version 1.0
The method document outlines the steps involved, assumptions taken, and data considered for solar and wind asset revenue forecasting.
1. Asset selection criteria
Revenues are modelled for generation units that meet the following criteria:
Operational Status: The unit is either operational or under construction.
Configuration: The unit is standalone and not connected to an energy storage system within the same power plant aggregate.
Capacity Threshold: The unit has an AC capacity greater than 5 MW. This threshold excludes distributed generation units, which typically lack dedicated price nodes (if applicable) and primarily operate under a self-consumption strategy.
Asset characteristics, including installed capacity, commissioning date, subsidy data and other relevant information, can be found in the Wood Mackenzie Lens P&R platform.
2. Asset revenue
Asset revenue is the total income generated by a solar or wind generation unit from its participation in the electricity market and other contractual or regulatory mechanisms. The revenue is calculated based on the following categories, commonly referred to as the revenue stacking approach.
Market revenue: Revenue earned from selling electricity and related products into competitive wholesale markets.
Subsidy and contractual revenue: Revenue earned from government subsidy programs, or long-term contracts, Power Purchase Agreements (PPAs) or Contracts for Difference (CfDs).
3. Production
The production values of each generating unit are estimated using the internal yield models and historical weather data. The primary meteorological data source we have chosen is the European Centre for Medium-Range Weather Forecasts (ECMWF) dataset, which provides a reanalysis global dataset called ERA5.
Our internal yield models process this weather data and provide generation values for individual generation units at the hourly level. The modelled production values are also benchmarked against realized production values, published by market operators or other sources. The following parameters are calculated for each generation unit:
Degradation impact (0.5% per year for Solar and 0.2% for Onshore wind, per Wood Mackenzie Analysis)
Pre-curtailed generation
Losses, includes the expected transmission losses. Loss assumptions are technology-specific and sourced from Wood Mackenzie analysis, covering electrical, transformer and availability losses.
Curtailment
Net energy production
3.1 Curtailment
The curtailment modelled here represents economic curtailment, which is estimated based on market price signals and curtailment price thresholds of the generation units. In the US, it's often equivalent to the Production Tax Credit (PTC).
In other markets, it will depend on the subsidy regime in place and the compensation terms included. For example, if the energy price is less than the curtailment threshold price, the generation unit curtails its production as it has no economic incentive to produce during those periods. The granularity of this estimate is on an hourly basis.
Two curtailment cases are modelled, 1) Base case: This reflects the realistic case under which the asset would operate, e.g., a US onshore wind asset with PTC for 10 years. 2) Negative price curtailment case: This reflects the case where production is curtailed when the energy price turns negative or less than zero.
3.2 Forecast generation profile
The 8760 hourly forecast generation profile is derived from modelled production values for a weather year and is aligned with the assumptions used in Wood Mackenzie’s long-term power price modelling - same weather year. The selected forecast profile, before degradation, curtailment and losses, is applied consistently across all future years until the generation unit’s decommissioning date.
3.3 Historical generation data
Historical and forecasted generation data are combined into a unified, asset-specific time series based on a defined cut-off date, referred to as the ‘Forecasted From’ date. This date is determined by comparing the latest available historical energy price data and actual generation data—whichever is earlier and is used as the transition point - between actual history and modelled forecast.
Actual historical generation is used up to and including this date; forecast generation and prices are applied thereafter. Historical data is sourced from publicly available settlement records and mapped to individual generation units. The historical data in the model gets updated on a defined frequency - this is currently weekly. The following market-specific approaches are applied:
For ERCOT, the 60-day SCED (Security Constrained Economic Dispatch) reports are processed for post-curtailed (metered energy) and pre-curtailed generation.
For CAISO, the EIA monthly generation reports provides monthly net generation. These reports, along with modelled asset-level hourly generation, zonal- and market-level net production data from ISO, curtailment reports from ISO and wholesale market prices were used to derive hourly curtailment curves. These curtailment curves are used to produce hourly post-curtailed and pre-curtailed generation at the asset level.
For rest of the ISOs in the North American market, the EIA monthly generation reports along with wholesale market prices were used to derive curtailment curves, which are then used to produce hourly post-curtailed and pre-curtailed generation at the asset level.
4. Revenue components and methodology
The revenue components included in the solar and wind revenue calculations are:
Energy revenue: Income from selling generated electricity at merchant market prices. For the historical period, this reflects revenue earned through sales in the real-time market, which is the primary market for most onshore wind and solar assets. The production used in the historical period are settled energy, so real-time power prices are used to estimate revenue in the historical period. For the forecast period, potential energy revenue is estimated using the asset-level forecast generation profile and Wood Mackenzie’s long-term day-ahead nodal power price forecasts. The generation used in this calculation corresponds to the net energy generated, i.e., after accounting for curtailment and losses. The day-ahead nodal energy prices are derived by a simulation engine that anchors Wood Mackenzie’s long term energy market views - zonal forecasts, reflecting the historical relationship between node and its associated hub and also add a stochastic layer to simulate realistic price spikes, volatility and negative price hours.
Capacity revenue: Payments received for maintaining availability to generate electricity during system peak demand periods, adjusted for seasonal and technological performance. Capacity revenue is calculated using our Long-Term Outlook (LTO) capacity price forecasts and the Effective Load Carrying Capability (ELCC), which serves as a derating factor reflecting the asset’s reliability contribution. ELCC values are sourced from Wood Mackenzie's market-specific accreditation methodology and reflect technology, vintage and zone-level accreditation as applicable.
Attributes revenue: Income earned from the sale or monetization of environmental attributes such as Renewable Energy Certificates (RECs), which represent the clean energy value of generation in the market. The value for these attributes for the forecast period is derived from our Long-Term Outlooks (LTO). For historical period, the attribute revenues are set to zero.
Ancillary revenue: Compensation for providing grid support services such as frequency regulation or reserves. Individual markets have different instruments and procure services in different intervals. The participation of wind and solar in these markets is usually negligible. Hence, they are not currently modelled here in this product.
Tax credits: Financial incentives that reduce tax liability based on renewable energy production. This methodology includes production-based credits only, such as the Production Tax Credit (PTC), which is applied to the net energy generated. PTC values are determined based on the awarded regime, eligibility, bonus multiplier, online date, and construction start year. Actual values are sourced from IRS publications and updated annually to reflect policy changes, including phase-out schedules. Future year PTC prices are forecasted using macroeconomic data, as tax credits are adjusted for inflation.
5. Prices
Prices cover the revenue components specified above, including wholesale electricity prices, capacity market prices, attribute prices and ancillary prices. Electricity prices includes both hub and nodal prices, when applicable. Tax credits are also included here, however they are not treated as cash revenue as detailed above.
Similar to generation data, historical and forecasted price data are combined into a unified, asset-specific time series based on a defined cut-off date, referred to as the ‘Forecasted From’ date. Actual historical data is used up to and including this date; forecasted price data is applied thereafter. This transition reflects a shift from RT price to DA price projections. See Sections 6 and 7 for how this is handled in the capture ratio and the planned DA/RT settlement feature. Further, historical data is sourced from publicly available settlement records and mapped to individual generation units. The historical data in the model gets updated on a defined frequency - weekly.
6. Derivative metrics and benchmarking
Total energy revenue ($): The total revenue obtained from the sale of generation, including the wholesale market sales, PPA settlements and/or subsidy-related settlements.
Total revenue ($): The total revenue represents the revenue obtained from the sale of generation and all products associated with the generation unit, including capacity payments, attribute revenue and tax credits. It is important to note that the production tax credits are not direct revenues but reduce tax liability, and while they appear in the asset's revenue stack, they function more as a financial offset than cash income.
Curtailment ratio (%): Curtailment ratio is the percentage of potential renewable energy generation that is intentionally reduced or not delivered to the grid.
Average energy revenue (per MWh): This is the production-weighted average energy revenue achieved by the generation unit in a specified time period, generally in a year.
Average total revenue (per MWh): This is the production-weighted average total revenue achieved by the generation unit in a specified time period, generally in a year.
Locational basis (per MWh): Locational basis refers to the price differential between a specific node and its associated market hub. This metric captures the impact of transmission constraints, congestion, and local supply-demand dynamics. It is a key factor in revenue modeling for generation units settled at nodal prices, especially in North American markets.
Revenue at risk due to locational basis ($): Revenue at risk from locational basis represents the potential loss in energy revenue due to price differences between the asset’s node and its associated market hub. It is calculated using pre-curtailed generation and the locational basis price, and reflects the impact of congestion and local market dynamics in potential revenue.
Capture ratio (%): Capture ratio measures how effectively a generation asset captures hub‑level market value, comparing its realized energy revenue to the revenue it would have earned if all net generation had cleared at the hub price. It isolates locational price effects by benchmarking performance to the hub.
Capture ratio = Energy revenue from nodal price / (Net generation * hub price)
Note: The capture ratio is calculated on a consistent price basis within each period — real-time (RT) prices historically and day-ahead (DA) projections in the forecast. This design is intentional: the historical period reflects actual market settlement behavior, including RT volatility, negative pricing, and congestion dynamics, while the forecast period reflects the DA market. In data tables, a column named 'Period' specifies whether the data is historical or forecast. The planned DA/RT settlement feature will extend full RT price simulation into the forecast horizon, enabling users to directly quantify the RT discount, stress-test operational strategies across both markets, and produce a fully continuous capture ratio series — further deepening the analytical power of this tool.
7. Planned features
Benchmarking: The model ranks the performance of the generation units within a specified boundary, for example, within a zone, market, or technology. The charts show the ranking of generation units across different parameters, with the highlighted bar indicating the selected asset's position relative to the group selected. The following metrics are ranked: curtailment ratio, total energy production, average energy revenue, average total revenue, and capture ratio.
PPA settlements: PPA settlements represent revenue earned through bilateral agreements with utilities, corporates, or other counterparties. These contracts typically specify fixed or indexed pricing structures and may include settlement mechanisms based on market price differentials. Revenue is calculated using the contracted strike price, reference market price (if applicable), and net energy generated.
Power price scenarios and stochastic paths: In addition to the base case, the product will enable users to choose alternative power price scenarios published by Wood Mackenzie to assess, compare and understand the impact of different power price forecasts on asset revenues. In addition to scenarios, stochastic price paths can be derived to estimate P50/P90 revenue.
Day-ahead/Real-time market settlements: Currently, the model forecasts revenue based on projected day-ahead nodal prices. Improvements are planned to use real-time prices and simulate production error that would allow user to test different operational strategies between day-ahead and real-time market, quantify the impact of forecast error and also estimate risks.
Contracted revenue: Contracted revenue refers to income earned through long-term agreements with mainly governments, typically designed to support renewable energy deployment. This category includes mechanisms such as Contracts for Difference (CfDs) and Feed-in Tariffs (FiTs).
These mechanisms vary by jurisdiction and are applied according to the specific terms allocated to each generation unit. The contracted or subsidy scheme details related to the individual generation unit can be found in the Wood Mackenzie Lens P&R platform. For periods beyond any contracted term, revenue is modelled on a fully merchant basis using the same nodal price framework.
Ancillary revenue: Solar and wind participation in the ancillary market will be modelled to assess revenue and allow users to vary participation levels to test operational strategies.
Data sources
Parameter | Data source |
Asset-specific hourly 8760 production profiles | Historical: Realized production data from available data sources (e.g., ISO) Forecast: Modelled 8760 profile, aligned with Wood Mackenzie power price modelling |
Annual degradation factor and loss factors (%) | Wood Mackenzie’s technology specific assumption |
Curtailment | Historical: Realized production data from available data sources and curtailment reports (e.g., ISO) Forecast: Economic curtailment modelled using price threshold (e.g., PTC) |
Nodal and Zonal prices | Historical: Market data (Real time prices) Forecast: Wood Mackenzie day-ahead nodal projection based on historical observations |
Renewable accreditation (ELCC) for assets in each price zone | Wood Mackenzie forecast |
Annual capacity and REC prices | Wood Mackenzie forecast |
Subsidy and tax credits | Asset specific values modelled based on guidelines and awarded conditions |