Identify Data and IT Requirements for Your New Forecasting System

Integrating improved generation forecasting into system operations can require new and/or expanded data and IT infrastructure to enable ongoing data collection, data processing, and real-time decision making based on updated forecast information.

---

Main Points

• Plant physical and operational data as well as weather forecast data are needed for a generation forecasting system.
• Many grid operators use relatively sophisticated information technology (IT) systems to integrate data from a variety of systems.
• Improved generation forecasting may require upgrades to existing data management and IT infrastructure.

---

First, See This

In addition to meteorological forecast data, a generation forecasting system requires physical and operational data from VRE plants. This table shows a summary.

See Table from page 19 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.

Now, Read This

System operators typically requires plant operators to provide master data and near real-time data on their plants.

VRE plants should be obligated to provide the data described below for VRE forecasting:

• Master data on the plant (or static data) such as total installed capacity, capacity of each generator, physical and technical properties of each generator (wind turbine or PV module), geographic location, point of interconnection, and expected annual average energy production. Note that this is not just for utility-scale VRE plants but also for smaller commercial and residential units. As has been observed in southern Germany, the penetration of rooftop solar PV has reached a level that leads to a reverse flow of energy—the flow from distribution to the transmission system. If system operators are unaware of all the generation in the grid, they will be handicapped in their efforts to manage reserve requirements and plan the dispatch of generators in the most economical manner.
• Near real-time data to system operators like power production (active and reactive), renewable energy resource (wind speed and direction or solar radiation and temperature), available capacity, curtailment, and other data, for each time block. The VRE plant owner should also be required to provide the available capacity forecast for the time blocks that correspond to WA [week ahead] and DA [day ahead] forecasts, which would inform the system operator about the maintenance schedule and when equipment currently under maintenance will be available to generate power. VRE plants should be required to provide such data using direct data transfer methods like web-based application programming interfaces. If centralized forecasting is adopted, the best practice is to implement a SCADA feed from VRE plants to provide (1) available capacity forecasts, which may be in the form of start and finish times of scheduled and unscheduled maintenance; (2) active and reactive power production in the past time block; and (3) weather parameters from on-site weather measurement station(s).

Excerpt from pages 19 – 20 of Scaling Up Renewable Energy Project Grid Integration Series: Variable Renewable Energy Forecasting by USAID.

Next, Read This

A forecasting system relies on IT infrastructure and data acquisition and control systems. Planning for an improved forecasting system may involve upgrading this infrastructure.

A forecasting system must convert the raw forecast data from various models into a flow of information useful to the system operator. The major components of a typical IT-based forecasting system include the following (Figure 4.1):

• front end/user interface (allows users to interact with the forecasting system)
• back end (connects components of a forecasting system)
• IT integration layer (enables communication within the forecasting system and with other systems)
• data repository (contains working data for the forecasting system)
• model engine (generates plant output forecasts based on the model).

To produce a forecast, the integration layer passes the forecast request and data from the back end to the model engine. Depending on the parameters of the request, the model engine executes with different time horizons, spatial resolution, or domains. The integration layer must also ensure that the engine receives all the data, correctly formatted, it needs to carry out the forecast. The integration layer receives the results of the fore end and updates other systems as necessary.

Front end. The front end provides a user interface for interacting with the forecasting system. It allows the user—in this case the grid or plant operator—to manage the system configuration; enter plant technical and geographical details; load meteorological observations and forecast data from external providers; enter upcoming O&M schedules; view the forecasted power output and uncertainty of the forecast; schedule the creation or retrieval of a forecast; and interact with other systems that rely on forecast output data, including Supervisory Control and Data Acquisition (SCADA)/Energy Management Systems (EMSs), dispatch, and market systems.

Back end. The most critical back end functions are system and data management. Ensuring that data are correct and available to other processes is essential to the functioning of the entire system. The back end also schedules and executes the processes involving data analysis and communicates with other components of the forecasting system.

Integration layer. The integration layer enables connection of the forecasting system to other systems run by the operator or other parties. It therefore must be developed in a way that lends itself to easy extension and connection to other systems as other components evolve and requirements change. For example, it may be necessary to provide a standardized application programming interface to other systems and ways to transfer data to other systems automatically and on demand, through a file transfer protocol (FTP) portal, web services, or even email. The integration layer address requirements of several system stakeholders and is responsible for ensuring proper access control for all users of the data and to the forecasting system itself. Users of the integration layer include the following:

• the forecasting system operator, which uses the integration layer to run the system and to transfer data to and from the front and back ends, the repository, and the model engine
• plant operators, which feed technical, upcoming availability, and O&M data to inform the forecast
• regulators, which provide oversight
• external data providers, such as meteorological agencies, which provide meteorological data for the forecast
• other interconnected systems run by the operator, including SCADA/EMS
• other system operators, which rely on the forecasting system to provide data or forecasts from the operator.

Data repository. The data repository serves as the central storage for all data needed by the system to make forecasts. Given the critical importance of the data, the different types and uses of data, and the size of the dataset, the repository must be flexible while remaining resilient to corruption and attack. Data include plant technical specifications; market data; weather data (forecast and observed); plant operating data (including production, wind speeds, and total insolation); and outputs (historical and forecast).

Model engine. Given the size, complexity, and computing requirements of most models, the model engine is usually separated as a standalone modular component. Separation permits swapping the engine to allow the use of newer and more accurate models, more detailed resolution models, additional computing power rented from other providers, and other types of flexibility. For example, an external forecast from the model engine provided to the user through the front might be replaced by a tailored in-house model based on software like SPSS, with significantly different data and processing needs.

Excerpt from pages 16 – 18 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.

And See This Diagram

This is a graphical depiction of the forecasting system components discussed above.

Figure from page 17 of Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy by ESMAP.

Suggested Actions & Next Steps

• Create a comprehensive list of data needed to achieve your forecasting goals. Review Chapter 4 (Data Communication) of Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions – Part 1: Forecast Solution Selection Process by IEA Wind Task 36.
• Create a corresponding list of IT infrastructure and communication and control systems needed. Review Section 3.2 (IT Infrastructure Considerations) of Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions – Part 1: Forecast Solution Selection Process by IEA Wind Task 36.
• Based on these lists, identify the gaps between currently available and needed data and IT assets for your desired system.
• Review these results with the TRC and plan for improvements or revise your goals as necessary.