5. Location Manageent

5.1. Content of the Forecast Sites Menu

In the “Forecast Sites” interface sites can be newly registered or managed. There have been setup 2 categories for this purpose:

  1. Registration of Forecast sites
  2. Management of registered sites

The following description will show what the 2 categories are used for.

5.2. Register Location

In the “Register Location” interface locations or forecast sites are registered for the first time. There are different choices of location types with their type specific variables to be registered. The common variables for all types of locations are the geographic information. This is a prerequisite in order to get a location registered in the MSEPS system.

The choises of location types are

A Wind Plant

B Solar Panel

C Weather variable location

D Generic

Type A, B and C are self explanatory, while type D locations require some explanation. These location types can be used for locations that are not necessarily point locations, but a midpoint of a area for e.g. demand forecasting. It in fact allows to register specific non-standard location types. See more details in section location-registration.

5.3. Configure Location

In the “Configure Location” interface the user can manage already registered locations. When entering the interface a table with the registered sites provides some overview of the most relevant parameter for the locations, such as the group, name, identifier and the geographic information (longitude, latitude).

For each table entry there is the possibility to carry out editing or delete the entry from the database. When editing is chosen, the same parameters as in the interface for the registration of a new location are shown and can be edited.

Note

Only permanent changes to a location should be edited in this interface. Availability of a location or temporarily changed capacity should NOT be edited in this interface. This should only be done in the availability interface.

5.4. Availability / Limitations

The “Availability / Limitations” interface is used to update or change availability and maximum export capacity of units in the real-time environment. In fact this interface provides the possibility to update any registered location and thereby influence the forecast immediately within the next forecast update cycle. The changes to availability and maximum export capacity can also immediately be made visible in the graphics by using the feature “restricted” in the legend. In that way, both restricted and unrestricted forecasts can be viewed (see detailed description in Forecast-Data-features).

To carry out an availability update, the unit to be updated has to be selected from a drop-down menu, where each <Location> has a choice of the mode for <availability> updates or updates of <maximum export> capacity.

5.4.1. Availability Update of a plant

By choosing the availability update of a plant the user can change/update a restriction in the near future of the installed capacity. This is either a reduced capacity, because some part of the plant is out of order or the entire plant is out of operation. The adjustment to the forecast process is that the installed capacity of that specific plant is reduced for the specified time.

The interface is designed for real-time usage and features as a standard 1 calendar week, as this is what is often required by Grid Codes to supply as “planned availability” and used by the maintenance team for plant maintenance work.

Note

The availability reduces the total unit capacity to the chosen values, while the maximum export capacity caps the generation at the chosen value. By pressing the submit button, you will be guided to the table of the selected unit.

5.4.2. Limitations Update of a Plant

By choosing the Limitations Update of a plant, abbreviated by MEC (Maximum Export Capacity) in the interface, a restriction in the near future of the maximum feed-in of a plant into the electricity grid is carried out.

In contrast to the availability change of a plant, the forecasts are adjusted in a different manner, namely by setting a lid to the allowed production of a plant. That means that the forecast is generated with 100% availability and restricted only when the total output of the plant would exceed the set MEC value.

5.5. File Upload

There are 2 type of files you can upload:

Type A: Non-specific file of type (e.g. .dat/.txt/.doc/.xls/.zip)

Type B: Specifically formatted Production or forecast timeseries data files

5.5.1. Type A Files (non-specific format)

The File upload feature for type A files is to enable the user to exchange information with WEPROG in a secure environment that are too large or inconvenient to send by Email.

5.5.2. Type B files (specifically formatted data files)

The file upload feature for type B files is to enable to be able to use additional information for the analysis of the forecast data supplied by WEPROG.

The files follow a specific format (see How should the data look like I want to upload?) and will be visible in the graphics in the Forecast Data menu (see user-specific-timeseries).

Each user has 4 additional timeseries variables u1..u4 and 1 timeseries to be defined as artifical neural network time series (ANN) available to be added to the system.

There are almost infinite many possibilities how to use the data and what kind of data can be added to the sytem. For example, you may upload production data that is only available historically or in hindcast mode for the analysis (see analysis-examples) or verification of the forecasts you have got (see also forecast-verification). After analysing or verifying data you may create a new “best guess” by using our tools to give user specific weight to specific forecast data (see).

Uploaded time series data can also be used to generate and train a artificial neural network (see
create-ANN)

To summarise, you can visualise and use any of the curves you upload - as additional timeseries in the forecast graphs - in the verification - for optimsed forecast calculations

The data that you upload can contain - historical production values - forecasts - trained data from internal neural networks