Flexibility Markets with ukpyn¶

This tutorial covers working with UK Power Networks flexibility market data using the flexibility orchestrator.

What you'll learn:

1. What flexibility markets are and why dispatches matter
2. Using the flexibility module with simple imports
3. Listing available datasets
4. Getting dispatch events with filtering
5. Using the generic flexibility.get() function
6. Exporting data to different formats

Prerequisites:

• Complete 01-getting-started.ipynb first
• UKPN_API_KEY environment variable configured
• These tutorials require additional dependencies. Install them with pip install "ukpyn[all]" — see Tutorial 01 for full setup instructions

1. Introduction to Flexibility Markets¶

What are Flexibility Markets?¶

Flexibility markets are mechanisms that allow UK Power Networks to procure services from distributed energy resources (DERs) to manage network constraints. Instead of traditional network reinforcement (building new infrastructure), flexibility services offer a more cost-effective and faster solution.

What are Dispatches?¶

A dispatch event occurs when UK Power Networks calls upon a flexibility provider to deliver their contracted service. This could involve:

• Demand reduction: Reducing electricity consumption during peak periods
• Generation increase: Increasing local generation to relieve network stress
• Storage discharge: Releasing stored energy when needed

Why Do Dispatches Matter?¶

Dispatch data is valuable for:

1. Researchers: Understanding how flexibility services are utilized across the network
2. Flexibility providers: Analyzing market activity and opportunities
3. Policy makers: Evaluating the effectiveness of flexibility markets
4. Network planners: Assessing flexibility's role in managing constraints

Service Types¶

Common flexibility service types include:

Service Type	Description
Sustain	Long-duration services (typically 2+ hours)
Secure	Response services for network security
Dynamic	Fast-response flexibility
Restore	Services for network restoration

In [ ]:

import ukpyn

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

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

2. Using the Flexibility Orchestrator¶

The flexibility module provides a simple, ergonomic interface to flexibility market data.

Simple Import¶

Import the flexibility module directly from ukpyn:

In [ ]:

from ukpyn import flexibility

print("Flexibility module loaded successfully!")
print(f"Module: {flexibility.__name__}")

# Expected output:
# Flexibility module loaded successfully!
# Module: ukpyn.orchestrators.flexibility

from ukpyn import flexibility print("Flexibility module loaded successfully!") print(f"Module: {flexibility.__name__}") # Expected output: # Flexibility module loaded successfully! # Module: ukpyn.orchestrators.flexibility

Available Functions¶

The flexibility module provides several convenience functions:

Function	Description
flexibility.get()	Generic function to get any flexibility dataset
flexibility.get_dispatches()	Get dispatch events with specific filters
flexibility.get_curtailment()	Get curtailment events
flexibility.export()	Export data to CSV, JSON, or other formats

Each function has both sync and async versions (e.g., get_async(), get_dispatches_async()).

3. Listing Available Datasets¶

Before fetching data, let's see what datasets are available in the flexibility module.

In [ ]:

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

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

# Expected output:
# Available flexibility datasets:
# ----------------------------------------
#   - dispatches
#   - dispatch_events

# List available datasets print("Available flexibility datasets:") print("-" * 40) for dataset in flexibility.available_datasets: print(f" - {dataset}") # Expected output: # Available flexibility datasets: # ---------------------------------------- # - dispatches # - dispatch_events

In [ ]:

# You can also access this as a module-level attribute
print(f"Number of datasets: {len(flexibility.available_datasets)}")
print(f"\nDatasets: {flexibility.available_datasets}")

# Expected output:
# Number of datasets: 3
#
# Datasets: ['dispatches', 'dispatch_events', 'tenders']

# You can also access this as a module-level attribute print(f"Number of datasets: {len(flexibility.available_datasets)}") print(f"\nDatasets: {flexibility.available_datasets}") # Expected output: # Number of datasets: 3 # # Datasets: ['dispatches', 'dispatch_events', 'tenders']

Dataset Descriptions¶

Dataset Name	Alias	Description
dispatches	dispatch_events	Flexibility dispatch events - instances where flexibility services were called upon

Note: dispatch_events is an alias for dispatches - they return the same data.

4. Getting Dispatch Events¶

The get_dispatches() function provides a convenient way to fetch dispatch events with built-in filtering options.

In [ ]:

# Fetch recent dispatch events (no filters)
dispatches = flexibility.get_dispatches(limit=10)

print(f"Total dispatch events: {dispatches.total_count}")
print(f"Records returned: {len(dispatches.records)}")

# Expected output:
# Total dispatch events: 1234  (actual number will vary)
# Records returned: 10

# Fetch recent dispatch events (no filters) dispatches = flexibility.get_dispatches(limit=10) print(f"Total dispatch events: {dispatches.total_count}") print(f"Records returned: {len(dispatches.records)}") # Expected output: # Total dispatch events: 1234 (actual number will vary) # Records returned: 10

In [ ]:

# Display the first few records
print("Sample dispatch events:")
print("=" * 60)

for i, record in enumerate(dispatches.records[:3], 1):
    print(f"\nRecord {i} (ID: {record.id})")
    print("-" * 40)
    if record.fields:
        for key, value in record.fields.items():
            print(f"  {key}: {value}")

# Expected output:
# Sample dispatch events:
# ============================================================
#
# Record 1 (ID: abc123...)
# ----------------------------------------
#   start_time_local: 2024-06-15T10:00:00
#   product: Sustain
#   zone: EPN
#   ...

# Display the first few records print("Sample dispatch events:") print("=" * 60) for i, record in enumerate(dispatches.records[:3], 1): print(f"\nRecord {i} (ID: {record.id})") print("-" * 40) if record.fields: for key, value in record.fields.items(): print(f" {key}: {value}") # Expected output: # Sample dispatch events: # ============================================================ # # Record 1 (ID: abc123...) # ---------------------------------------- # start_time_local: 2024-06-15T10:00:00 # product: Sustain # zone: EPN # ...

Filtering by Date Range¶

Use start_date and end_date to filter dispatches within a specific time period.

Date formats accepted:

• ISO format string: '2024-01-01'
• Python date object: date(2024, 1, 1)
• Python datetime object: datetime(2024, 1, 1)

In [ ]:

# Filter by date range using string format
q1_2024 = flexibility.get_dispatches(
    start_date="2024-01-01", end_date="2024-03-31", limit=50
)

print(f"Q1 2024 dispatches: {q1_2024.total_count}")
print(f"Records returned: {len(q1_2024.records)}")

# Expected output:
# Q1 2024 dispatches: 287  (actual number will vary)
# Records returned: 50

# Filter by date range using string format q1_2024 = flexibility.get_dispatches( start_date="2024-01-01", end_date="2024-03-31", limit=50 ) print(f"Q1 2024 dispatches: {q1_2024.total_count}") print(f"Records returned: {len(q1_2024.records)}") # Expected output: # Q1 2024 dispatches: 287 (actual number will vary) # Records returned: 50

In [ ]:

# Filter using Python date objects
from datetime import date, datetime

# Using date object
jan_dispatches = flexibility.get_dispatches(
    start_date=date(2024, 1, 1), end_date=date(2024, 1, 31), limit=20
)

print(f"January 2024 dispatches: {jan_dispatches.total_count}")

# Using datetime object
recent_dispatches = flexibility.get_dispatches(
    start_date=datetime(2024, 6, 1), limit=10
)

print(f"Dispatches since June 2024: {recent_dispatches.total_count}")

# Expected output:
# January 2024 dispatches: 95  (actual numbers will vary)
# Dispatches since June 2024: 450

# Filter using Python date objects from datetime import date, datetime # Using date object jan_dispatches = flexibility.get_dispatches( start_date=date(2024, 1, 1), end_date=date(2024, 1, 31), limit=20 ) print(f"January 2024 dispatches: {jan_dispatches.total_count}") # Using datetime object recent_dispatches = flexibility.get_dispatches( start_date=datetime(2024, 6, 1), limit=10 ) print(f"Dispatches since June 2024: {recent_dispatches.total_count}") # Expected output: # January 2024 dispatches: 95 (actual numbers will vary) # Dispatches since June 2024: 450

Filtering by Service Type¶

Use product to filter dispatches by the type of flexibility service.

In [ ]:

# Get dispatches for a specific service type
sustain_dispatches = flexibility.get_dispatches(product="Peak Reduction", limit=20)

print(f"Peak Reduction service dispatches: {sustain_dispatches.total_count}")
print(f"Records returned: {len(sustain_dispatches.records)}")

# Expected output:
# Peak Reduction service dispatches: 678  (actual number will vary)
# Records returned: 20

# Get dispatches for a specific service type sustain_dispatches = flexibility.get_dispatches(product="Peak Reduction", limit=20) print(f"Peak Reduction service dispatches: {sustain_dispatches.total_count}") print(f"Records returned: {len(sustain_dispatches.records)}") # Expected output: # Peak Reduction service dispatches: 678 (actual number will vary) # Records returned: 20

In [ ]:

# Combine date range and service type filters
filtered_dispatches = flexibility.get_dispatches(
    start_date="2024-01-01", end_date="2024-06-30", product="Secure", limit=100
)

print(f"Secure dispatches in H1 2024: {filtered_dispatches.total_count}")

# Display summary
if filtered_dispatches.records:
    print("\nSample records:")
    for record in filtered_dispatches.records[:3]:
        if record.fields:
            date_val = record.fields.get("start_time_local", "N/A")
            product = record.fields.get("product", "N/A")
            print(f"  - {date_val}: {product}")

# Expected output:
# Secure dispatches in H1 2024: 312  (actual number will vary)
#
# Sample records:
#   - 2024-06-28T...: Secure
#   - 2024-06-27T...: Secure
#   - 2024-06-25T...: Secure

# Combine date range and service type filters filtered_dispatches = flexibility.get_dispatches( start_date="2024-01-01", end_date="2024-06-30", product="Secure", limit=100 ) print(f"Secure dispatches in H1 2024: {filtered_dispatches.total_count}") # Display summary if filtered_dispatches.records: print("\nSample records:") for record in filtered_dispatches.records[:3]: if record.fields: date_val = record.fields.get("start_time_local", "N/A") product = record.fields.get("product", "N/A") print(f" - {date_val}: {product}") # Expected output: # Secure dispatches in H1 2024: 312 (actual number will vary) # # Sample records: # - 2024-06-28T...: Secure # - 2024-06-27T...: Secure # - 2024-06-25T...: Secure

Pagination¶

For large result sets, use limit and offset for pagination.

In [ ]:

# Paginate through dispatch records
page_size = 25

# Page 1
page1 = flexibility.get_dispatches(limit=page_size, offset=0)
print(f"Total records: {page1.total_count}")
print(f"Page 1: records 1-{len(page1.records)}")

# Page 2
page2 = flexibility.get_dispatches(limit=page_size, offset=25)
print(f"Page 2: records 26-{25 + len(page2.records)}")

# Calculate total pages
total_pages = (page1.total_count + page_size - 1) // page_size
print(f"\nTotal pages: {total_pages}")

# Expected output:
# Total records: 1234  (actual number will vary)
# Page 1: records 1-25
# Page 2: records 26-50
#
# Total pages: 50

# Paginate through dispatch records page_size = 25 # Page 1 page1 = flexibility.get_dispatches(limit=page_size, offset=0) print(f"Total records: {page1.total_count}") print(f"Page 1: records 1-{len(page1.records)}") # Page 2 page2 = flexibility.get_dispatches(limit=page_size, offset=25) print(f"Page 2: records 26-{25 + len(page2.records)}") # Calculate total pages total_pages = (page1.total_count + page_size - 1) // page_size print(f"\nTotal pages: {total_pages}") # Expected output: # Total records: 1234 (actual number will vary) # Page 1: records 1-25 # Page 2: records 26-50 # # Total pages: 50

5. Using the Generic get() Function¶

The flexibility.get() function provides a generic way to fetch any flexibility dataset.

In [ ]:

# Use the generic get() function
data = flexibility.get("dispatches", limit=10)

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

# Expected output:
# Dataset: dispatches
# Total records: 1234  (actual number will vary)
# Records returned: 10

# Use the generic get() function data = flexibility.get("dispatches", limit=10) print("Dataset: dispatches") print(f"Total records: {data.total_count}") print(f"Records returned: {len(data.records)}") # Expected output: # Dataset: dispatches # Total records: 1234 (actual number will vary) # Records returned: 10

In [ ]:

# Using the alias 'dispatch_events' works the same way
data_alias = flexibility.get("dispatch_events", limit=5)

print("Using 'dispatch_events' alias:")
print(f"Total records: {data_alias.total_count}")

# Expected output:
# Using 'dispatch_events' alias:
# Total records: 1234  (same as 'dispatches')

# Using the alias 'dispatch_events' works the same way data_alias = flexibility.get("dispatch_events", limit=5) print("Using 'dispatch_events' alias:") print(f"Total records: {data_alias.total_count}") # Expected output: # Using 'dispatch_events' alias: # Total records: 1234 (same as 'dispatches')

In [ ]:

# Add custom ODSQL filters with the where parameter
# This gives you full control over filtering
# Note: Use actual field names from the ODP schema

data_filtered = flexibility.get(
    "dispatches",
    limit=20,
    where="start_time_local >= '2024-01-01' AND start_time_local <= '2024-06-30'",
    order_by="-start_time_local",  # Sort by date descending (newest first)
)

print(f"Filtered dispatches (H1 2024): {data_filtered.total_count}")
print("\nFirst 3 records (sorted by date, newest first):")

for record in data_filtered.records[:3]:
    if record.fields:
        date_val = record.fields.get("start_time_local", "N/A")
        print(f"  - {date_val}")

# Expected output:
# Filtered dispatches (H1 2024): 567  (actual number will vary)
#
# First 3 records (sorted by date, newest first):
#   - 2024-06-30T...
#   - 2024-06-29T...
#   - 2024-06-28T...

# Add custom ODSQL filters with the where parameter # This gives you full control over filtering # Note: Use actual field names from the ODP schema data_filtered = flexibility.get( "dispatches", limit=20, where="start_time_local >= '2024-01-01' AND start_time_local <= '2024-06-30'", order_by="-start_time_local", # Sort by date descending (newest first) ) print(f"Filtered dispatches (H1 2024): {data_filtered.total_count}") print("\nFirst 3 records (sorted by date, newest first):") for record in data_filtered.records[:3]: if record.fields: date_val = record.fields.get("start_time_local", "N/A") print(f" - {date_val}") # Expected output: # Filtered dispatches (H1 2024): 567 (actual number will vary) # # First 3 records (sorted by date, newest first): # - 2024-06-30T... # - 2024-06-29T... # - 2024-06-28T...

In [ ]:

# Select specific fields to reduce response size
# Note: Use actual field names from the ODP schema
data_selected = flexibility.get(
    "dispatches", limit=10, select="start_time_local, product, zone"
)

print("Selected fields only (start_time_local, product, zone):")
print("-" * 50)

for record in data_selected.records[:5]:
    if record.fields:
        print(f"  {record.fields}")

# Expected output:
# Selected fields only (start_time_local, product, zone):
# --------------------------------------------------
#   {'start_time_local': '2024-06-30T...', 'product': 'Sustain', 'zone': 'EPN'}
#   ...

# Select specific fields to reduce response size # Note: Use actual field names from the ODP schema data_selected = flexibility.get( "dispatches", limit=10, select="start_time_local, product, zone" ) print("Selected fields only (start_time_local, product, zone):") print("-" * 50) for record in data_selected.records[:5]: if record.fields: print(f" {record.fields}") # Expected output: # Selected fields only (start_time_local, product, zone): # -------------------------------------------------- # {'start_time_local': '2024-06-30T...', 'product': 'Sustain', 'zone': 'EPN'} # ...

Error Handling¶

Handle errors gracefully when working with the API.

In [ ]:

# Handle invalid dataset names
try:
    invalid_data = flexibility.get("invalid_dataset")
except ValueError as e:
    print(f"Error: {e}")
    print(f"\nAvailable datasets: {flexibility.available_datasets}")

# Expected output:
# Error: Unknown dataset: 'invalid_dataset'. Available datasets: dispatches, dispatch_events
#
# Available datasets: ['dispatches', 'dispatch_events']

# Handle invalid dataset names try: invalid_data = flexibility.get("invalid_dataset") except ValueError as e: print(f"Error: {e}") print(f"\nAvailable datasets: {flexibility.available_datasets}") # Expected output: # Error: Unknown dataset: 'invalid_dataset'. Available datasets: dispatches, dispatch_events # # Available datasets: ['dispatches', 'dispatch_events']

In [ ]:

# Handle invalid date formats
try:
    bad_date = flexibility.get_dispatches(start_date="not-a-date")
except ValueError as e:
    print(f"Date format error: {e}")

# Expected output:
# Date format error: Invalid date format: 'not-a-date'. Expected ISO format (YYYY-MM-DD) or datetime string.

# Handle invalid date formats try: bad_date = flexibility.get_dispatches(start_date="not-a-date") except ValueError as e: print(f"Date format error: {e}") # Expected output: # Date format error: Invalid date format: 'not-a-date'. Expected ISO format (YYYY-MM-DD) or datetime string.

6. Exporting Data¶

Export flexibility data to various formats for analysis in other tools.

In [ ]:

# Export dispatches to CSV
csv_data = flexibility.export("dispatches", format="csv", limit=100)

print(f"Exported {len(csv_data)} bytes of CSV data")
print("\nPreview (first 500 characters):")
print("-" * 60)
print(csv_data.decode("utf-8")[:500])

# Expected output:
# Exported 15234 bytes of CSV data
#
# Preview (first 500 characters):
# ------------------------------------------------------------
# date;product;zone;mw_dispatched;...
# 2024-06-30;Secure;EPN;5.2;...
# ...

# Export dispatches to CSV csv_data = flexibility.export("dispatches", format="csv", limit=100) print(f"Exported {len(csv_data)} bytes of CSV data") print("\nPreview (first 500 characters):") print("-" * 60) print(csv_data.decode("utf-8")[:500]) # Expected output: # Exported 15234 bytes of CSV data # # Preview (first 500 characters): # ------------------------------------------------------------ # date;product;zone;mw_dispatched;... # 2024-06-30;Secure;EPN;5.2;... # ...

In [ ]:

# Save exported data to a file (optional)
from pathlib import Path

csv_data = flexibility.export("dispatches", format="csv", limit=100)

save_dir = None  # Set to a directory (e.g. "exports") to enable writing files.
if save_dir:
    output_file = Path(save_dir) / "dispatches_export.csv"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "wb") as f:
        f.write(csv_data)
    print(f"Saved to {output_file}")
else:
    print("File save skipped; set save_dir to enable writing.")

# Expected output:
# Saved dispatches_export.csv

# Save exported data to a file (optional) from pathlib import Path csv_data = flexibility.export("dispatches", format="csv", limit=100) save_dir = None # Set to a directory (e.g. "exports") to enable writing files. if save_dir: output_file = Path(save_dir) / "dispatches_export.csv" output_file.parent.mkdir(parents=True, exist_ok=True) with open(output_file, "wb") as f: f.write(csv_data) print(f"Saved to {output_file}") else: print("File save skipped; set save_dir to enable writing.") # Expected output: # Saved dispatches_export.csv

In [ ]:

# Export to JSON format
import json

json_data = flexibility.export("dispatches", format="json", limit=10)

# Parse and display
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:
# {
#   "date": "2024-06-30",
#   "product": "Secure",
#   ...
# }

# Export to JSON format import json json_data = flexibility.export("dispatches", format="json", limit=10) # Parse and display 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: # { # "date": "2024-06-30", # "product": "Secure", # ... # }

In [ ]:

# Export to Excel format (optional)
from pathlib import Path

xlsx_data = flexibility.export("dispatches", format="xlsx", limit=100)

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

# Expected output:
# Saved 24567 bytes to dispatches_export.xlsx

# Export to Excel format (optional) from pathlib import Path xlsx_data = flexibility.export("dispatches", format="xlsx", limit=100) save_dir = None # Set to a directory (e.g. "exports") to enable writing files. if save_dir: output_file = Path(save_dir) / "dispatches_export.xlsx" output_file.parent.mkdir(parents=True, exist_ok=True) with open(output_file, "wb") as f: f.write(xlsx_data) print(f"Saved {len(xlsx_data)} bytes to {output_file}") else: print( f"Exported {len(xlsx_data)} bytes (file save skipped; set save_dir to enable writing)." ) # Expected output: # Saved 24567 bytes to dispatches_export.xlsx

Working with pandas (Optional)¶

Load exported data into pandas for analysis.

In [ ]:

# Load into pandas DataFrame
try:
    from io import BytesIO

    import pandas as pd

    # Export and load into pandas
    csv_data = flexibility.export("dispatches", format="csv", limit=200)

    # OpenDataSoft CSV uses semicolon separator
    df = pd.read_csv(BytesIO(csv_data), sep=";")

    print(f"DataFrame shape: {df.shape}")
    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:
# DataFrame shape: (200, 8)
#
# Columns: ['date', 'product', 'zone', 'mw_dispatched', ...]
#
# First 5 rows:
#          date product  zone  mw_dispatched ...
# 0  2024-06-30      Secure   EPN           5.2 ...
# ...

# Load into pandas DataFrame try: from io import BytesIO import pandas as pd # Export and load into pandas csv_data = flexibility.export("dispatches", format="csv", limit=200) # OpenDataSoft CSV uses semicolon separator df = pd.read_csv(BytesIO(csv_data), sep=";") print(f"DataFrame shape: {df.shape}") 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: # DataFrame shape: (200, 8) # # Columns: ['date', 'product', 'zone', 'mw_dispatched', ...] # # First 5 rows: # date product zone mw_dispatched ... # 0 2024-06-30 Secure EPN 5.2 ... # ...

In [ ]:

# Quick analysis with pandas
try:
    from io import BytesIO

    import pandas as pd

    csv_data = flexibility.export("dispatches", format="csv", limit=500)
    df = pd.read_csv(BytesIO(csv_data), sep=";")

    # Count by product (service type)
    if "product" in df.columns:
        print("Dispatches by product type:")
        print(df["product"].value_counts())

except ImportError:
    print("pandas not installed")
except Exception as e:
    print(f"Analysis error: {e}")

# Expected output:
# Dispatches by product type:
# product
# Sustain    287
# Secure     156
# Dynamic     57
# Name: count, dtype: int64

# Quick analysis with pandas try: from io import BytesIO import pandas as pd csv_data = flexibility.export("dispatches", format="csv", limit=500) df = pd.read_csv(BytesIO(csv_data), sep=";") # Count by product (service type) if "product" in df.columns: print("Dispatches by product type:") print(df["product"].value_counts()) except ImportError: print("pandas not installed") except Exception as e: print(f"Analysis error: {e}") # Expected output: # Dispatches by product type: # product # Sustain 287 # Secure 156 # Dynamic 57 # Name: count, dtype: int64

Async Usage (Advanced)¶

For applications requiring async/await, use the async versions of the functions.

In [ ]:

# Async usage in Jupyter (await works directly in notebooks)

# Get dispatches asynchronously
async_dispatches = await flexibility.get_async("dispatches", limit=10)

print(f"Async fetch: {async_dispatches.total_count} total records")
print(f"Retrieved: {len(async_dispatches.records)} records")

# Expected output:
# Async fetch: 1234 total records
# Retrieved: 10 records

# Async usage in Jupyter (await works directly in notebooks) # Get dispatches asynchronously async_dispatches = await flexibility.get_async("dispatches", limit=10) print(f"Async fetch: {async_dispatches.total_count} total records") print(f"Retrieved: {len(async_dispatches.records)} records") # Expected output: # Async fetch: 1234 total records # Retrieved: 10 records

Clean Up¶

Remove any exported files created during this tutorial.

In [ ]:

# Clean up exported files (optional)
import os

files_to_remove = ["dispatches_export.csv", "dispatches_export.xlsx"]

for filename in files_to_remove:
    if os.path.exists(filename):
        os.remove(filename)
        print(f"Removed {filename}")

print("\nCleanup complete!")

# Expected output:
# Removed dispatches_export.csv
# Removed dispatches_export.xlsx
#
# Cleanup complete!

# Clean up exported files (optional) import os files_to_remove = ["dispatches_export.csv", "dispatches_export.xlsx"] for filename in files_to_remove: if os.path.exists(filename): os.remove(filename) print(f"Removed {filename}") print("\nCleanup complete!") # Expected output: # Removed dispatches_export.csv # Removed dispatches_export.xlsx # # Cleanup complete!

Summary¶

You've learned how to:

1. Understand flexibility markets and why dispatch data matters
2. Import the flexibility module with a simple from ukpyn import flexibility
3. List available datasets using flexibility.available_datasets
4. Get dispatch events with filtering by date range and service type
5. Use the generic get() function with custom ODSQL filters
6. Export data to CSV, JSON, and Excel formats

Key Functions¶

Function	Description
flexibility.available_datasets	List available dataset names
flexibility.get(dataset, ...)	Generic data fetch
flexibility.get_dispatches(...)	Fetch dispatches with filtering
flexibility.export(dataset, format, ...)	Export data to file format

Date Filter Parameters¶

Parameter	Type	Example
start_date	str, date, datetime	'2024-01-01'
end_date	str, date, datetime	date(2024, 12, 31)
product	str	'Secure'

Next Steps¶

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