Global data on wind and solar resource potential are available for high-level planning purposes, but generation forecasting depends on using localized weather data in forecasting models. There are various types of forecasting models, but all of them combine weather and plant data to determine a generation forecast. Centralized forecasting by the system operator is considered best practice. Various forecasting systems and services are commercially available, though systems can also be self-developed, depending on your identified needs and capabilities.
Main Points
- Global data sets of wind and solar resource potential can be used to identify high-resource areas or estimate generation for planning purposes.
- Generation forecasting requires localized weather data, a forecasting model, and plant data to estimate energy production.
- Centralized forecasting is best practice, but decentralized or hybrid forecasting may be used if existing high-quality forecasts by VRE generators or other entities are available.
- For simplicity, most countries use commercially available forecasting systems or services, though it is possible to build your own or bundle commercial solutions for separate components.
- It may be necessary to improve available meteorological services to support improved generation forecasting.
First, See These Resources
For planning and preliminary analysis purposes, publicly available tools like the Global Wind Atlas and Global Solar Atlas provide free downloadable maps and GIS data for estimating wind and solar resource potential. These are useful but not sufficient for meeting forecasting needs.
Next, See This Figure
To obtain higher quality forecasts, weather data is input into forecast models which integrate their results with plant data to provide generation forecasts.
See: Figure from page 8 of Advanced Forecasting of Variable Renewable Power Generation by IRENA.
Now Read This
Forecasts are generally done using physical or statistical methods, though sometimes these are combined in a hybrid forecasting approach.
Forecasting systems use meteorological variables or historical generation data to estimate the total plant energy output at any given time horizon.
Forecasting methods can be broadly divided into physical and statistical methods. Physical methods use weather data to populate a physical model of the atmosphere. Statistical methods use historical generation data to project plant output. Statistical methods work best for intra-hourly forecasts and up to three-hour ahead forecasts. Physical methods are used primarily for forecasting output beyond three to six hours, with some exceptions in solar, such as the application of total sky imagers for short-term forecasting for cloud prediction (Haupt 2018). In general, statistical models perform better for wind energy than for solar energy over short time horizons and physical models show better results for both wind and solar over long time horizons (Widén et al. 2015), because statistical models do not do a good job of predicting cloud coverage. Physical models sometimes used total sky imagers—digital cameras that produce high-quality images to show the entire sky to the horizon—for short-term high-resolution forecasting.
Hybrid forecasts combine results from forecasts produced by multiple methods in a single cohesive forecast, which is often more accurate than individual forecasts. TABLE 3.1 illustrates the types of forecasts available.
Forecasting models provide an estimate of the weather parameters at the plant site. Physical or statistical methods combine these forecasts with the power curve of a wind turbine or solar PV module—either in real-world use or from the theoretical estimate from the manufacturer—to convert them into accurate and useful data for system operators that reflect plant responses to meteorological forecasts (Foley and others 2012).
Read excerpt from page 5 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.
And See This Table
See table from page 6 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.
Next, Read This
In addition to the forecasting model used, a forecasting system can be centralized, decentralized, or a combination of both.
One of the design features of a forecasting system is whether the forecasting will be centralized (performed by the system operator) or decentralized (performed by plant operators) who feed the forecasts back to the dispatch center. Each method has benefits and challenges based on the type of information available.
The system operator requires a holistic view of the power system to ensure its safe operation, determine ancillary services and reserve margins, and so forth. A centralized forecasting system will result in more consistent (though not necessarily more accurate) results, because the same models and approaches will be used across the system. The system operator may also have access to information from a large number of plants across geographically dispersed locations that can help improve the forecast. A larger number of plants also results in scale economies on a per plant basis. However, a centralized system may have systematic biases that distort the forecast, either system-wide or for individual plants that do not conform precisely to the model’s assumptions.
A decentralized forecasting system may be better able to model individual plant output, but it lacks the benefits of the centralized approach. Individual plant operators have more precise information about the availability and real-time generation of the plant. A decentralized approach also provides more freedom to innovate the models to improve accuracy or reduce computing needs or increase the local spatial resolution of the model (NERC 2010).
Centralized and decentralized forecasting are complementary, not mutually exclusive. In India, for example, both the regional load dispatch centers and the VRE generators are required to issue forecasts.
Excerpt from page 16 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.
Now, Read This
Commercial forecasting systems are available and may be the easiest to implement, though sometimes bundling commercial solutions for different components or building your own system may be desirable for meeting your specific needs.
Read Excerpt: Page(s) 20-21 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.
Finally, Read This
It may be necessary to improve available weather forecasting services in order to increase the accuracy of generation forecasts. This is another potential role for policymakers.
In countries with poor weather forecasting services, highly accurate wind speed and radiation forecasts may not be available or can be prohibitively expensive. In such countries, it is good practice for governments or regional organizations to use existing meteorological research institutions in the region or establish new institutions to conduct research to improve forecasting accuracy for weather parameters that are important to VRE generation forecasting. These institutions would install meteorological stations, create weather models, downscale global weather data, perform statistical analysis for bias removal, model local weather phenomena, and generate accurate weather forecasts.
Excerpt from page 21 of Scaling Up Renewable Energy Project Grid Integration Series: Variable Renewable Energy Forecasting by USAID.
Suggested Actions & Next Steps
- Explore your region’s wind and solar resources using the Global Wind Atlas and Global Solar Atlas.
- Read more about forecasting models on pages 5 – 10 of ESMAP’s Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy.
- Reach out to potential forecasting software and/or subscription-based data vendors to inquire about costs and timelines. Some examples of service providers are CustomWeather, meteomatics, and meteoblue (note: this is not an endorsement of any of these companies or products).
- When discussing services provided by vendors, consider using the list of questions from Appendix B of Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions – Part 1: Forecast Solution Selection Process by IEA Wind Task 36.
- Evaluate potential sources of localized meteorological data and determine whether additional data is needed and what the timelines for developing that data might be.
- Consult with the TRC for additional information about VRE forecasting tools and/or subscription services they may be aware of and review your findings with them.