UNITED STATES

SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

___________________________________________

FORM 8-K

___________________________________________

CURRENT REPORT

PURSUANT TO SECTION 13 OR 15(d)

OF THE SECURITIES EXCHANGE ACT OF 1934

Date of report: November 7, 2019

(Date of earliest event reported)

___________________________________________

BLOOM ENERGY CORPORATION

(Exact name of Registrant as specified in its charter)

001-38598

Commission File Number

___________________________________________

Delaware	77-0565408
(State or other jurisdiction of incorporation or organization)	(I.R.S. Employer Identification Number)
4353 North First Street, San Jose, California	95134
(Address of principal executive offices)	(Zip Code)
408 543-1500
(Registrant’s telephone number, including area code)
Not Applicable
(Former name or former address, if changed since last report)

___________________________________________

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Check the appropriate box below if the Form 8-K filing is intended to simultaneously satisfy the filing obligation of the registrant under any of the following provisions:

¨            Written communications pursuant to Rule 425 under the Securities Act (17 CFR 230.425)

¨            Soliciting material pursuant to Rule 14a-12 under the Exchange Act (17 CFR 240.14a-12)

¨            Pre-commencement communications pursuant to Rule 14d-2(b) under the Exchange Act (17 CFR 240.14d-2(b))

¨            Pre-commencement communications pursuant to Rule 13e-4(c) under the Exchange Act (17 CFR 240.13e-4(c))

Securities registered pursuant to Section 12(b) of the Act:

Title of each class(1)		Trading Symbol(s)		Name of each exchange on which registered
Class A Common Stock $0.0001 par value		“BE”		New York Stock Exchange

(1)	Our Class B Common Stock is not registered but is convertible into shares of Class A Common Stock at the election of the holder.

Indicate by check mark whether the registrant is an emerging growth company as defined in Rule 405 of the Securities Act of 1933 (§230.405 of this chapter) or Rule 12b-2 of the Securities Exchange Act of 1934 (§240.12b-2 of this chapter).

Emerging growth company    þ

If an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act.  ¨

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Item 8.01 Other Events

On November 7, 2019, Bloom Energy Corporation (the “Company”) issued technical notes entitled "A Primer for Understanding Fuel Cell Power Module Life" and "How Bloom Reduces Emissions," copies of which are provided herewith as Exhibit 99.1 and Exhibit 99.2, respectively, and are incorporated herein by reference. The technical notes are also available on the Company's website at https://www.bloomenergy.com/resources under the "Technical Notes" tab.

Item 9.01                          Financial Statements and Exhibits

(d) Exhibits

Exhibit No.		Description
99.1		Technical Note entitled "A Primer for Understanding Fuel Cell Power Module Life"
99.2		Technical Note entitled "How Bloom Reduces Emissions"

SIGNATURE

Pursuant to the requirements of the Securities Exchange Act of 1934, the registrant has duly caused this report to be signed on its behalf by the undersigned thereunto duly authorized.

BLOOM ENERGY CORPORATION
Date:	November 7, 2019	By:		/s/ Randy Furr
				Randy Furr
				Chief Financial Officer

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TECHNICAL NOTE

A Primer to Understanding

Fuel Cell Power Module Life

Bloom Energy

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Executive Summary

Information with respect to how we operate the fuel cells in our Energy Servers is generally not publicly available, and given that, we want to provide some insight on the operating life cycle of our fuel cells. To that end, we have prepared this report to describe how we continuously track the performance of our fleet of solid oxide fuel cell (“SOFC”) power modules (“PMs”)¹, how we define the economic life in the field of our PM’s through a median time to replacement (“MTTR”) metric and how we apply this framework to publicly available data.

We utilize our fleet-wide monitoring data to clearly demonstrate year-over-year improvements to the MTTR of our PMs, from a median of 1.9 years for the fleet of PMs that reached commencement of operations (“COO”)² in 2011 (the “2011 Vintage”), to 4.7 years for our 2015 Vintage.

Vintage years beyond 2015 have not yet reached their respective MTTRs, and so estimating this value requires forecasting beyond the lifetime already demonstrated by actual run time in the field. We do so by (i) comparing like time periods between Vintages (i.e., the first year of performance of the 2015 Vintage vs. improved performance observed for the same period of the 2018 Vintage); and (ii) utilizing data from the extensive testing we conduct before releasing improvements to our stack and system design into the field. On the basis of these data, we estimate that the 2018 Vintage and later will have an expected MTTR of at least 5 years.

Finally, we show why certain publically available data – particularly those from the NYSERDA program – have incomplete data related to incorrect COO dates and efficiency calculations. However, these errors are easily corrected and can be bridged to demonstrate how the data provided for those systems lines up with those that have already demonstrated a 4.7 year MTTR for our 2015 Vintage fleet.

Solid Oxide Fuel Cells Overview

SOFCs generate power by electrochemically reacting fuel with oxygen ions harvested from ambient air. As shown in Figure 1 below, SOFCs are comprised of three layers of solid state materials: an anode, an electrolyte and a cathode (together, a “cell”). In addition, “interconnect” plates are sandwiched between the cells to manage fuel and air flows and to conduct current through repeated cell layers (for simplicity, interconnects are not pictured below).

¹ Our modular Energy Server design enables us to replace one PM, while the entire Energy Server remains in operation. Thus, our unit of replacement is at the PM level, and we therefore analyze the frequency thereof in this report.

² This value is generally very close to, or the same as, the “acceptance” date we use to recognize the revenue associated with the sale of an Energy Server.

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Figure 1: Solid Oxide Fuel Cell Technology

One cell produces approximately 25W of electricity. Stacked, alternating layers of cells and interconnects constitutes a fuel cell “stack.” Fuel cell PMs are comprised of multiple stacks and produce 50 kW at the beginning of life for the current product generation. A group of several PMs form a single Bloom Energy Server.

Bloom’s SOFC system architecture is shown below in Figure 2.

Figure 2: Energy Server Architecture

Cells, interconnects and the interfaces between them all have intrinsic ionic and electrical conductive resistance. The increase in resistance over time is a key variable affecting degradation in efficiency and our MTTR.

Field Life: Calculating, Forecasting and Utilizing MTTR

Our SOFC’s have a “life cycle” similar to that of an aircraft engine. They start their lives as newly manufactured stacks in a PM. They are installed at a customer site and achieve COO. They generate power for a certain period of time and at some point, our Remote Monitoring and Control Center (“RMCC”) determines that the PMs need to be removed and refurbished. Once removed, the PM’s are sent to our Repair and Overhaul Center, where we harvest the majority of the materials to refurbish the unit and return it to the field for continued generation of power.

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For a given PM, we define time to replacement (TTR) as the time between the COO date and the replacement for repair date.

That calculation is:

TTR (years) = (replacement date – COO Date)/365)

For a Vintage of PMs that reached COO in a given year, we calculate the median TTR (or MTTR) for that Vintage as the age by which 50% of the PMs in that fleet have been replaced (and, by extension, 50% of the PMs continue to operate in the field).

Our PMs do not generally experience sudden “catastrophic failure” in the field. Instead, we schedule their replacement, well into the future for a variety of reasons. We replace PMs considering not only the performance of the individual PM, but also that of its neighbors at a site and, in many instances, across a number of sites or fleet level (“Portfolio”). Our decision to remove an individual PM is therefore a function of the performance of a large group of PMs and Energy Servers in a Portfolio, as our performance commitments are generally set at the Portfolio level.

We have developed algorithms to optimize our replacement and refurbishment strategy to generate the highest performance at the lowest cost by analyzing a number of variables that include:

•	Individual PM, site-level and Portfolio-level electrical efficiency

•	Individual PM, site-level and Portfolio-level electrical output

•	Site level resiliency commitments

•	Service labor optimization (i.e. minimizing drive times and maximizing service impact on a per-trip basis)

•	Preventative maintenance

•	Diagnostic benefits (i.e., removing a laggard from service early to begin the work on root cause analysis)

•	Estimated costs for a given replacement date (i.e., some failure modes are less expensive when caught early)

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PMs that remain in service by definition do not have a replacement date, and so there is no MTTR to be calculated. However, we can estimate the expected MTTR (“MTTRe”) by estimating the impact that design improvements will have in increasing the MTTR of a newer vintage compared to the baseline of an older one.

To do so, we utilize field data from our RMCC to verify that design improvements are functioning as expected. As we improve the electrochemistry of our cells and stacks, as well as improve the operating environment and balance of plant that surrounds the cells and stacks, the rate of change in the resistance of the components subsides. This manifests as a reduced rate of performance degradation over time, which can be observed in the data in a number of ways.³ These improvements are first measured in the lab as we establish design of experiments (“DOEs”) to isolate beneficial changes to our cell, stack and PM hardware. We then validate these DOEs with field performance data to ensure that the improvements are operating as anticipated under field conditions. As we see these improvements manifest across many PMs, we update our MTTRe algorithms, as well as run model simulations, and we can conclude that the improvements versus older Vintage baselines will result in longer MTTRs.

³ The most precise of these is a metric that measures the area-normalized rate of change in resistance (or “area specific resistance degradation” (ASRD)) in the cell. As this resistance degradation slows, the cell’s deviation from its initial performance slows and the expected MTTR will increase.

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For example, Figure 3 below demonstrates the slower rate of efficiency degradation⁴ for our 2018 Vintage as compared to our 2015 Vintage.

Figure 3: 2015 Bloom 2.5 Fleet Cumulative Efficiency Profiles

One of the advantages of having a large operating fleet is that we can identify both statistical outperformers and laggards. This in turn helps us uncover the root causes of both longer- and shorter-than-median TTRs for any given PM. We utilize this data to create critical improvement programs to increase MTTR and reduce service costs both within current generation technology and our next generation technologies.

We utilize all of these data (historical MTTR values, current MTTRe values, as well as our engineering judgement for improvements currently underway) when pricing our operating and maintenance (“O&M”) contracts to ensure that a contract’s service revenues are sufficient to cover the required replacement and other necessary service activities.⁵  

MTTR Field Data

Figure 4 below shows our Remote Monitoring and Control Center (RMCC) in San Jose, CA where we monitor our global installed fleet (the “Global Fleet”) and continuously gather and analyze operating data down to the individual stack level to evaluate health and to optimize performance. For each PM, we monitor over 200 distinct variables or over 1,200 variables for a 300 kW Energy Server. We also have a mirror site in Mumbai, India, that provides both redundancy and resiliency of our RMCC operations as well as 24 x 7 hour coverage of our fleet.

⁴ Note that the Y-axis is in cumulative lifetime efficiency, which we show here as (i) this is generally the metric used in our O&M contracts (see below); and (ii) this normalizes for site level changes that include changes in gas composition and the inclusion of ancillary equipment such as gas pressure boosters. Also of note is that the rate of change of efficiency isn’t linear but rather “levels out” with longer operation, due to changes in the operating current.

⁵ Though PM replacement is the majority of the O&M cost, we also use similar estimating methods to calculate the future anticipated non-replacement costs associated with a given O&M contract.

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Figure 4: Bloom’s Remote Monitoring and Control Center in San Jose, CA

We have aggregated performance data in Table 1 below, which summarizes MTTR for all of our Vintages since 2011. This shows progressive improvement in MTTR year-after-year from 2011 through 2015 where an MTTR of 4.7 years has been reached. 2015 is the most recent vintage where MTTR can be directly calculated from field experience. Note the 90^th percentile TTR is also shown indicating the top 10% of each Vintage from 2013 through 2015 has already exceeded 5 years.

Table 1: Bloom Power Module Product Advancements

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It is also worth noting that 85% of our Global Fleet consists of PMs that reached COO between 2016 and 2019, and the remaining 15% of the Global Fleet is from 2014 and 2015 Vintage PMs. The earlier Vintages that had shorter MTTRs have been refurbished with all of the improvements and upgrades developed through continuous improvement programs to deliver the MTTR of our latest product vintages.

Comparison of Global Fleet Data to Sub-sets of Publically Available Data

A number of public sources of data exist that contain limited amounts of Energy Server performance data. These include 21 sites in the northeast covered by the NY State Energy Research and Development Authority (NYSERDA) program.

With respect to the NYSERDA data, the lifetime efficiency reported through the NYSERDA portal is not complete and incorrectly reports key variables for 75% of the sites due to a delay in the start of NYSERDA metering by 15 to 538 days. This means the NYSERDA cumulative efficiency calculation does not include the highest efficiency performance at the beginning of life resulting in errors ranging from 0.2% to 2.9% for these sites.

In addition, the reported efficiencies of the Energy Servers are impacted by the existence of certain ancillary equipment such as:

•	Gas pressure booster blowers used to increase low gas supply pressures in the region which introduce added electrical loads outside of the Energy Server reducing efficiency by approximately 2%; and

•	The use of Batteries for customer required resiliency solutions that reduce efficiency by about 1% due to the round trip efficiency losses for charging and discharging the batteries.

This ancillary equipment (booster blowers and batteries) are currently present in less than 5% of Bloom Energy Servers and thus are unique and not representative of the overall Bloom fleet. In the case of the NYSERDA sites, these ancillary loads have no effect on either the MTTR of the Energy Servers, or the rate of performance degradation described above. As noted above, efficiency alone does not dictate the MTTR. Given all of this, the uncorrected NYSERDA data cannot be used to calculate MTTR.

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Table 2 below shows the adjustments necessary to correct for NYSERDA metering delays and the presence of ancillary equipment to compare the NYSERDA data to the 2015 Vintage efficiency data discussed previously. In addition to the adjustments described above, there is also a 0.5% adjustment made to account for transmission losses from the Bloom Energy Server to the NYSERDA meter.

Table 2: Corrections of NYSERDA Efficiency Data

By plotting the adjusted NYSERDA cumulative efficiency, and comparing those data points for 2016 through 2018 sites to Bloom fleet data for the 2015 and 2018 Vintages, we show that the efficiency data generally show improvements over the 2015 vintage Bloom fleet that has already demonstrated a MTTR of 4.7 years.⁶  

Figure 5: NYSERDA Efficiency Data

⁶ Note the NYSERDA data point at approximately 2.0 years age and 54% cumulative efficiency appears to be a low outlier. The Bloom system meter at this site shows a cumulative efficiency 1.8% higher than the NYSERDA meter indicating a potential calibration issue here.

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Summary and Conclusions

Bloom has been installing and operating our Energy Servers for close to a decade. In addition to the advances in our SOFC technology through our research and development efforts, we have collected significant amounts of operating data through our RMCC, which we use herein to demonstrate the actual MTTRs of our Global Fleet. This has also allowed us to continually improve the operating performance of our PMs. We have observed the operating performance for our 2015 Vintage to achieve an MTTR of 4.7 years before being returned to our Repair and Overhaul Center for refurbishment.

Today, our ability to predict the performance of our PMs and Energy Servers is based not only on our technology leadership and research and development capability, but is also based on models built with extensive field data we have collected over the past decade. This unique capability is at the core of our ability to project our MTTR today, as well as the MTTRe tomorrow. These models show that our MTTRe on our recent vintages is more than 5 years.

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		TECHNICAL NOTE

How Bloom Reduces

Emissions

Bloom Energy
4353 North First Street	T 408 543 1500	info@bloomenergy.com
San Jose, CA 95134	F 408 543 1501	www.bloomenergy.com	@ Bloom Energy Corporation 2019. All Rights Reserved

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		TECHNICAL NOTE

Executive Summary

Bloom Energy’s mission is to make clean, reliable, and affordable energy for everyone in the world. Our solid oxide fuel cell product, the Bloom Energy Server, delivers highly reliable and resilient, ‘Always On’ clean electric power. Our Energy Servers generate electricity without combustion, utilizing natural gas, biogas, or hydrogen as fuel. At Bloom Energy, we work to contribute to the creation of sustainable communities by reducing carbon emissions and criteria air pollutants.

Our Energy Servers that run on hydrogen or biogas can produce carbon neutral power, and those fueled by natural gas produce carbon emissions. Our Energy Servers are however, among the most effective ways to displace less efficient centralized power plants with more efficient distributed generation, thereby achieving the combination of near-term emission reductions and increased resiliency. Power generation from our Energy Servers reduce carbon emissions and other air pollutants in the same manner as wind and solar generation — by displacing dirtier power plants. However, unlike wind and solar, our Energy Servers can do so around the clock.

To validate the net emissions reduction impact of our Energy Servers, Bloom commissioned a leading independent engineering firm, DNV-GL, to review the methodology used to determine our Energy Server’s emissions performance. DNV-GL found that our analysis relies upon valid reference data and computational approaches aligned with industry practice.

The results show that since Bloom began commercial deployments in 2011 our systems have achieved:

•	Approximately 2.33 million metric tonnes of CO2 reduction globally through 2019, equivalent to 18,900 acres of forest preservation or taking nearly one half of one million cars off the road for a year.1

•	Associated criteria pollutant reductions, including 5.05 million pounds of sulfur oxides (SOx), and 8.9 million lbs. of nitrogen oxides (NOx), equivalent to preventing approximately 5,200 lost work days and more than 30,000 days of restricted activity due to illness.2

    

In this paper, we review Bloom’s emissions profile to illustrate how our technology reduces emissions and delivers local air quality benefits. We’ll review our historical performance and how Bloom is positioned to continue leading the way toward a low carbon future.

Marginal Emissions: Comparing Absolute Emissions with Emissions from Displaced Alternatives

Establishing Bloom’s climate impact requires a comparison between its absolute emissions and the emissions from displaced alternatives. When a new, efficient distributed energy resource, such as a solar project or Bloom Energy Server, is brought online, it reduces the amount of power required from energy sources that generate “on the margin” — meaning those units that are operating to meet the last unit of energy demand.

The PJM regional transmission organization³ explains how this works, describing wholesale energy markets that function to dispatch generators as follows:⁴  

The price for wholesale electricity [is]…… set by organized wholesale markets. The clearing price for electricity in these wholesale markets is determined by an auction in which generation resources offer in a price at which they can supply a specific number of megawatt-hours of power.

If a resource submits a successful bid and will therefore be contributing its generation to meet demand, it is said to “clear” the market. The cheapest resource will “clear” the market first, followed by the next cheapest option and so forth until demand is met. When supply matches demand, the market is “cleared,” and the price of the last resource to offer in (plus other market operation charges) becomes the wholesale price of power.

                                                              

¹ https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator

² Based on California default values from the Clean Power Plan https://www3.epa.gov/ttnecas1/docs/ria/utilities_ria_final-clean-power-plan-existing-units_2015-08.pdf

³ PJM coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia

⁴ https://learn.pjm.com/electricity-basics/market-for-electricity.aspx

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		TECHNICAL NOTE

As a result of the wholesale energy market structure and the operating costs of power plants (see Figure 1 below), the “marginal generator” that is displaced from the power market when its power is no longer needed is typically a CO2 emitter and is generally the highest CO2 emitter operating at any given time.

Figure 1: Prioritization of Dispatch⁵ 

Energy providers on the margin are typically the most flexible but least efficient energy generation sources, which operate at the lowest electrical efficiency. This necessarily brings the highest levels of associated emissions, as more fuel is required to generate power per unit of electricity delivered. When more efficient or cost effective solutions displace marginal power sources, the highest cost resources are the first resources requested to be shut off.

Based on these current market dynamics, oil is the highest cost of these options, then coal where applicable, then natural gas. The average coal power plant has an emission rate of 2,065 lbs. of CO2/MWh while natural gas plants emit at 895 - 1,307 lbs.⁶ In comparison, Bloom Energy fuel cells have an emission rate of 679 - 833 lbs. CO2/MWh.⁷

Every unit of electricity that Bloom Energy Servers produce offsets a unit of electricity from a marginal source with corresponding benefits for emissions. Since Bloom’s carbon intensity is lower than the displaced alternatives, the net impact is measurable emissions reductions. Carbon impact measurement based on the displacement of marginal emissions is the standard for emissions accounting for distributed energy generation assets such as Bloom’s Energy Servers.

Bloom Energy Servers Compared to US Marginal Emissions – Carbon Impact & Air Quality

Figure 2 shows Bloom’s historical domestic absolute carbon emissions modelled against those that would have been produced by the generation of an equivalent amount of electricity from the marginal generators in the regions in which the units operate.⁸ The analysis represents Bloom’s combined historical average fleet emissions performance of both its first generation ES5700/10 systems and current ES-5 systems.

Bloom’s CO2 emissions reductions — the yellow line in Figure 2 — are based on comparison to historical EPA eGRID non-baseload data, which is issued every two years (not yet released for 2018). It serves as a transparent proxy for marginal emissions values across the relevant time period and regional footprint. Regional performance comparisons (see Figure 9: Regional Breakout below) illustrate that Bloom has reduced emissions compared to the power plants we have displaced (the marginal emitter) in every region in all years.

                                                              

⁵ PJM Learning Center Website https://learn.pjm.com/electricity-basics/market-for-electricity.aspx

⁶ 2017 EIA data from ‘Electric Power Annual’ Dataset

⁷ Bloom E5 Datasheet

⁸ https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

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		TECHNICAL NOTE

Figure 2: Carbon Impact

*2019e is pro-rated for the entire year based on Jan-Sep rate.

We’ve taken the same approach for evaluating air quality impact for SOx and NOx, two primary criteria pollutants also benchmarked in EPA’s eGRID non-baseload data. As demonstrated in Figure 3 below, Bloom’s output does not even register in the chart in relation to displaced marginal emissions.

Figure 3: Air Quality Impact

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		TECHNICAL NOTE

Carbon Impact Methodology

To begin determining our carbon emissions, we use the standard chemical conversions in the equation below to derive pounds of CO2 emitted per kWh from our natural gas-fueled Energy Servers, the volumes of which can be directly calculated based on an Energy Server’s net electrical efficiency (the fraction of the input chemical energy in the fuel converted into electrical energy).

Note: lower heating value (LHV) is converted to higher heating value (HHV) by a factor of 1.1078. It is also worth noting that this analysis captures the overall MWh produced by Bloom’s fleet outlined in Figure 2 to ensure any variations in system output are accurately and fully reflected in the calculations.

Bloom monitors and aggregates daily system efficiency levels down to the level of each Energy Server through use of the conversion below.

Using these conversions, Bloom can calculate the carbon emissions profile from its equipment, but that isn’t the same thing as Bloom’s climate impact. To measure emissions reductions, Bloom’s absolute emissions are then compared to the emissions from the generators we displace – the marginal emission.

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		TECHNICAL NOTE

Methodology Validated by Expert Organizations and Academia

Researchers at the Rochester Institute of Technology, Carnegie Mellon^9,10, UCSD, Yale, Dartmouth, the National Bureau of Economic Research¹¹, UC Berkeley¹², and UC Davis¹³ have published on the appropriateness of the marginal emissions based impact calculation methodology. Additionally, the following sample of organizations use this approach in program administration:

•	World Resources Institute

o	In guidance for voluntary carbon reporting under its GHG Protocol14

•	California Public Utilities Commission

o	In measuring performance under the Self Generation Incentive Program (SGIP)15

•	The UNFCC’s Clean Development Mechanism

o	In generating Certified Emissions Reductions from grid connected distributed energy projects under the Kyoto Protocol16

•	Business Renewables Center (BRC)

o	In guiding its 200 member brands to account for the impacts of power purchase agreements. NGO partners in the BRC include the Rocky Mountain Institute, World Wildlife Fund, World Resources Institute, Business for Social Responsibility, and CDP’s RE 100 Program and We Mean Business Coalition17

Marginal emissions proxies are tracked by the US Environmental Protection Agency (EPA), in its eGRID non-baseload reference data. The EPA suggests that this data is “recommended to estimate emission reductions from… projects that reduce consumption of grid supplied electricity.”¹⁸

Bloom follows this recommendation and utilizes this data to calculate our historical domestic emissions reductions by comparing our systems’ localized annual emissions to the marginal emissions displaced (see Figure 4 below for the geographical regions reported). For clarity, we also incorporate the EPA’s default values for line losses from transmission avoided by our distributed deployments.

Other sources of marginal emissions data and methodology exist, but eGRID data provides a consistent, transparent methodology covering all US regions over all of the years needed to produce an historical analysis for Bloom’s entire fleet. To confirm results of our analysis using hourly marginal emissions data, Bloom commissioned the non-profit organization WattTime to reconstruct the analysis using its 2018 proprietary model for California and found comparable results.

                                                              

⁹ Environ. Sci. Technol.2017512112988-12997

¹⁰ Environ. Sci. Technol. 2012, 46, 4742−4748

¹¹ Graff Zivin, J.S.,et al. Spatial and temporal heterogeneity of marginal emissions: Implications for electric cars and

other electricity-shifting policies. J. Econ. Behav. Organ. (2014),

¹² JAERE, volume 5, number 1. © 2018 by The Association of Environmental and Resource Economists

¹³ American Economic Journal: Economic Policy 2015, 7(3): 291-326

¹⁴ http://pdf.wri.org/GHGProtocol-Electricity.pdf

¹⁵ https://www.cpuc.ca.gov/uploadedFiles/CPUC_Public_Website/Content/Utilities_and_Industries/Energy/Energy_Programs/Demand_Side_Management/

Customer_Gen_and_Storage/2017_SGIP_AES_Impact_Evaluation.pdf

¹⁶ https://cdm.unfccc.int/methodologies/PAmethodologies/tools/am-tool-07-v4.0.pdf]

¹⁷ https://businessrenewables.org/what-we-do/

¹⁸ https://www.epa.gov/sites/production/files/2018-02/documents/egrid2016_technicalsupportdocument_0.pdf

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		TECHNICAL NOTE

Figure 4: eGRID Subregions¹⁹ 

Carbon Impact Breakdown

Utilizing the methodology described above, our analysis below shows that Bloom’s fleet has generated emissions reductions in every year and every region we operate since beginning scalable commercial deployments in 2011.

Figure 5 below demonstrates how power produced by a Bloom ES-5 system is more than 50% less carbon intensive than the national average of displaced alternatives based on 2016 eGRID data.

Figure 5

                                                              

¹⁹ https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

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Moving beyond a national average, in Figure 6 below, we also see that Bloom’s ES-5 systems are more carbon efficient than marginal emitters in every region we operate based on 2016 eGRID data.

Figure 6

For transparency, it is also important to understand how Bloom’s less efficient first generation ES5700/10 systems perform. The graphic below demonstrates how each fleet has performed year-over-year versus the marginal emissions average of the regions in which they operate. Although it is a characteristic of solid oxide fuel cells that the absolute emissions from the fleet increase each year as efficiency degrades over time, Figure 7 shows that such efficiency degradation does not materially affect the emissions reductions.

Figure 7

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		TECHNICAL NOTE

As with any thermal power plant, Bloom’s efficiency performance is the primary driver of absolute emissions in deployments where natural gas is used as fuel. Bloom provides our customers with warranties and guaranties regarding our Energy Servers’ efficiency, and we repair any Energy Server that fails to perform in accordance with these commitments.

Figure 8 below shows another view of Bloom’s efficiency performance, with the plot showing five-year average fleet efficiencies for both ES 5700/10 and ES5 equipment generations. As fleets age, we see average efficiency declines, but the degradation stabilizes, which ensures continued emissions reductions over the system’s life.

Figure 8

Finally, Figure 9 below shows emissions reductions quantification from the fleet across all the EPA subregions in which Bloom operates. Our fleet’s carbon efficiency ranges from 20-60% depending on the mix of marginal emitters active in a particular region.

Figure 9: Regional Breakout

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		TECHNICAL NOTE

10

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		TECHNICAL NOTE

Additional Emissions Reductions

Importantly, this data reflects the emissions results of Bloom’s entire deployed fleet: including systems Bloom owns, customers own, and third-party financiers own. We purposely do not distinguish between those ownership dynamics because we want to transparently demonstrate the nature of Bloom’s total equipment performance outside of transactional dynamics that might shift emissions accounting responsibility to one party or another.

The overall reported impact of Bloom’s Energy Servers includes additional emissions reductions beyond what is captured by the marginal emissions comparisons depicted in the graphs above, including:

•	18.78 MW of directed biogas transactions, neutralizing the carbon emissions from Bloom units equivalent to 552,250 MtCo2e20

•	14.35 MW of international deployments in India, Korea and Japan whose marginal grid emissions are generally higher, resulting in even greater emissions reductions than those cited in domestic comparisons and yield approximately 109,960 MtCo2e21

•	Displacement of emissions from the use of diesel generators at customer facilities totaling approximately 2 million pounds of known emissions savings to date

Air Quality Breakdown

Criteria pollutants are a class of smog forming air pollutants regulated by the EPA²², including NOx and SOx. They are the primary source of pollution and are produced during fossil fuel combustion power generation and when backup power generators are in use. Bloom’s non-combustion based fuel cells emit virtually no air pollutants.

The health and environmental impacts of combustion related pollutants are both very significant and readily quantifiable. In fact, calculations of the economic and health benefits associated with reducing NOx and particulate matter emissions have been found to exceed the economic and health benefits of reducing carbon emissions on a per ton basis.²³ In light of the overwhelming challenge presented by global climate change, the desire to reduce carbon emissions is appropriately the first and most important emissions reduction objective.

However, there is a steadily growing body of evidence indicating that local combustion related air pollution has far more serious and harmful consequences to human health and the environment than previously understood, including recent findings that:

•	Combustion related air pollution may be as harmful to your lungs as smoking cigarettes;24

•	Combustion related air pollution increases preterm birth risk;25

•	Combustion related air pollution causes dementia;26 and

•	Particulate matter is the largest environmental health risk factor in the nation, and the resulting health impacts are borne disproportionately by disadvantaged communities.27

                                                              

²⁰ Assumes system owners continued biogas sourcing at initial rates beyond initial contract term

²¹ Assumes Japanese marginal emissions values recommended by Ministry of Environment, Indian values from the Central Electricity Authority, and Korea based on US marginal emissions average as proxy

²² https://www.epa.gov/criteria-air-pollutants

²³ Institute for Policy Integrity, New York University School of Law, “How States Can Value Pollution Reductions from Distributed Energy Resources” July 2018, available at: https://policyintegrity.org/files/publications/E_Value_Brief_-_v2.pdf

²⁴ Wang M, Aaron CP, Madrigano J, et al. Association Between Long-term Exposure to Ambient Air Pollution and Change in Quantitatively Assessed Emphysema and Lung Function. JAMA. 2019;322(6):546-556. doi:10.1001/jama.2019.10255 Aubrey, Allison. Air Pollution May Be As Harmful To Your Lungs As Smoking Cigarettes, Study Finds. NPR. 13 August 2019. https://www.npr.org/sections/health-shots/2019/08/13/750581235/air-pollution-may-be-as-harmful-to-your-lungs-as-smoking-cigarettes-study-finds

²⁵ Mendola, P. et al. Air pollution and preterm birth: Do air pollution changes over time influence risk in consecutive pregnancies among low-risk women? International Journal of Environmental Research and Public Health, 2019. https://www.nih.gov/news-events/news-releases/nih-study-suggests-higher-air-pollution-exposure-during-second-pregnancy-may-increase-preterm-birth-risk

²⁶ Jung CR, et. al. Ozone, particulate matter, and newly diagnosed Alzheimer's disease: a population-based cohort study in Taiwan 2015. https://www.ncbi.nlm.nih.gov/pubmed/25310992 https://www.wired.com/story/air-pollution-dementia/

²⁷ Tessum et al. Inequity in consumption of goods and services adds to racial-ethnic disparities in air pollution exposure. PNAS March 26, 2019 116 (13) 6001-6006; first published March 11, 2019 https://doi.org/10.1073/pnas.1818859116

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Technology Performance Validation

The California Air Resources Board has certified Bloom Energy Servers as a Distributed Generation²⁸ technology due to its air quality emissions profile. This distinction is given to only the cleanest electricity generation technologies in California. As a part of Bloom’s certification process with the California Air Resources Board to become a Distributed Generation technology, Bloom went through third party validated testing of its ES5 Systems by the Avagadro Group (now Montrose Environmental) to determine that its emissions of nitrogen oxides, carbon monoxide and VOCs were below the certified limits.

Preventing Pollution and Reducing Emissions During Grid Outages with Microgrids

Bloom’s Energy Servers can form the basis of resilient microgrids, which have the capability to separate themselves from the grid and carrying critical load during an outage, the frequency, duration and severity of which are increasing every year. We have deployed more than 85 microgrids to date globally and our systems rode through 550 power outages in 2018 alone.

When Bloom microgrids are in place, they can prevent the need for both marginal generation and backup diesel generators, which emit both carbon and criteria pollutants into the communities surrounding displaced marginal generators as well as any community facing a prolonged power outage. Diesel generators also need testing, regularly emitting criteria pollutants even when there is no grid outage.

Impact Moving Forward

While we cannot fully predict the forward evolution of marginal emissions profiles, we anticipate that more baseload renewable power will continue to be brought online. With proper integration of renewables into the grid baseload, the marginal emissions rates are likely to stay constant and continue to be driven by inefficient carbon generators in the near to medium term.

The marginal emissions in a given region are often the last indicator to change when a grid is transitioning to renewable energy. For example, according to the California Independent System Operator (“CAISO”), the average marginal emissions rate for Northern California is listed as 984 lbs. of CO2/MWh — which is higher than Bloom’s Energy Server emission rate of 679 – 833 lbs. CO2/Mwh discussed above. The Northern California average marginal emissions rate is consistent with that of natural gas fired marginal generation, despite the fact that the CAISO grid mix has 31% renewables.²⁹

In new markets Bloom is actively exploring, including New Jersey, Maryland and Washington D.C., Bloom’s ES5 systems are more carbon efficient than the marginal generator in the eGRID subregion covering the states by more than 50%.

Still, Bloom’s commitment to climate action and a clean energy future is moving the company further into new fuels, industries, and technologies that hold the potential for even lower carbon intensity energy production. The journey has already begun, with our current Energy Servers providing carbon reductions in every region in which we operate, as articulated in this paper. But, where do we go from here?

Our Low Carbon Pathway

First, Bloom is actively developing international market opportunities in regions with dirtier grids and higher marginal emissions rates. Additionally, we are working to support new industries like shipping, which is currently powered largely by heavily polluting bunker fuel.

                                                              

²⁸ https://ww2.arb.ca.gov/node/1605/about

²⁹ http://www.caiso.com/Documents/GreenhouseGasEmissions-TrackingReport-Aug2019.pdf

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We are also focused on using renewable biogas as the fuel for our Energy Servers. The renewable natural gas market is maturing rapidly, as fuel sources are identified, pipeline capacity is constructed and project development, transactional and policy dynamics mature. Bloom is supporting the growth of this sector in order to help supply customers with the lowest carbon intensive fuel sources possible, but also to support rural communities and municipalities who would benefit from Bloom’s flexible, decentralized and resilient energy solution.

For scenarios in which renewable fuels are not available, Bloom is pushing technology and business model boundaries to pioneer carbon capture, utilization & storage (CCUS) potential from its Energy Servers. Because carbon and nitrogen never mix in Bloom’s fuel cells, it is both feasible and cost effective to capture C02, which can be stored in underground geologic formations or utilized in new products or processes like cement manufacturing and alternative fuel development.

Finally, Bloom sees the widespread deployment of renewable hydrogen fuel emerging as a goal on its low carbon pathway, given that no net greenhouse gases are produced in the process.

Conclusion    

Carbon mitigation is hugely important in the long term fight against global climate change. Reducing criteria pollutants has immediate, local and demonstrable impact on human health and wellness. Thanks to its distributed, Always On, non-combustion process of generating clean electricity, Bloom is engaged in both battles, working every day to reduce emissions, build resilience, and promote sustainable communities.

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