately for all purposes.

CLCPA Inventory Requirement

The CLCPA requires a report on GHG emissions, § 75-0105: Statewide greenhouse gas emissions report.  The regulation states that “No later than two years after the effective date of this article, and each year thereafter, the department shall issue a report on state-wide greenhouse gas emissions, expressed in tons of carbon dioxide equivalents, from all greenhouse gas emission sources in the state, including the relative contribution of each type of greenhouse gas and each type of source to the statewide total”.  The law specifies reductions in the future from 1990 emission levels so this section includes “§ 75-0105(5): The statewide greenhouse gas emissions report shall also include an estimate of what the statewide greenhouse gas emissions level was in 1990”.  DEC is interpreting this legislation to mean that they have to implement a new regulation that codifies the GHG emission inventory process.

It is not clear to me how much difference there is between the CLCPA inventory requirements and the methodology used for the NYSERDA report with one exception.  In particular, the legislation states that “§ 75-0105(3): The statewide greenhouse gas emissions report shall also include an estimate of greenhouse gas emissions associated with the generation of imported electricity and with the extraction and transmission of fossil fuels imported into the state which shall be counted as part of the statewide total.”  NYSERDA does not try to estimate extraction and transmission of fossil fuels emissions.

Lawmaking without Technical Expertise

I have no experience lobbying politicians to influence legislation and, frankly, have no desire to do so.  If I had been involved in this instance, I would have said that the legislation should specify the use of the NYSERDA inventory.  It has all the numbers you need, no development is necessary, and represents GHG emissions well.  Unfortunately, someone, somewhere convinced somebody to include the requirement for “an estimate of greenhouse gas emissions associated with the generation of imported electricity and with the extraction and transmission of fossil fuels imported into the state”.  At a minimum this component will have to be added to the NYSERDA inventory.  I do not know what else, if anything, DEC has deemed to be incompatible between the CLCPA law and the existing NYSERDA inventory.

As a numbers guy, I oppose including extraction and transmission emissions because the uncertainty bounds on the extraction and transmission values are large today and much worse for 1990.  In 1990 coal and oil generation was much higher than today and it is not clear to me how you could figure out the source and methodology used to extract the fuel.  The fuel specification data needed to estimate that information has been purged from company records, not only because it was so long ago but also because most of the companies who operated the 1990 facilities no longer own them.

Because including the extraction and transmission provision was a political decision, it is appropriate to discuss possible motives with the caveat that this has never been considered a skill of mine.  Clearly, including these numbers will increase the 1990 baseline and current emissions.  Note that the language says transmission and not transportation.  Coal and oil in New York are transported but natural gas is transmitted.  Therefore, my guess is that it was included by someone who wanted to make natural gas look bad by showing higher emissions.

Conclusion

The extraction and transmission emissions requirement has unintended consequences.  NY has a perfectly good emissions inventory but that provision means a lot of work has to be done to include those emissions. And DEC has no choice but to try to follow the requirements of the legislation.  As I noted above emissions inventories are difficult under any circumstances but when handicapped by requirements to adjust the numbers for ulterior motives the result is going to be significant effort for very little tangible benefit.

If it is correct that the extraction and transmission provisions were included by anti-natural gas advocates then I have to laugh.  I am convinced that the New York State anti-natural gas position is only supportable if the numbers are ignored or mis-understood and this is a vivid example.  In particular, the addition of extraction and transmission emissions to the 1990 baseline is going to show a larger relative decrease for coal and oil than natural gas.  Extracting coal has to be more GHG intensive than drilling and fracking a natural gas well.  Transporting the coal by rail or oil by pipeline to a refinery and then residual oil by ship or train to the power plants have much larger emissions than a natural gas pipeline to a power plant.  Worse still for the individuals who got this included is the fact that the higher baseline means that % reduction limits will also be higher.  In other words the CO2 limits will be higher so more CO2 can be emitted.

NYISO Carbon Pricing Letter to the Editor

Kevin Lanahan, Vice-President of External Affairs and Corporate Communications for the New York Independent System Operator (NYISO) had a letter to the editor of the Syracuse Post Standard published advocating their carbon pricing initiative.  I submitted a brief letter in response but was limited to 250 words.  This post expands on my concerns with carbon pricing in general and the NYISO carbon pricing initiative in particular.

Background

The NYISO letter to the editor describes how their carbon pricing initiative will work:

      • The state sets the carbon price, a certain amount per ton of CO2 being emitted.
      • Carbon-emitting power plants pay for the carbon they release into the atmosphere.
      • About half of the revenue goes to low-carbon or carbon-free resources like wind, solar and hydro.
      • The rest would be distributed back to consumers.

The NYISO has a fancy web page lauding their proposal that includes links to primary references describing their initiative, a link to a video describing how it will work and a scrolling list of supporters of the program.

I have been following this initiative since it was first proposed and have written ten posts specifically addressing aspects of the proposal.  I suggest reading my last post for a summary of my rationale to oppose the proposal. More recently as part of my comments (01/23/2020) on the New York State Department of Public Service Resource Adequacy Proceeding I responded to comments that suggested that it should be implemented.  This post summarizes those comments.

I have been involved in New York’s current carbon pricing program, the Regional Greenhouse Gas Initiative (RGGI), program process since its inception.  I believe that supporters of the NYISO carbon pricing initiative have not considered the RGGI results or practical limitations of the initiative.  When those factors are considered I have serious doubts that it will be efficient or cheaper than other alternatives. My comments will address each aspect of the NYISO letter.

Price Setting

According to the plan, the state will set the carbon price.  All indications are that the carbon price will be set to equal the Social Cost of Carbon (SCC).  That value is supposed to represent the future cost impact to society of a ton of CO2 emitted today. I addressed specific issues with the SCC in this context here and  here.  Despite those problems the State will likely go ahead and use it.  I believe New York will use a carbon price of $50 which is the global social cost of carbon at a 3% discount rate.

As an aside, there is a fundamental question underlying the State’s initiatives.  If the cost per ton removed of any reduction program exceed the SCC value, then it exceeds the expected societal costs of the emissions.   The question that has to be resolved is whether the State should invest in programs that do not meet this effectiveness threshold.  If the costs exceed the projected cost of the impacts, then what is the point?

CO2 Payments

The NYISO proposal would change the existing generator payment methodology by adding the carbon price as a function of generator’s hourly CO2 emissions so that each generator pays that price to the NYISO.  For a renewable generator there are no emissions so there is no carbon payment.  For a natural gas fired turbine that emits CO2 the hourly payment to NYISO would equal the tons emitted multiplied by the SCC value of ~$50 per ton.  It is important to understand that adding the cost of carbon not only costs consumers the cost of the carbon payment but also the cost for higher wholesale electricity.

The costs of this carbon pricing initiative are significant.  The average number of tons of CO2 emitted in New York in 2015 and 2016 was 32,106,042 tons so the carbon price at $50 per ton is $1.605 billion. I did a static calculation using 2015 and 2016 load and marginal emission rate data to estimate the effect of the carbon charge on wholesale electricity that increases generator net revenues. My analysis showed that in 2015 the total cost of the net revenues due to higher wholesale prices is $3.027 billion as compared to $1.321 billion calculated by applying the SCC to actual CO2 emissions. As discussed below some of the carbon price revenues will be returned to customers.  However, the increase in costs due to the change in market clearing price will not.

There are two other carbon price complications.  Leakage refers to the situation when a pollution reduction policy simply moves the pollution around rather than actually reducing it.  Ideally the best carbon pricing approach would apply to the entire globe and all energy sectors.  The NYISO carbon pricing initiative proposes to price just the New York electricity market.  The electric grid is inter-connected and obtaining emissions from outside New York is problematic in the first place.  Secondly the proposal will likely result in locational leakage when New York costs increase so energy production and emissions are not reduced but simply shift emission location out of the state.

Revenue Distribution

The NYISO letter says that “About half of the revenue goes to low-carbon or carbon-free resources like wind, solar and hydro.”, and “The rest would be distributed back to consumers.”  I am not sure how to interpret those statements.  I think they are saying that all the carbon price money collected will be returned to consumers and the increases in net revenues due to higher wholesale prices is going to low-carbon and carbon-free resources.  Even though I don’t think NYISO has been particularly forth-coming acknowledging the wholesale price component revenues I will be charitable and assume that my interpretation is correct.

Conclusion

I could go on but I think the summary from the Regulatory Assistance Project analysis for the State of Vermont is good.  It that found “we conclude that an attempt to reduce Vermont’s carbon emissions based on carbon pricing alone will cost more, and deliver less, than a program of carbon reductions that is based on practical public policies—policies that attack the main sources of carbon pollution through tailored, cost-effective programs geared to Vermont’s families, businesses, and physical conditions.”

Rare Snow Storms Today are Blatantly Obvious, Not

This post describes another evaluation of anecdotal evidence that is portrayed as evidence of global warming.  Previously I looked at a NPR radio segment in Great Lakes Vineyard Confronts Climate Change and examined a claim by a senior official at the Department of Environmental Conservation in Hay Harvest Climate Trend?.  This post addresses the frequency of snow storms in my home town.

Thread Posts

On the Facebook page “If you grew up” for my Oneonta, NY hometown group someone posted a conversation starter about the Old Farmer’s Almanac forecast for this winter.  This is the post that got me started:

“The winters we had when we are young are gone. It’s rare when we get a major storm. Amazing how much it has changed in 50 years – but people still deny the blatantly obvious.”

I responded to the first post with:

“If you look at the data rather than rely on anecdotal information and your memory (when I was a kid the snow came up to my waist all the time), then you will find that no one is ignoring the blatantly obvious. See for example: reference and reference”

That prompted this response:

“The source you cite is suspect at best. Reference”

I responded to that with this:

“My advice is to not trust anybody on any aspect of the global warming problem or purported solution.  Check out the numbers and data yourself.  The posts I referenced showed where they got their data and their data show that there is no “blatantly obvious” snowfall trend. Despite the “suspect” web site I stand by that position.”

That prompted this response:

“You are welcome to your opinions and to selectively choose which data you want to use, but opinions are just that. Some people also firmly believe that vaccines are horrible based on one dubious study that has been disproved, others believe the world is flat. The issue is that the climate is changing faster than it normally would due to man’s activity – some areas are getting dryer, others wetter, overall the temperature is rising but in some areas it will get colder. To dismiss the vast majority (@97%) of trained scientists and the blatantly obvious evidence in favor of the very loud deniers doesn’t make the 3%’s points valid. Reference”

My response:

“I said check the numbers and data yourself.  In this instance all I am saying is that don’t confuse weather and climate.  Just because it some observed weather phenomenon is different does not necessarily mean it is related to global warming.

When you combine saying that climate is changing faster than normal due to man’s activity with the suggestion that disagreeing dismisses the vast majority of trained scientists, it is misleading based on my check of the claim and survey.  I think trying to determine the rate of climate change and figure out how much of the observed climate change is due to mankind is too difficult to be conclusive.  I have not found any survey that asked the policy relevant question about the rate of change and man’s actitivity.  Instead they basically asked whether there was a greenhouse effect.

I also said don’t trust anyone.  Search on 97% climate scientist myth and then search to find the other side of the story then decide for yourself.  Trying to do a similar comparison for the rate of climate change and contribution of mankind is more difficult so I don’t think you can do something similar easily.  Although I think I can offer a trained scientist opinion don’t trust me even though I have two degrees in meteorology, certification as a consulting meteorologist and over 40 years experience.”

Approach

First, let’s define weather and climate.  According to the National Oceanic and Atmospheric Administration’s National Ocean Service “Weather reflects short-term conditions of the atmosphere while climate is the average daily weather for an extended period of time at a certain location.”  The referenced article goes on to explain “Climate is what you expect, weather is what you get.”

The Northeast Regional Climate Center data sets provide processed values for temperature and precipitation for many observing sites in New York.  If global warming is affecting the winter storm climatology then there should be trends in either or both the daily snowfall or snowfall accumulation data.  In a previous analysis of weather vs. climate perception I analyzed data from Ithaca, NY which is just about 90 miles away from Oneonta. Because it was readily available and I think representative of snowstorms in Oneonta, I just used the data from that site for daily snowfall and snowfall depth.  I also said trust no one so if anyone wants the data I can provide that for your own analysis.

I did a simple analysis of the daily snowfall and snowfall accumulation.  In both cases, I fit a linear regression model to describe the relationship between the daily values and time for the months November, December, January, February, and March. I use Statgraphics Centurion software from StatPoint Technologies, Inc. to do my statistical analyses because it enables the user to choose the best relationship from 27 different linear regression equations.  I determine which linear regression model provides the best fit and then use that model to describe the data. If the calculated probability value (P-value) is less than 0.05, there is a statistically significant relationship at the 95.0% confidence level and I defined the test result as significant.

Results

The purpose of this analysis was to determine if the frequency of major snowstorms in Central New York has changed due to climate change.  In this analysis I looked at climatic changes over the period of record from Ithaca and calculated the trend over the entire period for daily snowfall amounts and daily snowfall depth.  The Ithaca, NY Daily Snowfall Depth December 1899 through March 2019 figure shows quite a bit of variation.  Although there is a statistically significant increase in daily snow depth over this period the trend is only one inch per century.    Given the variation across the graph I do not consider this to be meaningful.  TheIthaca, NY Daily Snowfall (Inches) figure similarly shows so much variation that claiming any kind of trend is dicey but there also is a statistically significant increase in daily snow fall.  These data show that there is no trend towards fewer storms.

So why is there an impression that it is rare that Oneonta no longer gets major storms?  In the Ithaca, NY Extreme Daily Snow Depth and Snowfall (inches)table note that if the winters when you were young were around 1960 there were a couple of years when the snow depth was over 24” out of six in over 100 years.  Similarly, if your youth was in 1971 and 1972 there were two storms with snow over 16” out of the eight storms in over 100 years.  No question that would give the impression that the winters were different in your youth.

Another important point to keep in mind is that you could change the results by picking a different starting point for the analysis.  I also suspect that you could get different results by screening the data differently.  The choices I made were the simplest.  One you start tweaking input assumptions you can be rightfully accused of statistical manipulation.

Conclusion

These data illustrate the point I tried to make in the Facebook posts.  In my experience, whenever I have looked at the data behind a climate change related claim it was more complicated, more uncertain, and often contradictory to the claim made.   It is also difficult to separate whether the observed phenomenon is just weather variability or climate trend.

This post does not prove that there isn’t climate change, that mankind is not having an effect on climate, or that it isn’t appropriate to do something. If this were just a simple scientific disagreement it could be shrugged off as inconsequential.  Unfortunately, in New York State this kind of anecdotal evidence has been used to justify last summer’s Climate Leadership and Community Protection Act (CLCPA) which has been described as the most ambitious and comprehensive climate and clean energy legislation in the country when Cuomo signed the legislation.  The Citizens Budget Commission developed an overview of the CLCPA targets in Green in Perspective: 6 Facts to Help New Yorkers Understand the Climate Leadership and Community Protection Act. Look at those references to get a feel for what is in store for New York.

I have spent a lot of time trying to figure out what this law could mean.  For example, my latest estimate for the cost for the energy storage batteries needed when winds are light at night during the winter is $176.3 billion through 2050.  The United Kingdom has similar legislation in place and it was recently announced that their plan would require conversion of all gas central heating to electric.  I expect no less here.  As a result when I see anecdotal information used to justify these costly programs I have to respond.

NYS REV Investment Cost Effectiveness

Earlier this month I did a series of posts on the Citizens Budget Commission Getting Greener Report and this post addresses one of their recommendations.  In particular, they recommended that New York State “Establish a prioritization system to pursue renewables that provide the greatest GHG reductions at lowest cost.”  The Clean Energy Dashboard has data to start just such a system.

In my post on the New York Clean Energy Dashboard I noted that it provides data describing Reforming the Energy Vision program activity by electric and gas utilities and the New York State Energy Research and Development Authority (NYSERDA)”.  The dashboard has data that can be used to determine the cost efficiency of CO2 reductions.   In my previous post I presented the total cost effectiveness of all the programs and in this post, I break down the cost effectiveness by program for a first attempt at setting priorities.

Methodology

In order to determine which programs provide the greatest GHG reductions at the lowest cost we can filter Clean Energy Dashboard data twice and calculate a cost per ton reduced for each program.  To get emission reductions, the Clean Energy Dashboard needs filters for C02e Emission Reductions, Gross Annual (Metric Tons) and acquired savings.  I accessed the data tables and downloaded the data in the View Progress by Program tab that includes CO2e Emission Reductions, Gross Annual (Metric Tons).

To get the money spent to get those emission reductions, the Clean Energy Dashboard needs filters for Budget (dollars) and acquired savings.  We are asking for the money spent when “both the measure is installed and currently operational, and the funds associated with the measure or project have been expended.”  I access the data tables and downloaded the data in the View Progress by Program tab that lists the Budget (Dollars) data.

The next step was to combine the two data sets so that I could match the programs listed in each data set.  There was not a complete one-to-one correspondence between the programs so I manually edited the file to match all the programs possible.

Results

Once I had a data file with one-to-one correspondence, I found that there were three categories.  One program was included with emission reductions but no budget dollars and 52 programs had budget dollars but no emission reductions.  There were 107 programs with data for CO2e Emission Reductions and Budget dollars so that I could calculate cost effectiveness.

There was one Central Hudson program that had CO2e Emission Reductions but no budget funds.  In their Community Lighting program Central Hudson partners with local not-for-profit organizations to distribute free LED lightbulbs to eligible residential  customers and has reduced 334.21 tons of CO2e.

The 52 programs with spent money but no emission reductions were primarily NYSERDA projects.  These programs had an acquired budget total of $113,401,265. There were five Con Ed programs, two National Grid (NiMo) programs and the remaining 45 programs were all from NYSERDA.  I think I will return to these programs later because the emphasis in this post is on the programs where we can estimate cost effectiveness.  For now the New York State Clean Energy Dashboard Programs with Budget Money but No CO2e Emission Reductions table lists the programs.

For the 107 programs with both emission reductions and budget data, I divided the funds spent by the CO2 reductions to determine the CO2 invesment efficiency ($/ton) for each program.  The Carbon Dioxide Equivalent Cost Effectiveness Reductions table lists the cost effectiveness data for each program along with the administrator, program name and description, CO2e reduction and acquired funds spent ranked by the cost per ton.  I summarized the results in the Summary of Carbon Dioxide Equivalent Cost Effectiveness Reductions table.  Note that there were a total of 3,056797 CO2e emission reduction tons and an acquired budget total of $937,958572 which works out to an overall cost per ton rate of $306.84 per ton CO2e reduced.

The Social Cost of Carbon (SCC) is supposed to represent the future cost impact to society of a ton of CO2 emitted today.  I believe New York is going to base its carbon pricing on the $50 global social cost of carbon at a 3% discount rate.  In other words, if we spend more today than $50 per ton then we exceed the future cost impact to society. The summary table lists data by different multiples of the $50 SCC cost and shows that only two programs are below that criterion.  Seven programs are under $100, 19 more are under $250, and 79 programs exceed five times the SCC threshold.

Programs

There are a wide range of project types in the 107 programs included in the analysis.  There are concerns about the quality of the CO2 reduction estimates especially with regards to a verifiable methodology and how the costs for the programs are calculated.

I worry more about the CO2 reduction estimates.  For example, compare NYSE&G Energy Marketing Gas Program (#6 in the Carbon Dioxide Equivalent Cost Effectiveness Reductions table) with Central Hudson Behavorial Modification (#7).   The NYSE&G program is an online Energy Marketplace that offers instant rebates to residential customers who purchase qualifying  Wi-Fi thermostats.  I imagine that the program managers for this project found some analysis that provided a range of CO2 reduction estimates for wi-fi thermostat installations and then combined that estimate with the number of thermostats sold to come up with the reduction.  However, this estimate has to assume that the installation of the thermostat led to an energy-saving change at the homes in question and how much of a change.  Unfortunately this assessment is a relatively strong one as compared to the Central Hudson behaviorial modification program.  In that project Central Hudson set up a customized web portal for customers to access and track energy usage, and to access energy saving tips and savings.  The only way I to see how to quantify how that access changed behavior that in turn reduced CO2 emissions is to guess.

As noted previously, there are only two programs with claimed CO2 reduction effectiveness less than the SCC target value of $50.  The better of the two, NYSERDA Clean Energy Communities program, claims 220,182 tons in reductions at a cost of $4,947,773.  The program provided  assistance, technical support, and tools and resources to local governments for implementing clean energy High-Impact Actions in local communities.  The NYSERDA website describes the Clean Energy Communities program:

“Building a more sustainable New York starts with building more sustainable communities. Local governments affect energy choices in their communities, from government operations to homes, businesses, and community institutions.  Local governments in New York State can use the Clean Energy Communities program to implement clean energy actions, save energy costs, create jobs, and improve the environment. In addition to providing tools, resources, and technical assistance, the program recognizes and rewards leadership for the completion of clean energy projects.”

I classify this program as more unverifiable than most others.   Digging down into the project descriptions for this program you find for example providing incentives to a local government to install an electric vehicle charging station.  There is no way to estimate how much use that charging station has been used and how much CO2 was avoided.  As a result, I am not comfortable that this program did in fact produce the CO2 emission reductions claimed.

On the other hand, the second program that was better than the SCC target, NYSERDA Industrial Transition program (#2), provided technical and financial support to assist industrial and data center facilities with process improvement projects to help link energy and their core mission.  Those facilities very likely quantified the before and after implementation energy usage and could therefore provide a emission reduction that can be traced to verifiable information.  Therefore, I am more comfortable that this program did produce the CO2 emission reductions claimed.

There also are issues with the cost estimates.  On the face of it this should be simpler because the project managers must know how much money is being spent.  However, it gets more complicated when you consider the lifetime effectiveness of the investment.  For example, if you pay for LED lighting to replace  incandescent bulbs the investment is effective over the lifetime of the LED lights.  For my calculation I made the simple assumption that it is appropriate to use the total cost to get the reduction irregardless of the expected lifetime of the investment.

Conclusion

There is political, personal, and organizational pressure for larger as compared to smaller CO2 reductions for these programs.  As shown above, many of the programs have largely unverifiable emission reduction calculation methodologies.  I conclude it is likely that the emission reduction estimates for these programs are higher than we can actually expect to see.  The costs over time could be different too.

Nonetheless, these are numbers that can be used to meet the Citizens Budget Commission recommendation that New York State “Establish a prioritization system to pursue renewables that provide the greatest GHG reductions at lowest cost.”  This post provides a first cut list to set those priorities.

My bigger concern is the very poor performance of these programs relative to the SCC target levels.  As Resources for the Future explains:

One of the primary ways the SCC is used in policy design and evaluation is through benefit-cost analysis. A benefit-cost analysis compares the total economic benefits of a proposed policy to its total economic costs. Take, for example, a regulation that limits air pollution: its total benefits—including those from improvements to public health and the environment due to better air quality—would be compared against the implementation costs, such as the purchasing and installation of equipment to control air pollution. Benefit-cost analysis has been a required part of federal regulatory analysis since it was implemented by the Reagan administration in 1981.

The SCC is used in benefit-cost analysis to quantify the dollar-value of a policy’s effect on climate change due to changes in greenhouse gas emissions. For policies that increase emissions, the expected increase in emissions (in tons) is multiplied by the SCC, and the result is included as part of the total estimated costs of the policy. For policies that decrease emissions, the change in emissions is multiplied by the SCC, and the result is added to the expected benefits of the policy.

The bottom-line concern is whether New York State should be pursuing any of the 105 programs whose decrease in emissions exceeds the SCC cost benefit threshold of $50.  The performance of these programs is not encouraging.  There are 79 programs that exceed five times the $50 SCC threshold that total $801,173,268 but only claim reductions of 1,494,548 tons for a cost effectiveness of $536 per ton reduced.  Combine those programs with the 52 programs that claim no CO2 reductions, REV programs in the Clean Energy Dashboard have spent over $914 million with very little emissions reductions to show for it.

New York Clean Energy Dashboard

Updated May 22, 2020

Reforming the Energy Vision (REV) is Governor Andrew M. Cuomo’s comprehensive energy strategy for New York.  This post describes a component of the program – the Clean Energy Dashboard that summarizes results from the programs mandated by Cuomo’s energy program.

Overview

According to the Clean Energy Dashboard website, it is a “resource to provide you with a snapshot of program activity by electric and gas utilities and the New York State Energy Research and Development Authority (NYSERDA)”.   It “aggregates and provides information on utilities’ and NYSERDA’s programs” and is updated quarterly.  I spent a lot of time and had very little success trying to pick out the costs of REV so this is a promising development. In addition, the complete underlying dataset can be downloaded on Open NY.

There is a Users Guide for the dashboard that explains how the dashboard is laid out and how you can dial down within the system to extract specific information.  In the remainder of this section I will explain provide more detail for outsiders to the New York energy system.

The dashboard consists of a top graphic with a bar chart and filters to control what is displayed.  Below that there is a graph with the progress for the selected data.  There is an option to access the data tables.  When exercised you get data that can be downloaded and more detailed graphics.  Finally note that there is a glossary of terms at the bottom of the dashboard.

There are three filters on the top graphic.  Users can look at program activity for 13 metrics:

      • Budget (Dollars)
      • C02e Emission Reductions, Gross Annual (Metrie Tons)
      • C02e Emission Reductions, Gross Lifetime (Metrie Tons)
      • Electricity Peak Demand Reductions, Gross (MW) Electricity Savings, Gross Annual (MWh)
      • Electricity Savings, Gross Lifetime (MWh)
      • Fuel Savings, Gross Annual (MMBtu)
      • Fuel Savings, Gross Lifetime (MMBtu)
      • Participants (Count)
      • Renewable Energy Capacity, Gross (MW)
      • Renewable Energy Generation, Gross Annual (MWh)
      • Renewable Energy Generation, Gross Lifetime (MWh)
      • Total Energy Savings, Gross Annual (MMBtu equivalent)
      • Total Energy Savings, Gross Lifetime (MMBtu equivalent)

These metrics suggest that we should be able to determine total costs and costs per emission reductions, electricity savings, fuel savings, renewable energy development and total energy savings.

The dashboard lets the user view data by program administrator and primary end-use sector in the second top-line filter of the top graphic.  Program administrators are the nine regulated load-serving entities in New York State and the New York State Energy Research and Development Authority (NYSERDA).  “Load serving entities” is the current label for the original electric utility companies in New York and the energy service companies.  In this context it only refers to the original electric and gas utility companies that are regulated by the Public Service Commission:

      • Central Hudson
      • Consolidated Edison
      • National Grid (KEDLI) – formerly the Long Island Lighting Company
      • National Grid (KEDNY) – formerly Brooklyn Union Gas
      • National Grid (NiMo) – formerly Niagara Mohawk Power Corporation
      • National Fuel Gas
      • New York State Electric & Gas
      • Orange & Rockland
      • Rochester Gas & Electric

There are six primary end-use sectors:

      • Commercial
      • Industrial
      • Multifamily
      • Multisector
      • Residential
      • Transportation

I think this is self-explanatory.

On the right side of the top graphic are seven options to filter the data.  Four are straight-forward and require no further explanation.  The first option allows the user to filter by program administrator.  The third option filters by primary end-use sector.  The fourth one, fuel-type funding source, simply filters by electric or gas projects. The last simple one is the sixth filter that lists the program names.  The remaining filters require a bit more explanation.

There are three portfolios to choose from in the second option:

“The Clean Energy Fund (CEF), one of Reforming the Energy Vision’s (REV) three strategic pillars, is designed to deliver on New York State’s commitment to reduce ratepayer collections, drive economic development, and accelerate the use of clean energy and energy innovation. It will reshape the State’s energy efficiency, clean energy, and energy innovation programs.”

“On June 23, 2008, the Public Service Commission established the New York Energy Efficiency Portfolio Standard (EEPS) proceeding. As part of a statewide program to reduce New Yorkers’ electricity usage 15% of forecast levels by the year 2015, with comparable results in natural gas conservation, the Commission established interim targets and funding through the year 2011. The State’s utilities were required to file energy efficiency programs, and the New York State Energy Research and Development Authority, as well as independent parties, were invited to submit energy efficiency program proposals for Commission approval.  Since June 2009 the Commission has approved over 90 electric and gas energy efficiency programs, along with rules to guide implementation and measure results, through a series of orders.”

“In its February 26, 2015 Order (REV Order), the Commission required each utility to submit an annual Distributed System Implementation Plan (DSIP), which will serve as the template for utilities to develop and articulate an integrated approach to planning, investment and operations. As required by the February REV Order, the DSIP will be a comprehensive filing, to include information related to all Distributed Energy Resources, including energy efficiency, demand response, distributed storage and distributed generation. In order to ensure continued energy efficiency efforts during the transition to more REV-aligned activities, the order also established explicit energy efficiency budgets and targets for 2016 and set forth an annual process whereby utilities will propose post-2016 energy efficiency budgets and targets for approval. As part of that process, the order directed the filing of Energy Efficiency Transition Implementation Plans (ETIPs), to address the energy efficiency efforts specifically associated with proposed budgets and targets.”

The fifth filter is for LMI or Market Rate.  The glossary definition states “Low-to Moderate-Income (LMI), defined as households at or below 80% of State or Area Median Income; Market Rate, used when not LMI or for Residential/Multifamily households above 80% of State or Area Median Income.”  I think this is included so that environmental justice parameters can be calculated.

The seventh filter is for Committed or Acquired savings.  Committed energy savings are “considered committed when the funds associated with the measure are encumbered. This does not include acquired savings.” Encumbered funds are defines as “the current amount of funds that are tied to executed contracts, completed applications which have been determined to meet basic eligibility criteria but for which the program administrator does not have in hand an executed contract, and contracts awarded through competitive solicitations which are not yet executed.”  Acquired energy savings are “generally considered acquired when both the measure is installed and currently operational, and the funds associated with the measure or project have been expended.”

Example

The cost efficiency of CO2 reductions is an important parameter.  After all the Climate Leadership and Community Protection Act requires an 85% reduction in CO2 emissions by 2050 from 1990 levels so knowing how effectively the state program investments are reducing CO2 is important.  Table S-2 in the NYSERDA GHG emissions inventory states that 1990 emissions were 236.19 million metric tons of CO2 equivalent and that in 2016 emissions were down to 205.61.  That means the state has to reduce its emissions another 170.18 million metric tons.

In order to determine how effectively the mandated programs that Cuomo’s energy vision requires we can filter data twice and calculate a cost per ton reduced.  To get emission reductions, the Clean Energy Dashboard needs filters for C02e Emission Reductions, Gross Annual (Metric Tons) and acquired savings.  The resulting bar chart of cumulative progress by quarter shows that to date the programs have reduced emissions by 3.1 million metric tons.  I asked for the data download and downloaded a file that lists the quarterly CO2e emission reductions.  Summing the total of the projects, the reduction to date is 3,057,131 tons.

To get the money spent to get those emission reductions, the Clean Energy Dashboard needs filters for Budget (dollars) and acquired savings.  Remember that we are asking for the money spent when “both the measure is installed and currently operational, and the funds associated with the measure or project have been expended.”  The resulting bar chart of cumulative progress by quarter shows that to date the programs have spent $1,051.3 million  I asked for the data download and downloaded a file that lists the budget.  Summing the total of the program administers in the final quarter the total spent to date is $1,051,359,837.

Dividing the funds spent by the CO2 reductions we can determine that the CO2 investment efficiency is $343.90 per ton of CO2 equivalent reduced.  Given that the state has to reduce emissions another 170.18 million metric tons if we have to rely on these programs mandated by the State of New York then the cost to the state will be over $58 billion.  The Social Cost of Carbon (SCC) is supposed to represent the future price impact to society of a ton of CO2 emitted today.  This cost reduction efficiency is far in excess of the global social cost of carbon at a 3% discount rate of $50.  Because the social cost of carbon is an estimate of the economic damages that would result from emitting one additional ton of greenhouse gas this means that New York State’s reduction programs are not effectively reducing CO2 emissions.

Update May 22, 2020: NYSERDA contacted me and pointed out that if the lifetime savings were used then the CO2 investment efficiency in the preceding paragraph is $30.50 which would be less than the Social Cost of Carbon.  I should have noted the difference in the numbers due to annual versus lifetime savings.  Normally when an agency provides annual and lifetime CO2 emissions reductions, I note that in order to account for future emission reductions against historical levels the annual reduction parameter is more appropriate.  In other words, the annual reduction is comparable to annual 1990 emissions.  The lifetime reduction is not an apples to apples comparison with New York’s annual 1990 baseline.  I leave it to the reader to decide which is more appropriate.

There is a lot of information in the Dashboard data that I am sure many will find useful trying to figure out New York’s energy transformation attempt.

CLCPA Energy Storage Costs

In the summer of 2019 the Governor Cuomo and the New York State Legislature passed the Climate Leadership and Community Protection Act (CLCPA) which was described as the most ambitious and comprehensive climate and clean energy legislation in the country when Cuomo signed the legislation.

I have written a series of posts on the feasibility, implications and consequences of this aspect of the law based on evaluation of data.  This post combines the impacts of three references to extend my previous estimates of the energy storage cost component to include battery life expectancy.

I am a retired electric utility meteorologist with nearly 40 years experience analyzing the effects of meteorology on electric operations.  The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone.

Background

The NREL report Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System describes an analysis of the life expectancy of lithium-ion energy storage systems.  The abstract of the report notes that “The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions.”   The report concludes: “Without active thermal management, 7 years lifetime is possible provided the battery is cycled within a restricted 47% DOD operating range. With active thermal management, 10 years lifetime is possible provided the battery is cycled within a restricted 54% operating range.”  I used the 54% operating range limit in my estimates.

The Citizens Budget Commission developed an overview of the CLCPA targets in Green in Perspective: 6 Facts to Help New Yorkers Understand the Climate Leadership and Community Protection Act.  The goals of the law include these greenhouse gas (GHG) emission reduction targets:

      • Reduce GHG emissions to 60 percent of 1990 emissions levels in 2030;
      • Generate zero GHG emissions from electricity production by 2040; and
      • Ensure GHG emissions are less than 15 percent of 1990 emissions levels in 2050, with offsets to reduce net emissions to zero.

I assumed that all the additional generating resources needed out to 2050 were wind and solar.

The New York Independent System Operator (NYISO) sponsored a study entitled  NYISO Climate Change on Resilience Study – Phase 1 that included four different projections for the winter peak electric capacity for 2040 and 2050: NYISO Gold Book, reference scenario, policy scenario, and CLCPA scenario.  Their CLCPA scenario estimates the amount of renewable capacity necessary to meet the CLCPA targets based on the following two considerations: all of the new capacity will be renewable to meet the 2040 electricity production target and the load will increase due to electrification of heating and transportation.  I used their estimates of the total electric capacity for my estimates.

Analysis

My methodology for estimating energy storage requirements is based on evaluation of an example worst-case meteorological period.  In the future CLCPA electric energy sector of New York, electricity will only be generated from hydro, nuclear, on-shore wind, off-shore wind, utility-scale solar, behind-the-meter (residential) solar, and other renewable.  I estimate capacity for each renewable source category and apply those estimates to the energy requirements during the worst-cast period to calculate deficits and then estimate how much power will be needed to cover those deficits.  All my analyses show that there are periods when the winds are light at night so significant energy storage will be required.  For example, I recently calculated energy storage required for the NYISO Climate Change on Reslience Study 2040 and 2050 renewable energy capacity projections for a light wind night time worst-case period on Jan 3-4 2018.

In this analysis I extended that analysis by assuming that Li-Ion energy storage would be to operate the batteries to maximize longevity out to ten years, i.e., use active thermal management and cycle the battery within a restricted 54% operating range.  The Comparison of Energy Storage Required and Potential Price Necessary to Prevent Deficit on January 3-4 2018 table lists the results for the two projections without restricting the battery operating range and the Combined Energy Storage Capacity and Cost With Storage 54% Limitation table incorporates the 54% operating range restriction. In 2040 the renewable capacity projection of 56,071 MW  requires $52.5 billion without restricting the battery operating range but limiting the range to 54% increases the price to $96.0 billion.  In 2050, the renewable capacity is greater at 71,859 MW, so the battery storage required is smaller.  Without the battery operating range restriction energy storage is expected to cost $43.1 billion but with the 54% operating range restriction the cost is increased to $80.4 billion.  These estimates are for 2040 and 2050 and even with the range restrictions that means that the batteries installed in 2040 will have to be replaced by 2050.  Therefore, the expected cost of the batteries needed for just energy storage is the sum or $176.3 billion.

Sanity Check

These numbers are so large that my first impression was that I must have done something wrong.  After double checking the calculations I think they are correct.  That leaves the approach assumptions.  My methodology depends on the estimate of the renewable energy resources using a limited set of historical meteorological data, my assumptions about the proposed renewable energy output as a function of wind and sun input, the battery assumptions, and the estimates of future load when electrification is increased.  I believe these data show the absolute need for a feasibility study that refines these assumptions.  The input meteorological data has to be comprehensive enough to represent the location and availablity of all the input renewable resources.  The refined energy output from wind and solar resources has to use the actual equipment output information that is proprietary and dedacted from public descriptions of proposed projects.  My interpretation of the battery assumptions needs to be verified and refined.  Finally, more detailed estimates of future load during concurrent renewable energy output assessment periods are needed.

Despite the limitations of my work I believe that I can make a few conclusions whatever the results of the refined analysis.  There is no doubt in my mind that energy storage will be a primary driver for future costs.  Importantly, the largest energy storage requirements may be during period of low renewable energy resources that do not necessarily coincide with highest energy loads.

Although I am concerned about these energy storage estimates being so high, there are reasons that they could also be low.  I am only trying to estimate how many batteries will be needed for energy storage.  In the future system that eliminates fossil-fired sources, it is likely that transmission support services such as frequency regulation, spinning reserve, and voltage support will also have to be provided by batteries and it is unlikely that batteries designated for energy storage can provide those services too.   In additon, the future load projections that I used were based on annual numbers.  Air source heat pumps become much less efficient as temperatures decrease so widespread adoption will cause a spike in energy use at very low temperatures that can only be predicted by looking at shorter intervals.

Implications

I believe that energy storage is the limiting factor for increasing renewable energy resources.  I cannot say it enough that there has to be a comprehensive feasibility study based on renewable energy availability using measured meteorological data. I recommend that the resources available at the University at Albany Weather & Climate Enterprise be employed for the meteorological data input.  They recently released an assessment on what it will take for New York to reach the renewable energy goals in the Climate Leadership and Community Protection Act in a white paper entitled Toward 100 Percent Renewable Energy in New York. The white paper provides more extensive documentation on the NYS Mesonet that I recommend as the primary source of historical meteorological data. It notes that “The siting and operation of renewable energy facilities depends on accurate, representative measurements and power-production forecasts that are used to predict short-term output (minutes to days) as well as cumulative future power generation over the next 20-25 years.”

While estimating cost may be a primary output from the proposed feasibility study, I believe that the unique requirements of the New York City load pocket also have to be considered in the feasibility study.  There are specific transmission constraints for New York City that require in-city generation that have been implemented to prevent the reoccurence of blackouts.  Given that space to develop sufficient solar and wind resources within the city is unavailable that necessarily means that energy storage has to play a significant role.  Whether sufficient energy storage can be sited safely within the city has to be determined.

One potential approach to reduce energy storage costs is to over-build wind and solar facilities.  For example, Dr. Richard Perez at SUNY Albany recommends “oversizing and proactively curtailing wind and solar” resources.  However, as shown in my work there are significant periods of light winds at night when no over-building will eliminate the need for energy storage.  Moreover, there are issues related to over-building such as constraint payments.  Other options that could be considered include: energy efficiency, time and load sensitive rates, and a buildout of the transmission system to gain support from far away regions.  There are limitations to those options too.  There is a limit to energy efficiency gains when electrification of heating and transportation is increasing load at the same time.  Peak shaving with time and load sensitive rates is great in theory but when people need heat, they are going pay whatever is needed.  The idea that all we have to do is add enough transmission to import the power from someplace where the wind is blowing disregards the size of the weather systems that cause the worst-case conditions.

A quote attributed to Robert Louis Stevenson, “Sooner or later everyone sits down to a banquet of consequences”, is apropos.  It would be far better to do a comprehensive feasibility study as soon as possible to determine if the CLCPA targets can be met now than to try to muddle through trying to rush ahead implementing something that will have far worse consequences to the citizens of New York than the purported problem that was the rationale for the CLCPA.

NYISO Climate Impact Study Energy Storage Requirements

On December 17, 2019, the New York Independent System Operator (NYISO) Installed Capacity Working Group meeting included a presentation on the NYISO Climate Change on Resilience Study – Phase 1 by Eric Fox from Itron.  The study included estimates of future load expected as a result of the Climate Leadership and Community Protection Act.  This post primarily addresses that aspect of the study as it pertains to energy storage requirements.

Background

The purpose of the analysis was to develop long-term energy, peak, and 8,760 hourly load forecasts that reflect the potential impact of climate change.  Itron evaluated temperature trends using state climate impact studies and developed scenarios that reflect state policy goals with climate change impacts.  The summary of results states:

      • Analysis of weather trends across the state show statistically significant increase in average temperatures of 0.5 to 1.1 degree per decade
      • State average 0.7 degrees per decade
      • Temperatures on the coldest days are increasing faster than temperatures on the hottest days
      • Trend likely to continue through the future and could be worse depending on long-term greenhouse gas path
      • Warming trend will contribute to increase in summer peak demand and lower winter peak demands. Increase in cooling energy requirements will partially be offset by declining heating related requirements.
      • State policy to address greenhouse gas emissions will have more impact on loads than the impact due to temperature trends. The end-use modeling approach provides a framework for translating energy policy into impacts on energy, hourly loads, seasonal peak demands, and changes in emissions of greenhouse gases.

The primary topic of this post is the impact of state policies but I have to address the weather trends in this report.  As far as I can tell the primary source for the temperature trends presented is a study by two economists.  While on the face of it measuring temperature trends should be relatively simple in fact it is not and I have no faith that a couple of economists are aware of any nuances to these numbers.  To cut to the chase I do not find a claim that over my lifetime (approaching 70) that the average temperature of New York State has gone up nearly 5 degrees.  I am sure that if I dug out representative climatic data that I would find a different story but I don’t have the time.  Instead I refer readers to a recent post at a different site that shows that the temperature sensing network that was designed to be representative does not show such alarming trends.

More important is the report’s conclusion that temperatures on the coldest days are increasing faster than temperatures on the hottest days.  This is a typical result from analyses of the data but is often ignored.  The only reason I can imagine that it is not publicized more is that it can be argued that warmer winter days are more beneficial than not.  That is to say it does not fit the alarmist narrative.

Future Load Analysis

Itron’s analysis of the load impacts of the CLCPA focused on the residential sector.  They estimated the reduction in residential emissions needed to meet the CLCPA targets.  Then they calculated the cumulative increase in electricity needed overall and on the average per residence.  They found that in their policy case with accelerated energy efficiency gains and behind-the-meter PV adoption that the savings outweigh gains from electric vehicles and electrification. However, the CLCPA scenario has much higher electrification targets and state-wide annual energy use goes up about 50%.  They predict that the summer peak will increase from 43,317 MW in their reference case to 57,109 MW in the CLCPA scenario.  Importantly they expect that the electric load peak will shift from summer to winter.  They predict that the winter peak will increase from 31,131 MW in their reference case to 71,859 MW in the CLCPA scenario.

My primary concern in this post is to estimate the amount of energy storage that will be needed to meet the CLCPA targets.  My initial thought when I saw the projections available in this analysis was that I could repeat the evaluation of energy storage necessary that I did using the Citizen’s Budget Commission report with another projection.  While doing this analysis I came to the conclusion that the emphasis of this post should not be on those results but rather the future load projection calculations.

My methodology for estimating energy storage requirements is based on evaluation of an example worst-case meteorological period.  In the future CLCPA electric energy sector of New York, electricity will only be generated from hydro, nuclear, on-shore wind, off-shore wind, utility-scale solar, behind-the-meter (residential) solar, and other renewable.  I estimate capacity for each renewable source category and apply those estimates to the energy requirements during the worst-cast period to calculate deficits and then estimate how much power will be needed to cover those deficits.  All my analyses show that there are periods when the winds are light at night so significant energy storage will be required.

The NYISO Climate Change on Resilience Study – Phase 1 Forecast Comparison of Winter Peak figure lists four different projections for the winter peak electric capacity for 2020, 2030, 2040, and 2050.  The most notable thing is the massive increase in capcity needed for electrification of the residential, commercial, industrial and transportation sectors in the CLCPA scenario.  I calculated energy storage required for their 2040 and 2050 capacity projections for a light wind night time worst-case period on Jan 3-4 2018.

The Comparison of Energy Storage Required and Potential Price Necessary to Prevent Deficit on January 3-4 2018 table lists the results for the two projections.  For the 2040 capacity projection of 56,071 MW $52.5 billion would be needed for the Li Ion storage batteries necessary to provide electricity during the renewable deficit periods.  The increased capacity projected for 2050, 71,859 MW, reduces the battery storage cost to $43.1 billion.  As long as batteries are this expensive over-building renewable capacity is a cheaper alternative so developing a  way to optimize the costs of energy storage relative to the cost of additional renewable development would be a worthwhile investment.

My work on the CLCPA impacts on the electric system has emphasized energy storage requirements. My approach has three components: the potential renewable resources available, the generating resources capacity from each generation type, and how much load will be needed by the system. As shown below, the load projection for worst-case energy storage may differ from a worst-case analysis that uses the traditional peak energy load.

 

I believe an analysis of state-wide solar and wind resources is absolutely necessary in order to ultimately determine what could be available.  NYISO had the Analysis Group do a forward-looking assessment of the fuel and energy security of the New York electric grid during winter operations that targeted potential reliability risks and impacts under severe winter conditions and adverse circumstances regarding system resources, physical disruptions, and fuel availability.  The November 2019 final report was titled: Fuel and Energy Security In New York State: An Assessment of Winter Operational Risks for a Power System in Transition.  I have previously analyzed the effect of winter peaks and used a period, 12/29/17 to 1/12/2018, chosen based on a cursory check of extreme conditions.  The Analysis Group did an analysis over the last 25 years and this period was called out.  They defined extreme weather events including the largest increase above average daily load over a long period as 14 days from 12/25/2017 to 1/8/2018 and more extreme shorter periods where they found in the last 25 years the fourth lowest 3-day cold snap was 1/4/2018 to 1/7/2018.  Note that my attempt to estimate potential solar and wind resources came up with an overlapping time frame because energy storage requirements are driven by the renewable resources available.  The question that needs to be answered is whether there are periods when the loads are unremarkable but the availability of renewables is so low that even greater energy storage resources are needed.

The second component needed is the generating resources capacity from each generation type.  For my analysis of the Citizens Budget Commission results I calculated the MW capacity based on their power (GWh) estimates.  Itron listed capacity in MW for 2040 and 2050.  I arbitrarily split the relative amount for each renewable type and matched their totals.  Eventually I hope that the State does a least-cost optimization study that balances how much of each renewable generating source and energy storage is needed.

The final component is how much load will be needed by the system.  In this evaluation I arbitrarily chose to scale the observed load during the Jan. 3-4 2018 study period by the Citizen’s Budget Commission projected annual load in 2040 over the existing annual load.  As far as I can tell, both the CBC report and the Itron analysis estimated future loads from increased electrification based on annual numbers.  I believe that the increased load from heating electrification will increase the magnitude of the peak load more than the annual load will increase.  In particular, the preferred option for retrofitting home heating is an air source heat pump.  During extreme cold (less then zero degrees F) air source heat pumps don’t work efficiently so in order to keep warm people will have to rely on much less efficient radiant heating.  I have shown that this will exacerbate the winter peak and further complicates the winter peak load projections.  In order to incorporate this effect into the future winter peak projections detailed temperature and wind data should be used.

Conclusion

The NYISO Climate Change on Resilience Study – Phase 1  is a useful report and makes a couple of important points.  I am not a big fan of studies that try to estimate the impact of climate change on future operations simply because weather variability is larger than climate variability.  I believe trying to tease out a small climate effect is mostly bogus.  The report does note that “state policy to address greenhouse gas emissions will have more impact on loads than the impact due to temperature trends”.  They also come to the same conclusion that I have that the winter peak will be more important in the future.

There also are important limitations.  I think that future capacity estimates based on annual energy usage have to be replaced by estimates based on shorter time periods and that the air source heat pump inefficiency issue has to be considered.  The analysis underscores the importance of a comprehensive study of renewable resource availability relative to expected load when the CLCPA electrification requirements kick in because the peak energy storage requirement may not necessarily occur during the peak load period.

Two points need to be emphasized for the energy storage calculations using my methodology and the projected capacity from this report.  Energy storage costs are extraordinariy high.  In 2040 energy storage could cost $52.5 billion for 56,071 MW of capacity and in 2050 the cost would be $43.1 billion for , 71,859 MW of capacity.  Incredibly according to the NREL report Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System on the life expectancy of lithium-ion energy storage systems: “Without active thermal management, 7 years lifetime is possible provided the battery is cycled within a restricted 47% DOD operating range. With active thermal management, 10 years lifetime is possible provided the battery is cycled within a restricted 54% operating range.”  In other words the energy storage costs for the 2040 to 2050 time frame needs to sum the costs for a total of $95.6 billion! These numbers certainly support the notion that over-building renewable capacity will reduce energy storage costs and point to the need to optimize the tradeoff between renewable capacity and energy storage.

Citizens Budget Commission Getting Greener Report Summary

On December 9, 2019 the Citizens Budget Commission (CBC) released a report entitled Getting Greener: Cost-Effective Options for Achieving New York’s Greenhouse Gas Goals  that addresses the impacts of the Climate Leadership and Community Protection Act (CLCPA).   There is much to like about the report but I disagree with a few of their recommendations and have concerns about some of the methodology.  In order to do this report justice, I prepared three technical posts.  Once completed I realized that those posts were too wonky for a general audience so I have prepared this summary post.

If you have an interest in New York energy policy I recommend that you read the entire document.  It is well written, comprehensively covers many of the issues associated with the CLCPA, and makes estimates of the resources needed to implement the CLCPA.  My first post discussed their findings, the second post addressed their renewable energy forecast to meet the CLCPA and the final post calculated the energy storage requirement for an example winter peak period.  The numbers and analyses described in this summary are documented in those three posts.

Background

The CBC is a nonpartisan, nonprofit civic organization whose mission is “to achieve constructive change in the finances and services of New York City and New York State government”.  They claim to serve the public rather than narrow special interests try to preserve public resources, whether financial or human; and focus on the well-being of future New Yorkers which they say are “the most underrepresented group in city and state government”.

The CBC Energy Policy Committee managed the development of the report.  It was prepared for CBC by Seth Hulkower, President of Strategic Energy Advisory Services.  Apparently, this project has been in the works for a long time because the “initial findings of this report were presented at a CBC research conference held in New York City in December 2018”. The report was not completed until December 2019 because of New York’s changing policies over the past year.  In particular, the Climate Leadership and Community Protection Act (CLCPA) was promulgated in July 2019. They made revisions based on feedback from external reviewers and staff at the Public Service Commission and the New York League of Conservation Voters but noted that their willingness to assist in the research does not “imply any endorsement of the report’s findings and recommendations”.

Findings

In my post on the CBC findings  I annotated their summary.  The CBC findings support my position that the State needs to do a feasibility study to determine how the CLCPA could be implemented. The 2040 requirement to eliminate the use of fossil fuels by 2040 will require enormous investments and their findings point out the financial and flexibility risks if those investments are funded incorrectly.  They also raise the concern that existing sources of nuclear and hydro zero emitting generating power are not currently encouraged to remain in operation and suggest that discouraging natural gas infrastructure is counter-productive.  The final finding is the observation that there is much work to be done to implement the CLCPA targets for other sectors.  I agree with all these concerns.

The report makes a number of recommendations.   I agree that the State should prioritize investments based on performance, look beyond New York for additional sources of reduction support, eliminate self-imposed constraints on natural gas use, and retain our existing nuclear energy capacity as long as possible.  I do not think that a carbon pricing system will work if it only applies to New York or a limited region so I disagree with their recommendation for one even if it is across all sectors. Their transportation recommendation to think beyond electric vehicles for reductions makes sense where public transit investments could be cost-effective but that precludes rural areas.

Renewable Resources Projection

The CBC report is useful because it provides an estimate of the renewable resources required to meet the CLCPA 2040 fossil-free electric sector target.  The State has not admitted that 2040 load is going to be substantially higher than the current levels but this report makes a compelling case for a significant increase in annual load.  Their results indicate that “as New York moves to a path of decarbonizing heating and transportation in New York, the total electric demand will rise to 211,100 Gwh by 2040. To serve that demand with 100 percent non-emitting resources, nearly 94,000 Gwh of additional renewables will need to be added, a total that is roughly double the amount to be added from offshore wind (37,800 Gwh) and distributed solar (8,400 Gwh) now set by the CLCPA.”

I used their projections of the resources needed to meet the energy requirements (GWh) to estimate the power capacity (MW) needed.  As shown in the CBC Forecast of 2040 Capacity (MW) Resources to Meet CLCPA Goals table I calculated that New York would have to build 11,395 MW of residential solar, 16,117 MW of utility-scale solar, 18,457 MW of on-shore wind and 16,363 MW of off-shore wind to meet the increased load estimated by CBC.

I put those numbers in perspective.  For residential solar I used the rule of thumb that you need 66 square feet to generate 1kW of solar energy and that would require 36 solar panels.  That means that nearly 27 square miles of residential roofs would have to be covered by over 364.6 million solar panels to meet the 11,395 MW estimate.  For utility-scale solar I found a recent application that showed that each MW of utility scale solar will cover 7 acres so 112,816 acres or 176 square miles will be needed to meet the 16,117 MW of utility scale solar output estimate.  Assuming a 4.8 MW on-shore wind turbine would mean that over 3,845 on-shore wind turbines would be needed to meet the 18,457 MW output estimate.  One of the recently awarded off-shore wind project proposes to use 10.2 MW turbines and that means that 1,604 wind turbines would be needed to meet the 16,363 MW output estimate.

I do have one concern about the CBC forecast of resources to meet CLCPA goals.  In order to make a better estimate of the resources it is necessary to look at peak periods rather than just annual loads.  It is inappropriate to assume that a “smart” grid and more energy efficiency is going to eliminate electric load peaks so that they do not have to be considered.  Residential heating and transportation electrification will impact the winter peak very likely shifting the annual peak to winter simply because you cannot shift heating when it is very cold.   However, it is unfair to ask the CBC to address the winter peak expected load because it is a very complicated problem and would take a lot more effort.

I took a look at a winter peak period renewable resources derived from the CBC forecast.  I made a first cut attempt to estimate the capacity necessary to meet future energy load but I made a crude assumption that the peak load could be met with the resources needed to meet the annual energy estimate.  The results shown in the January 3-4 2018 Winter Peak With CBC Forecasted 2040 Capacity Resources to Meet CLCPA Goals table are influenced by the assumptions I made for off=shore wind turbine output because there were two no-wind energy output periods during the two-day winter peak period I analyzed.   I was surprised to see that the wind resource went to zero not only when the winds were light on January 3 but also when a deep low pressure developed and the wind speeds exceeded the upper wind speed cut-off I used on the very next day.  As a result of these conditions, there were twenty hours out of 48 hours that the output from all the resources available to New York in the CBC scenario for 2040 were negative and would require energy storage to keep the lights on, homes heated and vehicles charged.

Energy Storage

If the state goes ahead to build the amount of renewable energy that the CBC estimated would be necessary to meet the 2040 goal, it still does not preclude the need for energy storage.  The table with the January 3-4 data shows that there was a fifteen-hour period from January 3, 2018 at 1600 until January 4, 2018 at 0600 with hourly storage deficits totaling 134,545 MWh.  I used that period to calculate the amount of storage needed.

I assumed that the least cost energy storage approach would maximize energy storage duration based on lower costs per MWh in a recently released report from the National Renewable Energy Lab (NREL): “2018 U.S. Utility-Scale Photovoltaics-Plus-Energy Storage System Cost Benchmark”.  I arbitrarily chose different duration and capacity systems so that the battery systems covered the negative load to generation hours.  The Estimated Energy Storage Required and Potential Price Necessary to Prevent Deficit on January 3-4 2018 table summarizes the energy storage needs and my projection for the amount of different duration energy storage systems needed.  Finally, I adapted the results from the NREL study to estimate the cost of this amount of storage.  The table lists the estimated cost for the energy storage necessary to meet this winter peak period at a staggering $47 billion.

But that’s not all.  NREL reported on an analysis of the life expectancy of lithium-ion energy storage systems in 2017 in Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System.  The study tested batteries to simulate how long they would last in real-world conditions by reaching a certain depth of discharge rates and testing battery degradation over time.  Under NREL’s scenarios, an energy storage system is expected to last between seven and 10 years. The report states: “Without active thermal management, 7 years lifetime is possible provided the battery is cycled within a restricted 47% DOD operating range. With active thermal management, 10 years lifetime is possible provided the battery is cycled within a restricted 54% operating range.”

Conclusion

Although I am not on board with the CBC’s desire to do something because it is “necessary”, I think it is important that an organization that feels that is necessary realizes the magnitude of the effort and the very real possibility of massive future financial exposure.  The CBC report underscores the potential that doing something wrong would not be in the best interests of the State no matter how noble the intention.

There is great value in the estimate of future energy use provided in the CBC report because the State has yet to provide their estimates.  Although I think that there are limitations to their analysis, I also think they have erred on the conservative side.  The actual resources needed for peak period planning could well be substantially higher.

I need to re-iterate the inescapable conclusion that over-building more wind turbines or solar cells does not preclude the need for substantial energy storage.  The cost of the energy storage using LI ion batteries needed to meet an example winter peak period is estimated to be $47 billion dollars.  That value is probably conservative because of limitations on the operating range to extend life expectancy out to ten years.  The size of the numbers shown is sobering and cries out for an “official” projection from the State.

I can only conclude that the State of New York must do a feasibility study to refine the CBC analysis of future renewable resources needed as soon as possible.  The analysis has to be detailed enough that the energy storage requirements can be projected so that a full cost estimate of compliance with the CLCPA can be estimated.  In the absence of this analysis the State could well be headed down a financial sinkhole that will do much more harm than good.

Citizens Budget Commission Getting Greener – Energy Storage Estimate

Update: I have prepared three technical posts on this report: Once I completed these three posts, I realized that they were too wonky for a general audience.  The first post discussed their findings, the second post addressed their renewable energy forecast to meet the CLCPA, and the third post calculates the energy storage requirement for a winter peak period.  Because the CBC study is so important, I have prepared a less-technical summary that hits the highlights of all three posts.

On December 9, 2019 the Citizens Budget Commission (CBC) released a report entitled Getting Greener: Cost-Effective Options for Achieving New York’s Greenhouse Gas Goals  that addresses the impacts of the Climate Leadership and Community Protection Act (CLCPA).   There is much to like about the report but I disagree with a few of their recommendations and have concerns about some of the methodology.  In order to do this report justice, I have prepared three posts.  If you have an interest in New York energy policy I recommend that you read the entire document.  It is well written, comprehensively covers many of the issues associated with the CLCPA, and makes estimates of the resources needed to implement the CLCPA.  The first post discussed their findings and the second post addressed their renewable energy forecast to meet the CLCPA.  This post calculates the energy storage requirement for a winter peak period.

Background

The CBC is a nonpartisan, nonprofit civic organization whose mission is “to achieve constructive change in the finances and services of New York City and New York State government”.  They claim to serve the public rather than narrow special interests try to preserve public resources, whether financial or human; and focus on the well-being of future New Yorkers which they say are “the most underrepresented group in city and state government”.

The CBC Energy Policy Committee managed the development of the report.  It was prepared for CBC by Seth Hulkower, President of Strategic Energy Advisory Services.  Apparently, this project has been in the works for a long time because the “initial findings of this report were presented at a CBC research conference held in New York City in December 2018”. The report was not completed until December 2019 because of New York’s changing policies over the past year.  In particular, the Climate Leadership and Community Protection Act was promulgated in July 2019. They made revisions based on feedback from external reviewers and staff at the Public Service Commission and the New York League of Conservation Voters but noted that their willingness to assist in the research does not “imply any endorsement of the report’s findings and recommendations”.

Future Energy Storage Requirements

One of my primary concerns with the CLCPA is how much energy storage would be needed.  In order to determine how much is needed we need to know the difference between expected generation and expected load at the time of the greatest difference.   I applaud the CBC for developing an estimate of the renewable energy requirements of this legislation.  The MW capacity I derived from the CBC work can be used to estimate how much energy storage would have been required to meet an example winter peak period in January 2018 for a scenario that includes the added load due to heating and transportation and the 2040 renewable energy resources.  I previously made an estimate of the energy storage requirements for a summer peak period but I only considered existing load.

The first step is to determine the renewable capacity in MW expected to be necessary to meet the future load.  The CBC Forecast of 2040 Capacity (MW) Resources to Meet CLCPA Goals table lists those resources.  In the previous post I described how I used a combination of historical and meteorological data to estimate future output for the two-day winter peak period.  Results are shown in the January 3-4 2018 Winter Peak With CBC Forecasted 2040 Capacity Resources to Meet CLCPA Goals table. This table shows that there was a fifteen-hour period from January 3, 2018 at 1600 until January 4, 2018 at 0600 with hourly storage deficits totaling 134,545 MWh.  That period will be used to calculate the amount of storage needed.  In the earlier analysis I looked at five-minute data but this one will only look at hourly values.

In the absence of some sophisticated methodology to determine how best to provide the necessary energy storage, I arbitrarily limited the battery resource duration to eight hours and then picked different battery capacities until I had a positive margin (generation plus storage minus load).  I assumed that the least cost energy storage approach would maximize energy storage duration based on lower costs per MWh in a recently released report from the National Renewable Energy Lab (NREL): “2018 U.S. Utility-Scale Photovoltaics-Plus-Energy Storage System Cost Benchmark”.

In the Estimated Energy Storage Required and Potential Price Necessary to Prevent Deficit on January 3-4 2018 table I summarize the energy storage needs and my projection for the amount of different duration energy storage systems needed.  I chose three 8-hour duration battery systems of 1,200, 7,600 and 7,700 as the primary systems and four other battery systems to erase the remaining deficits.  Note that when I did this kind of analysis using 5-minute data that there were higher peaks amongst the hourly values.  If that is the case for this scenario, then it would require even larger batteries.

Future Energy Storage Costs

The aforementioned NREL battery benchmark report estimated costs for a limited number of battery durations.  I calculated costs for different duration energy storage costs in a post at What’s Up With That.  The Calculated Cost Breakdown $ per kWh Parameters for a US Li-ion Standalone Storage System for Different Durations table shows the methodology used to calculate the battery costs for this estimate.  The estimated cost for the energy storage necessary to meet this winter peak period is a staggering $47 billion.

But that’s not all.  NREL reported on an analysis of the life expectancy of lithium-ion energy storage systems in 2017 in Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System.  The study tested batteries to simulate how long they would last in real-world conditions by reaching a certain depth of discharge rates and testing battery degradation over time.  Under NREL’s scenarios, an energy storage system is expected to last between seven and 10 years. “Without active thermal management, 7 years lifetime is possible provided the battery is cycled within a restricted 47% DOD operating range. With active thermal management, 10 years lifetime is possible provided the battery is cycled within a restricted 54% operating range,” NREL said.

My estimate of the batteries needed assumed 100% discharge and the NREL results indicate that smaller battery discharge is necessary in order to prolong the life expectancy up to ten years.  In other words if by magic we could install all the renewable resources and all the necessary battery energy storage today, then by the time 2040 rolled around most of the renewable resources would be reaching the end of their life expectancy and we would have to start installing the third round of a significantly larger number of batteries than I projected.

 Conclusion

As documented in my previous post, energy storage will be required to keep the lights on, homes heated and cars charged on calm nights.  No amount of over-building solar and wind will remove that constraint.  The cost of the energy storage using LI ion batteries needed to meet an example winter peak period is estimated to be $47 billion dollars.  That value is probably conservative because of limitations on the operating range to extend life expectancy out to ten years.

I can only conclude that the State of New York must do a feasibility study to refine the CBC analysis of future renewable resources needed.  The analysis has to be detailed enough that the energy storage requirements can be projected so that a full cost estimate of compliance with the CLCPA can be estimated.