Transmission & Interconnections

This page documents the transmission and DC tie interconnection data products available through the ERCOT API.

DC Tie Flows

Report: NP6-626-CD Method: get_dc_tie_flows() Update Frequency: Hourly Retention: 7-8 days via API

Actual hourly flows for ERCOT’s four DC interconnections with neighboring grids.

ERCOT’s DC Ties

ERCOT has four non-synchronous DC interconnections:

DC_N - North tie to SPP (Southwest Power Pool)
DC_E - East tie to Mexico (CFE)
DC_L - Laredo tie to Mexico (CFE)
DC_R - Railroad tie to Mexico (CFE)

Flow Convention:

Negative values = Imports into ERCOT
Positive values = Exports from ERCOT

Basic Usage

from datetime import datetime
from lib.iso.ercot import ERCOTClient, ERCOTConfig

config = ERCOTConfig.from_ini_file()
client = ERCOTClient(config)

# Get DC tie flows for a specific day
start_dt = datetime(2025, 8, 15, 0, 0, 0)
end_dt = datetime(2025, 8, 15, 23, 59, 59)

dc_flows = client.get_dc_tie_flows(
    post_datetime_from=start_dt,
    post_datetime_to=end_dt
)

# Analyze flows by tie
import pandas as pd
df = pd.DataFrame(dc_flows['data'])

print("\nDC Tie Flow Summary:")
by_tie = df.groupby('dcTieName')['powerFlow'].agg(['mean', 'min', 'max', 'sum'])
print(by_tie)

# Calculate net position
net_flow = df['powerFlow'].sum() / len(df)
if net_flow < 0:
    print(f"\nERCOT was a NET IMPORTER: {abs(net_flow):,.1f} MW avg")
elif net_flow > 0:
    print(f"\nERCOT was a NET EXPORTER: {net_flow:,.1f} MW avg")
else:
    print("\nERCOT was BALANCED")

client.cleanup()

Data Fields:

postDatetime / operatingTime - Timestamp of measurement
dcTieName - DC tie identifier (DC_N, DC_E, DC_L, DC_R)
powerFlow - Power flow in MW (negative = import, positive = export)
operatingDate - Operating date
operatingHour - Operating hour

Advanced Analysis

# Analyze import/export patterns by time of day
df['hour'] = pd.to_datetime(df['operatingTime']).dt.hour

hourly_pattern = df.groupby('hour')['powerFlow'].mean()
print("\nAverage Flow by Hour:")
print(hourly_pattern.round(1))

# Find peak import/export hours
peak_import_hour = hourly_pattern.idxmin()
peak_export_hour = hourly_pattern.idxmax()

print(f"\nPeak Import Hour: {peak_import_hour}:00")
print(f"  Avg Flow: {hourly_pattern[peak_import_hour]:,.1f} MW")

print(f"\nPeak Export Hour: {peak_export_hour}:00")
print(f"  Avg Flow: {hourly_pattern[peak_export_hour]:,.1f} MW")

# Analyze by tie and direction
import_hours = df[df['powerFlow'] < 0].groupby('dcTieName')['powerFlow'].agg(['count', 'mean'])
export_hours = df[df['powerFlow'] > 0].groupby('dcTieName')['powerFlow'].agg(['count', 'mean'])

print("\nImports by Tie:")
print(import_hours)
print("\nExports by Tie:")
print(export_hours)

Use Cases:

Import/export pattern analysis
Grid interconnection studies
Cross-border energy flow tracking
Net interchange calculations
Regional market integration analysis

Dashboard: https://www.ercot.com/gridmktinfo/dashboards/dctieflows

Important Notes:

Real-time dashboard updates every 5 minutes
API data may have timing differences from dashboard
Only 7-8 days of data available via API
For historical data beyond 8 days, use EIA-930 API

—

SCED Binding Transmission Constraints

Report: NP6-86-CD Method: get_sced_binding_transmission_constraints() Update Frequency: Hourly (when constraints occur) Retention: 7 days via API

Shadow prices and details for binding or violated transmission constraints in SCED, including when DC ties or interfaces hit their limits.

Understanding Shadow Prices

Shadow prices represent the marginal cost ($/MW) of relieving a transmission constraint. High shadow prices indicate:

Significant congestion on the constrained element
Large price separation across the constraint
High cost to deliver additional power through the constraint

A shadow price of $15/MW means that relieving the constraint by 1 MW would reduce total system costs by $15.

Basic Usage

from datetime import datetime

# Get binding constraints for a specific day
start_dt = datetime(2025, 8, 15, 0, 0, 0)
end_dt = datetime(2025, 8, 15, 23, 59, 59)

constraints = client.get_sced_binding_transmission_constraints(
    sced_timestamp_from=start_dt,
    sced_timestamp_to=end_dt
)

# Find most expensive constraints
df = pd.DataFrame(constraints['data'])

if not df.empty:
    top_10 = df.nlargest(10, 'shadowPrice')[
        ['contingencyName', 'overloadedElementName', 'shadowPrice',
         'elementFlow', 'elementLimit']
    ]

    print("\nTop 10 Most Expensive Constraints:")
    print(top_10)

    print(f"\nTotal Constraint-Hours: {len(df)}")
    print(f"Average Shadow Price: ${df['shadowPrice'].mean():,.2f}/MW")
    print(f"Max Shadow Price: ${df['shadowPrice'].max():,.2f}/MW")
else:
    print("No binding constraints during this period")

Data Fields:

scedTimestamp - SCED timestamp
contingencyName - Contingency identifier
contingencyType - Type of contingency
overloadedElementName - Name of constrained element
fromStation / toStation - Element endpoints
voltageLevel - Voltage level
shadowPrice - Shadow price in $/MW
maxShadowPrice - Maximum penalty price
elementLimit - Element capacity limit in MW
elementFlow - Actual flow in MW
constraintType - Type of constraint

DC Tie Constraint Analysis

# Check if DC ties were constrained
dc_tie_constraints = df[
    df['overloadedElementName'].str.contains('DC|TIE', case=False, na=False)
]

if not dc_tie_constraints.empty:
    print(f"\n⚠️  Found {len(dc_tie_constraints)} DC tie constraint intervals")

    print("\nDC Tie Constraints:")
    for _, row in dc_tie_constraints.iterrows():
        element = row['overloadedElementName']
        shadow_price = row['shadowPrice']
        flow = row['elementFlow']
        limit = row['elementLimit']
        overload_pct = ((flow - limit) / limit * 100) if limit > 0 else 0

        print(f"\n  {element}")
        print(f"    Shadow Price: ${shadow_price:,.2f}/MW")
        print(f"    Flow: {flow:,.1f} MW / Limit: {limit:,.1f} MW")
        print(f"    Overload: {overload_pct:.1f}%")
else:
    print("\n✓ No DC tie constraints detected")

Congestion Pattern Analysis

# Analyze constraint frequency by element
constraint_freq = df['overloadedElementName'].value_counts().head(10)
print("\nMost Frequently Constrained Elements:")
print(constraint_freq)

# Calculate total congestion cost (approximate)
# Shadow price * (actual flow - limit) estimates the cost per interval
df['overload_MW'] = (df['elementFlow'] - df['elementLimit']).clip(lower=0)
df['congestion_cost'] = df['shadowPrice'] * df['overload_MW']

total_cost = df['congestion_cost'].sum()
print(f"\nEstimated Total Congestion Cost: ${total_cost:,.0f}")

# Analyze by time of day
df['hour'] = pd.to_datetime(df['scedTimestamp']).dt.hour
hourly_constraints = df.groupby('hour').size()

print("\nConstraint Count by Hour:")
print(hourly_constraints)

Use Cases:

Transmission congestion analysis
Identifying bottlenecks
DC tie limit monitoring
Interface constraint tracking
Congestion cost calculations
Transmission planning insights

—

Historical Data Beyond 7 Days

For DC tie flow data beyond the 7-8 day API window, use the EIA-930 API:

# EIA-930 provides hourly ERCOT interchange from July 2015-present
# Free public API (no authentication required)
# Dashboard: https://www.eia.gov/electricity/gridmonitor/

# Note: EIA-930 may aggregate ties differently than ERCOT's
# individual DC_N, DC_E, DC_L, DC_R breakdown

Consult your project’s EIA client for accessing historical interchange data.

—

CLI Usage

All transmission data products can be accessed via the command-line interface:

# DC tie flows (last 7 days available)
python isodart.py ercot transmission --trans-type dc_ties --start 2025-08-15 --duration 7

# Binding transmission constraints
python isodart.py ercot transmission --trans-type binding_constraints --start 2025-08-15 --duration 1

—

Example Scripts

See examples/ercot_transmission_analysis.py for a comprehensive example demonstrating:

DC tie flow pattern analysis
Net import/export calculations
Binding constraint identification
Congestion analysis

# Run all analyses
python examples/ercot_transmission_analysis.py --date 2025-08-15

# Run specific analysis
python examples/ercot_transmission_analysis.py --date 2025-08-15 --analysis ties
python examples/ercot_transmission_analysis.py --date 2025-08-15 --analysis constraints

—

Transmission & Interconnections

DC Tie Flows

ERCOT’s DC Ties

Basic Usage

Advanced Analysis

SCED Binding Transmission Constraints

Understanding Shadow Prices

Basic Usage

DC Tie Constraint Analysis

Congestion Pattern Analysis

Historical Data Beyond 7 Days

CLI Usage

Example Scripts

Related Documentation