New York Resource Adequacy Proceeding Comments

The New York State Public Service Commission (PSC) issued an order commencing a proceeding to examine how to reconcile resource adequacy programs and the State’s renewable energy and environmental emission reduction goals. This post describes the comments I submitted in this proceeding.

Materials and information are available in the Department of Public Services (DPS) resource adequacy matters docket Case 19-E-0530.  .  According to the Order Instituting Proceeding and Soliciting Comments, the inquiry is “necessitated by the Commission’s statutory obligations to ensure the provision of safe and adequate service at just and reasonable rates. Costs to consumers are a primary and ultimate consideration, recognizing that the necessary investments in resources must have sound economics.”

The PSC order solicited comments on the following questions.  Does the New York Independent System Operator (NYISO) have sufficient resource adequacy evaluation mechanisms in place to deal with the State’s ambitious renewable energy and environmental emission reduction goals?  Do the policies and market structure mechanisms insure just and reasonable consumer rates? There were several specific questions about existing products and their value with respect to costs.  Finally, there was a general question about the State’s role with respect to resource adequacy and request for recommendations for what to do next.

I submitted comments because I am not sure that the Climate Leadership and Community Protection Act (CLCPA) can be implemented so that it does not jeopardize safe and adequate energy service at just and reasonable rates. I based the comments on evaluations I did for previous posts on Solar Issues in Upstate New York , CLCPA Solar and Wind Capacity Requirements and CLCPA Energy Storage Requirements.

My filed documents (dated 9/16/2019 as a filing on behalf of an individual) illustrate my concerns with two examples.  I prepared a white paper that provides an initial estimate of the likely energy storage component requirement based on real world data.  It shows that at night when winds are light the energy produced from these sources will have to be supplanted with stored energy if New York shuts down all its fossil generation.  Given the extraordinary cost of battery energy storage I estimate that the batteries alone will cost over $12 billion to replace existing fossil generation and Indian Point after it retires.  The second example describes a potential problem with winter peak loads once the CLCPA is implemented.  Because of the stringency of the law, home heating is going to have to be electrified.  The preferred retrofit option is an air source heat pump.  However, they don’t produce heat when the temperature gets below zero so homeowners will need a backup system and the cheapest alternative is radiant heat which is much more inefficient.  As a result there will be a spike in electrical load that cannot be avoided.

Both examples used data from the NYS Mesonet.  I believe the best way to determine resource adequacy is to base the analysis on historical meteorological information as shown in the examples.  In order to determine the amount of energy storage you have to calculate how much wind and solar power is available and when.  In order to determine the effect of air source heat pumps meteorological data from the winter 2017-2018 peak load period was used.  I recommended that historical meteorological data be used to characterize potential solar and wind energy production to determine the feasibility of the CLCPA emission reduction target that eliminates emissions from electricity production by 2040.

In addition, I believe that the State needs to do a cumulative environmental impact assessment of this regulation.  The problem is that while an individual industrial wind facility or solar facility may not have a significant environmental impact the cumulative impact of all the facilities necessary to provide enough power to meet the reliability needs of the state could have significant environmental impacts.  For example, if one raptor gets killed by every ten wind turbines that might be acceptable but if we need a thousand wind turbines is one hundred raptors per year acceptable?

My final recommendation is for an independent review of the findings of the feasibility studies.  The CLCPA is the result of political pandering and the likelihood that a feasibility study would be subject to political influence is high.  The only way I can think of to prevent that is to establish an independent group to review the findings.  Membership should deliberately be chosen to represent both “sides” of vested interests in the outcomes.  They may not be able to come agree but their evaluation report can list where they have agreed to disagree and that will be useful for the public.

I think it is obvious that the resource adequacy proceeding must determine if the CLCPA can be implemented such that it does not jeopardize safe and adequate energy service at just and reasonable rates.  If renewable resources and energy storage are inadequate during the winter peak, then safe and adequate energy service could easily be jeopardized.  No jurisdiction has ever successfully reduced greenhouse gas emissions by developing renewable energy resources and managed to keep prices down and I see no reason that New York will be able to reverse that result.  Most importantly, the increase in energy prices will affect those who can least afford the increased costs.

If you are a resident of New York I ask that you submit comments to the DPS resource adequacy matters docket Case 19-E-0530 supporting the request for comprehensive, independent feasibility and cumulative environmental impact assessments.

Rocky Mountain Institute – Natural Gas Plants are Doomed

According to Bloomberg author David Baker Gas Plants Will Get Crushed by Wind, Solar by 2035.  The basis for this claim is a Rocky Mountain Institute (RMI) study.  This post looks at this claim in the context of New York State energy requirements.

Baker’s description of the study “The economics of clean energy portfolios”, states:

“Natural gas-fired power plants, which have crushed the economics of coal, are on the path to being undercut themselves by renewable power and big batteries, a study found.  By 2035, it will be more expensive to run 90% of gas plants being proposed in the U.S. than it will be to build new wind and solar farms equipped with storage systems, according to the report Monday from the Rocky Mountain Institute. It will happen so quickly that gas plants now on the drawing boards will become uneconomical before their owners finish paying for them, the study said.”

The RMI study claims that a “clean energy portfolio” can “provide the same services as power plants, often at net cost savings”.  These portfolios combine the following different resources: energy efficiency, demand flexibility, distributed and utility-scale battery energy storage, and variable renewable energy.  In previous work I have come to the conclusion that for New York State the critical planning scenario will be winter time heating caused high energy demand night-time loads when winds are calm. Keep in mind that during winter it is not only a short-term load concern but the shorter days and generally weaker winds mean that seasonal solar and wind resources are so low that seasonal storage will likely be necessary.  Let’s look at each of these resources in more detail in that context.

According to RMI the energy efficiency resource includes “Physical measures, software controls, or other strategies to reduce the amount of energy required to perform a given service (e.g., insulation and smart thermostats to reduce heating and cooling energy use)”.  Because there are tangible savings many structures already have insulation and smart thermostats.  In fact, I doubt that my home is all that unusual in that since we purchased the home in 1981, we added insulation to the attic a couple of times, insulated the walls, put in double paned insulated windows and doors, and have a smart thermostat.  Anything else we do will cost quite a bit and not get that big an energy reduction.  As a result, I believe that there is a limit to how much energy can be reduced with the proposed energy efficiency resource.  More importantly, the New York Climate Leadership and Community Protection Act has a greenhouse gas emission reduction target that will require electrification of wintertime heating.  RMI cannot claim a reduction in wintertime electric energy when there is a requirement for more winter electric energy use.

The study describes demand flexibility as: “Load controls to enable electricity consumption to shift through time without reducing overall energy use or service quality (e.g., thermal storage in water heater tanks, managed charging of electric vehicles)”.  In general, the theory that load controlling smart meters can make a significant difference is mostly “theory”.  As before in the case of wintertime heating, how much load shifting will be possible?  It is appropriate to point out that the four case studies that “proved” their claims were on the west coast, Florida, mid-Atlantic and in Texas.  None of these regions have winter peaks now and I doubt that even if winter heating is electrified it is unlikely to shift the peak to winter.

The RMI study defines variable renewable energy as: “Behind-the-meter and front-of-the-meter distributed and utility-scale solar photovoltaics (PV) and wind turbines that provide weather-dependent, non-dispatchable energy”.  The resources necessary in this study use a “clean energy portfolio optimizer” that “draws on the other components to define the constraints and objective function of a linear program that finds the lowest-cost portfolio of resources that can provide at least as much monthly energy, capacity during the 50 peak hours, and single-hour ramp capability during the highest period of system-level net-load ramp as the announced natural gas-fired power plant, while staying within resource potential limitations.”  Therein lies a potential fatal flaw for New York.  RMI minimizes the magnitude of peak load impacts with its energy efficiency and demand flexibility resources but averages out renewable energy deficits by using monthly energy and a limited number of peak hours.  The only way to determine if their portfolio will work is by evaluating shorter time periods, at longest an hourly period but even shorter would be better, over years of real-world data.  Based on my analysis of real-world examples I believe it is possible that the worst-case planning scenario will not be the peak load but the minimum renewable energy output.

Finally, the study includes battery energy storage: “Dedicated battery storage assets, either in front of the meter or behind the meter, providing energy balancing and flexibility via controlled charging and discharging.”  RMI published a study in 2015 that describes 13 different services that battery energy storage can provide.  There is a large gap between saying that batteries can provide the services and how they would do that.  Again, an hour-by-hour feasibility needs to be done to determine if this is possible.  In addition, RMI claims more value by stacking these services from “the same device or fleet of services”.  In other words, they claim that one battery system might be used, for example, for frequency regulation, voltage support, and energy storage.  A much more sophisticated study than this overview study is needed to determine whether that is feasible.  Frequency regulation and voltage support might require batteries to be at mid-charge levels to balance peaks and valleys whereas you want your energy storage to be at the maximum charge for use when renewables are not available.

In general, this study chooses how it wants to treat its resources.  There are hopeful assumptions for distributed resources and battery energy storage that have no track record.  There is no consideration of life-cycle resources needed for all the batteries, solar panels and wind turbines. Finally, while the treatment of the technological components necessary to provide the resources are overly optimistic in my opinion, their treatment of costs is much worse.  Both current costs and expected cost expectations in the future are more aspirational than rational.

No doubt this study will be cited as proof that natural gas is not necessary for the future because renewables can do everything they do cheaper.  It would take an electrical engineer with transmission and generation expertise to fully evaluate this study.  However, there are enough broad assertions and convenient assumptions that I do not take this study as definitive evidence that a clean energy portfolio will be able to replace natural gas fired power plants anytime soon in New York.

Climate Forecast Lessons from Dorian

Although I am a meteorologist with over 40 years of experience, I have been told that does not qualify me to have an “expert” opinion on the science of climate change.  Nonetheless, I believe my background and experience qualifies me to make a few points about the model-based projections of climate change relative to the forecasts for Hurricane Dorian.  Don’t ever forget that model projections are the basis for the “climate crisis” rhetoric that we are bombarded with on a daily basis.

A quick internet search found this very well done forecast for Dorian on August 29, 2019.  Meteorologist Tim Pandajis from WVEC Channel 13 in Norfolk, VA explains the current status of the storm on August 29, the forecast for the next several days, but also explains many of the reasons why the forecast is uncertain.  I particularly liked his explanation because it includes spaghetti plots.  At 8:04 in the video he shows how different models are seeing things differently and his presentation shows how different models predict how the storm will move and the timing.  Of course as it turned out Dorian behaved quite differently than any of the forecasts.

Given the constant changes to the forecasts for Dorian I am sure many recall the old saying that meteorology is the only profession where you can be wrong most of the time and still keep your job.  Reality is much different.  For me there are two things to keep in mind.  On September 1 the storm reached peak intensity but it also stalled.  The forecast intensity for the rest of the storm only went down when it became obvious that the storm intensity was going down.  The reason the intensity went down is that the hurricane sat in one place for so long that it brought cold water up to the surface.  Hurricanes need warm water to maintain intensity or grow and the cold water affected the intensity.  It is interesting that the models did not incorporate that effect or did not incorporate enough of that effect.  However, I am confident that the models will be revised to address that in the future.

When I graduated with my MS of in meteorology in 1976 three to five-day forecasts were not that good but they have improved a lot.  I ascribe that improvement in large part because weather forecasts are always being tested.  Whenever there is a poor forecast the models and the forecasters learn from that and improve their products going forward.  The climate forecasts that predict imminent and inevitable climate catastrophe do not have that advantage.  The National Weather Service defines 30-year averages as a climatic normal.  Using that time-period a climate model forecast should be tested against a 30-year weather average of observations.  Clearly there are many fewer opportunities to test a climate forecast model as opposed to a weather forecast. In addition, my experience with simpler models is that you can get the “right” answer for the wrong reason.  Weather forecast models address this problem by the large number of tests.  If they adjust the model for the wrong reason it may work once but the error will show up later so a different adjustment is tried until they get it right.  Climate models will never be able to correct if they have the wrong reason in our lifetimes.

The final lesson from Dorian is forecasting uncertainty.  As Tim Pamdajis showed with spaghetti plots in his presentation there was enough uncertainty to make a difference on hurricane response actions to take for the forecasts on August 29.  On the other hand, the climate model projections are portrayed in the media and by advocates as absolutely certain.  None of the caveats provided by the modelers are acknowledged in the hue and cry about a climate emergency.  The reality is that there are a range of modeled projections for future climate and, for the most part, only the most extreme impact results are publicized and those are the ones that are the basis for the “climate emergency”.

These lessons from Dorian support my belief that climate model forecasts cannot be trusted enough to believe that there is a climate emergency.  I am not alone.  Richard Lindzen commented on climate modeling for greenhouse gas effects:

“Here is the currently popular narrative concerning this system. The climate, a complex multifactor system, can be summarized in just one variable, the globally averaged temperature change, and is primarily controlled by the 1-2% perturbation in the energy budget due to a single variable – carbon dioxide – among many variables of comparable importance.  This is an extraordinary pair of claims based on reasoning that borders on magical thinking.”

My takeaway message from Dorian.  Everyone has experience with weather forecast model predictions.  Intuitively I imagine most people have some suspicions about the validity of any predictions of the climate in 100 years.  This post illustrates reasons why those suspicions are well-founded.  In no way does that mean that the climate is not warming or that greenhouse gas emissions might not have an effect in the future.  However, in my opinion the imminent, inevitable climate catastrophe forecast is a very low probability for this and many other reasons.  If you want to do something to reduce potential climate impacts then do the “no regrets” like energy conservation and energy efficiency, and invest in research to make carbon dioxide free energy production cheaper than energy production from fossil sources which would make conversions a no regrets solution.  Unfortunately this is not the message from any of the Democratic candidates for President.

One final point relates to the effect of global warming on the storm itself.  I am sure you have heard the stories that Dorian supports the catastrophic concerns.  I don’t have time to address this in particular but I believe that the following refute the proposition that Dorian is somehow indicative of a global warming crisis.

    • Judith Curry “Alarmism enforcement” on hurricanes and global warming argues that there are a few climate scientists whose behavior “is violating the norms of science and in my opinion is unethical”. She also provides links to two papers from the World Meteorological Organization (WMO) Task Team on Tropical Cyclones that do not support the crisis allegation:

Tropical Cyclones and Climate Change Assessment: Part I. Detection and Attribution

Tropical Cyclones and Climate Change Assessment: Part II. Projected Response to Anthropogenic Warming

Connect New York “Climate Change in New York” Panel Discussion

Updated response from the host September 5, 2019 follows

On August 26,2019 Public Broadcasting Service WCNY Syracuse NY aired the Connect New York program “Climate Change in New York, a Changing Landscape”.   I stopped listening within the first two minutes because there were three gross mis-characterizations in that time and that was too much for me to swallow.  This post documents those three mis-characterizations.

Their description of the show states:

“Summer 2019 has been an illustration of climate change in New York – from a record breaking heat wave to flooding along the shores of Lake Ontario. In July, Governor Cuomo signed one of the most aggressive climate bills in the nation. We ask climate experts if the new law will be enough when the International Panel on Climate Change has warned that the world has 11 years left to act.”

In the opening monologue of the show host Susan Arbetter said: “Summer 2019 has been a graphic illustration of climate change from a record-breaking heat wave in France to flooding along the shores of Lake Ontario.”  After introducing the panel Ms. Arbetter referenced the UN Intergovernmental Panel on Climate Change asking Sandra Steingraber why we have to act quickly.  Dr. Steingraber said “Climate change now is a real emergency” and I stopped watching.  I believe that the heat wave and high water only represent extreme weather within the range of natural variability and that there is no climate emergency.   One of my pragmatic environmentalist’s principles is Alberto Brandolini’s  Baloney Asymmetry Principle: “The amount of energy necessary to refute BS is an order of magnitude bigger than to produce it.”  The explanation of the reason why Lake Ontario flooding is not an illustration of climate change exemplifies that principle.

If climate change were the cause of record Lake Ontario levels and resulting flooding then we would expect that there would be a trend of increasing lake levels.    That presumption is very easy to check. The US Army Corps of Engineers, Detroit Office provides monthly mean lake-wide average levels for all the Great Lakes.  The Great Lakes water levels 1918 to 2018 figure shows these data for all the lakes.  A quick scan does not reveal any obvious trend for Lake Ontario.  Moreover there are high lake levels in 1943, 1947, 1951, 1952, 1973, and 1974 as well has values in 2017 and the record breaking levels in 2019.

There is another factor to keep in mind relative to the Lake Ontario historical water levels.  When the Moses-Saunders dam on the St. Lawrence River was completed in 1958 it enabled some control of Lake Ontario water levels.  The International Lake Ontario – St. Lawrence River Board implemented Plan 2014 to ensure that releases at the Moses-Saunders Dam comply with the International Joint Commission’s 8 December 2016 Supplementary Order effective January 2017 entitled: Regulation Plan 2014 for the Lake Ontario and the St. Lawrence River Compendium Document.  I will not try to determine whether the dam had any effect on the recent high water levels but there are those that believe that is the case.

In order to determine if there is a possible trend I fit a linear regression model to determine if there was a statistically significant trend. I use Statgraphics Centurion software from StatPoint Technologies, Inc. to do my statistical analyses because it provides flexible plotting and regression tools.  Statgraphics enables the user to choose the best relationship from 27 different linear regression equations.  It is also nice because it presents clear summaries for the non-statistician like me.

I found the maximum monthly Lake Ontario water level for each year and plotted those values versus the year.  The Maximum Annual Monthly Lake Ontario Lake Levels 1950 to 2019 figure plots the water levels that have been coordinated with Canada from 1918 to 2018 and 2019 data through July that I extracted from the monthly reports.  According to the statistical program there is a statistically significant relationship at the 95% confidence level between Lake Ontario Maximum Monthly Level and Year because the P-value in the ANOVA table is less than 0.05.  I have listed the statistics and Statgraphics descriptions in Lake Ontario Annual Maximum Water Level Statistics 1950 to 2019.

At first glance host Susan Arbetter appears to be justified saying that Lake Ontario water levels are rising in response to anthropogenic climate change.  Based on their backgrounds I doubt that any members of the expert panel disagreed either. The expert panel consisted of Rachel May, NYS Senator who was an environmental sustainability educator at SUNY ESF with no science degrees; Sandra Steingraber, a Distinguished Scholar in Residence at Ithaca College where she writes about climate change, ecology, and the links between human health and the environment;  Mark Dunlea, founder of the Green Education and Legal Fund whose web page states that he is a graduate of RPI (Management) and Albany Law School; and Yvonne Chu a member of Climate Change Awareness and Action who has a BS in Environmental Science from SUNY Plattsburgh.

However there is an inconvenient fact.  The Intergovernmental Panel on Climate Change claims the effect of anthropogenic greenhouse gas emissions on the climate system “has a 95–100% probability of causing the currently observed and unprecedented warming of the climate since the mid-twentieth century”. As a result anthropogenic climate change could only have affected water level change after 1950. To test this I separated the Lake Ontario water level data into two sets: before and after 1950.  Maximum Annual Monthly Lake Ontario Lake Levels 1918 to 1949 figure lists the water levels from 1918 to 1949. According to the statistical program there is a statistically significant relationship at the 95% confidence level between Lake Ontario Maximum Monthly Level and Year over this time period because the P-value in the ANOVA table is less than 0.05.  I have listed the statistics in Lake Ontario Annual Maximum Water Level Statistics 1918 to 1949.

However, as shown in Maximum Annual Monthly Lake Ontario Lake Levels 1950 to 2019, the relationship is much weaker after 1950.  According to the statistical program there is not a statistically significant relationship at the 95% confidence level between Lake Ontario Maximum Monthly Level and Year over this time period because the P-value in the ANOVA table is greater than 0.05.  I have listed the statistics in Lake Ontario Annual Maximum Water Level Statistics 1950 to 2019.

Because there is no statistically significant trend after 1950, the disastrous flooding of 2019 is more likely weather related than indicative of climate change.  I refer you to another of my pragmatic environmentalist principles the Golden Rule of Climate Extremes.  Dr. Cliff Mass christened this rule as “The more extreme a climate or weather record is, the greater the contribution of natural variability”.  I am confident that were I to do the same kind of analysis for the French heat wave this summer it would be another example of this golden rule.

If you recall, Ms. Arbetter referenced the UN Intergovernmental Panel on Climate Change asking Sandra Steingraber why we have to act quickly.  She said “Climate change now is a real emergency”.  Again I refer you to Dr. Cliff Mass who has explained that climate change is probably not an existential threat.  He believes it is a serious problem and I agree.  Note, however, over-hyping the reality could very well come back and hurt the cause.

Ms.  Arbetter summed up the Lake Ontario flooding as “pitting the status quo against science”.  I have shown that her “science” was fatally flawed.  Her expert panel only included advocates without the technical expertise to differentiate between weather and climate.  Where does that leave the viewers who watched this show?  Eventually the public will catch on that this alleged imminent, inevitable climate emergency that requires costly and sweeping changes to society is not as advertised.

I am heartened that WCNY has not joined the Columbia Journalism Review “Covering Climate Now” effort.  However, this Connect NY program was entirely consistent with the intent of that effort to strengthen the media’s focus on the climate crisis.  According to the Connect NY web page the program offers “insightful discussion, information, and analysis on timely topics that affect residents across the Empire State”.  However, it seems to me the program was not an honest attempt to present both sides of this topic but rather a platform to present opinions of one side of this issue.

Update: I sent a letter to the station with these explanations.  I received the following response on September 5, 2019:

Dear Roger,

I appreciate your email.  The climate program that aired on WCNY in August was the second “Connect: NY” program we have produced on the issue.  The first program aired on February 25th and featured the climate debate from the business perspective.   If you watch both of them, I think you’ll have a fuller appreciation of the range of perspectives we have featured on the air on this issue.

Thank you again for engaging.

warmly,

Susan Arbetter

 

 

CLCPA Energy Storage Requirements

Updated 31 August, 2019 in response to comments – changes in italics

On July 18, 2019 New York Governor Andrew Cuomo signed the Climate Leadership and Community Protection Act (CLCPA), which establishes targets for decreasing greenhouse gas emissions, increasing renewable electricity production, and improving energy efficiency. This is one of a series of posts on the ramifications of the “most aggressive climate law in the United States”. This post lays out an initial guess for the energy storage needed for CLCPA wind and solar resources at levels greater than announced to date.

CLCPA Target Overview

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 are truly aspirational:

Reduce greenhouse gas (GHG) emissions:

    • 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.
      • GHG offsets means that for every ton emitted into the air one ton is removed via GHG capture of some sort. For example, a company or individual can pay a landowner to leave trees standing that would otherwise be removed or plant additional trees to offset GHG emissions.

Increase renewable electricity:

    • Increase renewable sources to 70 percent by 2030; and

Develop or support:

    • 9 gigawatts (GW) of offshore wind electric generation by 2035;
    • 6 GW of distributed photovoltaic solar generation by 2025; and
    • 3 GW of energy storage capacity by 2030.
    • Conserve 185 trillion British thermal units (TBTUs) of annual end-use energy use by 2025, of which at least 20 percent should be from energy efficiency improvements in disadvantaged communities.
    • The CLCPA also requires between 35 percent and 40 percent of spending on clean energy or efficiency programs be in disadvantaged communities and mandates an air monitoring program in at least four such communities.

Simple Wind and Solar Capacity Model

I believe that CLCPA advocates have not figured out that an electric system that is completely dependent upon renewables will require much more energy storage than commonly assumed. I follow Michel at the Trust, yet Verify blog because he evaluates Belgian “green” technology quantitatively and has given me many insights into potential issues that might also arise in New York. Moreover, like me he prefers using real-world data. In a recent post Michel evaluated the potential effect of increased electricity production from intermittent energy sources in Belgium with a simple solar and wind capacity increase data analysis “model”. He downloaded solar generation, wind generation, and total load data for an entire year. The solar and wind data were summed together for every time period, in his case 15 minutes. Then he projected solar and wind by multiplying the observed sum by different values. The results graphically showed that adding a lot more intermittent wind and solar capacity increases production peaks but does not increase production nearly as much during production valleys. In addition, the results show that as renewable capacity increases more balancing mechanisms will be required.

In a previous post I adapted his methodology to New York State for 2018 with his help and analyzed data from August 2018 which represents the month with the most deficit periods. I believe that the CLCPA claims that renewable energy can completely replace the current fossil fuel load are extraordinary. As such, its proponents have to provide extraordinary evidence that it can work. In this post I look at the required balancing mechanisms for solar and wind to replace existing fossil generation in New York.

In the previous post I estimated how much energy storage may be required by incorporating reasonable assumptions about the future using assumptions about the availability of nuclear, solar, and wind using the Trust, yet Verify simple approach. The biggest future change is the forced shutdown of the Indian Pont nuclear facility in the next several years. In my previous analysis I used “best case” estimates that assumed that solar and wind are available at their rated capacities every hour in my test period. Because those sources are intermittent the amount of time when they are available at full load is not constant. For example, solar availability varies during the day and over the month of August there will be periods when the wind is blowing less than optimal. On the other hand assuming that Indian Point capacity is not available at its rated capability is a reasonable assumption because it usually runs at full load except for maintenance.

The ultimate result in that post estimated the wind and solar capacity from an aggressive CLCPA implementation plan.  In that post and this one I want to estimate the least amount of energy storage needed in the future so I increased renewable additions more than have been announced to date.  I don’t think there will be any significant increase in hydro or the other renewable category sources of methane, refuse, or wood firing and they are not intermittent so I made no changes to those categories. Because New York is shutting down 2,067 MW of nuclear at Indian Point in the next several years I subtracted that amount from every hour. I multiplied the existing onshore wind resource twenty times to estimate future availability. The CLCPA plan currently calls for 9,000 MW of off-shore wind power but I doubled that amount. The CLCPA plan also calls for 6,000 MW of solar PV power but I doubled that amount too. In order to account for daylight I added 6,000 MW to every time period from 0700 to 1955. In order to account for wind intermittency I made some assumptions about availability and scaled the offshore wind resource down when the on shore resource was below half of the observed maximum.

As shown in August 2018 Simple Model Aggressive CLCPA Renewables vs. Fossil Load, there are many periods of surpluses (all the renewables minus the existing fossil resource shown in blue) but there are still periods with deficits even with the best case assumptions about renewable availability. The remainder of this post examines one of the deficit periods in more detail.

Refined Renewable Resource Estimates.

In order to more realistically estimate the potential renewable resources available during one of these periods real world observations need to be included. For this analysis it is assumed that the onshore wind assumption that additional wind would be proportional to existing wind is adequate. However, I did try to modify the offshore wind and the solar components. In order to do that I chose a shorter period and collected meteorological data to get a better estimate of potential solar and off-shore wind capacity. I arbitrarily chose a deficit period on the early morning of August 8, 2018 when winds were light and the sun was either not up or not at full strength to look at the potential magnitude of energy storage required to balance the deficit.

In order to characterize the off-shore wind potential I found a National Oceanic and Atmospheric Administration buoy located 30 NM south of Islip, NY (40°15’3″ N 73°9’52” W) that I used to represent NY offshore wind resource availability. I downloaded hourly NDBC data for 2018 and scanned the data. As noted August 8 had light winds. The weather map for 8 August 2019 shows that there was a large high pressure system dominating the east coast. As a result, I am confident that this buoy characterizes NY offshore wind speeds and thus the resource of NY offshore wind.

This analysis characterizes wind energy as a function of observed wind as follows. I found a wind turbine power output variation curve, developed a straight line equation for the curve and estimated that the output of 18,000 MW of New York offshore wind equals 1714 times the wind speed minus 6000. I assumed that the observed wind speed at the hub height is proportional to the logarithm of the height above ground. For the calculations I assumed a hub height of 85 m and a surface roughness of 0.0003 while the buoy anemometer height is 4 m. The NY offshore wind output capacity in MW was calculated for every hour using this approach.

The solar output is a function of the observed solar irradiation in watts per meter squared. I assumed that 12,000 MW of solar capacity could be added in response to the CLCPA but that will be installed state wide. I downloaded solar insolation maps from the NYS Mesonet archive. I accessed the solar irradiation map in the spatial analysis directory to get solar irradiation maps and as an added bonus the maps also include gridded winds. NYS Mesonet Solar Irradiance Map 8 August 2018 at 1525 UTC is an example of these maps and can be reproduced at this link. In this case there is a lot of variation across the state which makes a state-wide single number estimate of solar irradiation weak but sufficient for this first cut analysis. I estimate that the highest irradiance was 900 W/m2 and the lowest was around 100 W/m2. To do this right one would have to determine where the solar panels might be located to weight the observations. For this hour I guessed 600 W/m2 for the state. I assumed that the 12,000 MW of solar cells produced 12,000 MW when the solar irradiation equals 800 watts per square meter (the PVUSA test condition) and I did not account for any other factors such as the cell temperature or any losses. So my naïve formula for solar output was simply the observed input solar irradiation times 12,000 divided by 800.

The Deficit Example of Simple Model of Intermittent Wind and Solar Generation vs. Fossil Generation and Indian Point Shutdown table lists 5-minute from 0300 to 0955 EDT on August 18, 2018 when the assumed aggressive CLCPA renewable capacity could not replace the existing fossil capacity and loss of the Indian Point nuclear facility.   The first three data columns list the total NYISO state-wide generation load, the NYISO total load, and the fossil generation load. The next four columns list the onshore wind load, CLCPA solar load, and the CLCPE off-shore wind load calculated as described above with the total shown in another column. The next three columns present the meteorological data used. Finally the sum of the onshore wind load, CLCPA solar load, and the CLCPE off-shore wind load minus the existing fossil and the Indian Point capacity of 2,067 MW is listed. In this period all the five-minute periods were negative. The first conclusion is that the post-CLCPA constraint may not be the peak load but instead a night-time low wind period.

Energy Storage Requirements and Costs

I have never seen an analysis that attempted to determine how much storage capacity would be required to meet a real-world generation capacity deficit. Clearly the total capacity has to exceed the observed deficit. In this case I estimate that the total deficit equals the sum of the average of the 12 5-minute deficits each hour or 33,548 MWh. I think that the maximum output of the energy storage has to equal the largest 5-minute deficit or 8,131 MW.

After that it is not clear how best to divvy up the energy storage requirements. 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 reported on my estimates for different duration energy storage costs in a post at What’s Up With That.  In this analysis I included the costs of the battery and did not include developer costs to site, permit and connect the facility to the grid.

In the Estimated Energy Storage Required and Potential Price table I summarize the energy storage needs and my projection for the amount of different duration energy storage needed for the seven hour deficit period with my over-built renewables future scenario. In the first hour of the deficit period the hourly average was 1,140 MW but the peak was 1,390 MW so I project 1,400 MW at 7-hour duration could be used. The next hour had the peak 5-minute deficit of 8,131 MW. In order to meet that and subsequent hours I project 1,300 MW at 6-hour duration, 2,750 MW at 5-hour duration and 2,690 MW at 1-hour duration would cover that peak and most of the subsequent deficits. In order to cover subsequent peaks I added 1,200 MW at 2-hour duration and 620 MW at 1-hour duration. The total MWh stored (37,160) exceeds the observed total deficit (33,548) by 3,612 so there is a lot of room for refining this analysis but that has to be weighed against the fact that no attempt was made to find the worst case period which has to be done at some point.

The total costs are staggering. In order to cover the deficit of energy produced by solar and wind resources at an aggressive level over current on-shore wind and proposed CLCPA solar and wind, $12.5 billion dollars of energy storage will be required to replace existing fossil generation and Indian Point. Nobody in the State has suggested how much energy storage will be required and the 3,000 MW of energy storage capacity by 2030 goal has not included any duration goals. In context 11,260 MW of energy storage capacity is needed according to this analysis and there are large amounts of seven, six and five hour duration energy storage capacity required.  Needless to say, no State estimates have covered the expected costs of their storage goal much less what might ultimately be needed.

Conclusion

In order to determine the cost and feasibility of the CLCPA the State needs to do a similar analysis using real world data and historical load data. The analysis should attempt to site likely renewable energy resources and use the NYS Mesonet data to determine potential resource availability for as long a period as possible. The goal of the analysis would be to determine the energy storage capacity required to meet the CLCPA so that a cost estimate can be prepared.

CLCPA Solar and Wind Capacity Requirements

CLCPA Solar and Wind Capacity Requirements

On July 18, 2019 New York Governor Andrew Cuomo signed the Climate Leadership and Community Protection Act (CLCPA), which establishes targets for decreasing greenhouse gas emissions, increasing renewable electricity production, and improving energy efficiency. This is one of a series of posts on the ramifications of the “most aggressive climate law in the United States”.  This post addresses the wind and solar capacity necessary to implement the CLCPA by looking at a “best case” scenario.

CLCPA Target Overview

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 are truly aspirational:

Reduce greenhouse gas (GHG) emissions:

    • 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.
    • GHG offsets means that for every ton emitted into the air one ton is removed via GHG capture of some sort. For example, a company or individual can pay a landowner to leave trees standing that would otherwise be removed or plant additional trees to offset GHG emissions.

Increase renewable electricity:

    • Increase renewable sources to 70 percent by 2030; and

Develop or support:

    • 9 gigawatts (GW) of offshore wind electric generation by 2035;
    • 6 GW of distributed photovoltaic solar generation by 2025; and
    • 3 GW of energy storage capacity by 2030.
    • Conserve 185 trillion British thermal units (TBTUs) of annual end-use energy use by 2025, of which at least 20 percent should be from energy efficiency improvements in disadvantaged communities.
    • The CLCPA also requires between 35 percent and 40 percent of spending on clean energy or efficiency programs be in disadvantaged communities and mandates an air monitoring program in at least four such communities.

Simple Wind and Solar Capacity Model

I follow Michel at the Trust, yet Verify blog because he evaluates Belgian “green” technology quantitatively and has given many insights into potential issues that might also arise in New York. Moreover, like me he prefers working with real-world data. In a recent post Michel evaluated the potential effect of increased electricity production from intermittent energy sources with a simple solar and wind capacity increase data analysis model. He down-loaded solar generation, wind generation, and total load data for an entire year from the ELIA site. The solar and wind data were summed together for every time period, in his case 15 minutes. Then he projected solar and wind resources by multiplying the observed sum by different values.

Please go to the post and review the methodology and results. The results show that additional intermittent wind and solar capacity increases production peaks but does not increase production nearly as much during production valleys. In addition, the results show that as capacity increases more balancing mechanisms will be required. In my opinion the best part of the analysis was that the graphical results clearly showed these impacts.

As you can see in the comments I complimented Michel for the clarity of the analysis and asked if his model could be applied with New York data. He responded that it would be possible and I sent a link to the New York production data. I had intended to process the data for him to input but Michel graciously did the processing himself. (Fortunately for me because I no longer have access to data processing software, apparently I am the only one who wants to be able to use FORTRAN, so I have to brute force process data in a spreadsheet.) His results for 15x wind plus other renewables relative to total load are reproduced here.

Simple Wind and Solar Capacity Model with New York Data

Michel’s results used the historical data available at the New York Independent System Operator (NYISO) real-time fuel mix data dashboard. I will respond to his comments in the original post in more detail here.

Michel correctly determined that I only want to look at wind and “other renewables”.   I agree that the intermittent source results will not be as clear-cut as the Belgian data where wind and solar are registered on their own, showing the pure effect of the intermittent energy sources. The problem trying to estimate the effect of New York solar capacity increases is that solar is buried in “other renewables” which includes methane, refuse, or wood firing. Those other sources are not intermittent so we get mixed signals.

Michel used solar and wind capacity data but could not find corresponding New York capacity data, so he didn’t correct for potentially increasing capacity over the year. Unfortunately the NYISO data base does not provide a nice spreadsheet format capacity report like the ELIA generating facilities link. However, I don’t think there is enough added capacity to make a difference for this analysis. On the other hand Michel found that Belgian wind capacity increased by 500 MW and solar capacity increased by over 400 MW so he had to correct for that or the results would have been flawed.

Michel notes that the result is quite different from the Belgian data. In the first place New York is bigger. The ELIA link notes that total capacity in Belgium is 15,660 MW. The NYISO data are buried in Table III-3a Capability by Zone and Type in their annual Load and Capacity Data Report. In the summer the total capacity in New York was 39,245 MW in 2019. Secondly, peak loads are different. New York State production is highest in summer and lower in winter, just the opposite as Belgium. He correctly infers that air conditioning drives the peak load to the summer.

He correctly assumed that there is less solar capacity relative to wind in New York because solar capacity is so small that it does not have its own category. In the NYISO capability table there are only 31.5 MW of solar capacity. The ELIA solar-PV generation data link notes that “Elia has updated the register of total installed solar capacity in Belgium. As a result, the installed solar capacity increases with 416.27 MW” well over ten times as much as NY. However, the link also states that the monitored solar PV capacity is 3,369.05 MW. I assume that this refers to distributed solar PV capacity and also suggests the New York would be well served to start monitoring this capacity as well. The NYISO claims that there are 1,862 MW of solar PV nameplate capacity behind the meter.

Michel observes that consumption is higher in New York than Belgium and the share of intermittent energy smaller. As a result, the point where surpluses and shortages cancel out (without taking the losses into account) will be higher (somewhat higher than 25.5x, versus 8.5x for Belgium).

New York Simple Wind and Solar Capacity Model for August 2018

Michel’s model results indicate that August 2018 has many shortages so I looked at August 2018 data myself using a spreadsheet. My primary concern is the effect of the CLCPA on future capacity keeping in mind that the target is to eliminate fossil fuel use so I compared solar and wind only to fossil load, i.e., the output from the generators listed as fossil in Table III-3a: Capability by Zone and Type. Using the same data as Michel but only using renewables to replace fossil load gives a similar result. Note in the table August 2018 Simple Model 26 x New York Wind + Other Renewables vs. fossil load that surpluses are blue and deficits are red. There are more surpluses simply because fossil load is less than total load. Note that even if the wind and other renewable categories are increased 26 times the current rate existing fossil cannot be replaced without a lot of shortages.

I believe that the CLCPA claims that renewable energy can completely replace the current fossil fuel load are extraordinary. As such, its proponents have to provide extraordinary evidence that it can work. I have tried to modify the data to incorporate reasonable assumptions about the future using “best case” assumptions about the availability of solar and wind. These are “best case” estimates because I assumed that solar and wind are available at their rated capacities every hour in my test period. Because those sources are intermittent the amount of time when they are available at full load is not constant. For example, solar availability varies during the day and over the month of August there will be periods when the wind is blowing less than optimal. On the other hand assuming that Indian Point capacity is not available at its rated capability is a reasonable assumption because it usually runs at full load except for maintenance.

I don’t think there will be any significant increase in hydro or the other renewable category sources of methane, refuse, or wood firing and they are not intermittent so I made no changes to those categories. Incredibly New York is shutting down 2,067 MW of nuclear at Indian Point in the next several years because public perception of nuclear is a more important consideration than the existential threat of climate change. I subtracted that amount from every hour. The CLCPA plan currently calls for 9,000 MW of off-shore wind power so I added that amount to every hour. The CLCPA plan also calls for 6,000 MW of solar PV power. In order to account for daylight I added 6,000 MW to every time period from 0700 to 1955. The results in August 2018 Simple Model CLCPA Renewables vs. fossil load show the same thing: adding solar and wind capacity significantly adds to surplus loads but does not reduce the deficits nearly as much even if it were available at the full capacity every hour.

I tried to estimate capacity from an even more aggressive implementation plan (doubling the offshore wind and solar additions to 18,000 MW and 12,000 MW respectively). However, doing that would show positive numbers unless there is a correction for off shore wind intermittency if I simply added another 9,000 MW of wind to every hour. In order to account for wind intermittency I scaled the offshore wind resource down when the on shore resource reached half of the observed maximum. I scaled the resource proportional to the observed decrease in the 99th to the 70th percentile on-shore resource to the 50th. For example, when the on shore wind resource was at the 50th percentile I estimated that the off-shore wind resource was proportional to the 99th divided by the maximum observed onshore wind resource. I made similar corrections for even lower levels and I believe this is conservative. Again, as shown in August 2018 Simple Model Aggressive CLCPA Renewables vs. Fossil Load, the surplus increases by adding solar and wind capacity at full capacity but we still will have to deal with significant deficits.

My takeaway point is that even with unrealistic assumptions about the “best case” availability of solar and wind capacity, there are periods with significant deficits. In order to prove the extraordinary claim that solar and wind can replace existing fossil the State of New York, a similar type of analysis using actual data to estimate realistic energy production must be done. That is the only way to provide the extraordinary proof showing just how much energy storage will be required to prevent deficits. I will take a preliminary look at the energy storage ramifications of this in a future post.

 

New York City Energy Storage Peaking Turbine Replacement

The biggest air quality issue in New York State is compliance with the National Ambient Air Quality Standard for Ozone. In order to meet that limit the New York State of Department of Environmental Conservation (DEC) proposed regulations earlier this year to lower allowable nitrogen oxide (NOx) emissions from simple cycle and regenerative combustion turbines during the ozone season. The problem is that these turbines are needed to keep the lights on during periods when needed most so replacement is not very simple as I explained in an earlier post.

This post describes the State’s evaluation of the politically correct alternative, energy storage, to provide the power generated by these turbines. This post addresses the report findings for turbines that could be directly replaced by energy storage. I want to emphasize that the following represents my opinion and not the opinion of any of my previous employers or any other company with which I have been associated. I have been following the operational implications of these turbines and their effect on ozone for over 20 years.

Background

The evaluation of using energy storage to replace these peaking units is part of the New York State Energy Storage Roadmap announced by Governor Cuomo in June 2018. As part of that effort the Department of Public Service (DPS) established an Energy Storage Deployment Program. On July 1, 2019, Energy Storage Deployment Program Report – Unit by Unit Peaker Study was submitted to the docket for Case 18‐E‐0130 – In the Matter of Energy Storage Deployment Program. DPS staff, working with New York State Energy Research and Development Authority (NYSERDA), Long Island Power Authority (LIPA), New York Independent System Operator (NYISO), NY Department of Environmental Conservation (DEC), Con Edison, and consulting firm Energy and Environmental Economics, Inc. (E3) prepared the report. The DPS December 12, 2018 Order Establishing Energy Storage Goal and Deployment Policy directed them to develop a unit‐by‐unit operational and emission profile study and methodology to determine which downstate peaking power plant generating units are potential candidates for repowering or replacement. My previous post describes these peaking turbines and more detail on the rationale for replacement so I will not repeat that material here.

The December 2018 DPS Energy Storage Goal and Deployment Policy specified what was to be included in the analysis. It was to “include a series of reliability and operational assessment studies looking at the equivalent level of ‘clean resources’ that could provide the same level of reliability as the existing peaker units. Hybridization and repowering with energy storage, as well as replacement with stand-alone energy storage, should be explicitly examined, according to the Roadmap.”

According to the description in Energy Storage Deployment Program Report – Unit by Unit Peaker Study:

The analysis relies on historical 2013 hourly operational and emissions data for the approximately 4,500 MW of affected peaking units across the state (almost entirely concentrated in New York City, Long Island, and the Lower Hudson Valley) to examine the technical feasibility of energy storage or energy storage paired with solar providing equivalent historical generation of the peaking units. Peaker operational and emissions data from 2013 was chosen because this reflects the peak NYISO demand year, and the correspondingly high levels of peaker operation which occurred in July 2013. This served as a proxy for representing peak‐level system operations, although theoretical peak system operations may impose incremental needs beyond those of 2013. The study did not consider system changes after 2013 that may impact how conventional peaking units and energy storage resources operate in the future, such as retirements of existing units, changes in the overall levels and patterns of demand, new transmission solutions, and/or the addition of more intermittent, renewable energy.

Analysis

I am not a fan of the approach used in this analysis because I think it gives some mis-leading unit specific information. In the first place they considered all turbines as candidates not understanding that the primary purpose of some turbines is not to provide power during high load demand periods. They wasted effort considering the Jamestown Public Utilities turbine in Western New York that runs on the order of half the time. Peaking turbines are defined as units with an “average annual capacity factor of 10.0 percent or less over the past three years”. In addition there are turbines at steam boiler facilities that are necessary for “black start” situations when there is a blackout and the power necessary to start up the boiler is unavailable from the grid. Because that is a very rare instance the units are also run to provide power for peak power periods. In my opinion it would not be cost effective to dedicate energy storage for this application. You could not use it for peak loads because you never know when the grid power won’t be available. In conclusion the report considered units that should not have been included.

According to Table A-1 in the report, there are 3,780 MW of peaking turbines in New York. The report concludes that “Overall, at least 275 MW of peaking units, or around six percent of the total rated capacity of the fleet, are found to be potential candidates for replacement with 6‐hour energy storage sized to the maximum 2013 output of each peaking unit.” That means that a 6-hr energy storage system would be able to replace 7% of the existing peaking turbine capacity. The report goes on to say that “This number increases to over 500 MW when using 8‐hour duration storage”, but that only increases the replacement of existing capacity to 13%.

I don’t disagree with their conclusion that “Energy storage or a combination of energy storage and solar can contribute towards meeting NOx limits for a large number of units”. However there is a long way between “can contribute” and “will actually be an option used”. This is a preliminary scoping study. It notes that the “minimum size storage required to meet the NOx requirements can vary between units of the same facility” but does not recognize that the variation between sister units at a facility does not mean that one unit is more of a candidate than another.  The reality is that affected sources will adopt a facility‐wide strategy to meet the NOx limits and those strategies were not examined in this report.

There are other issues as noted in the Conclusion and Recommendations for Further Study. They note that “A more detailed analysis will be needed to understand the reliability impacts of specific unit replacements, especially as loads and resources change with greater electrification of transport and buildings and higher penetrations of renewables.” Many of these peaking units are in load pockets and changes in the load will drive whether energy storage is viable.

Costs

The report states that “A more detailed and thorough benefit‐cost analysis would need to be performed to understand the true economic viability of the replacement and/or hybridization options presented in this analysis.” Therein lies the biggest issue of energy storage – the cost. For those of us outside of Albany who care about costs a recently released report from the National Renewable Energy Lab (NREL): “2018 U.S. Utility-Scale Photovoltaics-Plus-Energy Storage System Cost Benchmark” provides information that can be used to estimate the costs of the energy storage option.

The NREL study lists costs for durations up to four-hours but the DPS report also includes six-hour and eight-hour durations. Table 3 in the NREL document, Detailed Cost Breakdown for a 60-MW U.S. Li-ion Standalone Storage System with Durations of 0.5–4 Hours, provides the information necessary to extend their projections to those different durations. I fit a linear regression model to describe the relationship between the specific costs and energy storage duration from the NREL table. 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. In this evaluation, in every instance, the reciprocal-X model (Y = a + b/X) statistic was the best choice and every regression had an R-squared coefficient great than 99.9% which indicates a strong relationship and suggests that these estimates are good enough for this analysis.

The NREL analysis includes all the costs for a greenfield energy storage project so I calculated values of retrofit potential costs that exclude the land acquisition costs. I estimate the installed cost for energy to be $343/kWh for an eight-hour battery system, $355/kWh for a six-hour battery system, and $380/kWh for a four-hour battery system.

The table NYC Energy Storage Peaker Replacement Summary lists data from the DPS study and calculated values. Table E1 in the DPS report lists the total nameplate capacity (MW) of peaking units that can potentially be fully replaced with storage to meet the 2025 NOx limits at 100% sizing to each unit’s 2013 peak generation. Note that I did not include the upstate turbine included in the DPS report in this analysis because it is not a peaking turbine. There are 36 MW of peaking unit capacity in New York City and Long Island that can be replaced with four hours of storage, 229 MW that can be replaced with six hours of storage, and 463 MW with eight hours of storage for a total of 728 MW. This is 18% of the 2013 peak load in New York City and on Long Island. Table A estimates the replacement cost estimate using the NREL report numbers and shows that replacing 18% of the load with Li-ion battery storage would cost $1.8 billion.

The cost per ton removed further demonstrates the staggering cost implications. I could not figure out which particular units were candidates for replacement because my analysis of Table B-1 did not result in the same number of units in each category. As a result I could not calculate the unit-specific cost per ton removed. Instead I just used the total emissions from all the sources the report’s Table 3: Peaking Units 2013 Operational Data. Table B shows the costs if all the emissions from all the peaking units came only from the 728 MW that can be replaced by energy storage. The cost to remove a ton of NOx is over $900,000 per ton and cost to remove a ton of CO2 is over $1,000 per ton. In order to put those numbers in perspective consider that the social cost of carbon (the alleged societal cost per ton of CO2 emitted) is currently around $50 by the Obama administration method and less than $5 by the Trump administration.

Conclusion

The report concludes “Overall, the findings suggest that there is an opportunity to consider replacing or hybridizing a substantial portion of the peaking units subject to DEC’s proposed NOx rule with a fleet of storage resources paired with solar. Such an outcome would potentially deliver significant environmental benefits, advance the state’s carbon reduction and clean energy goals, as well as benefit historically disadvantaged populations and communities such as environmental justice areas in line with the goals of the Climate Leadership and Community Protection Act.” However these results show that the cost of energy storage replacement is at least an order of magnitude greater than the cost of carbon’s impacts so this opportunity is not a cost-effective way to advance the state’s carbon reduction and clean energy goals.