Scenario

A "scenario" is a case study performed on a network. It is characterized by a scenario type (e.g., SteadyGas, SteadyACPF, QuasiDynamicACOPF, DCUCOPF, etc.), a time window (start time, end time), a set of settings, controls, and constraints making up the scenario (i.e., a set of events and conditions), and a description of how such set changes over time by using profiles.

The mathematical model describing a scenario can be either a "simulation" or an "optimization". The execution of a scenario in SAInt is generically referred to as "finding a solution".

The information contained in a scenario is saved to a file with the file extension *.*sce (e.g., *.esce for electric network scenario, *.gsce for gas network scenario, and *.tsce for thermal network scenario).

For each network, an unlimited number of scenarios can be defined and executed.

1. Scenario type

Each scenario type is applied to a specific network type as shown in Table 1. A scenario is executed in the time window defined by the user. Additional scenario settings can be found at "Scenario events". These settings depend on the scenario type chosen. A quasi dynamic scenario executed for a single time is a steady state.

Refer to the "Glossary" page for the definition of the different time inputs.

Table 1. Scenario types per energy carrier and network type.
Network Type	Scenario Type	Mathematical Model Type	Description
Electric	`QuasiDynamic - (U)ACPF`	Simulation	Quasi Dynamic (Unbalanced) Alternating Current Power Flow Simulation of power flows in an electric network using nodal active and reactive power balance equations. In unbalanced power flow, all three phases of the system are modeled.
Electric	`QuasiDynamicACOPF`	Optimization	Quasi Dynamic Alternating Current Optimal Power Flow Optimization of the operational costs of generators subject to nodal voltage, transmission and generator constraints, as well as active and reactive power balance equations.
Electric	`QuasiDynamicDCPF`	Simulation	Quasi Dynamic Direct Current Power Flow Simulation of power flows in an electric network using a DC-approximation for the active and reactive power balance equations.
Electric	`QuasiDynamicDCOPF`	Optimization	Quasi Dynamic Direct Current Optimal Power Flow Optimization of the operational costs of generators subject to transmission and generator constraints, and a DC-approximation for the active and reactive power balance equations.
Electric	`DCUCOPF`	Optimization	Direct Current Unit Commitment Optimal Power Flow Optimization of the production costs of generators subject to unit commitment of generators, transmission constraints using a DC-approximation for the active and reactive power balance equations. The optimization includes at least one time horizon that includes multiple timestep.
Electric	`CEM`	Optimization	Capacity Expansion Model Optimization to identify the least-cost mix of power system resources, considering future scenarios like new policies, technologies, demand forecasts, and fuel price projections. The optimization horizon can consider one year or multiple years with investments happening in multiple stages. The typical outputs include the optimal capacities of investment candidates, capital and operational costs for installations, etc.
Gas	`SteadyGas`	Simulation	Steady State Gas Hydraulic Simulation Hydraulic simulation of a gas network in steady state conditions.
Gas	`DynamicGas`	Simulation	Dynamic Gas Hydraulic Simulation Hydraulic simulation of a gas network in dynamic conditions.
Thermal	`SteadyThermal`	Simulation	Steady State Thermal Hydraulic Simulation Thermal simulation of a thermal network in steady state conditions.
Thermal	`QuasiDynamicThermal`	Simulation	Quasi-Dynamic Thermal Hydraulic Simulation Thermal simulation of a Thermal network in quasi-dynamic conditions.
Hub	`SteadyGas - SteadyAC(O)PF/DC(O)PF - SteadyUACPF`	Simulation	Steady Coupled Simulation Simulation of a gas and electric network in steady state conditions.
Hub	`DynamicGas - DynamicAC(O)PF/DC(O)PF`	Simulation	Dynamic Coupled Simulation Simulation of a gas and electric network in dynamic conditions.

The "O" in the scenario type name stands for optimization.
A hub network contains the coupling objects between networks.
DCUCOPF is also referred as production cost model.

2. Scenario event

An "event" is a definition of a change in settings, controls, or constraints (boundary conditions) of an object at a specific time during the execution of a scenario. It is mainly characterized by a start time (when?), which describes at what execution time the event should be considered, a parameter (what?), which defines which boundary condition of the object should be changed, and a value (how?), which indicates which value should be set for the event parameter.

3. Scenario profile

A "profile" is a collection of ordered equidistant data points that includes information on how these data points are processed in terms of the time step, interpolation, sampling, and periodicity. Profiles can be assigned to an event if the value of the event should change over time.

A profile that is not assigned to an event is not considered during the execution of a scenario.