Coal Cost Crossover Energy Innovation VCE FINAL

Transcript

1 www.energyinnovation.org 98 Battery Street; San Francisco, CA 94111 [email protected] energyinnovation.org COAL COST CROSSOVER: THE ECONOMIC VIABILITY OF EXISTI NG LOCAL COAL COMPARED TO NEW RCES WIND AND SOLAR RESOU 1 VATION O’BOYLE, ENERGY INNO ERIC GIMON AND MIKE ● MARCH 201 9 . M . CLACK AND SARAH MCK EE, VIBRANT CLEAN EN CHRISTOPHER T ERGY America has officially entered the “coal cost TABLE OF CONTENTS crossover” – where existing coal is increasingly more expensive than cleaner alternatives. Today, 2 INTRODUCTION AND RES ULTS approximately local wind and solar could replace 5 CORE DATASETS 74 percent of the U.S. coal fleet at an immediate savings to customers. By 2025, this number grows COAL TO RENEWABLES C OST to 86 percent of the coal fleet. 8 CROSSOVER 2 This analysis complements existing research into – COAL AT RISK NUMBERS 9 FINDINGS the costs of clean energy undercutting coal costs, DISCUSSION 11 by foc using on which coal plants could be replaced CONCLUSION 13 locally (within 35 miles of the existing coal plant) at a saving. – 15 APPENDIX A SOLAR PV DATASET It suggests local decision - makers should consider APPENDIX B – WIND DATASET 19 plans for a smooth shut - — plants down of these old APPENDIX C – COAL PLANT DATASET 23 assessing their options for reliable replacement of 3 that electricity , as well as financial options for 1 , Jeremy Fisher, Uday Varadarajan, The authors would like to thank Joe Daniel, Harriet Moyer Aptekar Ric O’Connell, Taylor McNair, and Sonia Aggarwal for their helpful feedback on this report. Any remaining errors are the responsibility of the authors. 2 , Carbon Tracker Institute, No Country for Coal Gen – Below 2° C and Regulatory Risk for US Coal Power Owners 2c - September 2017, https://www.carbontracker.org/reports/no - country - for - coal - - risk regulatory - and - gen - ow bel - ; Pacificorp, for - coal - power - owners/ us 2019 Integrated Resource Plan (IRP) Public Input Meeting , December 2018, - ocument_cw_01.pdf https://www.eenews.net/assets/2018/12/05/d . 3 Operational Analysis of the Eastern Interconnection at Very Joshua Novacheck, Greg Brinkman, and Gian Porro, High Renewable Penetrations , National Renewable Energy Laboratory, September 2018, https://www.nrel.gov/docs/fy18osti/71465.pdf ; Mark Dyson and Alex Engel, A Low - Cost Energy Future for Western Cooperatives, Rocky Mountain Institute, 2018, https://www.rmi.org/wp - . content/uploads/2018/08/RMI_Low_Cost_Energy_Future_for_Western_Cooperatives_2018.pdf 1

2 4 communities . dependent on those plants effective - Ultimately, this report begins a longer conversation about the most cost replacement for coal, which may include combinations of local or remote wind, solar, transmission, storage, and demand response. INTRODUCTION & RESULTS in the United States, or more precisely at a “cost crossover.” Coal generation is at a crossroads 5 Due to the rapid recent cost decline of wind and solar, the combined fuel, maintenance, and forward costs of coal - fired power from many existing coal plants is now more other going - expensive than the all in costs of new wind or solar projects. This cost crossover raises - substantial questions for regulators and utilities as to why these wer coal plants should keep running instead of new renewable po n this analysis: Definitions i plants. “Local” means within 35 miles. To determine which coal plants are facing this cost crossover with coal means local wind or “At risk” renewables, Energy Innovation partnered with Vibrant Clean 6 solar could replace the coal plant’s Energy (VCE) to compile a dataset of coal, wind, and solar costs. total output (on a kilowatt - hou r For simplicity, the modeling compares each coal pl ant’s marginal - basis) at an all in cost lower than ) to the lowest levelized cost of energy cost of energy ( MCOE the existing coal plant’s ongoing (LCOE) for wind or solar resource localized around that coal plant. marginal costs. Restricting replacement to local resources makes this analysis olar all travel from conservative, considering most coal, wind, and s “Substantially at risk” coal means more remote locations to load centers via transmission. local wind or solar could replace the coal plant’s total output at an Our research finds that in 2018, 211 gigawatts (GW) of existing in cost >25% lower than the - all (end of 2017) U.S. coal capacity, or 74 percent of the national existing coal plant’s ongoing fleet, was at risk from local wind or solar that co uld provide the arginal costs. m risk coal - same amount of electricity more cheaply. By 2025, at 7 nearly the entire U.S. fleet. increases to 246 GW – 4 Sonia Aggarwal, “Billions At Stake: Should We Invest In Struggling Power Plants Or Communities Facing Closures?” we - Forbes , August 23, 2018, https://www.forbes.com/sites/energyinnovation/2018/08/23/billions - at - stake - should - invest - facing - closures/#b62238a1f687 . in - struggling - power - plants - or - communities - 5 Megan Mahajan, “Plunging Prices Mean Building Ne w Renewable Energy Is Cheaper Than Running Existing Coal,” , December 3, 2018, Forbes - https:// www.forbes.com/sites/energyinnovation/2018/12/03/plunging - prices - mean renewable existing - . building - new coal/ - running - than - cheaper - is - energy - - 6 VCE’s WIS:dom model uses granular wind speed and solar irradiance data for nine - square - kilometer (3 - km x 3 - km) ce lls across the entire U.S. to paint an accurate picture of LCOE, making this a uniquely granular analysis. 7 st , 2018. - The VCE compiled dataset computes approximately 286 GW of coal fired power plants as of January 1 firing with natural gas has occurred, in part, due to the cost pressure that - Since that date, rapid retirements and re we identify in this study. 2

3 at risk from Furthermore in 2018, 9 4 GW of existing U.S. coal capacity was deemed substantially new local wind and solar that could undercut ongoing costs of existing coal by at least 25 – percent. By 2025, substantially at risk coal increases to 140 GW – almost half the U.S. fleet even as federal renewable energy tax credits phase out. Given uncertainties in publicly available ” could, with high confidence, be substantially at risk coal cost data, the tier of coal plants “ 3

4 State - - state data detailing replaced with renewable energy at an immediate cost savings. by as a companion to this report . these findings are availa ble - forward costs for the vast majority of coal plants fall between The VCE dataset reveals the going MWh – 111 / megawatt - hours ( ) . Costs in 2018 for solar are more tightly clustered, between $33 – $28 tional resource quality, falling 52 / MWh, while wind costs vary more widely based on loca between $13 – 88 / MWh, with a high number of very costly outliers in windless regions. The crossover between new renewable and coal running costs is just one important part of shutting down existing coal plants replaci ng coal plants with new wind and solar energy is – much more complex in practice. The purpose of this report is to act as a conversation primer for stakeholders and policymakers where the math points to cheaper options that could replace coal plants at a sa vings to customers. Any decision on how to proceed will require further modeling of grid impacts and alternative sources of reliability services, as well as the possibility for even - mile maximum radi us considered in cheaper renewable replacements further away than the 35 8 this report. Regardless, any coal plant failing the cost crossover test should be a wake - up call for policymakers and local stakeholders that an opportunity for productive change exists in the immediate vicinity of that plant. local renewables in the immediate vicinity of coal plants implies wind and solar could Building replace local jobs, expand the tax base, reuse existing transmission, and locate in the same utility service territory. But these constraints are quite restrictive. Ut ility planners, regulators, and customers could save additional money by looking further afield. For example, Colorado plans to 9 The replace its coal fleet with strategically located wind and solar resources around the state. can accurately analyze the viability of transitioning from VCE WIS:dom model and others dispatchable power sources like coal to variable resources like wind and solar. The unpaid capital balance owed to investors in coal plants falls outside a coal plant’s MCOE. should not factor into the economic viability of the plant (after all, it’s easier Though this balance to repay debt if utilities are meeting current obligations more cheaply), potential stranded asset value of at risk coal plants reaches into the tens of billions. A recent series of America’s Power - 10 Plan policy briefs highlight different financial tools policymakers can consider to retire uneconomic coal - fired generation while balancing consumer, community, and investor concerns. 8 VCE’s algorithm selected wind or solar resources immediately adjacent to the coal plant, and moved outward until renewable energy replaced the out put of the coal plant. 35 miles is the furthest away from the coal plant the model had to go to fill this need. The algorithm is described in Appendix C. 9 “Colorado Energy Plan,” Xcel Energy, https://www.xcelenergy.com/company/rates_and_regulations/resource_plans/colorado_energy_plan . 10 “Managing The Utility Financial Transition From Coal to Clean,” Energy Innovation: Policy and Technology LLC, https://energyinnovation.org/publication/managing - the - utility - financial - transition - from - coal - to - clean - 2/ . 4

5 CORE DATASETS by - plant analysis: LCOE and rces to construct its unique plant This report uses two data sou - MCOE. Current and future LCOE data for wind and solar projects are on a fine resolution scale, allowing policymakers to directly see wind and solar opportunities in their geography. VCE has - resolution wind and solar LCOE maps across the U.S. using detailed weather created several high 2 - km models for power production geographic resolution , multiple wind hub - heights, nine at a five - minute temporal resolution. and a Modeling details are provided in Appendices A & B. The wind and solar LCOE maps in this report include 2018 LCOE estimates by VCE for each technology, including current tax benefits and regional cost modifiers. They clearly show attractive pricing for bo th technologies across the U.S. as low as $15 per MWh for wind and $28/MWh for solar in 2018. Note that wind LCOEs have more geographic variation and hence the color scales differ from the solar color scales. We also include the VCE 2025 estimates of wi nd and solar LCOE using the low - case NREL Annual 11 cost projections. In 2025, despite the loss of federal tax Technology Baseline (ATB) 12 incentives, future cost declines mean that future pricing continues to be attractive. High - wind and solar LCOE maps resolution images of the are available for download, allowing users to zoom in at a fine - scale. - . coal plants. This dataset VCE also provided plant - by plant estimates of the current MCOE for U.S was created for existing U.S. coal fired power plants by combining publicly available information. - Data was collected from FERC Form 1, EIA Form 860, and EIA Form 923 for the calendar year 2017. The extracted information includ es amount of fuel burned, average power plant heat rate, emission factors, capital investments, pollution controls, fixed operations and maintenance (O&M) costs, and variable O&M costs. operation and maintenance (O&M) of The MCOE combines fuel and variable costs based on the power plants, as well as the fixed O&M and the ongoing capital spending for pollution controls and other upgrades to the power plant. Those later fixed costs were converted to $/MWh, using plant - specific capacity factors. For plants in regular use (capacity factors over 33%) this analysis shows a wide range of MCOEs, from $25 / MWh to $104 / MWh. For smaller capacity factors, the MCOE values quickly climb even higher, as O&M expenses are spread over fewer and fewer hours, and efficie ncy plummets. High - resolution images of the maps showing coal operational costs compared new renewables. 11 “Annual Technology Baseline,” National Renewable Energy Laboratory, August 2018, https://atb.nrel.gov/ . These estimates are justifiable due to cost declines today that indicate we’re already reaching the NREL ATB mid - case numbers. 2018 - vintage cont racts for wind and solar are available from Level 10. 12 “Renewable Electricity Production Tax Credit (PTC),” United States Department of Energy, https://www.ener gy.gov/savings/renewable - electricity - production - tax - credit - ptc 5

6 ovoltaic projects in 2018 using VCE dataset Map of the levelized cost of energy for U.S. solar phot Map of the levelized cost of energy for U.S. wind projects in 2018 using VCE dataset 6

7 Map of the levelized cost of energy for U.S. solar photovoltaic projects in 2025 using VCE dataset Map of the levelized cost of energy for U.S. wind projects in 2025 using VCE dataset 7

8 The coal plant dataset provides additional information that can be used for further analysis. First, it includes location and installed capacity of each coal fired power pla nt. Second, it includes the - heat rates, capacity factors, ages, and plant names for ease of reference on the MCOE construction. COAL TO RENEWABLES CROSS O VER COST In order to compare the costs of building new renewables with the ongoing costs of running co al plants, this report combines the two datasets above to present simplified cost crossover math. Examining each coal fired power plant in the dataset, VCE determined how nearby wind and - solar could be used to replace that coal plant. To determine the ri sk profile of the coal generation to wind and solar replacement, we compared the MCOE of the coal - fired power plant with the LCOE of the total wind or solar output required to replace all the coal megawatt hours (VCE looked only at either all wind or all s olar replacement). The VCE algorithm logic is explained in Appendix C. In short, it replaces all the MWhs generated from each coal plant annually using local wind or local solar in a search pattern for sites that are 13 available for deployment steadily inc reasing in distance. The maximum distance the algorithm required to identify replacement wind or solar resources for any given power plant was 35 miles, with a resulting average of 16 miles; these are very local replacements on the scale of the aps being presented with this report. Sites deemed unsuitable for development by national m the VCE site screening algorithm were excluded from the assessment. The algorithm did not Its output is look further afield for cheaper combinations of distant resources and transmission. strictly the LCOE of local wind or solar required to replace each coal plant, transformed into a percentage difference between the MCOE of the existing coal generation and new local wind and solar. Any plant with a negative percentage diffe rence for solar or wind replacement was deemed at risk, and “ substantially at risk ” if the differential was less than - 25% with local resources. The quantity of energy replacement is only compared in terms of annual generation and doesn’t capture the time - based value of energy and grid services from a dispatchable (if not always so flexible) coal plant. Further useful analysis could compare a coal plant with a “ virtual power plant, ” combining wind, solar, storage and demand - side resources to more closely mimic or improve on the dispatch of the coal plant and reliability services. But, as mentioned above, while the VCE analysis includes the cost of interconnecting new local wind and solar, the search algorithm does not look further afield for even cheaper resources once it has replaced the required MWhs. In Colorado, for example, no coal plant is at risk from local wind in this analysis, but we know that wind in the eastern part of the state easily competes 13 Suitability is determined using the VCE site screening algorithms that remove all protected areas, urban areas, critical flora and fauna, as well as topographical constraints on construction . Further, the algorithm provides buffer space for habitation and other land uses around the potential resource candidate technology. 8

9 - state transmi ssion. In light of these factors, cost crossover with coal and is accessible via in would likely be more common if transmission expansion were taken into account. N R ISK FINDINGS UMBERS – COAL AT Using the cost crossover algorithm, VCE determined that in 2018 more than 49 GW of coal were substantially at risk from local wind and more than 69 GW are substantially at risk from local solar, meaning they could be replaced with local renewable energy re sources at least 25 percent cheaper than the running costs of the coal. By 2025, local wind and solar could respectively replace roughly 76 GW and 111 GW of coal 14 fired power plants. Combining th e generation at 25 percent lower costs than running the coal - risk with 94 wind and solar datasets, VCE finds that 211 GW of coal capacity, or 74 percent, is at GW substantially at risk from 2018 possible local wind and solar. Assuming the NREL lower cost technology baseline case for 2025, substantially at - risk co al increases to 140 GW (with sunset tax support), or almost half of the U.S. fleet. Combined Combined Solar Wind Wind Solar RE Cost MW (2018) (2018) (2025) (2018) (2025) (2025) Coal >25% less substantial ly 93,812 140,073 111,077 49,165 69,117 75,778 than coal risk at 0 - 25% less 16 246,306 118,085 178,871 Coal at risk 7 ,201 229,001 210,842 than coal Coal 0 - 25% m ore potentially 75,806 40,342 57,647 168,563 107,777 119,447 than coal at risk Coal >25% more deemed 21,608 101,792 7,866 46,289 15,706 49,620 than coal safe risk coal by state, as this is often the most relevant - We also report the substantially at 15 risk coal plant jurisdiction for the future of any at : - 14 Using NREL ATB low. 15 There are two states where the MW of coal in the substantially at - risk categories falls from 2018 to 2025. This is because those plants are right on the cusp ( - 25%) of that category and a slight increase in local costs, due to PTC ve the less risky category. sunset causes them to mo 9

10 Note that many Midwestern states see a significant increase in substantially - risk coal by 2025, at reflecting the continuing drop in price for local solar and the high marginal costs of these coal plants. Solar costs have less geographic variation and are therefore projected to become locally but Midwestern states could also readily access rich wind accessible in more places than wind, resources to the west with more transmission. The sharpest patterns are regional. Almost all coal plants in the PJM footprint are at risk to coal replacement on a straight energy value comparison by 2025. Of course, coal plants in PJM 16 unfriendly to solar garner a large fraction of their revenue from capacity markets that are (in part because they make no allowances for seasonal performance or time of - day value). This - keeps them afloat, with a h uge opportunity cost to customers. Another strong regional trend is in the Southeast, where almost all coal plants are substantially at risk to replacement by local solar in 2025 (solar energy is often available there at half the cost of coal power using the NREL lowest - cost scenario). The trend is so strong that it is hard to imagine Southeastern utilities not relying heavily on solar and complementary load shifting resources to replace the coal and save customers money. ar: Much of the U.S. coal fleet is simply becoming uneconomic and The overall conclusion is cle analysts, utilities, other stakeholders, regulators, and policymakers need to take a critical look at 16 Jacob Mays, David Morton, and Richard P. O’Neill, “Asymmetric Risk and Fuel Neutrality in Capacity Markets,” United States Association for Energy Economics Working Paper No. 19 - 385 (February 8, 2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3330932 ; Robbie Orvis and Eric Gimon, “The State of Wholesale Power Markets: What’s Wrong With Proposed Changes in Eastern RTOs?” Utility Dive, June 20, 2017, state https://www.utilitydive.com/news/the - of - - wholesale - power - markets - whats - wrong - with - proposed - changes - in/445417/ . 10

11 each and every coal plant in their jurisdiction. - fired generation solely on an annual basis, but as In this analysis, wind and solar replac e all coal previously stated, a limitation of this analysis is that replacing annual generation does not capture coal generation dispatch timing. Despite its notorious inflexibility, coal is mostly dispat chable, while wind and solar are variable sources of energy whose output, even in aggregate, does not necessarily match demand. But so - called “baseload” coal economics igh capacity typically require high capacity factors, limiting their use as flexibility resources (h factors require avoiding frequent ramping up and down) and creating a premium for what flexibility they offer, as consumers must pay the costs of running higher cost energy sources - - year round to access that flexibility. omes between the marginal economics of coal versus wind and solar, the The wider the gap bec more coal plants will have to depend on their perceived capacity value to recover costs. Their capacity factors may drop even more, widening the gap, and opening a window for dedicated resources like demand response, storage, and existing flexible resources to fill their niche. DISCUSSION This report suggests a sunset scenario for coal power. Not all plants will retire immediately — a steady flow of exits is more likely, especially wher e capacity markets and monopoly utilities — but all stakeholders support uneconomic coal generation at the expense of new renewables must prepare for the looming economic reality. The first step for merchant owners, utilities, regulators, and other stakeho lders is taking a hard look at coal retirement. For regulated utility assets, integrated resource plans (IRP) and other - term planning analytical efforts should always include coal retirement scenarios. Indiana long utility NIPSCO has shown how smart anal ysis can flip planning directions: Their most recent planning effort recommended replacing all their coal in the next decade with r enewable energy, 17 including wind and solar, along with battery storage . Consumer advocates elsewhere should be asking whethe r coal plants receiving state - regulated cost recovery but operating in 18 should be allowed to run at loss to the transparent competitive regional energy markets 19 detriment of consumers’ pocketbooks . 17 The Times of Fired Electricity Generation Within 10 Years,” A ndrew Steele, “NIPSCO Plan Would Eliminate Coal - , September 19, 2018, Northwest Indiana - plan - would - eliminate - https://www.nwitimes.com/business/local/nipsco 5180 - - electricity - generation - within coal years/article_269a1f6b - 1a24 - fired - 976d - 27a474d8ee47.html . - 18 United States D epartment of Energy, Principles for Increasing the Accessibility and Transparency of Power System Planning , January 2017, https://www.energy.gov/sites/prod/files/2017/01/f34/Principles%20for%20Increasing%20the%20Accessibility%20a nd%20Transparency%20of%20Power%20System%20Planning.pdf . 19 Joseph Daniel, “The Coal Bailout Nobody is Tal king About,” Union of Concerned Scientists (blog), September 24, 2018, https://blog.ucsusa.org/joseph - daniel/the - coal - bailout - ) is - is - talking - about . A virtual power plant ( VPP nobody distributed energy a cloud - based distributed power plant that aggregates the capacities of heterogeneous 11

12 Consumer advocates faced with utility inertia, environment al advocates concerned about unpriced coal externalities, and advanced energy solutions providers eager to open opportunities can push back against reliability or dispatchability arguments by comparing the economics of any single coal plant with a combinat ion of local (or distant but easily accessible) renewables with compl e mentary demand - side and storage resources, or virtual power plants 20 - in replacement also proves more economic than an at (VPP) risk coal plant, it - . If a VPP drop minimum can provide an estimate of the savings available from coal plant retirement. A more holistic approach leveraging other existing assets on the grid can prove to be even 21 - cheaper for integrating low showed how Colorado cost renewables. For example, a VCE study ace all could repl s aging coal plants with a mix of wind, solar, natural gas , and storage to save it the state’s electric customers $250 million annually without affecting reliability. This more than se Colorado appears on the tail example is especially notable in the context of this report, becau end of states with coal plants at risk from renewables within 35 miles. Because coal plants in the central and eastern part of Colorado are most economically replaced eastern part of the state , not with local resources (although solar does with cheap wind from the start becoming a local option by 2025), our cost crossover analysis does not flag many of these plants as at risk (see 2025 LCOE wind map). In fact, this is true for most of the West, where high - rces in the $15 – quality wind resou 25 / MWh range are often only accessible through large transmission projects. Understanding the geographic dimensions of renewable costs – the and proper modeling are therefore key to planning analysis opportunities visible in our maps – - and decision making. For coal plants in a vertically integrated jurisdiction like Colorado, and in hybrid setups where coal plants participate in wholesale markets but long - term costs are covered by ratepayers (e.g. nd Midcontinent Independent System Operator), it is also many states in Southwest Power Pool a useful to look not just at the MCOE of a given coal plant, but also at the remaining balance of long term costs. Captive customers are on the hook for these costs. If an at - risk coal plant - ut is not paid off, significant incremental savings await ratepayers, especially if the retires b remaining amortization balance can be refinanced at a lower cost than typical utility rates of 22 . return resources (DERs) for the purposes of enhancing power generation, as well as trading or selling power on the electricity market. 20 The Economics of Clean Energy Portfolios Mark Dyson, Alexander Engel, and Jamil Farbes, , Rocky Mountain Institute, 2018, https://rmi.org/wp - . content/uploads/2018/05/RMI_Economics_Of_Clean_Energy_Portfolios_2018.pdf 21 Vibrant Clean Energy, “New Study Finds That Replacing Aging Coal Plants With Wind and Solar Saves Colo rado $2.5 Billion by 2040 While Sharply Slashing Emissions,” January 8, 2019, https://www.vibrantcleanenergy.com/wp - content/uploads/2019/ - VCE_CO_CoalPlantRetireStudy(CRS).pdf . 01/CEI 22 “Financial Transition,” America’s Power Plan, December 2018, https://americaspowerplan.com/power - transformati on - solutions/financial - transition/ . 12

13 term contracts, Colorado offers another When evaluating coal replacement by other long - interesting example because of its competitive IRP process where potential suppliers bid against each other to meet future needs. This kind of bidding transparently surfaced cost numbers that cost crossover was possible. revealed some of the first signs that In competitive markets run by Independent System Operators (ISOs), cost crossover analysis indicates where markets are likely out of balance with current economic realities. Obviously, if nd wholesale prices remain below coal plant MCOE, coal plant owners are taking all the risk a plants will bow to economic pressure and retire. For example, in 2018 Texas’ ERCOT system had , 23 In PJM’s most recent look at at least five coal plants close or announce plans to close. 24 incorporating ambitious fractions of renewables , the largest amounts of solar generation hours of coal to solar replacement - considered are nowhere near the hundreds of terawatt implied in this report’s analysis. With proper planning and more technology agnostic rules, - tremendous value can be unlocked for customers served by ISOs and utilities. CONCLUSION Coal is a dirty and expensive way to generate electricity. The National Academies estimated that 25 non - in 2005, U.S. coal generation alone caused at least $62 billion in climate related damages. Coal’s remaining rationale was that it was cheap if externalities weren’t included, but even that new rationale is vanishing. Our report shows that coal is increasingly uneconomic against local wind and solar resources. The next refuge for those with an economic stake in coal generation is reliability, or claims that the grid cannot run reliably without it. This report cannot directly address that contention, but 26 27 tudies more holistic studies like the VCE Colorado or Minnesota s renewable or the NREL integration studies do undercut this point. 23 Miranda Green, “Texas Coal Plant Announces Plans to Shut Down,” The Hill , September 25, 2018, environment/408369 http s://thehill.com/policy/energy - - texas - based - coal - fired - plant - announces - retirement . 24 https:/ /www.pjm.com/committees - and “Renewable Integration Study Reports,” PJM Interconnection, - groups/subcommittees/irs/pris.aspx . 25 The National Academies, Hidden Costs of Energy , 2009, https://www.nap.edu/resource/12794/Hidden_Costs_Key_Findings_final.pdf . 26 Vibrant Clean Energy, “New Study Finds That Replacing Aging Coal Plants With Wind and Solar Saves Colorado $2.5 Billion by 2040 While Sharply Slashing Emissions,” January 8, 2019, https://www.vibrantcleanenergy.com/wp - VCE_CO_CoalPlantRetireStudy(CRS).pdf content/uploads/2019/01/CEI - Smarter ; Vibrant Clean Energy, Minnesota’s Grid, July 31, 2018, https://www.mcknight.org/wp content/uploads/Minnesotas - - . SmarterGrid_FullReport_NewFormat.pdf 27 Joshua Novacheck, Greg B rinkman, and Gian Porro, Operational Analysis of the Eastern Interconnection at Very High Renewable Penetrations , National Renewable Energy Laboratory, September 2018, https://www.nrel.gov/docs/f ; “Renewable Electricity Futures Study,” National Renewable Energy y18osti/71465.pdf Laboratory, https://www.nrel.gov/analysis/re - futures.html ; “Interconnections Seam Study,” National Renewable E nergy Laboratory, https://www.nrel.gov/analysis/seams.html ; “Eastern Renewable Generation Integration Study,” . National Renewable Energy Laboratory, https://www.nrel.gov/grid/ergis.html 13

14 Other resources will be required to complement wind and solar and provide essential reliability ergy available services, but the increasingly attractive relative value proposition for the raw en from wind and solar versus more expensive coal generation can generate more and more money to directly address grid challenges. Steep declines in costs for resources like battery storage will stretch that money even more. Furthermore, it is becoming clear that wind and solar can become an asset rather than a liability when it comes to essential reliability services due to their 28 highly responsive power electronics. Large majorities of Americans support increasing the use of solar and wind energy in their 29 s . The data in this report provide an economic rationale for a coal phase - out in the next state decade led by wind and solar, happening a lot quicker than most had imagined. It’s time to get on with the coal to - clean transition. - 28 Energy and Environmental Economics, Inc., Investigating the Economic Value of Flexible Solar Power Plant Economic https://www.ethree.com/wp Operation , October 2018, - - content/uploads/2018/10/Investigating - the - Power Plant of - Flexible - Solar - - - Value - Operation.pdf . 29 “Findings From the Fall 2018 NSEE,” Gerald R. Ford School of Publ ic Policy, http://closup.umich.edu/national - surveys - on - energy - and - environment/nsee - 2018 - fall/renewables.php . 14

15 X A APPENDI SOLAR PV POWER DATAS ET To create a high resolution levelized cost of electricity (LCOE) dataset a power dataset is (VCE) has created such a power dataset for solar PV across required. Vibrant Clean Energy, LLC a geographic resolution of three km and a temporal the United States. The power dataset is at resolution of five minutes. The solar PV power dataset spans five calendar years. To construct the solar PV power dataset, VCE acquired the full three - dimensional (native) fields of the National Oceanic a nd Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR). VCE has continued to expand the only 3 D archive of the HRRR for both assimilation - hours and forecast out to 36 hours. The numerical weather prediction (NWP) model data from NOAA is crucial because it includes 20 - 50,000 observations collected and quality controlled by the National Weather Service (NWS). The observations include ground - based measurements, satellites, aircraft, radar, balloon launches, and more. In addition, VCE acqu ired the GOES - East and GOES - West Satellite telemetry for the visible band, three Infrared bands, and the water vapor band. The temporal resolution of the satellite data is 15 - minutes. The satellite dataset spans the same time period as the NOAA HRRR datase t. The satellite dataset has been collected because it is well understood that NWP are poorer at cloud resolving than satellites in terms of thickness and dispersion. Further, the dual satellite imagery facilitates stereographic projections of the clouds f or computing the shading, reflection and absorption of solar irradiance in many grid cells. Finally, VCE collected the NOAA SURFRAD and SOLRAD high - precision ground - based measurements for solar irradiance. This will be used in the deep - learning AI algorith m contained in VCE’s Solar Irradiance Model (SIM). Not every variable in the HRRR dataset is used for the solar PV power estimates. For the proprietary algorithm created by VCE, the Solar Power Model (SPM), we extract: the wind speed at two meters, the inc oming shortwave radiation, the incoming longwave radiation, the outgoing shortwave radiation, the outgoing longwave radiation, the clouds in the column above the ground resource sites, the hydrometeors in the column, the temperature, the clouds and hydrome teors in the beam direction, and the estimated aerosols. In addition to collecting data from the HRRR, GOES, SURFRAD, and SOLRAD, VCE must compute - Sun some critical variables that have a significant influence on solar irradiance: The Earth distance, the de clination angle, the hour angle, the azimuth angle, the zenith angle for every single site across the United States. An important addition is the Equation of Time that can disrupt accuracy at five - minute resolution if not included. The HRRR dataset is at hourly resolution. It is at this stage that we convert the hourly data into five - minute data. We do this using the tendencies (derivatives) within the HRRR model as well as statistical methods to create a continuous function between hours. The five - minute resolution allows use of cloud scattering and other variables in the HRRR that can be useful to determining time periods than the hourly data. solar PV power at shorter - 15

16 30 17 . We The procedure to create the datasets is somewhat similar to that described in Clack, 20 recap the major points here for completeness. The first part of the procedure is to create the Direct Normal Irradiance (DNI), the Diffuse Horizontal Irradiance (DHI), and the Global Horizontal Irradiance (GHI). We require all he solar panels respond differently to the DNI and DHI; particularly with components because t heating of the panels and the photoelectric effect. The SIM trains the learning algorithm (AI) with the ground based observations from the SOLRAD and SURFRAD sites. These are conside red the - “truth” with their measurement errors incorporated. The GOES and NWP datasets are the - based measurements. Of course, a small components to be combined to produce the ground subset is held back from the training algorithm to validate the approach. T he approach begins with a shallow - learning sequence (as in Clack, 2017); but then continues with deeper learning that recombines different variables in unconventional ways to increase the precision of the upon. The training is performed repeatedly estimates. There are ~630,000 observations to train with different data levels available. For example, one satellite only available; part of a satellite missing, all satellites missing, some hydrometeors missing, etc. It is important to note that the nearest one - m inute average of the ground - based observation is used for the five - minute estimates. We combine errors of measurements and five - minute variance for the observations. This is a deliberate choice; the SIM is comparing a point to a grid cell average. We do no t want to over - fit the learning. Note that the SURFRAD and SOLRAD sites span the U.S. and are in different urban environments. urban/non - The conclusion of the SIM is where the deep learning algorithm applies the computed - minute time periods. The outputs are GHI, DNI, e U.S. for all five coefficients to all sites across th DHI, hour angle, azimuth angle, zenith angle, declination angle, clouds, aerosols, temperature, and wind speed at two meters. Once the SIM outputs are created, the procedure moves to the se cond stage, which is the SPM. - crystalline. The SPM include parameters for different solar PV types. The standard used is mono The SPM computes the power estimates based on the SIM outputs, the angle of the panels, the shading, the tracking assumed, and the terrain / elevation. The SIM outputs include temperature and wind speeds, that allows computation of the heating of the panels that influences the power production vastly. The Invert Loading Ratio (ILR) is assumed to be 1.2. The SPM has the ability to per form the computations with any level of ILR; but this would add too many degrees of freedom if it was not consistent across the U.S. - The SPM computes the instantaneous CF for each three - km site for each five minute time step. The power can be above 100 pe rcent rated power because of temperature dependency, cloud brightening, Inverter loading, and snow cover. The SPM is limited to only allowing 130 percent of 30 Christopher T. M. Clack, “Model ing Solar Irradiance and Solar PV Power Output to Create a Resource Assessment Using Linear Multiple Multivariate Regression,” Journal of Applied Meteorology and Climatology , (January 2017): 109 - 125, https://journals.ametsoc.org/doi/pdf/10.1175/JAMC - D - 16 - 0175.1 . 16

17 the rated capacity. This is to avoid overloading the inverters. The rated power is defined by the s olar panels installed. This is because for costs later, we use the cost for installing at 1.0 ILR, so we require consistent definitions. The solar PV power dataset is the final output of SPM. Currently, the dataset covers multiple tilts a xis tracking (North - South facing, tilted at latitude) and two - for fixed PV, one - axis tracking. It does also include rooftop solar PV estimates, which is based on roof space, average tilt of roof, shading, and pitch of roof in each three - km grid cell. The one - axis tracking i s the most widely adopted in the U.S., but the other versions allow comparison for production and optimal siting at - a later date. For example, northern states would benefit from two Axis tracking for higher solar d offset the additional cost of construction. production in the winter months, which woul However, the far south could use fixed axis tilted at an optimal angle and save on having tracker 31 technologies. A short visualization of the solar PV dataset is available online. AND COSTS INCENTIVES Once the solar PV power dataset is created, we can start to apply costs to the resource sites. In the previous section, we have only dealt with the physics of the solar irradiance and power; and not how economics alters the picture for site preference. To apply cos ts, we use the National Renewable Energy Laboratory (NREL) Annual Technology Baseline (ATB) 2018. The NREL ATB 2018 provide costs for numerous years and technologies. We have chosen to use the 2018 costs axis - along with the 2025 (low and mid) projection. The so lar cost is referenced to the one tracking for each site across the U.S. The economic life of the solar PV plant is estimated to be 25 years. The Weighted Average Cost of Capital is assumed to be 5.87 percent (real). The fixed and variable costs are a lso pulled from the NREL ATB 2018. The federal incentives of the Production Tax Credit (PTC) and Investment Tax Credit (ITC) are applied with their current sunset dates. Only the ITC is applied to the solar sites. The U.S. is extremely diverse in its costs for labor, materials and permitting. The algorithm used for the modeling includes a component that applies state - level multipliers to the cost of the solar PV construction. They are applied at the state - level because of data availability. The multipliers range from 87.5 percent to 105.0 percent. Further there is cost multipliers for the different technologies. For the one axis tracker there is a 15 percent premium for building the tracker - system compared with the fixed panels, with no tilt. TRANSMISSION CO NSIDERATIONS The VCE WIS:dom optimization model includes detailed transmission datasets. The transmission datasets include the transmission lines, their voltage, the transmission substations and their capacities. For each three - km site from the solar PV po wer dataset, a geodesic is computed to 31 “Solar Power For Day 10 of 2014 – Coincident With Winds,” Christopher Clack, Youtube video (May 23, 2018), https://www.youtube.com/watch?v=d22m0KHy5Fs . 17

18 the nearest substation. The cost of the solar resource site is increased by the cost to construct the transmission line to the nearest substation. Further, if the WIS:dom model determines the pacity, the solar site will incur a cost to upgrade the transmission substation is close to ca substation as well. This is relaxed for the 2025 versions, because the grid topology is likely to change by that date. LCOE MAPS DATA LINKS AND , the final step is to produce the LCOE mapping. With the costs and power datasets completed - km resource site. The capacity The power dataset is converted to capacity factors for each three factor is combined with the costs to produce the LCOE. Essentially, total costs (capital, fixed, transmission, multipliers) divided by potential generation (CF * Size * 8760). We allowed only - - South. one axis tracking tilted at latitude and orientated North VCE has created NetCDF files that include the LCOE data for 2018, 2025 Low, and 2025 Mid. Further, VCE has vi sualized the LCOE data in PDF. The images allow easy zoom capabilities into regions of the United States to be used by all. The data files allow more precise analysis using the LCOE mapping. The location of the data files is: https://www.vibrantcleanenergy.com/wp - /LCOE - content/uploads/201 9 / 03 Mapping/SolarLCOE_Data.zip The locations of the images are: - content/uploads/2019/03/LCOE - https://vibrantcleanenergy.com/wp Mapping/SolarPVLCOEMap_2018_cobrand_samescale.pdf https://vibrantcleanenergy.com/wp - content/uploads/2019/03/LCOE - Mapping/SolarPVLCOEMap_2025L_cobrand_samescale.pdf https://vibrantcleanenergy.c - content/uploads/2019/03/LCOE - om/wp Mapping/SolarPVLCOEMap_2025M_cobrand_samescale.pdf 18

19 APPENDIX B WIND POWER DATASET To create a high resolution levelized cost of electricity (LCOE) dataset a power dataset is required. Vibrant Clean Energy, LLC has created such a power dataset for wind across the United States. The power dataset is at a geographic resolution of three km and a temporal resolution of five minutes. The wind power dataset spans five calendar years. To construct the wind power dataset, V CE acquired the full three dimensional (native) fields of - the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR). VCE has continued to expand the only 3 D archive of the HRRR for both assimilation - hours and forecast out to 36 hours. The numerical weather prediction (NWP) model data from NOAA is crucial because it includes 20 - 50,000 observations collected and quality controlled by the National Weather Service (NWS). The observations include ground - , based measurements satellites, aircraft, radar, balloon launches, and more. Not every variable in the HRRR dataset are used for the wind power estimates. For the proprietary algorithm created by VCE, the Wind Power Model (WPM), we extract: he wind speeds from 20 m to 240 m above ground level in 10 meter increments, the wind direction at each height, the air density at each height, turbulent kinetic energy at each height, temperature at each height, hydrometeors at each height, and the clouds at each height. The HRRR model i s in hybrid - sigma coordinates and these are interpolated to height above ground level using cubic spline interpolation. The HRRR dataset is at hourly resolution. It is at this stage that we convert the hourly data into five - minute data. We do this using th e tendencies (derivatives) within the HRRR model as well as minute resolution - statistical methods to create a continuous function between hours. The five allows use of wind gusts and other variables in the HRRR that can be useful to determining wind power - time periods than the hourly data. at shorter 32 The procedure to create the datasets is somewhat similar to that described in Clack et al., 2016 33 . We recap the major points here for completeness. and Choukulkar et al., 2016 The first part to convert weather variables to power estimates is to create the Rotor Equivalent Wind Speed (REWS). The REWS is a scalar value that estimate the average wind speed across the xpanded to include NWP entire rotor swept area. In Clack et al., 2016 the methods were e models and the full power equation; which accounts for the discretization of the wind speed and derivatives for NWP. Further, in Choukulkar et al., 2016, the method was further expanded to 32 Christopher T. M. Clack, et al., “Demonstrating The Effect of Vertical and Directional Shear for Resource Mapping Wind Energy of Wind Power,” 19, (November 2015): 1687 - 1697, https://www.vibrantcleanenergy.com/wp - content/uploads/2016/11/Demonstrating_the_effect_of_vertical_and.pdf . 33 Aditya Choukulkar, et al., “A New Formulation for Rotor Equivalent Wind Speed for Wind Resource Assessment and Wind Power Forecasting,” Wind Energy 19, (September 2015): 1439 - 1452, Wind_Energy.pdf https://www.vib rantcleanenergy.com/wp - content/uploads/2016/11/Choukulkar_et_al - 2016 - 19

20 ce on the power equations. The REWS formulation include the turbulent kinetic energy influen - reviewed papers. The REWS also takes into account the sheer and can be found in those peer veer across the rotor swept area. A similar procedure is required for the tempera ture, clouds, 34 , 35 s and air density. Two of the wind data set can be found online v ideo visualization . Once the REWS and other variables are created for the wind power dataset, the power estimates must be constructed. This is done using the wind power equations from Clack et al., 2016 and Chou kulkar et al., 2016. The WPM uses a combination of wind turbines from each wind resource class to create a generic wind turbine for each. The generic wind turbine has a coefficient of power curve that is a function (rather than a data table). The coefficie C nt of power (or ) is the P efficiency of the wind turbine to extract power from the wind. It is used within the power equation. A more common tool is the power curve; however, this is more limited because it does not allow changes in air density, and is le ss sensitive to the cube of the wind speed (when using the REWS formulation). This is particularly important when considering the full power equation and turbulent kinetic energy. Once the WPM has completed there is wind power for the optimal turbine class for each three - km across the United states for each five - minute interval for a five - year period. A visualization of 36 the wind power (at 80m AGL) is available online. The current iteration of the wind power dataset has power for 80 meters, 100 meters, 120 meters, 140 meters, and 160 meters. It includes terrestrial and offshore wind resources. AND COSTS INCENTIVES Once the wind power dataset is created, we can start to apply costs to the resource sites. In the ysics of the wind; and not how economics alters previous section, we have only dealt with the ph the picture for site preference. To apply costs, we use the National Renewable Energy Laboratory (NREL) Annual Technology Baseline (ATB) 2018. The NREL ATB 2018 provide costs for numerous years and technologi es. We have chosen to use the 2018 costs along with the 2025 (low and mid) projection. The wind cost is referenced to the optimal type for each site across the – United States including for offshore wind. The economic life of the wind turbines is estimated to be 30 years for terrestrial and 25 years for offshore. The Weighted Average Cost of Capital is assumed to be 5.87 percent (real). The fixed and variable costs are also pulled from the NREL ATB 2018. The federal incentives of the Production Tax Credit ( PTC) and Investment Tax Credit (ITC) are applied with their current sunset dates. The PTC is applied to the terrestrial wind, while the ITC is 34 “10m Winds For Day 10 of 2014,” Christopher Clack, Youtube Video (May 23, 2018), https://www.youtube.com/watc . h?v=HU_m56X0FCM 35 “Hurricane Arthur in 2014 – 10m Wind Speeds,” Christopher Clack, Youtube video (May 8, 2018), https://www.youtube.com/watch?v=VeTGnzg4ngs . 36 “Wind Power Across United States ( 4 days),” Christopher Clack, Youtube video (November 29, 2018), https://www.youtube.com/watch?v=K5kqch2QNzU . 20

21 applied to the offshore wind sites. The algorithm used for the modeling takes into account that the PTC is only a pplied for 10 years after construction. The U.S. is extremely diverse in its costs for labor, materials and permitting. The algorithm used - level multipliers to the cost of the wind for the modeling includes a component that applies state are applied at the state - level because of data availability. The multipliers construction. They range from 97.5 percent to 112.5 percent. TRANSMISSION CONSIDE RATIONS The VCE WIS:dom optimization model includes detailed transmission datasets. The transmission de the transmission lines, their voltage, the transmission substations and their datasets inclu capacities. For each three - km site from the wind power dataset, a geodesic is computed to the o construct the nearest substation. The cost of the wind resource site is increased by the cost t transmission line to the nearest substation. Further, if the WIS:dom model determines the substation is close to capacity, the wind site will incur a cost to upgrade the transmission substation as well. This is relaxed for the 2025 versions , because the grid topology is likely to change by that date. LCOE MAPS AND DATA LINKS With the costs and power datasets completed, the final step is to produce the LCOE mapping. The power dataset is converted to capacity factors for each three km resource site. The capacity - factor is combined with the costs to produce the LCOE. Essentially, total costs (capital, fixed, transmission, multipliers) divided by potential generation (CF * Size * 8760). We allowed up to 100 meter AGL for 2018 and up to 120 meter for 2025. The algorithm selects the optimal height for the hub based on the reduction in the LCOE. It will increase the hub height from 80 meters to 100 meters if it reduces the LCOE by more than $7.50/MWh and from 100 meters to 120 meters if it reduces th e LCOE by more than $12.50/MWh. Essentially, if it chooses a 120 meter hub height, the cost of wind power is estimated to be $20/MWh cheaper than at 80 meters. VCE has created NetCDF files that include the LCOE data along with the optimal hub heights for 2018, 2025 Low, and 2025 Mid. Further, VCE has visualized the LCOE data in PDF. The images allow easy zoom capabilities into regions of the United States to be used by all. The data files allow more precise analysis using the LCOE mapping. The location of the data files is: https://www.vibrantcleanenergy.com/wp content/uploads/2019/03/LCOE - - Mapping/WindLCOE_Data.zip The locations of the images are: https://vibrantcleanenergy.com/wp - content/uploads/2019/03/LCOE - Mapping/WindLCOEMap_2018_cobrand.pdf https://vibrantcleanenergy.com/wp - content/uploads/2019/03/LCOE - Mapping/WindLCOEMap_2025L_cobrand.pdf 21

22 - content/uploads/2019/03/LCOE https://vibrantcleanenergy.com/wp - Mapping/WindLCOEMap_2025M_cobrand.pdf 22

23 APPENDIX C - FIRED POWER PLA NT DATASET COAL ing coal - fired power A marginal cost of electricity (MCOE) dataset can be created for the exist plants across the United States by combining publicly available information. The data collected 37 38 39 from FERC Form 1 , EIA Form 860 and EIA Form 923 for the calendar year 2017. The information extracted includes the amount of fuel burned , the average heat rate of the power plants, the emission factors, the capital investments, the pollution controls, the fixed operations and maintenance costs, and the variable operations and maintenance costs. as the frequency of update for public information, Due to the scale of the coal dataset as well inevitably there are some inconsistencies that appear in the analysis when referencing other datasets. VCE has done its best to avoid such inconsistencies in the dataset, but some will likely remain. The highest occurrence of inconsistencies will be due to: retired plants after 2017, repowering of coal plants with natural gas, naming conventions between datasets, and nameplate capacity numbers. The coal fuel cost for the construction of the MCOE dataset i s taken from the National Renewable Energy Laboratory (NREL) Annual Technology Baseline (ATB) 2018. The national average for the 2018 calendar year is used. The fuel data collected from publicly available sources for 2017 was used to adjust the national co al price to the individual power plants. If there are multiple units at a coal - fired power plant, the data was combined into a single value for the entire plant. The coal - fired power plant fuel costs are multiplied by the annual average heat publicly available data. This results in a fuel cost for each power plant in $ / MWh. rates from the In addition to fuel costs, there are variable costs based on the operations and maintenance (O&M) of the power plant. The variable O&M was extracted from the NREL ATB 2 018 and applied regionally. The values were correlated to the publicly available data. The variable O&M was constructed in $ / MWh. The final costs included in the MCOE are the fixed O&M costs and the ongoing capital spending for pollution controls and ot her upgrades to the power plant. These costs are applied to the coal - fired power plants based on estimates constructed from the publicly available data. To convert these fixed costs to $ / MWh, the capacity factors for each of the power plants were utilize d. The final MCOE dataset is in $ / MWh and is the addition of the fuel costs, the variable O&M costs and the fixed costs. The combined MCOE costs are dependent on the capacity factors. The MCOE dataset was constructed to compare the cost building new wind and solar to replace the 37 “Form 1 – Electric Utility Annual Report,” Federal Energy Regulatory Commission, https://www.ferc.gov/docs - filing/forms/form 1/data.asp . - 38 “Form EIA - 860 Detailed Data With Previous Form Data,” United States Energy Information Administration, https://www.eia.gov/electricity/data/eia860/ . 39 “Form EIA - 923 Detailed Data With Previous Form Data,” United States Energy Information Administration, https://www.eia.gov/electricity/data/eia923/ . 23

24 fired power plants. Since the MCOE is sensitive to the capacity generation from each of the coal - fired power plant, it should be noted that adding new wind and solar to - factor of the coal e capacity factor, thereby increasing the MCOE. replace the coal generation would lower th The coal MCOE dataset comes with additional information that can be used for further analysis. fired power plant is included. Second, the First, the location and installed capacity of each coal - heat rates, ca pacity factors, age, and plant names are also included for ease of reference for the construction of the MCOE. Finally, the construction costs were estimated to compute the remaining debt for each coal - fired power plant. These debt costs were created using the publicly available data, the age of the power plants and the cumulative generation and revenue for that power plant. The debt costs were included in the LCOE, but not the MCOE. COAL REPLACEMENT ALG ORITHM - ted, it can be used to determine the ability for Once the coal fired power plant dataset is crea wind and solar to replace those coal plants. The LCOE for wind and solar were created previously. The LCOE calculation includes the transmission costs for interconnection, the resource potential and the loca lized costs for construction. The LCOE values were computed for 2018 and 2025. The replacement of the coal generation with wind and solar is determined by comparing the - fired power plant with the LCOE of the total wind or solar required to replace MCOE of the coal all the coal megawatt hours. The algorithm for replacing the coal generation has the following basic structure: Select the coal 1. fired power plant to replace; - 2. Find the closest wind or solar resource site; 3. Determine the generation from the wind or s olar site and reduce the coal generation required to be replaced; 4. Save the installed capacity of wind or solar along with the LCOE; 5. Find the next closest wind or solar resource site; 6. Repeat steps 3 – 5 until the coal generation to replace becomes zero; 7. Compu te the LCOE for the replacement wind or solar generation; Repeat steps 1 – 7 until all the coal - fired power plants are replaced. 8. - fired power plant is replaced The algorithm continues until the entire generation for each coal with wind or solar. The output f rom the algorithm is the LCOE of the wind or solar required to replace the coal generation. That data is transformed into a percentage difference between the MCOE of the existing coal generation and the new wind and solar. The algorithm could be expanded in the future to include the addition of storage and a limit to the amount of installed capacity allowed to replace the coal - fired power plants. 24

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