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Build Your Own Texas: Episode 1

See below for methodology, assumptions, and limitations.

Episode 1: Tex Mix – The Generation Mix You Have vs. The Generation Mix You Could Have

There has been a lot of discussion with respect to the Texas electricity grid this year, particularly since Winter Storm Uri hit the state in February 2021. Regulators, utility executives, and customer advocacy groups are reviewing and assessing the reasons for widespread blackouts in Electric Reliability Council of Texas (ERCOT), the state’s principal electricity grid, and considering how to prevent an energy shortage from happening in the future. These investigations will ultimately uncover why plants failed and how to better prepare these same plants and the overall system for the next incident.

Beyond uncovering reasons for plant failures, this event has sparked discussion about the generation mix of Texas (and other grids) as many jurisdictions seek to shift to lower CO2-emitting resources. Some experts suggest that the events in Texas may be the strongest case yet for renewable energy (see this article as an example). This position made us consider a key question:

  • How would a different generation mix have performed in ERCOT?

The focus is not on why plants performed the way they did, but instead, given what we know about how they did perform, would a different mix of generation sources have done any better at meeting the demand? If so, at what cost and emissions profile? To answer this question, we built an interactive model to determine how the ERCOT grid would have performed with a different generation mix. If you have ever wondered what Texas could have looked like as a carbon-neutral state, now is your chance to build it. We encourage you to consider these key questions when interacting with this tool:

  • What would happen if you significantly increased solar capacity?
  • How much storage would you build to supplement renewable generation?
  • Would you add more nuclear capacity as baseload generation?
  • What about the amount of wind energy needed to serve load?
  • How difficult would it have been to meet periods of high demand in times of low renewable output or conversely minimizing excess production when renewable output was very high, but demand is low?
  • What would you use as a replacement for coal- or gas-fired generation?
  • What is the impact of your changes on the cost of energy, as measured by Levelized Cost of Energy (LCOE)?
  • What is the impact on carbon emissions?

The Model

Our interactive model allows for the adjustment of generation resources to see if that proxy generation portfolio could have reduced emissions while still meeting the state’s actual electricity demand over the study period (January 2020 through April 2021).

In building the model, we reviewed daily actual electricity output and capacity factors by generation type for the period leading up to and including Winter Storm Uri. Total daily generation can be used as a reasonable proxy for the measure of demand (i.e., the system produced the power to meet demand, ignoring any imports or exports). By varying the total capacity of each generation source and applying the known capacity factor from that day, the model calculates what resource category or type would have been used. Surplus energy to the daily demand is noted in light red, while any energy deficit is shown in bold red. Using average resource costs for each generation type, the model estimates the impacts on the cost of energy.

Results of the Model

As a first step, a baseline scenario was developed using the following assumptions:

  • Collected historical generation by type (i.e., nuclear, solar, wind, etc.) by day over the study period
  • Used Lazard LCOE to compare costs across generation types.[1]
  • Set the baseline portfolio for wind, solar, natural gas, and coal-fired generation as the LCOE mid-point of Lazard’s unsubsidized pricing
  • Used NuScale Small Modular Reactor (SMR) LCOE of $65/MWh for nuclear resources
  • Calculated a portfolio price based on current generation mix as a reference point for alternative portfolios

[1] Because this analysis is a thought exercise regarding the ERCOT generation mix, the model requires to build all new generation sources.

In developing the baseline scenario, it becomes apparent, that there are a few times throughout the study period where meeting demand without fossil fuels would have proven difficult. There are two instances of particularly high demand at a time when renewable output was low for extended periods: September 2020 and February 2021.

The February 2021 Winter Storm Uri (February 13-17) is obviously the most well-known instance.

The second such period, and less well known, was in September 2020 when renewable production was low for several days, while high temperatures led to demand increases that required a ramp-up of natural gas generation.

Specifically, a drop in wind generation (from 35% capacity factor on September 9, 2020 to 4% on September 15, 2020) resulted in significant under-production of zero-carbon resources, and natural gas generation was called upon to meet demand and maintain system reliability.

Furthermore, demand response efforts were present during these two events, so actual demand was likely higher than what is reflected at those times.

Conversely, there are times over the study period where renewables comprised a significant portion of the generation mix and any major increase above current capacity would have created “overproduction.” See example from December 17, 2020 to January 4, 2021 below.

Using the model, we ran an “increased renewable, no storage” scenario to see how building an alternative generation portfolio would have performed over the study period. In this scenario, we increased wind and solar capacity by more than four times the current capacity and kept the existing nuclear plants and hydro sites at their current operating capacity. We removed natural gas and coal-fired resources from the generation mix and did not consider storage alternatives.

This “increased renewable, no storage” scenario demonstrates that building exclusively intermittent resources to meet high-demand periods, like Winter Storm Uri, will result in excess energy many times throughout the study period when demand is low. There are additional scenarios that can be created in the model where excess energy is created during the study period. This excess energy can be created by generation resources whose operating characteristics are limited in their ability to ramp with demand.

As a result of various model iterations (i.e., changing assumptions and reviewing results), we identified the following questions, which will be explored in future episodes.

  • What opportunities exist if we create excess energy from changing the current generation mix?
  • What is the changed land use to support a different generation mix?
  • What would be the “least-cost” answer in terms of renewables and storage?
  • Can SMRs be constructed economically to be a carbon-free source?
  • Is there a generation mix that can significantly reduce coal and gas and minimize carbon emissions to near zero?

We encourage you to interact with this model and test how your potential generation mix would have impacted carbon emissions and the cost of energy. As you interact with this tool, there will be a significant number of questions that arise from this “what-if” analysis.

Please contact us to discuss our perspective on the questions that our clients are asking, our perspective on addressing these questions, and any potential implications of various solutions that arise from these different “what-if” scenarios.

Model Methodology:
  • Collected daily demand and generation data from ERCOT
  • Collected capacity from March 2021 (from ERCOT), by generation source (e.g., nuclear, natural gas, etc.)
  • Calculated daily capacity factor, by generation source
  • Calculated total generation by generation source based on the daily capacity factor
  • Assumed Energy storage (batteries) are charged using energy from renewable resources; when all battery charging is complete, any additional energy beyond that needed to meet demand is deemed excess energy
  • Energy storage is sized based on the average output of solar and wind, i.e., when a selection of 1 on the capacity toggle, and the average daily output of solar and wind is 250,000 MWh there would be a storage capacity of 250,000 MWh
Key Assumptions and Data Sources:
  • Demand is implied by total historical generation but could have been higher in select time periods (e.g., Winter Storm Uri) due to imports or demand response
  • Generation cost by generation source is based on Lazard October, 2020 levelized cost of energy (LCOE) reflected in $ per MWh. LCOE refers to the estimates of the revenue required to build and operate a generator over a specified cost recovery period. Note that LCOE is highly dependent upon financing parameters (debt-equity mix, cost of capital, depreciation schedule), installed cost, and fuel costs.  Lazard’s analysis, updated annually, is frequently used in industry discussions.
  • Used NuScale forecasted LCOE of $65/MWh as per NuScale SMR Technology – An Ideal Solution for Repurposing U.S. Coal Plant Infrastructure and Revitalizing Communities, 2021
  • The analysis excludes demand from distributed energy resources
  • The analysis also excludes renewable integration costs from calculations of LCOE
  • Generation includes proportionally larger technologies and not smaller (in % of total generation) technologies such as thermal peaking plants, hydropower, and biomass
  • Cost of Storage is defined as the generation supplied by storage for a given day, multiplied by the per MWh LCOE price. We will cover different ways to apply the LCOE of storage in a future storage focused episode
  • Emissions calculated using the following values, per EPA
    • Coal: 2.21 lbs./kWh (20.3% of total baseline generation)
    • Natural gas: 0.91 lbs./kWh (47.4% of total baseline generation)
Limitations of the Analysis:
  • The goal of the model is to provide a simplistic view of energy-only characteristics to illustrate the what-if generation mixes and the impact on cost of energy and carbon emissions
  • Analysis does not include the various detailed elements such as ramping non-variable fuel cost assumptions, price of energy- calculation
  • Since this analysis uses daily data, it excludes any intraday changes or requirements of the generation mix such as charging or discharging batteries throughout the day
  • The analysis considers current technology costs and does not consider changes in technology costs
  • The carbon-free scenario considers a limited number of non-carbon generation resources (i.e., solar, nuclear, wind, and energy storage) and does not consider new technologies under development such carbon capture and storage technologies
  • Calculating specific wholesale electricity power prices would require a detailed system modeling

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Additional Contributing Authors: Bill Hosken, Cory O’Brien, Morgan Schadegg, and Chris Vlahoplus.

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Contributing Authors

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