Exploding Duck Curve: What Does It Cost to Achieve 100% Renewable Electricity and What Are the Implications?

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Eric Selmon                                  Hugh Wynne

Office: +1-646-843-7200              Office: +1-917-999-8556

Email: eselmon@ssrllc.com         Email: hwynne@ssrllc.com

SEE LAST PAGE OF THIS REPORT FOR IMPORTANT DISCLOSURES

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April 1, 2019

Exploding Duck Curve:

What Does It Cost to Achieve 100% Renewable Electricity and What Are the Implications?

In this research report, we assess the scale and cost of the renewable resources required to transition California to a 100% renewable supply of electricity with a degree of reliability comparable to that of California’s power grid today.  We estimate the levelized cost of electricity from such a system at $213 per MWh; compared with the 2018 average price for full requirements electricity of $53/MWh, this implies an increase of $160/MWh or 300%.  The high cost of 100% renewable energy reflects the intermittency of wind and solar power; to ensure current standards of reliability, we estimate the scale of the renewable generation and battery storage resources required  at 450 GW, as against the 75 GW of capacity required by the state today.  Such a system would be capable of generating 300 million MWh of electricity  in excess generation of the state’s needs, with significant implications for the economics of electric vehicles and power prices in neighboring states.

Exhibit 1: An All-Renewable System Is an Extremely Costly Source of Full Requirements Power

Costs Increase 250-300% As Installed Capacity Balloons Generating Too Much Electricity

  

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

  • The Background: Senate Bill 100, passed by the California legislature in August 2018 and signed by Governor Brown in September, has set a target of supplying 100% of retail electricity sales in the state from zero-carbon resources by 2045. With California’s last operating nuclear power plant, Diablo Canyon, scheduled to retire in 2025, Senate Bill 100 puts the state on the course towards 100% renewable generation. When ranked against the world’s largest countries, California’s GDP of $2.9 trillion now ranks fifth in the world, surpassing that of the United Kingdom. What are the implications of transitioning an economy of this scale to 100% renewable electricity?
  • The Purpose of This Analysis: The objective of our analysis is to:
  • estimate the cost of supplying 100% of California’s electricity demand from renewable resources,
  • determine which combination of wind, solar, and energy storage would meet demand at lowest cost,
  • identify critical implications of an all-renewable strategy, including the vast scale of renewable resources required, the excess energy that these resources would supply, and the difficulty of ensuring reliable, round-the-clock (“full requirements”) power without traditional dispatchable generation.
  • Our Objective and Constraints: The objective of our modelling is to determine the least costly mix of California’s various renewable resources capable of supplying the energy needed to meet the state’s total electricity demand during every hour of the year. Our model therefore balances (i) the state’s aggregate demand for electricity for each hour of 2017 (hourly load in kWh), with (ii) the output of a fleet of wind, solar, hydroelectric and geothermal resources, complemented by energy storage, and sized to ensure an adequate supply of renewable energy in each hour of the year. Our model takes into account California’s historical reliance on the region’s hydroelectric and geothermal resources. To estimate the output of new wind and solar generation assets, our model uses the hourly output in 2017 of the wind and solar resources available in typical locations in California (Tehachapi Pass for wind and LA for solar). Critically, the target of 100% renewable generation precludes us from relying on conventional generation, such as gas turbine generators, which today are used to backstop intermittent renewable resources such as wind and solar. Consequently, during hours of the year when the output of renewable energy resources is lowest (usually on winter nights), our model relies on energy storage to bridge the gap. Finally, we have designed this hypothetical renewable fleet and associated energy storage so as to minimize the cost of supplying only renewable electricity to meet the state’s hourly load. (See the appendix for more detail.)
  • Methodology: The wind, solar and storage capacity required to meet these objectives far exceed the state’s installed capacity of these resources. Given the need for massive capacity additions, our analysis estimates the cost to build new facilities as well as the cost to operate and maintain them. Specifically, we have estimated the current levelized cost of energy (LCOE) [1] of these combined resources and imputed it to all the wind, solar and storage resources required to meet California’s 100% renewable target.
  • In contrast, we do not expect the capacity of the hydroelectric and geothermal resources available to California to grow in future. Therefore, rather than estimate the cost of hydroelectric and geothermal energy on the basis of new-build economics, as we have for wind and solar, we impute a cost to hydroelectric and geothermal generation equal to the around-the-clock price of wholesale power on the California ISO (Independent System Operator) in 2017. Given the long historical track record of these resources, our analysis assumes the volume of hydroelectric and geothermal generation that California will use to meet demand will equal its average monthly reliance on these resources over the last 20 years.
  • The Results: Taking into account California’s access to existing hydroelectric and geothermal resources, the lowest cost mix of wind, solar and energy storage resources adequate to meet state’s demand for electricity during each hour of the year comprises 15 GW of wind capacity, 250 GW of solar capacity and 710 GWh of storage capacity (equivalent to 177.5 GW, given the typical four hour duration of discharge of lithium ion batteries). These requirements compare with a current installed fleet of 9.2 GW of wind, 13.8 GW of solar and 0.2 GWh of installed energy storage.
  • The aggregate cost of these wind, solar and storage resources, based on their current construction cost, plus the market value of the hydroelectric and geothermal generation that we estimate California will use, is equivalent to $213 per MWh of retail electricity supplied in the state. This compares with an average around-the-clock price for full requirements electricity[2] in the state of $53/MWh in 2018, implying an increase of $160/MWh or 300%. Spread across California’s 230 million MWh of annual electricity demand, this cost increase represents an enormous economic burden on the state, equivalent to some $37 billion annually.
  • We estimate the cost of transitioning to 100% renewable generation will decline over time as the installed cost of wind, solar and storage resources falls and the energy efficiency of these resources increases. Based on our estimates of the LCOEs of these resources in 2025, we estimate the total cost of supplying California’s power demand with fully renewable electricity would fall to $147/MWh, expressed in constant 2019 dollars, from $213/MWh in 2019. By 2030, we estimate the cost will fall further, to $127/MWh in constant 2019 dollars. Compared to the $53/MWh cost of full requirements electricity in 2018, however, this still represents an increase of $74/MWh, equivalent to $17 billion annually.
  • A slightly more modest target, of 99.9% renewable energy, would further reduce the cost. Were California to meet 99.9% of its electricity needs with renewable energy, and rely on gas fired capacity for the remainder, we estimate the total cost of electricity supplied could be reduced from $213/MWh in 2019 to $186, a reduction of 13%; from $147/MWh in 2024 to $132, or 10%; and from $127/MWh in 2030 to $114, or 10%.
  • Why Is a 100% Renewable System So Expensive? The very high cost of transitioning to 100% renewable energy reflects the enormous scale of the renewable generation and battery storage capacity required to ensure a reliable supply of electricity. A 100% renewable power system, if it is to supply electricity reliably during every hour of the year, must be sized to generate and store sufficient energy for every hour of demand — even those hours that occur at the end of prolonged periods of low renewable generation. The difference in cost between a 100% and a 99.9% renewable energy system can be used to illustrate the difficulty of achieving this goal.
    • A system capable of supplying 99.9% of California’s current electricity demand with renewable energy would require 50 GW of wind, 130 GW of solar and 130 GW of battery storage capacity, in addition to 10 GW of existing geothermal and hydroelectric capacity, for a total of some 320 GW of renewable energy and battery capacity. By comparison, California’s power demand is met today by some 75 GW of conventional and renewable power generation capacity (see Exhibits 2 & 3).
    • Such a system, though endowed with renewable generation and battery storage capacity equivalent to over 4x the capacity supplying California today, would nonetheless have failed to meet the state’s total electricity needs during 20 hours in 2017. During 10 of these hours, the system would have fallen short of supplying the state’s electricity needs by 50% or more and, during 4 of those hours, the system would have fallen short by more than 75% (see Exhibits 4 & 5).
    • The cause of this shortfall, which would have occurred between January 7 and January 12, 2017, is a combination of (i) low solar generation attributable to short winter days and the low elevation of the sun in the sky, aggravated by (ii) cloudy and rainy winter weather, further depressing solar output, and (iii) still, windless nights (see Exhibit 6). Three days of such weather would have caused California to fully deplete the 520 GWh charge of its 130 GW battery fleet. The poor solar and wind conditions persisted for another two days, however, during which the total renewable generation on the system would have fallen short of prevailing demand for 20 hours, accumulating a total deficit of supply relative to demand of some 230 GWh (see Exhibit 5).
    • The lowest cost means of overcoming this shortfall is to rely on the state’s existing fleet of simple and combined cycle gas turbine peakers to serve demand during the hours of deficit renewable generation. If we exclude this option, however, the lowest cost alternative is to expand the capacity of the state’s battery storage by 48 GW and the capacity of its combined wind and solar fleets by 85 GW, at an additional capital cost of $125 billion. The effect is to increase the total capacity of the system from 320 to 450 GW, versus 75 GW today, and to raise the levelized cost of full requirements electricity in the state from $186 to $213/MWh, versus $53/MWh today (see Exhibits 3 & 7).
  • Conclusions. The enormous cost of an all-renewable system renders it highly likely that most regions will continue to rely on conventional generation to ensure an adequate supply of firm, dispatchable capacity during all hours of the year – even if the bulk of electricity is supplied by renewable resources. In future research reports, we will explore the potential cost savings from such a strategy in California and other regional markets, assessing the resources required to achieve 80% and 90% renewable generation and their implications for the cost of electricity.
  • As the proportion of renewable electricity in total supply rises, the criterion for selecting among conventional generation technologies will shift, from the lowest all-in cost of energy (as conventional generators will no longer be the primary suppliers of energy) to the lowest cost of capacity (where conventional generators will continue to play a critical role). Such a shift will benefit generation technologies with a low capital cost per MW of capacity, such as simple cycle gas turbines, over those with a low all-in cost per MWh of generation, such as combined cycle gas turbine generators. Among simple cycle gas turbines, this should also favor technologies with greater flexibility, including lower costs to start the turbine and faster ramping speeds, such as aeroderivatives.
  • Regions that make the opposite choice, and transition toward all-renewable systems, will find that in building renewable and storage capacity adequate to meet demand during all hours of the year, they create systems capable of generating far more electricity than they need. An all-renewable system, with the enormous renewable generation and battery storage resources required to meet demand even under the most adverse conditions (prolonged periods of low wind and solar generation), will generate electricity significantly in excess of demand during all the remaining hours of the year (when wind and solar generation are far higher). If California’s renewable and storage resources were sized to supply 100% of the state’s electricity demand during each hour of the year, we calculate that the system would be capable of generating more than twice the annual electricity demand of the state (see Exhibit 8).
  • The potential scale of this excess generation is truly enormous: in California, a 100% renewable system would be capable of generating an estimated 307 million MWh of electricity in excess of the needs of the state – an excess equal to 133% of the annual electricity consumption of California and over 80% of the combined electricity consumption of the surrounding states (Washington, Oregon, Idaho, Nevada, Utah and Arizona). Options to absorb or avoid this tsunami of electricity could include:
    • exports to regions with non-coincident peaks in demand, including metropolitan centers in states to the east, such as Arizona, Nevada and Utah;
    • imports of renewable generation from these states, allowing reduced in-state generation;
    • accelerating the transition of the region’s vehicle fleet from internal combustion to electric motors, thereby reducing carbon emissions from transportation;
    • developing other sources of demand for electricity by promoting regional investment in such electricity intensive industries as the smelting of aluminum, copper or steel; oil refining and the production of basic chemicals; and the manufacture of pulp, paper and cement.

Exhibit 2: Total System Capacity of the California ISO Today Compared

To 99.9% and 100% Renewable Energy Scenarios

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 3: Current Renewable Energy Resources in California ISO

vs. 99.9% and 100% Renewable Scenarios (GW)

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 4: Hourly Renewable Generation and Battery Discharge Compared to CAISO Load, from January 7 to January 12, 2017 (GW)

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 5: Shortfall in the Supply of Renewable Energy to Serve Load,

Expressed as % of CAISO Load, from January 7 to 12, 2017 (99.9% Case)

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 6: Capacity Factor of Typical Californian Solar and Wind Farms

From January 7 to January 12, 2017 (1)

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1. Reflects the 2017 hourly output of wind capacity at the Tehachapi Pass and solar capacity in the Los Angeles area.

Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 7: Levelized Cost of Energy from Wind, Solar and Battery Storage Projects Compared to LCOE of an All-Renewable Power System in California ($/MWh) (1)

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1. The levelized cost of energy equals the annual cash cost to build and operate a power plant, divided by the annual energy output of the plant over its useful life, discounted back to the present and expressed in $/MWh. The LCOE can be regarded as the minimum fixed price at which a project’s electricity must be sold in order to recover the total cost of the project, including construction cost, operation and maintenance expense, taxes and the target return on invested capital, over the lifetime of the project.

Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Exhibit 8: Total CAISO Output in Millions of MWh, Broken Down Into Generation Required to Serve Load and Generation in Excess of Load (Curtailed Output)

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Source: MADA Analytics, Lazard’s Levelized Cost of Energy and Levelized Cost of Storage 2018, S&P Global, SSR analysis

Appendix: Software and Inputs

The Software

The modelling software we used to perform our analysis is MADA Energy Processing Solutions (MEPS), and was developed by MADA Analytics, a data-analytics energy-storage software company, of which Eric Selmon is a co-founder, shareholder and director. MEPS is designed to optimize combinations of various renewable generating technologies with energy storage to achieve a defined goal (usually highest return or lowest cost). It does so by conducting an hourly analysis of all of the different possible combinations of the generation and storage technologies within a user-set range, and calculating the power output, performance and financial results of each combination across all of the hours over the life of the projects.

For this analysis, we assumed that only solar, wind and battery storage, plus existing hydroelectric, geothermal and biomass generation, would be used to meet demand.  We optimized for the lowest cost combination of these resources that was able to meet system demand in every hour of the year. We also ran an alternative scenario where renewables supply only 99.9% of demand, allowing us to quantify the impact on cost as the targets for renewable penetration decrease, as well as the dispatchable peaking capacity (i.e. gas fired peakers) required for system reliability as renewable targets fall.

Critically, MEPS’ focus on the balance between hourly supply and demand provides allows for a much more accurate result than an analysis that uses only the aggregate annual or monthly data on peak demand and resource availability, because it addresses the fundamental need of the power grid to match supply and demand at all times. Based on the hourly output of typical wind and solar resources in California in 2017, and CAISO’s hourly load in that year, we were able to identify those hours of the year when the system was most vulnerable to a shortfall of supply as a result of prolonged periods of low renewable generation, resulting in the full discharge of the system’s battery storage capacity.

MEPS can also incorporate the cost of conventional generation capacity to backstop the supply from intermittent renewable resources. As discussed in the body of this research report, the output of a system designed to meet 99.9% California’s power demand with renewable energy would have fallen short of demand for 20 hours in 2017. MEPS allow us to design the lowest cost solution to offset this gap (using gas fired generation to meet the shortfall in supply during these 20 hours), and to calculate an adjusted LCOE that includes the cost of these additional resources.

 

Hourly Data Inputs

To operate, the MADA software requires hourly data on solar insolation, windspeed and demand for each location. While the software can handle tremendous levels of complexity, we used an Excel-based pilot version of the software, so the time to run each simulation increased greatly with the complexity. Therefore, to simplify the analysis, we used data for solar insolation and windspeed from a single, typical location (Tehachapi Pass for wind and LA for solar) and we used only a single year of data (2017) for the solar, wind and demand data.

To account for the availability of existing hydroelectric, geothermal and biomass generation, we reduced the hourly demand required to be met by the wind, solar and storage resources in the model by the average hourly hydro and geothermal output since 2000, and average hourly biomass over the most recent full year, 2017.

Possible Biases in the Data

Using a single location and year to estimate the volatility of renewable generation, and a single year to estimate the volatility of system load, may introduce distortions in our analysis.

In particular, measuring solar and wind generation at a single location will tend to underestimate the stability of the state-wide supply of renewable energy, thus causing the model to overestimate the scale of solar and wind resources required to meet demand during every hour of the year. That said, using a single location and year to estimate the amount of hourly solar generation should have only a limited impact on the results of our analysis, as solar insolation does not vary much in California from year to year, nor is there high regional variation across the state during any given hour. By contrast, windspeeds can vary significantly from year to year and are very location specific. Relying on windspeed data from just the Tehachapi Pass, therefore, likely underestimates the stability of the California’s total wind output from hour to hour and therefore overestimates the resources required to meet demand. However, because weather fronts, which drive the general presence or absence of wind at any time, cover large areas, we believe the impact is limited.

Importantly, other assumptions used in our analysis will tend to understate the resources required to meet demand during each hour of the year, thus introducing a contrary bias. Our use of a long term average for hydroelectric generation means that we have not accounted for the full impact of drought conditions, which would require additional resources to offset. Critically, also, our use of a single year of demand data tends to underestimate the volatility of demand, again causing our model to underestimate the need for generation and storage resources.

Finally, given the enormous scale and cost of the renewable and storage resources required to meet hourly demand, we are confident that the overall conclusions of our analysis are robust despite the potential biases in the data.

©2019, SSR LLC, 225 High Ridge Road, Stamford, CT 06905. All rights reserved. The information contained in this report has been obtained from sources believed to be reliable, and its accuracy and completeness is not guaranteed. No representation or warranty, express or implied, is made as to the fairness, accuracy, completeness or correctness of the information and opinions contained herein.  The views and other information provided are subject to change without notice.  This report is issued without regard to the specific investment objectives, financial situation or particular needs of any specific recipient and is not construed as a solicitation or an offer to buy or sell any securities or related financial instruments. Past performance is not necessarily a guide to future results.

  1. The levelized cost of energy is expressed in $/MWh and equals (i) the annual cash cost to build and operate a power plant, discounted back to the present, divided by (ii) the annual energy output of the plant over its useful life, also discounted back to the present. The LCOE can be regarded as the minimum fixed price at which a project’s electricity must be sold in order to recover the total cost of the project, including construction cost, operation and maintenance expense, taxes and the target return on invested capital, over the lifetime of the project. 
  2. The full requirements price of electricity is the price sufficient to pay for the resources required to supply 100% of the electricity demand prevailing during each hour of the year. It includes the cost of energy, capacity, transmission and ancillary services. 
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