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<< Click to Display Table of Contents >> Analyzer > Long term planning > Core concepts |
A Scenario case represents a long-term planning scenario.
It defines a set of assumptions about how demand will develop over time, for example:
•Expected development.
•High-growth scenario.
•Conservative development scenario.
A scenario case is used to compare different possible futures.
Only one scenario case is used at a time when running a calculation.
Each scenario case contains one or more Scenario assumptions.
A Scenario assumption defines how demand growth is applied to a specific part of the network.
Each assumption:
•Belongs to a scenario case.
•Is linked to a planning area or network object (for example a station or feeder).
•Contains one or more modelling components.
•Defines how load growth should be modelled.
Scenario assumptions define where and how load growth is applied in the network. They provide the input for the calculation and do not store any results.
Demand growth is defined using two types of modelling components:
•Connection-driven demand
•Electrification growth (DER – Distributed Energy Resources)
Connection-driven demand represents load growth from planned developments within a defined planning area or network object.
Examples include:
•New residential areas.
•Commercial or industrial sites.
•Public infrastructure.
•Charging installations.
You define the development using a load type and a quantity.
The load type determines how the demand is measured (for example number of apartments or square meters) and how it contributes to the network.
The system automatically calculates the resulting peak load based on predefined values for each load type. This means that you do not need to enter peak load manually.
Load growth can be assigned to a specific time period by defining a start year and, optionally, an end year. If a period is specified, the load is distributed over time and accumulated for each year in the study.
Electrification growth (DER) is used to model demand increases driven by broader trends, such as electrification.
Instead of modelling individual projects, this approach represents gradual growth over time, for example:
•Increased use of electric vehicles.
•Higher electricity demand in residential areas.
The model is based on statistical data, such as:
•Number of apartments.
•Number of houses.
•Expected adoption of technologies (for example EV chargers).
This data is defined in Statistical projections, which describe how these factors develop year by year.
Based on these projections, the system:
•Estimates how many new loads (for example EV chargers) are added over time.
•Converts them into peak load using predefined load types.
•Distributes the load across the network.
The result is a yearly forecast of additional demand that can be analysed at different levels of the network, such as substations or planning areas.
Statistical projections provide the data used to model electrification growth (DER) over time.
A projection is a dataset with values for each year, for example:
•Number of apartments.
•Number of houses.
•EV chargers per household.
•Population.
Each projection represents one type of data and describes how it develops over time.
These projections are linked to electrification growth (DER) and are used by the system to calculate how demand increases year by year.
Statistical projections do not represent scenarios on their own. Instead, they are input data used within scenario assumptions to model long-term development.
Load growth is not applied equally across the network. Instead, it is distributed across different network levels using predefined coincidence factors for:
•Service connection
•Low-voltage (LV) network
•Secondary substation
•Primary substation
•High-voltage (HV) network
Coincidence factors describe how load is distributed and aggregated at different hierarchy levels.
This makes it possible to analyse how demand growth affects the network at different levels, for example:
•Local impact in the LV network
•Secondary station loading
•Primary station capacity impact
•Effects on the upstream HV network
The coincidence structure ensures consistent scaling from connection-level demand to system-level impact.