Frequently asked questions about the UK & Europe forecast methodology.
General Questions
What geographic regions does the model cover?
The model covers 15 different regions in Europe, including Great Britain, Germany, France, Spain, Portugal, Italy, Belgium, Netherlands, Austria, Switzerland, Poland, Norway, Sweden, Denmark, and Ireland. A region is defined as a country or a combination of countries that make a bidding zone (e.g., Germany and Luxembourg).
What time horizon does the forecast cover?
Our European power price forecast runs out to 2060, with 15-minute granularity for prices and dispatch outputs.
What temporal resolution is used in the model?
The model runs at 15-minute granularity to build a full time series of electricity and ancillary services prices. This aligns with European wholesale power markets, after the day-ahead market moved from hourly to 15-minute trading intervals on 30th September 2025.
Power Prices
How are day-ahead prices determined?
Day-ahead prices are determined by building a supply stack that matches supply with demand in each 15-minute period. The model considers generator costs (fuel, carbon, start-up), availability, network constraints, and interconnector flows to determine the clearing price.
How do you model intraday prices?
Intraday prices are modelled by considering forecast errors between day-ahead and actual (outturn) values for demand, wind, and solar generation. We also account for human behaviours like herding that can amplify price movements.
What drives price volatility in the model?
Price volatility is driven by several factors: renewable intermittency, demand variability, plant outages, interconnector constraints, and storage operations. The model captures both short-term (within-day) and longer-term (seasonal) volatility patterns.
Battery Storage
How is battery degradation modelled?
Battery degradation is modelled as a function of total cycles. We assume that usable capacity decreases over time based on cycling patterns. Users can toggle degradation on/off and specify repowering schedules in custom runs.
What battery durations are included?
The model includes batteries of various durations: 1h, 2h, 4h, 6h, and 8h. Each duration has different characteristics in terms of market participation and revenue potential.
How are frequency response revenues calculated?
Frequency response revenues are calculated using a dispatch model that co-optimises battery operation across Dynamic Containment, Dynamic Moderation, and Dynamic Regulation markets. Market saturation effects are included as battery capacity grows.
Model Inputs & Assumptions
Where do capacity forecasts come from?
Generation capacity forecasts are based on current installed capacities (from ENTSO-E and national data), combined with our in-house capacity expansion model that makes investment decisions based on economics and reliability requirements.
What commodity price sources are used?
Commodity prices are sourced from: forward curves from futures contracts, long-term forecasts from NESO, Deloitte, and Oxford Economics, and our internal Modo Energy hydrogen model.
How are renewable load factors determined?
Renewable load factors are derived from historical weather data using Renewables Ninja, based on a reference weather year (typically 2018). These profiles capture the resource-specific variability for wind and solar generation.
Calibration & Validation
How is the model validated?
The model is validated through extensive backtesting against historical data. We compare modelled prices with actual prices across multiple metrics: mean price levels, price distributions, intraday shapes (duck curves), and top-bottom spreads.
How are battery revenues calibrated?
Battery revenues are calibrated using the Modo Energy ME BESS Index, which tracks real-world revenues for battery assets in Great Britain. We compare modelled revenues with the 75th percentile of actual fleet performance to derive calibration factors:
- 80% — Central scenario
- 75% — Low scenario
- 85% — High scenario
Technical Details
What optimisation approach is used?
The model uses mixed-integer linear programming (MILP) for dispatch optimisation and linear programming (LP) for the capacity expansion model. Storage operations are co-optimised with generation dispatch to minimise total system cost.
How are network constraints handled?
Network constraints are modelled using a net transfer capacity (NTC) approach for interconnectors and boundary flow limits for internal constraints. The model captures asymmetric capacity limits based on direction of power flow.
What is the model’s computational approach?
The capacity expansion model uses 20 representative days per year with hourly dispatch, rolling forward year-by-year. The fundamentals model runs at 15-minute granularity for the full forecast horizon.