LTDS Network Planning with ukpyn¶

This tutorial covers the Long Term Development Statement (LTDS) orchestrator for accessing network planning data from UK Power Networks.

What you'll learn:

What is LTDS and why it's useful
Using the LTDS orchestrator with simple imports
Listing available datasets
Getting Table 3a (observed peak demand)
Getting Table 2a/2b (transformer data)
Getting Table 5 (generation data)
Getting Table 6 (connection interest)
Getting infrastructure projects
Exporting data to CSV

Prerequisites: Complete 01-getting-started.ipynb first.

These tutorials require additional dependencies. Install them with pip install "ukpyn[all]" — see Tutorial 01 for full setup instructions

1. What is LTDS?¶

The Long Term Development Statement (LTDS) is a key regulatory document published by UK Power Networks twice yearly. It provides detailed information about the distribution network that is essential for:

Network planners understanding infrastructure capacity
Developers assessing connection opportunities
Researchers analysing grid constraints and generation patterns
Local authorities planning decarbonisation initiatives

LTDS Tables Overview¶

Table	Description	Key Use Cases
Table 2a/2b	Transformer specifications	Capacity planning, asset modelling
Table 3a	Observed peak demand	Load forecasting, headroom analysis
Table 5	Generation capacity	Renewable energy mapping, curtailment analysis
Table 6	Connection interest	Development pipeline, constraint identification
Projects	Infrastructure schemes	Investment planning, reinforcement tracking

Licence Areas¶

UK Power Networks operates across three licence areas:

EPN - Eastern Power Networks (East of England)
LPN - London Power Networks (Greater London)
SPN - South Eastern Power Networks (South East England)

2. Using the LTDS Orchestrator¶

The LTDS orchestrator provides a simple, ergonomic interface for accessing LTDS data. Import it directly from ukpyn:

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import ukpyn

ukpyn.check_api_key()
print("API key configured!")
import ukpyn

ukpyn.check_api_key()
print("API key configured!")

API key configured!

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# Import the LTDS orchestrator
from ukpyn import ltds

print("LTDS orchestrator imported successfully!")
# We have a convenient way to check that the orchestrator is working, by printing the methods on the object.

print(repr(ltds))
# Import the LTDS orchestrator
from ukpyn import ltds

print("LTDS orchestrator imported successfully!")
# We have a convenient way to check that the orchestrator is working, by printing the methods on the object.

print(repr(ltds))

LTDS orchestrator imported successfully!
LTDSOrchestrator(methods=[export, get, get_async, get_cim, get_projects, get_table_1, get_table_2a, get_table_2b, get_table_3a, get_table_3b, get_table_4a, get_table_4b, get_table_5, get_table_6, get_table_7, get_table_8])

3. Listing Available Datasets¶

The ltds.available_datasets property shows all datasets you can access through the orchestrator.

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# List all available LTDS datasets
print("Available LTDS datasets:")
print("-" * 40)

for dataset in ltds.available_datasets:
    print(f"  - {dataset}")

print(f"\nTotal: {len(ltds.available_datasets)} datasets")

# Expected output:
# Available LTDS datasets:
# ----------------------------------------
#   - table_2a
#   - transformer_2w
#   - table_2b
#   - transformer_3w
#   - table_3a
#   - observed_peak_demand
#   - table_3a_transposed
#   - table_5
#   - generation
#   - table_6
#   - connection_interest
#   - infrastructure_projects
#   - projects
#   - cim
#
# Total: 14 datasets
# List all available LTDS datasets
print("Available LTDS datasets:")
print("-" * 40)

for dataset in ltds.available_datasets:
    print(f"  - {dataset}")

print(f"\nTotal: {len(ltds.available_datasets)} datasets")

# Expected output:
# Available LTDS datasets:
# ----------------------------------------
#   - table_2a
#   - transformer_2w
#   - table_2b
#   - transformer_3w
#   - table_3a
#   - observed_peak_demand
#   - table_3a_transposed
#   - table_5
#   - generation
#   - table_6
#   - connection_interest
#   - infrastructure_projects
#   - projects
#   - cim
#
# Total: 14 datasets

Available LTDS datasets:
----------------------------------------
  - table_1
  - circuit_data
  - table_2a
  - transformer_2w
  - table_2b
  - transformer_3w
  - table_3a
  - observed_demand
  - observed_peak_demand
  - table_3a_transposed
  - table_3b
  - forecast_demand
  - table_4a
  - fault_level_3ph
  - table_4b
  - fault_level_earth
  - table_5
  - generation
  - table_6
  - connection_interest
  - table_7
  - restrictions
  - table_8
  - fault_data
  - projects
  - infrastructure_projects
  - cim

Total: 27 datasets

Using the Generic `get()` Function¶

The ltds.get() function allows you to fetch any LTDS dataset by name:

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# Fetch data using the generic get() function
# Import the LTDS orchestrator
from ukpyn import ltds

response = ltds.get("table_3a", limit=5)

data = response.records

print("Dataset: table_3a")
print(f"Total records: {response.total_count}")
print(f"Records returned: {len(response.records)}")

# Show first record fields
if response.records:
    print("\nSample record fields:")
    for key, value in list(response.records[0].fields.items())[:5]:
        print(f"  {key}: {value}")

# Expected output:
# Dataset: table_3a
# Total records: 1523
# Records returned: 5
#
# Sample record fields:
#   licence_area: EPN
#   substation: ALDEBURGH PRIMARY
#   year: 2023
#   peak_demand_mw: 12.5
#   ...
# Fetch data using the generic get() function
# Import the LTDS orchestrator
from ukpyn import ltds

response = ltds.get("table_3a", limit=5)

data = response.records

print("Dataset: table_3a")
print(f"Total records: {response.total_count}")
print(f"Records returned: {len(response.records)}")

# Show first record fields
if response.records:
    print("\nSample record fields:")
    for key, value in list(response.records[0].fields.items())[:5]:
        print(f"  {key}: {value}")

# Expected output:
# Dataset: table_3a
# Total records: 1523
# Records returned: 5
#
# Sample record fields:
#   licence_area: EPN
#   substation: ALDEBURGH PRIMARY
#   year: 2023
#   peak_demand_mw: 12.5
#   ...

Dataset: table_3a
Total records: 1964
Records returned: 5

Sample record fields:
  gridsupplypoint: Amersham
  substation: Coldharbour Farm 11kV
  season: Summer
  maximum_demand_24_25_mw: 2.9
  maximum_demand_24_25_pf: 0.96

4. Table 3a - Observed Peak Demand¶

Table 3a contains observed load data for primary substations. This is essential for:

Understanding current network utilisation
Identifying substations approaching capacity
Load forecasting and trend analysis

Use the dedicated get_table_3a() function for convenient filtering:

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# Get Table 3a data for a specific licence area
epn_demand = ltds.get_table_3a(licence_area="EPN", limit=10)

print("EPN Observed Peak Demand Data")
print(f"Total records: {epn_demand.total_count}")
print(f"Records returned: {len(epn_demand.records)}")
print("-" * 60)

for record in epn_demand.records[:5]:
    fields = record.fields
    substation = fields.get("substation", "Unknown")
    peak = fields.get(
        "maximum_demand_24_25_mw", "N/A"
    )  # Adjust field name based on actual dataset
    season = fields.get("season", "N/A")
    print(f"  {substation}: {peak} MW (Season: {season})")

# Expected output:
# EPN Observed Peak Demand Data
# Total records: 523
# Records returned: 10
# ------------------------------------------------------------
#   ALDEBURGH PRIMARY: 12.5 MW (Season: Winter)
#   ALTON WATER PRIMARY: 8.3 MW (Season: Winter)
#   AMPTON PRIMARY: 15.2 MW (Season: Winter)
#   ...
# Get Table 3a data for a specific licence area
epn_demand = ltds.get_table_3a(licence_area="EPN", limit=10)

print("EPN Observed Peak Demand Data")
print(f"Total records: {epn_demand.total_count}")
print(f"Records returned: {len(epn_demand.records)}")
print("-" * 60)

for record in epn_demand.records[:5]:
    fields = record.fields
    substation = fields.get("substation", "Unknown")
    peak = fields.get(
        "maximum_demand_24_25_mw", "N/A"
    )  # Adjust field name based on actual dataset
    season = fields.get("season", "N/A")
    print(f"  {substation}: {peak} MW (Season: {season})")

# Expected output:
# EPN Observed Peak Demand Data
# Total records: 523
# Records returned: 10
# ------------------------------------------------------------
#   ALDEBURGH PRIMARY: 12.5 MW (Season: Winter)
#   ALTON WATER PRIMARY: 8.3 MW (Season: Winter)
#   AMPTON PRIMARY: 15.2 MW (Season: Winter)
#   ...

EPN Observed Peak Demand Data
Total records: 1110
Records returned: 10
------------------------------------------------------------
  Ilmer Primary 11kV: 7.0 MW (Season: Summer)
  Thame Primary 11kV: 9.4 MW (Season: Summer)
  Wendover Primary 11kV: 11.4 MW (Season: Winter)
  Romford Primary 11kV: 23.2 MW (Season: Summer)
  Selinas Ln Primary 11kV: 9.5 MW (Season: Summer)

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# Filter by licence area
lpn_2023 = ltds.get_table_3a(
    licence_area="London Power Networks (LPN)", limit=10
)  # add  other filters as needed

print("London Power Networks - 2023 Peak Demand")
print(f"Total records: {lpn_2023.total_count}")
print("-" * 60)

for record in lpn_2023.records[:5]:
    fields = record.fields
    print(
        f"  {fields.get('substation', 'Unknown')}: {fields.get('maximum_demand_24_25_mw', 'N/A')} MW"
    )

# Expected output:
# London Power Networks - 2023 Peak Demand
# Total records: 187
# ------------------------------------------------------------
#   ABBEY ROAD PRIMARY: 22.1 MW
#   ACTON LANE PRIMARY: 18.7 MW
#   ...
# Filter by licence area
lpn_2023 = ltds.get_table_3a(
    licence_area="London Power Networks (LPN)", limit=10
)  # add  other filters as needed

print("London Power Networks - 2023 Peak Demand")
print(f"Total records: {lpn_2023.total_count}")
print("-" * 60)

for record in lpn_2023.records[:5]:
    fields = record.fields
    print(
        f"  {fields.get('substation', 'Unknown')}: {fields.get('maximum_demand_24_25_mw', 'N/A')} MW"
    )

# Expected output:
# London Power Networks - 2023 Peak Demand
# Total records: 187
# ------------------------------------------------------------
#   ABBEY ROAD PRIMARY: 22.1 MW
#   ACTON LANE PRIMARY: 18.7 MW
#   ...

London Power Networks - 2023 Peak Demand
Total records: 258
------------------------------------------------------------
  Axe St 11kV: 31.9 MW
  Barking West 11kV: 14.2 MW
  Whiston Rd 11kV: 27.9 MW
  Churchfields 11kV: 34.4 MW
  Forest Hill 11kV: 14.7 MW

5. Table 2a/2b - Transformer Data¶

Tables 2a and 2b contain transformer specifications:

Table 2a: Two-winding transformers (1x HV, 1x LV)
Table 2b: Three-winding transformers (1x HV, 2x LV)

This data is useful for capacity planning and network modelling.

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# Get Table 2a - Two-winding transformer data
transformers_2w = ltds.get_table_2a(licence_area="EPN", limit=10)

print("EPN Two-Winding Transformers (Table 2a)")
print(f"Total records: {transformers_2w.total_count}")
print("-" * 60)

for record in transformers_2w.records[:5]:
    fields = record.fields
    substation = fields.get("gridsupplypoint", "Unknown")
    winter_rating = fields.get("transformer_rating_mva_winter", "N/A")
    summer_rating = fields.get("transformer_rating_mva_summer", "N/A")
    voltage_hv = fields.get("voltage_hv", "N/A")
    voltage_lv = fields.get("voltage_lv", "N/A")
    print(
        f"  {substation}: {winter_rating}/{summer_rating} MVA ({voltage_hv}/{voltage_lv} kV)"
    )

# Expected output:
# EPN Two-Winding Transformers (Table 2a)
# Total records: 234
# ------------------------------------------------------------
#   Amersham: 15/12 MVA (33/11.5 kV)
#   Barking 132kV: 117/90 MVA (132/33 kV)
#   ...
# Get Table 2a - Two-winding transformer data
transformers_2w = ltds.get_table_2a(licence_area="EPN", limit=10)

print("EPN Two-Winding Transformers (Table 2a)")
print(f"Total records: {transformers_2w.total_count}")
print("-" * 60)

for record in transformers_2w.records[:5]:
    fields = record.fields
    substation = fields.get("gridsupplypoint", "Unknown")
    winter_rating = fields.get("transformer_rating_mva_winter", "N/A")
    summer_rating = fields.get("transformer_rating_mva_summer", "N/A")
    voltage_hv = fields.get("voltage_hv", "N/A")
    voltage_lv = fields.get("voltage_lv", "N/A")
    print(
        f"  {substation}: {winter_rating}/{summer_rating} MVA ({voltage_hv}/{voltage_lv} kV)"
    )

# Expected output:
# EPN Two-Winding Transformers (Table 2a)
# Total records: 234
# ------------------------------------------------------------
#   Amersham: 15/12 MVA (33/11.5 kV)
#   Barking 132kV: 117/90 MVA (132/33 kV)
#   ...

EPN Two-Winding Transformers (Table 2a)
Total records: 1153
------------------------------------------------------------
  Amersham: 15/12 MVA (33/11.5 kV)
  Amersham: 18/15 MVA (33/11 kV)
  Barking 132kV: 117/90 MVA (132/33 kV)
  Barking 132kV: 13/10 MVA (33/11 kV)
  Barking West 33kV: 24/18 MVA (33/11 kV)

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# Get Table 2b - Three-winding transformer data
transformers_3w = ltds.get_table_2b(licence_area="EPN", limit=10)

print("EPN Three-Winding Transformers (Table 2b)")
print(f"Total records: {transformers_3w.total_count}")
print("-" * 60)

for record in transformers_3w.records[:5]:
    fields = record.fields
    substation = fields.get("hv_substation", "Unknown")
    summer_rating = fields.get("transformer_rating_mva_summer_hv", "N/A")
    winter_rating = fields.get("transformer_rating_mva_winter_hv", "N/A")
    print(f"  {substation}: {winter_rating}/{summer_rating} MVA")

# Expected output:
# EPN Three-Winding Transformers (Table 2b)
# Total records: 45
# ------------------------------------------------------------
#   BRAMFORD GRID: 240 MVA
#   BURWELL GRID: 180 MVA
#   ...
# Get Table 2b - Three-winding transformer data
transformers_3w = ltds.get_table_2b(licence_area="EPN", limit=10)

print("EPN Three-Winding Transformers (Table 2b)")
print(f"Total records: {transformers_3w.total_count}")
print("-" * 60)

for record in transformers_3w.records[:5]:
    fields = record.fields
    substation = fields.get("hv_substation", "Unknown")
    summer_rating = fields.get("transformer_rating_mva_summer_hv", "N/A")
    winter_rating = fields.get("transformer_rating_mva_winter_hv", "N/A")
    print(f"  {substation}: {winter_rating}/{summer_rating} MVA")

# Expected output:
# EPN Three-Winding Transformers (Table 2b)
# Total records: 45
# ------------------------------------------------------------
#   BRAMFORD GRID: 240 MVA
#   BURWELL GRID: 180 MVA
#   ...

EPN Three-Winding Transformers (Table 2b)
Total records: 31
------------------------------------------------------------
  Chelmsford North Grid 132kV: 78/60 MVA
  Chelmsford North Grid 132kV: 78/60 MVA
  Waltham Park 132kV: 78/60 MVA
  Bushey Mill Grid 132kV: 78/60 MVA
  EPN 0205: 117/90 MVA

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# Search for a specific substation by name
# Transformer table data has hv and lv substations, so we may need to adjust the search field based on actual dataset structure
# search for 'Ashford' in hv_sustation or lv_substation as needed based on actual dataset structure

search_results = ltds.get(
    "table_2a",
    refine={
        "licencearea": "South Eastern Power Networks (SPN)"
    },  # Exact match filter here
    where="transformer_rating_mva_winter > 50",  # Numeric filter
    limit=20,
)

print("SPN Transformers > 50 MVA")
print(f"Found: {search_results.total_count} records")
print("-" * 60)

for record in search_results.records[:10]:
    fields = record.fields
    gsp = fields.get("gridsupplypoint", "N/A")
    winter_rating = fields.get("transformer_rating_mva_winter", "N/A")
    summer_rating = fields.get("transformer_rating_mva_summer", "N/A")
    voltage_hv = fields.get("voltage_hv", "N/A")
    voltage_lv = fields.get("voltage_lv", "N/A")
    print(
        f"  {gsp}: {winter_rating}/{summer_rating} MVA ({voltage_hv}/{voltage_lv} kV)"
    )

# Expected output:
# SPN Transformers > 50 MVA
# Found: 15 records
# ------------------------------------------------------------
#   Ashford Grid 33kV: 90/75 MVA (132/33 kV)
#   Canterbury Grid: 120/100 MVA (132/33 kV)
#   ...
# Search for a specific substation by name
# Transformer table data has hv and lv substations, so we may need to adjust the search field based on actual dataset structure
# search for 'Ashford' in hv_sustation or lv_substation as needed based on actual dataset structure

search_results = ltds.get(
    "table_2a",
    refine={
        "licencearea": "South Eastern Power Networks (SPN)"
    },  # Exact match filter here
    where="transformer_rating_mva_winter > 50",  # Numeric filter
    limit=20,
)

print("SPN Transformers > 50 MVA")
print(f"Found: {search_results.total_count} records")
print("-" * 60)

for record in search_results.records[:10]:
    fields = record.fields
    gsp = fields.get("gridsupplypoint", "N/A")
    winter_rating = fields.get("transformer_rating_mva_winter", "N/A")
    summer_rating = fields.get("transformer_rating_mva_summer", "N/A")
    voltage_hv = fields.get("voltage_hv", "N/A")
    voltage_lv = fields.get("voltage_lv", "N/A")
    print(
        f"  {gsp}: {winter_rating}/{summer_rating} MVA ({voltage_hv}/{voltage_lv} kV)"
    )

# Expected output:
# SPN Transformers > 50 MVA
# Found: 15 records
# ------------------------------------------------------------
#   Ashford Grid 33kV: 90/75 MVA (132/33 kV)
#   Canterbury Grid: 120/100 MVA (132/33 kV)
#   ...

SPN Transformers > 50 MVA
Found: 0 records
------------------------------------------------------------

6. Table 5 - Generation Data¶

Table 5 shows the capacity of existing distributed generation connected at each primary substation. This includes:

Solar PV installations
Wind generation
Battery storage
Other distributed energy resources

Essential for understanding renewable energy penetration and export constraints.

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# Get generation data for a licence area
generation = ltds.get_table_5(licence_area="EPN", limit=10)

print("EPN Generation Capacity (Table 5)")
print(f"Total records: {generation.total_count}")
print("-" * 60)

for record in generation.records[:5]:
    fields = record.fields
    gridsupplypoint = fields.get("gridsupplypoint", "Unknown")
    tech = fields.get("fuel_type", "Unknown")
    capacity = fields.get("installedcapacity_mva", "N/A")
    print(f"  {gridsupplypoint} ({tech}): {capacity} MVA")

# Expected output:
# EPN Generation Capacity (Table 5)
# Total records: 1247
# ------------------------------------------------------------
#   ALDEBURGH PRIMARY (Solar): 5.2 MW
#   ALTON WATER PRIMARY (Wind): 12.0 MW
#   ...
# Get generation data for a licence area
generation = ltds.get_table_5(licence_area="EPN", limit=10)

print("EPN Generation Capacity (Table 5)")
print(f"Total records: {generation.total_count}")
print("-" * 60)

for record in generation.records[:5]:
    fields = record.fields
    gridsupplypoint = fields.get("gridsupplypoint", "Unknown")
    tech = fields.get("fuel_type", "Unknown")
    capacity = fields.get("installedcapacity_mva", "N/A")
    print(f"  {gridsupplypoint} ({tech}): {capacity} MVA")

# Expected output:
# EPN Generation Capacity (Table 5)
# Total records: 1247
# ------------------------------------------------------------
#   ALDEBURGH PRIMARY (Solar): 5.2 MW
#   ALTON WATER PRIMARY (Wind): 12.0 MW
#   ...

EPN Generation Capacity (Table 5)
Total records: 493
------------------------------------------------------------
  Amersham (Energy Storage (>=1MW)): 210.5 MVA
  Amersham (Energy Storage (>=1MW)): 104.2 MVA
  BIGGLESWADE 132KV (Photovoltaic (>=1MW)): 157.7 MVA
  Bramford (Energy Storage (>=1MW)): 147.4 MVA
  Brimsdown (EPN) (Photovoltaic (>=1MW)): 157.8 MVA

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# Filter by technology type - Solar generation
solar = ltds.get("table_5", where="fuel_type LIKE '%Photovoltaic%'", limit=10)
print("Solar Generation Across All Licence Areas")
print(f"Total records: {solar.total_count}")
print("-" * 60)

for record in solar.records[:5]:
    fields = record.fields
    area = fields.get("licencearea", "?")
    substation = fields.get("substation", "Unknown")
    capacity = fields.get("installedcapacity_mva", "N/A")
    print(f"  [{area}] {substation}: {capacity} MVA")

# Expected output:
# Solar Generation Across All Licence Areas
# Total records: 892
# ------------------------------------------------------------
#   [South Eastern Power Networks (SPN)] None: 5.7 MVA
#   [South Eastern Power Networks (SPN)] Walton 11kV: 1.3 MVA
#   ...
# Filter by technology type - Solar generation
solar = ltds.get("table_5", where="fuel_type LIKE '%Photovoltaic%'", limit=10)
print("Solar Generation Across All Licence Areas")
print(f"Total records: {solar.total_count}")
print("-" * 60)

for record in solar.records[:5]:
    fields = record.fields
    area = fields.get("licencearea", "?")
    substation = fields.get("substation", "Unknown")
    capacity = fields.get("installedcapacity_mva", "N/A")
    print(f"  [{area}] {substation}: {capacity} MVA")

# Expected output:
# Solar Generation Across All Licence Areas
# Total records: 892
# ------------------------------------------------------------
#   [South Eastern Power Networks (SPN)] None: 5.7 MVA
#   [South Eastern Power Networks (SPN)] Walton 11kV: 1.3 MVA
#   ...

Solar Generation Across All Licence Areas
Total records: 292
------------------------------------------------------------
  [South Eastern Power Networks (SPN)] None: 5.7 MVA
  [South Eastern Power Networks (SPN)] Walton 11kV: 1.3 MVA
  [Eastern Power Networks (EPN)] None: 157.7 MVA
  [Eastern Power Networks (EPN)] None: 157.8 MVA
  [Eastern Power Networks (EPN)] None: 404.0 MVA

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# Combine filters: Wind generation in SPN
spn_wind = ltds.get(
    "table_5",
    where='licencearea="South Eastern Power Networks (SPN)" AND fuel_type LIKE "%Wind%"',
    limit=10,
)

print("Wind Generation in South Eastern Power Networks")
print(f"Total records: {spn_wind.total_count}")
print("-" * 60)

for record in spn_wind.records:
    fields = record.fields
    print(f"  {fields.get('substation')}: {fields.get('installedcapacity_mva')} MVA")

# Expected output:
# Wind Generation in South Eastern Power Networks
# Total records: 23
# ------------------------------------------------------------
#   DUNGENESS PRIMARY: 25.0 MW
#   RYE PRIMARY: 8.5 MW
#   ...
# Combine filters: Wind generation in SPN
spn_wind = ltds.get(
    "table_5",
    where='licencearea="South Eastern Power Networks (SPN)" AND fuel_type LIKE "%Wind%"',
    limit=10,
)

print("Wind Generation in South Eastern Power Networks")
print(f"Total records: {spn_wind.total_count}")
print("-" * 60)

for record in spn_wind.records:
    fields = record.fields
    print(f"  {fields.get('substation')}: {fields.get('installedcapacity_mva')} MVA")

# Expected output:
# Wind Generation in South Eastern Power Networks
# Total records: 23
# ------------------------------------------------------------
#   DUNGENESS PRIMARY: 25.0 MW
#   RYE PRIMARY: 8.5 MW
#   ...

Wind Generation in South Eastern Power Networks
Total records: 6
------------------------------------------------------------
  None: 331.6 MVA
  Herne Bay Grid 33kV: 146.8 MVA
  None: 67.4 MVA
  Eastchurch Prison 6.6kV: 4.8 MVA
  Sheerness Grid 33kV: 20.5 MVA
  Polegate Grid 33kV: 7.5 MVA

7. Table 6 - Connection Interest¶

Table 6 indicates the level of new connection interest at each primary substation. This shows the pipeline of:

Queued generation connections
Queued demand connections
Accepted but not yet connected projects

Critical for understanding future network constraints and development hotspots.

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# Get connection interest data
connections = ltds.get_table_6(licence_area="LPN", limit=10)

print("London Power Networks - Connection Interest (Table 6)")
print(f"Total records: {connections.total_count}")
print("-" * 60)

for record in connections.records[:5]:
    fields = record.fields
    substation = fields.get("substation", "Unknown")
    gen_queue = fields.get("generation_numbers_received_total_capacity", "N/A")
    demand_queue = fields.get("demand_numbers_received_total_capacity", "N/A")
    print(f"  {substation}:")
    print(f"    Generation queue: {gen_queue} MW")
    print(f"    Demand queue: {demand_queue} MW")

# Expected output:
# London Power Networks - Connection Interest (Table 6)
# Total records: 187
# ------------------------------------------------------------
#   ABBEY ROAD PRIMARY:
#     Generation queue: 2.5 MW
#     Demand queue: 15.0 MW
#   ...
# Get connection interest data
connections = ltds.get_table_6(licence_area="LPN", limit=10)

print("London Power Networks - Connection Interest (Table 6)")
print(f"Total records: {connections.total_count}")
print("-" * 60)

for record in connections.records[:5]:
    fields = record.fields
    substation = fields.get("substation", "Unknown")
    gen_queue = fields.get("generation_numbers_received_total_capacity", "N/A")
    demand_queue = fields.get("demand_numbers_received_total_capacity", "N/A")
    print(f"  {substation}:")
    print(f"    Generation queue: {gen_queue} MW")
    print(f"    Demand queue: {demand_queue} MW")

# Expected output:
# London Power Networks - Connection Interest (Table 6)
# Total records: 187
# ------------------------------------------------------------
#   ABBEY ROAD PRIMARY:
#     Generation queue: 2.5 MW
#     Demand queue: 15.0 MW
#   ...

London Power Networks - Connection Interest (Table 6)
Total records: 133
------------------------------------------------------------
  Barking 132kV GIS:
    Generation queue: None MW
    Demand queue: 50.0 MW
  Nelson St 11kV:
    Generation queue: None MW
    Demand queue: 20.2 MW
  Sydenham Park 11kV:
    Generation queue: None MW
    Demand queue: 1.8 MW
  City Road 132kV:
    Generation queue: None MW
    Demand queue: 1.5 MW
  Devonshire Square 11kV:
    Generation queue: None MW
    Demand queue: 5.8 MW

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# Search for connection interest at specific substations
battersea = ltds.get("table_6", where='substation LIKE "%Romford%"')

print("Connection Interest - Substations matching 'ROMFORD'")
print(f"Found: {battersea.total_count} records")
print("-" * 60)

for record in battersea.records:
    fields = record.fields
    print(f"\n  {fields.get('substation')}")
    for key, value in fields.items():
        if key != "substation" and value is not None:
            print(f"    {key}: {value}")

# Expected output:
# Connection Interest - Substations matching 'ROMFORD'
# Found: 2 records
# ------------------------------------------------------------
#   ROMFORD PRIMARY
#     licence_area: LPN
#     generation_queue_mw: 5.0
#     demand_queue_mw: 25.0
#   ...
# Search for connection interest at specific substations
battersea = ltds.get("table_6", where='substation LIKE "%Romford%"')

print("Connection Interest - Substations matching 'ROMFORD'")
print(f"Found: {battersea.total_count} records")
print("-" * 60)

for record in battersea.records:
    fields = record.fields
    print(f"\n  {fields.get('substation')}")
    for key, value in fields.items():
        if key != "substation" and value is not None:
            print(f"    {key}: {value}")

# Expected output:
# Connection Interest - Substations matching 'ROMFORD'
# Found: 2 records
# ------------------------------------------------------------
#   ROMFORD PRIMARY
#     licence_area: LPN
#     generation_queue_mw: 5.0
#     demand_queue_mw: 25.0
#   ...

Connection Interest - Substations matching 'ROMFORD'
Found: 2 records
------------------------------------------------------------

  Romford Primary 11kV
    gridsupplypoint: Barking 132kV
    proposed_connection_voltage_kv: 11
    status_of_connection: Connection offers made (not yet accepted by customer)
    demand_numbers_received_total_number: 1.0
    demand_numbers_received_total_capacity: 4.4
    spatial_coordinates: {'lon': 0.181853, 'lat': 51.574901}
    sitefunctionallocation: EPN-S0000000H7150
    licencearea: Eastern Power Networks (EPN)

  Romford Primary 11kV
    gridsupplypoint: Barking 132kV
    proposed_connection_voltage_kv: 11
    status_of_connection: Budget Estimates Provided
    demand_numbers_received_total_number: 2.0
    demand_numbers_received_total_capacity: 2.8
    spatial_coordinates: {'lon': 0.181853, 'lat': 51.574901}
    sitefunctionallocation: EPN-S0000000H7150
    licencearea: Eastern Power Networks (EPN)

8. Infrastructure Projects¶

The infrastructure projects dataset contains information about UK Power Networks' network investment and reinforcement schemes, including:

Network reinforcement projects
New connection schemes
Asset replacement programmes
Innovation projects

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# Get infrastructure projects with convenience function
projects = ltds.get_projects(limit=10)

print("UKPN Infrastructure Projects")
print(f"Total records: {projects.total_count}")
print("-" * 60)

for record in projects.records[:5]:
    fields = record.fields
    name = fields.get("ltds_name", "Unknown")
    dno = fields.get("dno", "N/A")
    asset_type = fields.get("asset_type_or_quantity", "Unknown")
    completion_year = fields.get("expected_completion_year", "TBD")
    justification = fields.get("justification_for_the_need", "N/A")
    print(f"  {name}")
    print(f"    DNO: {dno}")
    print(f"    Asset: {asset_type}")
    print(f"    Justification: {justification}")
    print(f"    Expected Completion: {completion_year}")
    print()

# Expected output:
# UKPN Infrastructure Projects
# Total records: 342
# ------------------------------------------------------------
#   Burwell Local Grid 33kV
#     DNO: EPN
#     Asset: 1 x 33kV CB, 2 x grid transformers uprated
#     Justification: Load related reinforcement
#     Expected Completion: 2026
#
#   Canterbury Grid Upgrade
#     DNO: SPN
#     Asset: 2 x 132/33kV transformers
#     Justification: Asset replacement
#     Expected Completion: 2025
#   ...
# Get infrastructure projects with convenience function
projects = ltds.get_projects(limit=10)

print("UKPN Infrastructure Projects")
print(f"Total records: {projects.total_count}")
print("-" * 60)

for record in projects.records[:5]:
    fields = record.fields
    name = fields.get("ltds_name", "Unknown")
    dno = fields.get("dno", "N/A")
    asset_type = fields.get("asset_type_or_quantity", "Unknown")
    completion_year = fields.get("expected_completion_year", "TBD")
    justification = fields.get("justification_for_the_need", "N/A")
    print(f"  {name}")
    print(f"    DNO: {dno}")
    print(f"    Asset: {asset_type}")
    print(f"    Justification: {justification}")
    print(f"    Expected Completion: {completion_year}")
    print()

# Expected output:
# UKPN Infrastructure Projects
# Total records: 342
# ------------------------------------------------------------
#   Burwell Local Grid 33kV
#     DNO: EPN
#     Asset: 1 x 33kV CB, 2 x grid transformers uprated
#     Justification: Load related reinforcement
#     Expected Completion: 2026
#
#   Canterbury Grid Upgrade
#     DNO: SPN
#     Asset: 2 x 132/33kV transformers
#     Justification: Asset replacement
#     Expected Completion: 2025
#   ...

UKPN Infrastructure Projects
Total records: 41
------------------------------------------------------------
  Burwell Local Grid 33kV
    DNO: EPN
    Asset: 1 x 33kV CB, 2 x grid transformers uprated
    Justification: Load related reinforcement
    Expected Completion: 2026

  East Cambridge Grid
    DNO: EPN
    Asset: Establish new Grid substation
    Justification: Load related reinforcement
    Expected Completion: 2027

  Fulbourn Grid - Sawston 33kV
    DNO: EPN
    Asset: Additional 33kV Circuit
    Justification: Load related reinforcement
    Expected Completion: 2026

  Radnor Primary
    DNO: EPN
    Asset: Installation of a third 33kV circuit and third primary transformer
    Justification: Load related reinforcement
    Expected Completion: 2026

  Brockenhurst Primary 11kV/Mill Hill Primary 11kV
    DNO: EPN
    Asset: Asset replacement. Increase in Transformer Capacity and 11kV Network Reinforcement
    Justification: Asset replacement
    Expected Completion: 2026

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# Filter projects by local authority using convenience function
islington_projects = ltds.get_projects(local_authority="Islington", limit=10)

print("Islington Infrastructure Projects")
print(f"Total: {islington_projects.total_count}")
print("-" * 60)

for record in islington_projects.records[:5]:
    fields = record.fields
    name = fields.get("ltds_name", "Unknown")
    asset_type = fields.get("asset_type_or_quantity", "Unknown")
    completion = fields.get("expected_completion_year", "TBD")
    substation = fields.get("substation_or_circuit_ple_name", "Unknown")
    justification = fields.get("justification_for_the_need", "N/A")
    print(f"  {name}")
    print(f"    Substation: {substation}")
    print(f"    Asset: {asset_type}")
    print(f"    Justification: {justification}")
    print(f"    Expected completion: {completion}")
    print()

# Expected output:
# Islington Infrastructure Projects
# Total: 5
# ------------------------------------------------------------
#   Holloway Road Primary Reinforcement
#     Substation: Holloway Road Primary
#     Asset: 1 x 11kV transformer
#     Justification: Load related reinforcement
#     Expected completion: 2026
#   ...
# Filter projects by local authority using convenience function
islington_projects = ltds.get_projects(local_authority="Islington", limit=10)

print("Islington Infrastructure Projects")
print(f"Total: {islington_projects.total_count}")
print("-" * 60)

for record in islington_projects.records[:5]:
    fields = record.fields
    name = fields.get("ltds_name", "Unknown")
    asset_type = fields.get("asset_type_or_quantity", "Unknown")
    completion = fields.get("expected_completion_year", "TBD")
    substation = fields.get("substation_or_circuit_ple_name", "Unknown")
    justification = fields.get("justification_for_the_need", "N/A")
    print(f"  {name}")
    print(f"    Substation: {substation}")
    print(f"    Asset: {asset_type}")
    print(f"    Justification: {justification}")
    print(f"    Expected completion: {completion}")
    print()

# Expected output:
# Islington Infrastructure Projects
# Total: 5
# ------------------------------------------------------------
#   Holloway Road Primary Reinforcement
#     Substation: Holloway Road Primary
#     Asset: 1 x 11kV transformer
#     Justification: Load related reinforcement
#     Expected completion: 2026
#   ...

Islington Infrastructure Projects
Total: 1
------------------------------------------------------------
  Islington 132kV
    Substation: Islington 132kV
    Asset: Replacement of Super Grid Transformers supplying site
    Justification: Asset replacement
    Expected completion: 2028

9. Exporting Data to CSV¶

The LTDS orchestrator includes an export() function for downloading data in various formats including CSV, JSON, and Excel.

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# Export Table 3a to CSV
from pathlib import Path

csv_data = ltds.export("table_3a", format="csv", limit=100)

# Save to file only when save_dir is set
save_dir = None  # Set to a directory (e.g. "exports") to enable writing files.
if save_dir:
    output_file = Path(save_dir) / "ltds_table_3a.csv"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "wb") as f:
        f.write(csv_data)
    print(f"Exported {len(csv_data)} bytes to {output_file}")
else:
    print(
        f"Exported {len(csv_data)} bytes (file save skipped; set save_dir to enable writing)."
    )

# Preview first few lines
print("\nPreview (first 500 characters):")
print(csv_data.decode("utf-8")[:500])

# Expected output:
# Exported 12453 bytes
#
# Preview (first 500 characters):
# licence_area;substation;year;peak_demand_mw;...
# EPN;ALDEBURGH PRIMARY;2023;12.5;...
# ...
# Export Table 3a to CSV
from pathlib import Path

csv_data = ltds.export("table_3a", format="csv", limit=100)

# Save to file only when save_dir is set
save_dir = None  # Set to a directory (e.g. "exports") to enable writing files.
if save_dir:
    output_file = Path(save_dir) / "ltds_table_3a.csv"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "wb") as f:
        f.write(csv_data)
    print(f"Exported {len(csv_data)} bytes to {output_file}")
else:
    print(
        f"Exported {len(csv_data)} bytes (file save skipped; set save_dir to enable writing)."
    )

# Preview first few lines
print("\nPreview (first 500 characters):")
print(csv_data.decode("utf-8")[:500])

# Expected output:
# Exported 12453 bytes
#
# Preview (first 500 characters):
# licence_area;substation;year;peak_demand_mw;...
# EPN;ALDEBURGH PRIMARY;2023;12.5;...
# ...

Exported 15099 bytes (file save skipped; set save_dir to enable writing).

Preview (first 500 characters):
gridsupplypoint;substation;season;maximum_demand_24_25_mw;maximum_demand_24_25_pf;forecast_m_d_mw_25_26;forecast_m_d_mw_26_27;forecast_m_d_mw_27_28;forecast_m_d_mw_28_29;forecast_m_d_mw_29_30;firm_capacity_mw;minimum_load_scaling_factor;unutilised_capacity_percent;functional_location;licencearea;id
Amersham;Coldharbour Farm 11kV;Summer;2.9;0.96;2.9;2.9;2.9;2.8;2.8;11.5;28.8%;74.78260869565217;EPN-S0000000H8009;Eastern Power Networks (EPN);4
Barking 132kV;Chase Cross Primary 11kV;Winter;14.6;0

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# Export generation data to CSV
from pathlib import Path

gen_csv = ltds.export("table_5", format="csv", limit=200)

save_dir = None  # Set to a directory (e.g. "exports") to enable writing files.
if save_dir:
    output_file = Path(save_dir) / "ltds_generation.csv"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "wb") as f:
        f.write(gen_csv)
    print(f"Exported generation data: {len(gen_csv)} bytes to {output_file}")
else:
    print(
        f"Exported generation data: {len(gen_csv)} bytes (file save skipped; set save_dir to enable writing)."
    )

# Expected output:
# Exported generation data: 24891 bytes
# Export generation data to CSV
from pathlib import Path

gen_csv = ltds.export("table_5", format="csv", limit=200)

save_dir = None  # Set to a directory (e.g. "exports") to enable writing files.
if save_dir:
    output_file = Path(save_dir) / "ltds_generation.csv"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "wb") as f:
        f.write(gen_csv)
    print(f"Exported generation data: {len(gen_csv)} bytes to {output_file}")
else:
    print(
        f"Exported generation data: {len(gen_csv)} bytes (file save skipped; set save_dir to enable writing)."
    )

# Expected output:
# Exported generation data: 24891 bytes

Exported generation data: 26627 bytes (file save skipped; set save_dir to enable writing).

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# Export to JSON format
import json

json_data = ltds.export("table_6", format="json", limit=10)

# Parse and pretty-print
data = json.loads(json_data)
print(f"Exported {len(data)} records as JSON")
print("\nFirst record:")
print(json.dumps(data[0], indent=2))

# Expected output:
# Exported 10 records as JSON
#
# First record:
# {
#   "licence_area": "EPN",
#   "substation": "ALDEBURGH PRIMARY",
#   "generation_queue_mw": 1.5,
#   ...
# }
# Export to JSON format
import json

json_data = ltds.export("table_6", format="json", limit=10)

# Parse and pretty-print
data = json.loads(json_data)
print(f"Exported {len(data)} records as JSON")
print("\nFirst record:")
print(json.dumps(data[0], indent=2))

# Expected output:
# Exported 10 records as JSON
#
# First record:
# {
#   "licence_area": "EPN",
#   "substation": "ALDEBURGH PRIMARY",
#   "generation_queue_mw": 1.5,
#   ...
# }

Exported 10 records as JSON

First record:
{
  "gridsupplypoint": "Barking 132kV",
  "substation": "Romford Primary 11kV",
  "proposed_connection_voltage_kv": "11",
  "status_of_connection": "Connection offers made (not yet accepted by customer)",
  "demand_numbers_received_total_number": 1.0,
  "demand_numbers_received_total_capacity": 4.4,
  "generation_numbers_received_total_number": null,
  "generation_numbers_received_total_capacity": null,
  "spatial_coordinates": {
    "lon": 0.181853,
    "lat": 51.574901
  },
  "sitefunctionallocation": "EPN-S0000000H7150",
  "licencearea": "Eastern Power Networks (EPN)",
  "id": 4
}

Loading CSV into pandas¶

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# Load exported CSV into pandas for analysis
try:
    from io import BytesIO

    import pandas as pd

    # Export and load directly
    csv_bytes = ltds.export("table_3a", format="csv", limit=500)
    df = pd.read_csv(BytesIO(csv_bytes), sep=";")

    print(f"Loaded DataFrame: {df.shape[0]} rows x {df.shape[1]} columns")
    print(f"\nColumns: {list(df.columns)}")
    print("\nFirst 5 rows:")
    display(df.head())

except ImportError:
    print("pandas not installed. Install with: pip install pandas")

# Expected output:
# Loaded DataFrame: 500 rows x 12 columns
#
# Columns: ['licence_area', 'substation', 'year', 'peak_demand_mw', ...]
#
# First 5 rows:
#   licence_area          substation  year  peak_demand_mw  ...
# 0          EPN  ALDEBURGH PRIMARY  2023            12.5  ...
# ...
# Load exported CSV into pandas for analysis
try:
    from io import BytesIO

    import pandas as pd

    # Export and load directly
    csv_bytes = ltds.export("table_3a", format="csv", limit=500)
    df = pd.read_csv(BytesIO(csv_bytes), sep=";")

    print(f"Loaded DataFrame: {df.shape[0]} rows x {df.shape[1]} columns")
    print(f"\nColumns: {list(df.columns)}")
    print("\nFirst 5 rows:")
    display(df.head())

except ImportError:
    print("pandas not installed. Install with: pip install pandas")

# Expected output:
# Loaded DataFrame: 500 rows x 12 columns
#
# Columns: ['licence_area', 'substation', 'year', 'peak_demand_mw', ...]
#
# First 5 rows:
#   licence_area          substation  year  peak_demand_mw  ...
# 0          EPN  ALDEBURGH PRIMARY  2023            12.5  ...
# ...

Loaded DataFrame: 500 rows x 16 columns

Columns: ['gridsupplypoint', 'substation', 'season', 'maximum_demand_24_25_mw', 'maximum_demand_24_25_pf', 'forecast_m_d_mw_25_26', 'forecast_m_d_mw_26_27', 'forecast_m_d_mw_27_28', 'forecast_m_d_mw_28_29', 'forecast_m_d_mw_29_30', 'firm_capacity_mw', 'minimum_load_scaling_factor', 'unutilised_capacity_percent', 'functional_location', 'licencearea', 'id']

First 5 rows:

	gridsupplypoint	substation	season	maximum_demand_24_25_mw	maximum_demand_24_25_pf	forecast_m_d_mw_25_26	forecast_m_d_mw_26_27	forecast_m_d_mw_27_28	forecast_m_d_mw_28_29	forecast_m_d_mw_29_30	firm_capacity_mw	minimum_load_scaling_factor	unutilised_capacity_percent	functional_location	licencearea	id
0	Amersham	Coldharbour Farm 11kV	Summer	2.9	0.96	2.9	2.9	2.9	2.8	2.8	11.5	28.8%	74.782609	EPN-S0000000H8009	Eastern Power Networks (EPN)	4
1	Barking 132kV	Chase Cross Primary 11kV	Winter	14.6	0.96	14.8	15.6	15.8	16.2	16.8	18.5	NaN	21.081081	EPN-S0000000H7142	Eastern Power Networks (EPN)	15
2	Barking 132kV	Crowlands Grid 33kV	Winter	54.6	0.96	54.8	76.1	88.3	100.8	113.5	109.7	NaN	50.227894	EPN-S0000000D7124	Eastern Power Networks (EPN)	17
3	Barking 132kV	Romford Primary 11kV	Winter	28.8	0.96	28.7	30.3	30.8	31.5	32.3	42.3	NaN	31.914894	EPN-S0000000H7150	Eastern Power Networks (EPN)	19
4	Barking 132kV	Selinas Ln Primary 11kV	Winter	13.5	0.96	13.6	14.7	15.3	16.0	16.7	25.0	NaN	46.000000	EPN-S0000000H7133	Eastern Power Networks (EPN)	21

Working with Async (Advanced)¶

For Jupyter notebooks or async applications, you can use the async versions of the functions:

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# Async version - works natively in Jupyter
response = await ltds.get_async("table_3a", limit=5)

print("Async fetch complete!")
print(f"Records: {len(response.records)}")

# Expected output:
# Async fetch complete!
# Records: 5
# Async version - works natively in Jupyter
response = await ltds.get_async("table_3a", limit=5)

print("Async fetch complete!")
print(f"Records: {len(response.records)}")

# Expected output:
# Async fetch complete!
# Records: 5

Async fetch complete!
Records: 5

Summary¶

You've learned how to:

Import the LTDS orchestrator with from ukpyn import ltds
List available datasets using ltds.available_datasets
Fetch data using the generic ltds.get() or table-specific functions
Filter by licence area (EPN, LPN, SPN) and other parameters
Access Table 3a for peak demand data
Access Tables 2a/2b for transformer specifications
Access Table 5 for generation capacity by technology
Access Table 6 for connection interest pipelines
Get infrastructure projects and filter by status
Export data to CSV, JSON, and other formats

Function Reference¶

Function	Description
`ltds.get(dataset, ...)`	Generic fetch for any LTDS dataset
`ltds.get_table_2a(...)`	Transformer data (2-winding)
`ltds.get_table_2b(...)`	Transformer data (3-winding)
`ltds.get_table_3a(...)`	Observed peak demand
`ltds.get_table_5(...)`	Generation capacity
`ltds.get_table_6(...)`	Connection interest
`ltds.get_projects(...)`	Infrastructure projects
`ltds.get_cim(...)`	Common Information Model data
`ltds.export(...)`	Export data to CSV/JSON/Excel

Next Steps¶

Explore the 03-analysis-patterns.ipynb for data analysis with pandas
Check out the examples folder for community contributions
Visit the UK Power Networks Open Data Portal for more datasets