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Joseph Daniel, Senior Energy Analyst – Union of Concerned Scientists

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1 Joseph Daniel, Senior Energy Analyst – Union of Concerned Scientists
Out-of-merit Generation of Regulated Coal Plants in Organized Energy Markets Joseph Daniel, Senior Energy Analyst – Union of Concerned Scientists

2 Module 1: Screening analysis, Methodology
𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐷𝑆 𝑖 = 𝐢 𝑖 π‘š βˆ’πΆ 𝑖 𝑝 Where 𝐷𝑆 𝑖 : Dark Spread, the profit margin per unit output in a given hour 𝐢 𝑖 π‘š : cost of market purchase in that hour, at that node (locational marginal price) 𝐢 𝑖 𝑝 :π‘π‘Ÿπ‘œπ‘‘π‘’π‘π‘–π‘‘π‘œπ‘› π‘π‘œπ‘ π‘‘ 𝑖𝑛 π‘‘β„Žπ‘Žπ‘‘ β„Žπ‘œπ‘’π‘Ÿ Expected CF = # hours 𝐷𝑆 𝑖 > 0 / # hours (8,760) Actual CF = 𝐺 𝑖 𝑔 Capacity Γ— 8,760

3 (incl. municipality and coops)
Module 1: Screening analysis, Results for 2017 PJM MISO Merchant Generators Rate Regulated (incl. municipality and coops) Vertical axis is actual value: Horizontal axis is expected value: Would expect outcomes to fall on or near diagonal line (y=x) Predominantly rate regulated coal plants that operate above expected value ERCOT SPP

4 Module 2: Cash Flow Analysis, Methodology
𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 Γ— 𝐺 π‘Ž 𝑛 𝐺 π‘Ž 𝑔 Where 𝐺 𝑖 𝑛 :𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 𝑖 𝑔 :π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 π‘Ž 𝑛 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ 𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 π‘Ž 𝑔 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 assumed for units not reporting 𝐺 π‘Ž 𝑛 𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 Γ— 𝐺 π‘Ž 𝑛 𝐺 π‘Ž 𝑔 Where 𝐺 𝑖 𝑛 :𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 𝑖 𝑔 :π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 π‘Ž 𝑛 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ 𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 π‘Ž 𝑔 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐷𝑆 𝑖 = 𝐢 𝑖 π‘š βˆ’ 𝐢 𝑖 𝑝 Where 𝐷𝑆 𝑖 : The profit margin per unit output in a given hour, β€œDarkest Spread” (more robust than Dark Spread) 𝐢 𝑖 π‘š : cost of market purchase in that hour, defined as the LMP 𝐢 𝑖 𝑝 :π‘π‘Ÿπ‘œπ‘‘π‘’π‘π‘–π‘‘π‘œπ‘› π‘π‘œπ‘ π‘‘ 𝑖𝑛 π‘‘β„Žπ‘Žπ‘‘ β„Žπ‘œπ‘’π‘Ÿ 𝛽 π‘Ž = 𝑖= 𝐺 𝑖 𝑛 Γ— 𝐷𝑆 𝑖 𝛽 π‘Ž represent the annual economic margin in total dollars 𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 Γ— 𝐺 π‘Ž 𝑛 𝐺 π‘Ž 𝑔 Where 𝐺 𝑖 𝑛 :𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 𝑖 𝑔 :π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 π‘Ž 𝑛 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ 𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 π‘Ž 𝑔 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 Γ— 𝐺 π‘Ž 𝑛 𝐺 π‘Ž 𝑔 Where 𝐺 𝑖 𝑛 :𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 𝑖 𝑔 :π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 π‘Ž 𝑛 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ 𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 π‘Ž 𝑔 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐢 𝑝 = 𝐢 𝑓 + 𝐢 𝑣 + 𝐢 𝑒 Where (expressed in $/MWh) 𝐢 𝑝 : marginal cost of production 𝐢 𝑓 : fuel cost 𝐢 𝑣 : variable O&M costs 𝐢 𝑒 : emissions costs 𝐺 𝑖 𝑛 = 𝐺 𝑖 𝑔 Γ— 𝐺 π‘Ž 𝑛 𝐺 π‘Ž 𝑔 Where 𝐺 𝑖 𝑛 :𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 𝑖 𝑔 :π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 β„Žπ‘œπ‘’π‘Ÿ 𝑖 𝐺 π‘Ž 𝑛 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ 𝑛𝑒𝑑 π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› 𝐺 π‘Ž 𝑔 :π‘Žπ‘›π‘›π‘’π‘Žπ‘™ π‘”π‘Ÿπ‘œπ‘ π‘  π‘”π‘’π‘›π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›

5 𝛽 Results: Net, 3-years Rate Regulated Merchant Generators PJM MISO
The two β€œworst” units are merchant waste-coal cogeneration units in PJM and extend below graph. Weighted Average Margin ($/MWh) SPP ERCOT NOTE: Each bar represents one coal unit, width of bars are not proportional to size (capacity) of that unit. Ex: ERCOT had fewest units, so the width of the bars are greatest.

6 Cumulative monthly gross losses ($millions) 2015-2017
𝛽 Results: Gross, 3-years Rate Regulated Merchant Generators PJM MISO Cumulative monthly gross losses ($millions) SPP ERCOT NOTE: Each bar represents one coal unit, width of bars are not proportional to size (capacity) of that unit. Ex: ERCOT had fewest units, so the width of the bars are greatest.

7 3-year Cumulative Monthly Losses RTO/ISO
Top 15 Worst Actors (all over 100 million over 3 years) 3-year Cumulative Monthly Losses RTO/ISO Elm Road Generating Station $ (425) Million MISO Dolet Hills $ (407) Million MISO/SPP Mount Carmel Cogeneration $ (290) Million PJM Pirkey $ (228) Million SPP Northeastern Power Cogen Facility $ (192) Million Westwood Generating Station $ (173) Million John E. Amos $ (159) Million Whitewater Valley $ (143) Million Big Cajun 2 $ (137) Million Conesville $ (136) Million Montour $ (129) Million San Miguel $ (127) Million ERCOT J. Sherman Cooper $ (120) Million Sioux $ (115) Million Indian River

8 Over $4.6 billion in market losses over three years
Results for 𝛽 (Monthly Granularity) Represents only the sum of all months where Beta was negative. PJM Regulated Merchant 2015 -$259 Million -$333 Million 2016 -$86 Million -$335 Million 2017 -$354 Million -$695 Million Total -$699 Million -$1,362 Million MISO Regulated Merchant 2015 -$681 Million -$18 Million 2016 -$566 Million -$13 Million 2017 -$270 Million -$5 Million Total -$1,518 Million -$36 Million SPP Regulated Merchant 2015 -$258 Million -$21 Million 2016 -$163 Million -$7 Million 2017 -$443 Million -$15 Million Total -$865 Million -$43 Million ERCOT Regulated Merchant 2015 -$36 Million $n/a 2016 -$39 Million 2017 -$79 Million Total -$154 Million Over $4.6 billion in market losses over three years NOTE: These numbers are gross, not net; values don’t account for impacts of merit order on LMP and new clearing price of replacement energy.

9 Future Research Questions?
Why are merchant units behaving this way? Are affiliate transactions distorting the market? Is guaranteed cost recovery distorting the market? How much of the out-of-merit dispatch can be excused by system constraints? What is the impact on LMP (and other generators)? Are plants that are refusing to turn off creating congestion? Negative LMPs? Should regulators (PUCs) disallow costs associated with uneconomic dispatch?

10 Conclusions and Implications
Not isolated to SPP, all markets impacted Assumption of rational actors in organized markets with rate-regulated assets may be flawed Calls into question the extent of consumer benefits associated with markets LMP not a good proxy for avoided costs

11 Definitions, Caveats, Assumptions
Units excluded: Not all EGU’s report hourly data, those units are omitted Primarily impacts units less than 25MW Only includes units are units whose primary fuel group is listed as coal Includes waste coal, pet coke, lignite, bit., and sub bit. Units that have converted to dual fuel, or co-fire biomass, or list coal as secondary or tertiary fuel are excluded Units that retired prior to June 2018 were excluded Merchant owners don’t report fuel cost data to EIA, S&P data used as back fill Units that joined RTO during study period only included costs and revenues after join date Units that dispatch into multiple RTOs were analyzed only in β€œprimary” RTO Definitions, Caveats, Assumptions

12 Data Sources, and References
Energy Information Agency Form 860 Federal Energy Regulatory Commission Form 1 Environmental Protection Agency Air Markets Program Database S&P Global Market Intelligence Daniel, J. (2017): Backdoor Subsidies for Coal in the Southwest Power Pool: Are Utilities in SPP Forcing Captive Customers to Subsidize Uneconomic Coal and Simultaneously Distorting the Market?, Sierra Club. Washington, D.C. Nelson, W., Liu, S. (2018) Half of U.S. Coal Fleet on Shaky Economic Footing: Coal Plant Operating Margins Nationwide. Bloomberg New Energy Finance. New York, NY. Bloomberg New Energy Finance. (2017). Trends in US power, gas, and renewable economics. DLA Energy World Wide Energy Conference. New York, NY. Data Sources, and References

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