A utility has the ability to provide forecast data for various types of DER resources that are present within the electrical network. The NMS categorizes DERs into the following forecasts categories:

The following sections will describe how to configure them, and provide the required forecast data.

A utility has the ability to configure different weather zones within the NMS data model such that different distributed generation profiles can be used within each of the different zones to characterize the real-time and forecasted generation output. For example, if the "coastal" zone is cloudy, a user could set the forecast for PV to use a cloudy profile in the coastal zone, but all other zones would use the clear profile. The weather zone configuration is also used by loads to adjust load profiles as temperature changes. DERs technology types (for example, PV, Wind, and so on) that are mapped to weather zones should be weather affected DERs only. For example, a large utility scale diesel generator would not be mapped to a weather zone since its forecast and output is independent of weather conditions.

If a utility wishes to configure weather zone functionality, data needs to exist that maps each distributed generation and load resource in the NMS data model to one of the applicable zones. The preferable location for this data would be the GIS, but, if needed, the data could be placed in the customer specific Power Flow Engineering Data workbook. This data needs to be brought across during the model build into the ZONE columns of the PF_DIST_GEN and PF_LOADS tables. The value populated will need to map to a configured zone in the PF_WEATHER_ZONE table.

Once Weather Zones have been configured, the utility has the ability to provide a forecast for each zone that forecasts how the DER resources for each power source will behave for the current day plus the next six days. This forecast should also include a temperature forecast for each applicable zone; this will be used to adjust load profiles as temperature changes.

During implementation, a utility needs to determine how they would like to use the weather zone forecast functionality.

Whichever method is used, the entire forecast for each zone must be provided with the same method (profiles or scaling factor) for each power source.

Temperature forecasts can be provided for US customers using a product adapter that interfaces to a weather data server. Alternatively, an implementer could build a custom adapter that would provide temperature forecasts on a zone basis from an alternate resource such as utility specific forecasts (see the Oracle Utilities Network Management System Adapters Guide for more information).

Large scale utility DERs are categorized as units that are large in scale and unaffected by weather. An example would be a diesel generator or large battery that has an output that is unaffected by weather. For these units, the forecast is provided for each individual unit and they are not aggregated together like DERs within weather zones. It may be entirely plausible to have two large batteries in close proximity to each other but their forecasts could be drastically different.

If a utility wishes to categorize a DER resource as being outside of a weather zone the ZONE field within the PF_DIST_GEN database table should be configured within the alias of the unit. The preferable location for this data would be the GIS, but if needed the data could be entered in the utility's Power Flow Engineering Data workbook. This data will need to be brought across during the model build. A corresponding power source also needs to be configured for the unit (for example, Diesel, Gas, Battery, and so on).

Once the large scale DERs have been configured, the utility has the ability to provide a forecast for each unit that forecasts the output for the current day plus the next six days. It is expected that an external system would be used to populate the forecasts for these units and the data is stored in the PF_DER_FORECAST database table. Within this table a key corresponding to a distributed generation profile can be provided (for example, on, off, peak shave, and so on). Alternatively, a utility could provide direct scaling factors to use for each hour instead of what profile to use for each hour, the input method can be configured with the distGenDefault SRS rule. With either method the entire forecast for all units must be provided with the same method (profiles or scaling factor) for each power source. The NMS has a DERMS adapter product that is capable of taking a CSV or RDBMS based forecast from an external system for use by the NMS (see the Oracle Utilities Network Management System Adapters Guide for more information).

A utility has the ability to configure different demand response groups (DR groups) within the NMS data model such that different DR profiles can be used within each of the different groups to characterize the real-time and forecasted DR scenarios. For example, the ability to simulate a DR group dropping load for 2 hours during a peak load day.

If a utility wishes to configure demand response group functionality, data would need to exist that maps each DR resource in the NMS data model to one of the applicable demand response groups. The preferable location for this data would be the GIS, but, if needed, the data could be placed the utility's Power Flow Engineering Data workbook. This data will need to be brought across during the model build into the ZONE column of the PF_DIST_GEN table. The value populated will need to map to a configured zone in the PF_DEMAND_RESPONSE_GROUPS table.

Once the demand response groups have been configured, the utility has the ability to provide a forecast for each demand response group that forecasts the output for the current day plus the next six days. It is expected that an external system would be used to populate the forecasts for these units and the data is stored with NMS DB table PF_DEMAND_RESPONSE_FORECAST. This table can have a key corresponding to a distributed generation profile (for example, on, off, and so on). Alternatively, a utility could provide direct scaling factors to use for each hour instead of what profile to use for each hour; the input method can be configured with the distGenDefault SRS Rule. With either method, the entire forecast for all units must be provided with the same method (profiles or scaling factor) for each power source. The NMS has a DERMS adapter product that is capable of taking a CSV or RDBMS based forecast from an external system for use by the NMS (see the Oracle Utilities Network Management System Adapters Guide for more information).