# An Open-source Model for Simulation and Corrective Measure Assessment of the
2021 Texas Power Outage

Dongqi Wu  Department of Electrical and Computer Engineering, Texas A&M
University, College Station, Texas, USA  Co-first author  Xiangtian Zheng
Department of Electrical and Computer Engineering, Texas A&M University,
College Station, Texas, USA  Co-first author  Yixing Xu  Breakthrough Energy
Sciences, Seattle, Washington, USA  Daniel Olsen  Breakthrough Energy
Sciences, Seattle, Washington, USA  Bainan Xia  Breakthrough Energy Sciences,
Seattle, Washington, USA  Chanan Singh  Department of Electrical and Computer
Engineering, Texas A&M University, College Station, Texas, USA  Le Xie
Department of Electrical and Computer Engineering, Texas A&M University,
College Station, Texas, USA  Texas A&M Energy Institute, College Station,
Texas, USA  Corresponding author<EMAIL_ADDRESS>
###### Abstract

Unprecedented winter storms that hit across Texas in February 2021 have caused
at least 4.5 million customers to experience load shedding due to the wide-
ranging generation capacity outage and record-breaking electricity demand.
While much remains to be investigated on what, how, and why such wide-spread
power outages occurred across Texas, it is imperative for the broader research
community to develop insights based on a coherent electric grid model and data
set. In this paper, we collaboratively release an open-source large-scale
baseline model that is synthetic but nevertheless provides a realistic
representation of the actual energy grid, accompanied by open-source cross-
domain data sets. Leveraging the synthetic grid model, we reproduce the
blackout event and critically assess several corrective measures that could
have mitigated the blackout under such extreme weather conditions. We uncover
the regional disparity of load shedding. The analysis also quantifies the
sensitivity of several corrective measures with respect to mitigating the
power outage, as measured in energy not served (ENS). This approach and
methodology are generalizable for other regions experiencing significant
energy portfolio transitions.

## Main

The extreme winter storm and associated electricity outages in February 2021
are estimated to have caused more than 70 deaths [1] and $200 billion economic
loss [2] in the state of Texas. Besides the official brief review [3] and
ongoing internal investigation on the Electric Reliability Council of Texas
(ERCOT), there have been preliminary reports from non-peer-reviewed articles
[4, 5, 6] and press interviews [7, 8] on potential causes and technical
solutions for this blackout event. Given the complexity and confidentiality of
the actual electric grid model and relevant information, it becomes very
challenging for the broader research community to develop analysis and provide
credible insights on such major events.

While researchers recently have contributed to the creation of large-scale
synthetic grid models [9] for analysis such as macro-scope energy portfolio
transition [10, 11], cross-domain and open-source approaches to reproduce the
Texas blackout event and quantify impact from corrective measures against
extreme frigid weather are still at a nascent stage, with several gaps in
existing research. First, existing open-source large-scale synthetic grid
models are not ready-to-use for the event reproduction without rigorous
calibration. Second, the lack of aggregated and processed event timeline data
prevents exhaustive simulation and further investigation. Last but not least,
the lack of consistent quantified criteria renders studies on the
effectiveness of potential corrective measures and their combined effect
incomparable.

Here, we collaboratively develop an open-source large-scale synthetic baseline
grid [12], providing a realistic representation of the actual ERCOT electric
grid, accompanied by the open-source data set along the event timeline. This
ready-to-use multi-platform synthetic grid model is calibrated based on open-
source data sets, including generation by source, load by weather zones,
generation unit outage timeline, load shedding record, etc. To the best of our
knowledge, it is the first fully open-source approach to model, simulate,
benchmark, as well as propose corrective measures against the 2021 Texas power
outage. The reproduction results transparently replay the timeline of the
change of system generation capacity, load demand and load shedding associated
with the key events, aiding public researchers in combing the event
development process, investigating the event causes, and providing possible
technical solutions. Additionally, we propose and evaluate multiple technical
solutions that can possibly mitigate the electricity scarcity under such
extreme weather conditions, including energy system winterization,
interconnected HVDC lines, up-scaled demand response program and strategic
energy storage facilities. Leveraging the synthetic grid, we perform
quantitative analysis on the corrective measures in the aspect of reducing the
extent of blackout events. Our results indicate the strong disparity among the
winterization effectiveness for generation units of various source types and
geographical regions, the quantitative assessment of certain corrective
portfolios, and the interdependence of per-unit performance of corrective
measures.

## Open-source Synthetic Grid Model and Data

We first collaboratively develop an open-source, large-scale, synthetic
baseline grid that provides a realistic representation of the actual ERCOT
electric grid, and then integrate generation and load-related data along the
event timeline, which are both publicly available on Github [12]. The original
sources are detailed in the documentations in the Github repository (see Data
and Code Availability). In this paper, the synthetic model creation
particularly focuses on feasibility of the direct current optimal power flow
(DCOPF) solution without transient stability assessment for the following
reasons. First, DCOPF is commonly used for generation dispatch in the real-
world system operation under normal conditions, due to its computation
efficiency compared to the alternating current optimal power flow (ACOPF).
Second, although maintaining the system transient stability affects the system
operation under emergency conditions, the major decisions that determine the
blackout event thread, such as generation dispatch and load shedding, mostly
depend on the power flow solution feasibility. Finally, regulations on
critical energy/electric infrastructure information (CEII) limit the
disclosure of information about actual power grid components, and hence
dynamic model parameter calibration for generators is impractical due to the
lack of publicly available information.

For the purpose of blackout event reproduction and ‘what-if’ analysis, we
create a comprehensive blackout event dataset via collection from publicly
available sources [13, 3, 14, 15, 16] and estimation (see Methods). This
dataset integrates actual load by weather zone, actual generation by source,
7-day-ahead load forecast by source, solar and wind generation forecast,
generation units outage, actual available generation capacity, actual load
shedding and customer power outage into a single ready-to-use format. Here, we
define the counterfactual load as the 7-day-ahead load forecast and the
simulated load shedding as the gap between the post-shed and counterfactual
load, and introduce the estimated generation capacity by weather zone based on
rated generation capacity, thermal generation units outage and actual
renewable generation (see Methods), all of which play important roles in the
event reproduction and what-if analysis.

The synthetic ERCOT grid model is adapted from an existing test system [17]
and rigorously calibrated in several aspects, namely generator units capacity,
and transmission line rating (see Methods). The geographical load distribution
comes from the existing grid model [9], while their real-time magnitudes in
simulation are adjusted according to the real load dataset or calculated ones
depending on whether the real load data are available. The generator units
capacities are updated to the actual available generation capacity [15] in
January 2021. Without modifying the network topology, some transmission lines
are upgraded to ensure that the model remains feasible in the period leading
up to the blackouts and that no renewable generators are unreasonably
curtailed due to congestion.

Integrating the open-source datasets, the ready-to-use multi-platform
synthetic grid model is the first open-source simulation package dedicated to
potentially provide firm interdisciplinary insights into the particular real-
world blackout event. To give an intuitive impression on the blackout event,
we provide an event overview along with regional generation outages and
customer power outages (Fig. 1). The timeline of the whole blackout event
(Fig. 1-a) that contains the actual total load, actual and estimated
generation capacity shows the electricity scarcity due to the high load demand
and wide-ranging generation outage. The actual generation and generation
outage across eight ERCOT weather zones (Fig. 1-b) show that the generation
outage at the darkest hour mainly consists of natural gas thermal generation
outages across ERCOT and renewable generation outages in the North, West, Far
West and South zones. We also find the regional disparity of load shedding
(Fig. 1-c) from the aggregated county-level utility-reported customer outage
data [16] during the "darkest" period, namely from 8 p.m. February 15 to 11
a.m. February 16. Specifically, the satellite counties around Houston in the
Coast zone and several counties distributed in the West zone suffered the most
severe outages. We notice the significant gap between the estimated generation
capacity and actual online capacity before February 15 and increasing mismatch
between them after noon on February 16 that are in line with expectations due
to several reasons explained in Supplementary Note 1. We have observed that
there exists a substantial mismatch between actual load and either actual
online or estimated generation capacity, which is beyond the reserve limit.
This mismatch may be attributed to multiple reasons, such as transient
stability requirements, reactive power demands, and capped wholesale market
price [18], which deserve more investigation but are nevertheless outside the
scope of this paper. To show the complex but realistic features of the
synthetic grid, we visualize the topology of the whole synthetic grid (Fig.
2), of which load distribution, generation units capacity and transmission
lines rating are calibrated based on the static ERCOT grid-related data (see
Methods). In the following analysis, we will leverage the synthetic model
along with the blackout event dataset to reproduce the blackout event via
simulation and perform quantitative assessments of multiple corrective
measures against extreme frigid weather.

## Reproduction of the 2021 ERCOT Blackout Event

To reproduce the ERCOT blackout event from February 15 to February 18, we
simulate the synthetic grid model using the aggregated data, where realistic
load shedding allocation and DCOPF are key steps. We take the estimated
generation capacity (the binding constraint for load shedding) and
counterfactual load (the ebb-flow pattern of load) as the simulation inputs.
To achieve the fidelity of load shedding, we mimic the guides of load shedding
and restoration [19, 20] to determine the total load shedding amount at any
given moment, and perform appropriate spatial allocation of load shedding to
reflect its regional disparity (see Methods). We reproduce the blackout event
by iteratively solving DCOPF given the post-shedding load (see Methods), which
reveals the hourly change of geographically distributed load, generation and
load shedding across the ERCOT in detail.

We demonstrate the fidelity of the synthetic grid and the associated
simulation methods by the reproduction results of load shedding and generation
composition (Fig. 3). To quantify the severity of the blackout event, we use
the power system reliability index energy-not-served [21] (ENS), defined as
the integral of load shedding over the event timeline, to quantitatively
evaluate the load shedding throughout the rest of this paper. We first
demonstrate the fidelity of the geographical load distribution and the
designed load shedding algorithm by the good match between the actual and
simulated total load shedding (Fig. 3-a) that respectively represent a total
of $998.8$ GWh and $929.6$ GWh ENS. The unavoidable mismatch attributes to the
combined effects of errors in synthetic grid modelling and system operation
under emergency conditions (see Supplementary Note 2 for the remark on the
mismatch). We then validate that the simulation well captures the regional
disparity of load shedding across eight weather zones [22] by comparing the
simulated zone-level normalized load shedding with the real one (Fig. 3-b). It
shows that Far West experienced the most disproportional load shedding among
all zones and Coast has suffered from a significantly worse condition compared
to the other two most populous zones: North Central and South Central.
Finally, we validate the fidelity of generation units capacity by type and
generation cost curves used for DCOPF by showing the almost perfect match
between actual and simulated generation composition throughout the event (Fig.
3-c). The reproduction results validate the synthetic baseline model, the
related data and the associated simulation methods, which provide a reliable
basis for the following what-if analysis. Additionally, the reproduction
results can transparently show the change of load, generation and load
shedding along the timeline, aiding public multidisciplinary researchers in
combing the event development process, investigating the event causes and
providing possible technical solutions. The transparency and reproducibility
of the synthetic grid model also allow public researchers to contribute to
further model development and calibration.

## Quantitative Assessment of Corrective Measures against Extreme Frigid
Weather

In order to provide firm insights into potential solutions against problems
revealed by extreme frigid weather, we start from investigating four possible
corrective measures, namely, generation units winterization, interconnection
HVDC lines, up-scaled demand response program, and strategic energy storage
facility (see Supplementary Figure 2 for conceptual diagram). Generation units
winterization only refers to the adapted winterization treatments for electric
energy generation units, assuming no winterization is currently applied for
any units. Particularly, we reserve the impacts of wide-ranging natural gas
scarcity [6] due to failures in the non-winterized natural gas supply chain
(see Methods and Supplementary Note 4). Additional HVDC interconnections,
besides two existing HVDC ties, respectively connect from the Western
Interconnection to the West zone and from the Eastern Interconnection to the
Coast zone, require necessary transmission lines upgrade around the locations
of their converter stations (see Methods). Up-scaled demand response refers to
various incentive programs distributed across ERCOT that require voluntary
reduction of electric energy demand. Energy storage refers to the large
utility scale storage facilities that absorb the excessive energy during off-
peak hours and release it at high power during emergency hours. Here we treat
the energy storage as the first-aid measure while taking the other three as
the sustained electricity supply measure, especially viewing the generation
units winterization as the primary preventive measure. Therefore, we conduct
quantitative assessment of all corrective measures from different perspectives
in the following analysis.

Taking these corrective measure settings into account, we first quantify the
impacts of each single sustained electricity supply measure of distinct
extents in load shedding (Fig. 4). We find that 60% generation units
winterization can effectively reduce the ENS from 929.6 GWh to 40.8 GWh (Fig.
4-a), and about 80% generation units winterization can prevent the blackout
entirely, where we reserve the impacts of non-winterized natural gas supply
chain. We also find that HVDC lines and up-scaled demand response of equal
capacity have similar but different effectiveness on mitigating the
electricity scarcity (Fig. 4-b,c), which respectively reduces the ENS by
$64.1$ GWh and $67.5$ GWh per 1 GW capacity (see Supplementary Note 6 for more
details). Since energy system winterization is the most straightforward
solution against extreme frigid weather, we attach additional importance to
prioritizing the winterization of generation units of specific source types in
different regions. We perform the quantitative assessment of the effectiveness
of facility winterization by source and region on the electricity scarcity
mitigation. The results indicate the distinct performance of per-GW generation
units winterization (see Supplementary Note 7 for more details). Based on
this, we suggest the priority of winterization for the disabled nuclear
generation units located in the South Central, natural gas generation units
across ERCOT, coal generation units in the Coast and wind generation units in
the West.

Given the quantitative assessment of single sustained electricity supply
measure, we investigate the performance of several sustained electricity
supply portfolios and assess the first-aid capability of energy storage on the
basis of sustained sources. We have selected three appropriate winterization
portfolios based on the foregoing priority analysis (see Supplementary Table
1) to provide a quantitative assessment in terms of additional ENS reduction
contributed by both HVDC and demand response (Fig. 5). First, we find that the
performance of HVDC and demand response are slightly different but almost
equivalent under certain cases of winterization portfolio. Second, we find the
per-GW performance of HVDC and demand response decreases as the winterization
capacity increases. For first-aid outage mitigation at the load shedding peak
hour, we focus on load shedding peak clipping by the strategic energy storage
facilities on top of sustained corrective measure portfolios, each refers to
one of the three winterization portfolios (see Supplementary Table 1) together
with HVDC and demand response of 2 GW. We find that the performance of per-GWh
capacity reduces as the total energy storage capacity increases, or along with
increasingly sufficient sustained supply corrective measures (Fig. 6).

To summarize the key findings obtained in the foregoing quantitative analysis,
we find the strong disparity of generation units winterization of various
source types in multiple regions, and the interdependence of per-unit
performance of corrective measures, based on the quantitative assessment of
certain corrective portfolios.

## Discussion

We introduced an open-source cross-domain synthetic ERCOT grid model
associated with the blackout event-related data, which is the first-of-its-
kind for broader energy researchers to provide insights into the 2021 Texas
power outage event. Simulation results based on this open synthetic model are
shown to have captured key characteristics of the real-world event, uncovering
the key regional disparity of load shedding. The quantitative assessment of
the corrective measures and portfolios has indicated the strong disparity of
winterization effectiveness among generation units of various types in
multiple regions and the interdependence of per-unit performance among
corrective measures. It can immediately inform policy makers of the priority
of generation units winterization, the quantitative assessment of certain
portfolios on mitigating the blackout and the necessity of launching
systematic investigations on the combined effects of corrective measures,
which can potentially be generalized for other regions around the world which
are experiencing the dual challenge of energy portfolio transition and extreme
weather conditions.

This open-source, cross-domain, data-driven approach to analyzing a real-world
power grid during extreme events provides a fresh perspective to allow broader
climate and energy research communities to have high fidelity characterization
of what exactly happened and what could have been corrected in a large power
system, as nations and regions aggressively tackle energy and climate
challenges. The transparency and reproducibility of the synthetic grid model
will contribute its unique value to the future energy policy making and
technological solution discussion in the broader context of energy portfolio
transition and frequent extreme climate, which otherwise are mostly closed-
door research in a few government agencies and transmission organizations on
confidential real-world models.

For the future public research and internal ERCOT investigation, it will be
fruitful to provide solid and reasonable explanations to the following
observations that are unanswered in this paper. First, we notice the wide-
ranging natural gas generation capacity outage and de-rating are not simply
due to the freezing temperature, but also to natural gas scarcity and
interruption in the supply chain. It is particularly important to estimate and
predict the impacts of interdependence between two energy infrastructure
systems on the overall energy system reliability and energy market stability
on both sides under extreme weather conditions. Second, we notice the price-
ceiling-hitting whole-sale electricity price [18] at $9,000 per MW, that
lasted for three days ending on February 18. Its quantified impacts on
generation dispatch and load restoration still remain unknown. More
investigation is necessary for demonstrating and developing a benign power
market mechanism that can encourage improving the reliability and resiliency
of power grids. Last but not least, the interdependence of per-unit
performance among corrective measures emphasizes the necessity of systemic
assessment on the combined cost-effectiveness of technical solution bundles.

## Methods

### Data Aggregation

In order to reproduce the blackout event and perform quantitative what-if
analysis, we integrate the blackout related data during the event period
between February 14 to February 18. The original sources for all datasets are
provided in the Data and Code Availability section. We integrate the datasets
via two ways, namely data collection from multiple resources [13, 15, 14, 16]
and data estimation.

  * •

_Data Collection_ : We collect actual load, actual generation, 7-day-ahead
load forecast and 7-day-ahead solar generation forecast data from the ERCOT
regular data channel [13]. We collect generation units outage data from the
source [14] dedicated for the blackout event, which specifies the outage
period, outaged capacity, source type, and location of outaged and de-rated
generator units. Note that only part of all generation outages are included in
this source, since some resource entities do not provide ERCOT consent to
disclose, and outages shorter than two hours may not be included. We obtain
generation capacity data from the Energy Information Admission (EIA)
generation inventory data source [15]. Additionally, we get the customer power
outage data [16] from PowerOutage, which has city-level utility-reported
number of customers suffering power outage.

  * •

_Data Estimation_ : We define the estimated generation capacity as the sum of
total maximum online capacity of thermal and nuclear generation and total
real-time varying available wind and solar generation capacity. Here the
maximum online capacity of thermal and nuclear generation are equal to the
seasonal maximum capacity [15] subtracted by the generation units outage [14],
while the real-time varying capacity of wind and solar generation is the
aforementioned collected wind and solar generation data [13]. We define the
load shedding as the gap between the actual and counterfactual load data. Here
the counterfactual load data refer to the 7-day-ahead load forecast. We also
estimate the counterfactual wind generation as described in the literature
[10] using the associated weather data [23] during the blackout event period.
Wind generation estimation in this way can achieve the highest granularity.
Due to the lack of weather data required by solar generation estimation model,
the 7-day-ahead forecast solar generation is the alternative way for the
counterfactual estimation. Great matches between actual and counterfactual
wind and solar generation profiles before February 9 (see Supplementary Figure
1) demonstrates the accuracy of counterfactual generation, and it also
indicates that the impacts of the winter storm on renewable generation
approximately started from February 9.

### Synthetic Grid Creation

The synthetic ERCOT grid model is adapted from the existing test system [17],
and rigorously calibrated in several aspects, namely, system topology,
geographical load distribution, generation units capacity and transmission
lines rating. Note that although the latest released synthetic Texas 7,000-bus
grid[24] offers more granularity in terms of buses and zones, the zones do not
line up with the data available from ERCOT, rendering remarkable difficulty
for model calibration. Therefore, we still choose this 2,000-bus case as the
base model.

  * •

_Load distribution_ : The geographical load distribution comes from the
original design of the synthetic grid model [9], of which the relative sizes
are reasonably determined by the demographic and geographical information. The
real-time sizes of loads in simulation are further adjusted to the real hourly
load dataset or calculated counterfactual ones. As the public datasets only
disclose aggregated load of each weather zone, all loads within each weather
zone are proportionally scaled such that the sum of all loads in each area
equals the published numbers.

  * •

_Generation units capacity_ : The existing test system contains generation
capacity inventory up to 2016 [9] plus the largest generators added in the
period from 2017 to 2019, with the entire generator fleet scaled to match
totals by type and zone at the start of 2020 [17]. To update this dataset to
2021 conditions, we add eight new generators: two natural gas generators (a
total of 427 MW), one biomass generator (100 MW), four solar generators (742
MW), and one wind generator (220 MW). These generators were added at high-
voltage buses in the synthetic network closest to their real locations.
Finally, the generation fleet was scaled such that totals by type and weather
zone match the totals from EIA Form 860-M, December 2020 (to account for any
uncaptured additions, retirements, de-ratings, etc.).

  * •

_Transmission lines rating:_ The topology of the system is adopted from the
synthetic Texas 2,000-bus grid as described in the literature [9]. Without
modifying the topology of the transmission network, their capacity ratings and
imdepances are tuned to emulate transmission expansion to accommodate the
additional generation resources, additional HVDC interconnections and 2021
demand. Transmission lines are upgraded as necessary to ensure network
feasibility and to avoid unrealistic curtailment of variable renewable
generators. Specifically, 59 out of 3,206 branches are upgraded, representing
108.4 GW-miles of transmission lines upgrades (out of a total of 19,374 GW-
miles in the base grid. These transmission upgrades are mostly in the Far West
(29) and North (14) weather zones, where growth of demand and generation
resources has been greatest relative to the 2016 transmission network
capacities.

### Simulation based on Synthetic Grid Model

####  Load Shedding and Restoration Operation Principles

In ERCOT operation protocols, when the system-wide reserve drops to a
dangerous level that qualifies for Energy Emergency Alert (EEA) conditions,
the grid operator will use different resources from various participants of
the ERCOT market to maintain grid security. In EEA levels 1 and 2, ERCOT will
first contact industrial loads that agreed to be disconnected during
emergencies and call upon available demand response programs. In EEA level 3
events during which the operating reserve capacity is below 1000 MW, ERCOT
will ask transmission companies to shed load, typically done through rotating
outages. In our simulations, we aim to follow a similar process in determining
the total amount of load to shed while maintaining simplicity and generality.
For each snapshot, we start by applying the counterfactual load and try to
find a feasible power flow solution. If the available capacity cannot satisfy
the full demand or the supply is limited by transmission line congestion,
DCOPF will be infeasible. In that case, we then gradually reduce the load
across the network until a feasible solution is found. In the reproduction
simulation, the spatial distribution of shedded load obtained from the ERCOT
historic demand data during the event is adopted into the simulation to more
closely mimic the timeline of the event. Similarly, if the system-wide reserve
is high enough and there is active load shedding from the past hours, we
attempt to slowly reconnect them back until the operation reserve has been
depleted. The full logic flow of load shed in simulation is listed in
Algorithm 1.

Load renewable generation profile of hour $t$, $P_{g}^{t}$ into model

Modify thermal generator capacity based on unit outage data

Load counterfactual load of hour $t$, $P_{l}^{t}$ into model

Apply load shedding from the past hour $P_{ls}^{t-1}$ to load buses

Compute system-wide capacity reserve
$P_{r}^{t}=\sum_{i}(P_{g,i}^{t}-P_{l,i}^{t})+P_{ls}^{t-1}$ $\forall i\in$
[list of all buses]

Attempt to solve DCOPF on the base profile

if DCOPF is infeasible or $P_{r}^{t}<P_{r,min}$ then

while DCOPF is infeasible or $P_{r}^{t}<P_{r,min}$ do

Increase load shedding: $P_{ls}^{t}=P_{ls}^{t}+\Delta P_{ls}$

Update $P_{r}^{t}=P_{r}^{t}+\Delta P_{ls}$

end while

else if Operation reserve $P_{r}^{t}>P_{r,min}$ and $P_{ls}^{t}>0$ then

while OPF is feasible and $P_{r}^{t}>P_{r,min}$ and $P_{ls}^{t}>0$ do

Decrease load shedding: $P_{ls}^{t}=P_{ls}^{t}-\Delta P_{ls}$

Update $P_{r}^{t}=P_{r}^{t}-\Delta P_{ls}$

end while

end if

Save $P_{ls}^{t}$ as the minimum load shedding for hour $t$

Algorithm 1 Iterative Load Shedding in Simulation

#### Details of Blackout Event Reproduction

  * •

_Load and Load Forecast Profile_ : In the reproduction of the outage event, we
apply the real historic load during the period between February 12 and
February 18 to the synthetic Texas network. The ERCOT load data are divided
into eight weather zones in Texas, each containing a specific set of counties.
In the simulation, we scale the base value of every load bus within each
weather zone such that the total load capacity in the zone equals the ERCOT
load forecast data on the same hour, which is used as the counterfactual load
profile.

  * •

_Renewable Generator Capacity_ : We have estimated the unit-level wind and
solar capacity data during the event periods using the combination of
available weather data and ERCOT generation-mix data. In the reproduction of
the outage event, the unit-level data are all scaled according to the total
actual renewable generation data published on EIA [15]. These data are set to
be the maximum real power output for renewable generators and is subject to
further curtailments, should congestion in the lines occur. The renewable
generators are set to have zero cost in the DCOPF formulation to prioritize
them over thermal energy sources.

  * •

_Dispatch-able Generator Capacity_ : We use the ERCOT unit-level outage
report[14] to determine the maximum real power output capacity for thermal
generators. Although the maximum rate capacity for thermal plants is fixed
through the event, many thermal generators have experienced outage or de-
rating due to various reasons, including limited fuel supply, facility
freezing and planned maintenance. As the generators in the synthetic networks
are equivalent generators, exacting matching with the outage report is not
possible. Instead, the outage data are first aggregated to county-level and
used to set the maximum capacity of all thermal generators of the same fuel
type in each county.

  * •

_Formulation of DCOPF_ : Using deterministic system demand and renewable
generation data, the actual output of dispatchable generators (coal, natural
gas and nuclear) and the power flow pattern in the network is determined by
Direct-Current Optimal Power Flow (DCOPF). DCOPF is an optimization
formulation that computes the most economic real power output assignment for
all dispatch-able generators in a network, such that all transmission line
flow is below their thermal limit and the total real power generation equals
the total demand across the network. The Matlab package Matpower is used to
solve DCOPF for all simulations. A detailed description and formulation of
DCOPF implemented by Matpower can be found in their documentation [25].

  * •

_Cost Curve_ : The cost curves of generators in the original synthetic
network[9] are revised to make an even closer match with reality. We have run
a year-round multi-period DCOPF simulation using 2016 ERCOT renewable and load
profile data to confirm that the total generation from each of the fuel
sources is sufficiently closely matching historical data. A detailed
experiment procedure and result comparison can be found in the literature
[10].

#### Generator Outage and Corrective Measure Modelling

To quantitatively assess the impact of generator outage on the severity of the
outage event and evaluate the effectiveness of potential corrective measures
such as the additional HVDC interconnections, generation units weatherization,
up-scaled demand response programs and energy storage, it is necessary to
model each of these elements appropriately in our synthetic grid. The detailed
modelling method is documented below:

  * •

_Generator Outage_ : The unit-level generator outage data is retrieved from
the ERCOT public report [26, 14]. As detailed generator information is not
available to the public (especially for natural gas, wind and solar
generators), we have 606 equivalent generators across the network such that
the area-wide aggregated generation capacity and profile can match real ERCOT
records. To match the real outage data with synthetic equivalent generator, we
pre-process the data by aggregating the total outage capacity for each county
and fuel type. The county-level outage data are used to scale the maximum real
power output of all corresponding generators in the same county and of the
same fuel type in the synthetic network.

  * •

_Generator Weatherization_ : Weatherization is a preventive measure to reduce
the impact of extreme weather conditions on the functionality of
infrastructures. Different types of generators require different types of
weatherization treatments: wind turbines require blade and gearbox heating
while gas plants may need anti-frost treatment for facilities. Despite the
potentially big difference in weatherization cost and complexity, our focus is
more about evaluating the effectiveness of weatherization among different
generator types and geographical regions. To this end, we only specify the
amount of MW of weatherized generators for each fuel type in each weather zone
and try to compare the effectiveness on the reduction of ENS as a result of
weatherization. We have also considered the scarcity of natural gas supply
before and during the winter storm by approximating the de-rating of the
winterized generators caused by gas supply shortage, using disclosed unit de-
rating data from ERCOT (see Supplementary Note 5). When computing the area-
level available capacity during counterfactual simulation, we only apply
weatherization to generators that were completely out, as the de-rating of the
running generators was highly likely caused by lack of gas supply. The
weatherized generators are also de-rated based on the extent of de-rating of
running gas generators in the same county.

  * •

_HVDC Ties_ : We have included two existing HVDC ties to the Eastern
Interconnection in our synthetic network model. These two existing conveter
stations are modelled as fixed equivalent loads for DCOPF computation. In each
hour, the real tie flow data from ERCOT is assigned as real power demand for
the equivalent loads and the sign is determined by the direction of DC tie
flow. This is meant to make the DC tie flow exactly the same as real data. For
counterfactual studies, we have included two additional HVDC ties, one to the
Western Interconnection and another to the Eastern Interconnection. We have
adopted a commonly used design [27] in determining the location for the
converter stations and upgrade the transmission lines around the converter
stations (see Supplementary Method 1). As the additional HVDC ties are
introduced to be counterfactual corrective measures to supply any extra power
during the event, their actual output power needs to be adjusted according to
the ERCOT system demand. Hence, the new DC ties are modelled differently as
equivalent generators with a cost function representing the hypothetical cost
to buy power from neighboring states. The magnitude and direction of their tie
flow is determined by OPF result.

  * •

_Demand Response_ : Demand response refers to various incentive programs that
encourage voluntary reduction of electric energy demand during peak or
emergency hours. Although most demand response programs in ERCOT are still in
development at the current stage and mostly target large industrial,
commercial or aggregated customers, it has the potential to scale up quickly
and provide a valuable demand side resource during similar energy emergencies.
In our simulation, the effect of a demand response program is modelled as a
form of voluntary load reduction with prior agreement between load serving
entities and customers which does not count to ENS. During energy emergencies,
load resources from demand response programs are the first to be committed
before all of the other more costly steps. Hence, we also prioritize available
demand response capacity over storage and forced load shedding. When the
system reserve is below 2300 MW which corresponds to ERCOT EEA level 1, the
loads across the synthetic network are reduced up to the maximum allowed
demand response capacity.

  * •

_Energy Storage_ : The purpose of energy storage is to absorb excessive energy
from renewable sources during off-peak hours and release stored energy during
peak and emergency hours. Unlike other types of corrective measures that are
more suitable in reducing the overall severity of the outage by providing
sustained additional power supply, the advantage of energy storage lies in its
ability to provide relatively large power output during a short period, which
can be used to bridge through "most difficult" hours. To emphasize this, we
assume all storage capacities are fully charged prior to the event and commit
them during the hours when the level of forced load shed is around the
highest. The contribution of energy storage is reflected in the reduction of
peak load shed capacity.

## Data and Code Availability

The open-source synthetic ERCOT model and the corresponding dataset used for
blackout event reproduction and quantified what-if analysis are publicly
available on Github[12]. A tutorial and examples for running the code using
provided dataset are also available in the Github repository.

## References

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## Figures & Tables

Figure 1: Overview of the 2021 ERCOT blackout event from the perspective of
generation capacity and load shedding. a, Blackout event overview in terms of
generation capacity and actual load associated with the key event labels. The
significant gap between the estimated generation capacity and actual online
capacity before February 15 and increasing mismatch between them after noon on
February 16 are in line with expectations due to several reasons explained in
Supplementary Note 1. b, Online and offline generation capacity by source in
ERCOT weather zones at 12:00 p.m. February 16. The ring size is determined by
the zone-level total generation capacity and its color represents the type of
generation capacity. The block color represents the weather zone where the
county is located (grey blocks are not within eight weather zones). c,
Normalized county-level customer outage percentage during the "darkest"
period. The block color represents the county-level customer outage percentage
[16], where the ERCOT average outage percentage is 31% (grey blocks mean no
data available). The "darkest" period is ranging from 8 p.m. February 15 to 11
a.m. February 16, possessing the largest load shedding amount.

Figure 2: Visualization of the large-scale synthetic ERCOT grid. Here the
branch width is proportional to the transmission line rating. This synthetic
grid contains 606 generators, 1,350 loads and 3,206 branches, of which the
generation units capacity, transmission line rating and load distribution are
calibrated based on the available ERCOT grid-related data.  Figure 3: Blackout
event reproduction via simulation on the synthetic grid in comparison with the
real data. a, Comparison between the simulated and actual total load shedding
curves. b, Zone-level normalized load shedding with 95% confidence interval
(CI) during the period between 8 p.m. February 15 and 12 p.m. February 16 that
has the highest load shedding. FW, NC and SC respectively represent Far West,
North Central and South Central zones. Here the normalized load shedding
refers to the ratio of load shedding percentage over online load percentage
for each zone. We use the estimated load shedding data based on the
counterfactual and actual load data from ERCOT rather than customer outage
data [16] as the real benchmark (See Supplementary Note 3). c, Comparison
between actual and simulated percentage of generation compositions of various
source types throughout the event.

][H]

Figure 4: Quantitative assessment of each single sustained supply measure in terms of load shedding (GW). a, The impacts of additional facility winterization on load shedding with different percentages from $20\%$ to $60\%$. These percentages correspond to the winterization for generation capacity of 21.7 GW, 43.4 GW and 65.1 GW. b, The impact of additional HVDC lines on load shedding with the capacity ranging from 1 to 4 GW. c, The impact of up-scaled demand response program on load shedding with the capacity ranging from 1 to 4 GW. Note that winterized generator units and demand response programs are deployed across ERCOT without priority in any specific area. Here the original curve refers to the load shedding in the event reproduction with no corrective measures.  Source Type | Far West | West | North | East | Coast | North Central | South Central | South
---|---|---|---|---|---|---|---|---
Nuclear | — | — | — | — | — | — | 38,205 | —
Natural Gas | $37,058$ | $12,338$ | $18,993$ | $16,908$ | $25,129$ | $29,236$ | $18,905$ | $23,279$
Coal | — | — | — | $\phantom{00,}266$ | $18,719$ | $\phantom{0}1,967$ | $\phantom{00,}266$ | $\phantom{0}5,768$
Wind | $\phantom{0}6,865$ | $15,178$ | $\phantom{0}4,479$ | $\phantom{0}5,188$ | $\phantom{00,}795$ | — | — | —
Table 1: Quantitative assessment of generation units winterization by source
and region in terms of ENS reduction (MWh). Each entry in the table shows the
resulting reduction of ENS (in MWh) for 1 GW generation capacity winterization
of each source type in each weather zone. A crossed-out entry means that the
lack of associated generation outage data renders the evaluation of this
certain winterization non-applicable. Figure 5: Additional ENS reduction (GWh)
by HVDC and demand response given different winterization portfolios. a, b, c,
respectively show the additional ENS reduction contributed by HVDC and demand
response that is represented by both bubble size and color, given different
total winterized generation capacity. Here the winterization portfolios in
three cases are determined based on the results in Table 1 (see Supplementary
Table 1 for more details). The ENS reduction contributed by winterization
portfolio alone are respectively 266.2 GWh, 467.0 GWh and 628.9 GWh for the
case of 10 GW, 20 GW and 30 GW winterized capacity.

Figure 6: Load shedding peak clipping of energy storage facility. The baseline
refers to the model without other corrective measures, while the portfolio 1,
2 and 3 respectively consist of one of three winterization portfolios in
Supplementary Table 1 together with HVDC and demand response of 2 GW capacity.