Scenario Design#

This guide covers how to design effective scenarios for your analysis.

Scenario Types#

Historical Scenarios#

Model past years using actual data (not FES projections).

Historical_2022:
  description: "Validation against 2022 outturn"
  modelled_year: 2022
  renewables_year: 2022    # Must match for consistency
  demand_year: 2022
  network_model: "ETYS"
  # No FES_scenario needed

Use cases:

  • Model validation against historical outturn

  • Understanding past system behavior

  • Baseline for comparison

Data sources:

  • Thermal generation: DUKES statistics

  • Renewables: REPD (Renewable Energy Planning Database)

  • Demand: ESPENI profiles

Future Scenarios#

Project future years using NESO Future Energy Scenarios.

Future_2035:
  description: "2035 under Holistic Transition"
  modelled_year: 2035
  renewables_year: 2019    # Historical weather pattern
  demand_year: 2035
  network_model: "ETYS"
  FES_year: 2024
  FES_scenario: "Holistic Transition"

Data sources:

  • Thermal/Renewables/Storage: FES projections

  • Demand: FES annual totals with historical profiles

  • Network: ETYS base topology with planned upgrades (optional)

Tip

ETYS network upgrades are controlled by etys_upgrades.enabled and etys_upgrades.upgrade_year. The ETYS publication year is set via etys.year (supports 2022, 2023, 2024). See Configuration Reference for details.

Market Scenarios#

Market scenarios are historical or future scenarios with an additional market: block. They still build the same physical network, but then branch into a copperplate wholesale solve and, unless wholesale_only: true, a constrained balancing redispatch solve.

Historical_2024_market:
  description: "Historical 2024 wholesale and balancing market run"
  modelled_year: 2024
  renewables_year: 2024
  demand_year: 2024
  network_model: "ETYS"
  solve_period:
    enabled: true
    start: "2024-01-01 00:00"
    end: "2024-01-07 23:00"
  market:
    enabled: true
    wholesale:
      mode: "rolling_day_ahead"
      window_hours: 24
    balancing:
      mode: "rolling"
      window_hours: 1
      bid_offer_source: "auto"

Use market.wholesale_only: true for wholesale price and schedule analysis without BM redispatch. See Market Dispatch for detailed configuration.

FES Pathway Selection#

Holistic Transition#

Balanced approach with multiple low-carbon vectors.

FES_scenario: "Holistic Transition"

  • Moderate electrification

  • Green hydrogen development

  • Biomass and CCS

  • Good for “central” scenarios

Electric Engagement#

Maximum electrification pathway.

FES_scenario: "Electric Engagement"

  • Very high electricity demand

  • Extensive heat pump deployment

  • Limited hydrogen role

  • Tests grid capacity limits

Hydrogen Evolution#

Hydrogen-centric decarbonization.

FES_scenario: "Hydrogen Evolution"

  • Blue hydrogen from natural gas + CCS

  • Hydrogen for heat and transport

  • Lower electricity demand growth

  • Industrial hydrogen clusters

Falling Short#

Slower progress scenario.

FES_scenario: "Falling Short"

  • Misses 2050 net zero

  • Tests system resilience

  • Useful for stress testing

Weather Year Selection#

The renewables_year determines the weather pattern for renewable generation.

renewables_year: 2019  # Choose based on your analysis goals

High Wind Years#

  • 2015, 2019: Above-average wind output

Low Wind Years#

  • 2010, 2021: Below-average wind

Extreme Events#

  • 2018: “Beast from the East” cold snap

  • Check ERA5 data for specific events

Tip

For robust analysis, run multiple weather years and compare results.

Time Period Selection#

Full Year#

For complete annual analysis:

solve_period:
  enabled: false

Representative Weeks#

For faster analysis:

solve_period:
  enabled: true
  start: "2035-01-13 00:00"   # Winter week
  end: "2035-01-19 23:00"

Typical Periods#

Period

Dates

Characteristics

Winter peak

Jan 10-17

High demand, low solar

Summer low

Aug 1-8

Low demand, high solar

Autumn wind

Nov 1-8

High wind, moderate demand

Spring transition

Apr 15-22

Variable conditions

Stress Testing#

For extreme conditions:

# Find the peak demand day in your weather year
solve_period:
  enabled: true
  start: "2035-01-15 00:00"
  end: "2035-01-22 23:00"

Scenario Checklist#

Before running a new scenario, verify:

  • modelled_year makes sense for your analysis

  • renewables_year has available cutout data

  • FES_scenario is valid (for future years)

  • solve_period is appropriate for your question

  • Solver settings are configured

  • Run python scripts/validate_scenarios.py