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	`SteadyACPF` and `QuasiDynamicACPF`	Simulation	Quasi Dynamic Balanced Alternating Current Power Flow Single- or multi-time step (quasi-dynamic), phase-balanced, steady-state, alternating current (AC) power flow simulation. Simulation of power flows in an electric network considering: voltage drop/change equations across the from- and to-node of lines and transformers; nodal AC balance equations; reactive power limits of generators; a distributed active power compensation for multi-generator network models. The resulting mathematical model is a set of non-linear equations solved using a Newton-Raphson linearization algorithm.
Electric	`SteadyACOPF` and `QuasiDynamicACOPF`	Optimization	Quasi Dynamic Balanced Alternating Current Optimal Power Flow Single- or multi-time step (quasi-dynamic), phase-balanced, steady-state alternating current (AC) power flow optimization. Optimization of active power generation dispatch costs (objective function) in an electric network subject to: voltage drop/change equations across the from- and to-node of lines and transformers; nodal AC balance equations; nodal voltage limits; active and reactive power limits of generators; current, active power, and apparent power limits of lines and transformers. The resulting mathematical model is a non-linear optimization model (NLP) solved with Gurobi’s LP-Solver using a sequential linear programming algorithm (SLP).
Electric	`SteadyUACPF` and `QuasiDynamicUACPF`	Simulation	Quasi Dynamic Unbalanced Alternating Current Power Flow Single- or multi-time step (quasi-dynamic), single-, two- or three-phase unbalanced, steady-state, alternating current (AC) power flow simulation. Simulation of power flows in an electric network considering: voltage drop/change equations per phase for lines and transformers; nodal AC balance equations per phase; generator reactive power limits; a distributed active power compensation model for multi-generator networks; single-, or two-phase-, wye-, or delta-connected loads. The resulting mathematical model is a set of non-linear equations which is solved using a Newton-Raphson linearization algorithm.
Electric	`DCUCOPF`	Optimization	Linearized Unit Commitment AC-Power Flow Optimization Multi-time step optimization of the decisions on unit commitment and economic dispatch considering generator, storage, and transmission constraints using a linear approximation of the non-linear AC power flow equations ( i.e., (a) nodal voltage = nominal voltage, (b) resistance << reactance, (c) negligible voltage phase angle difference across lines and transformers). Each optimization time horizon can have a look-ahead period to inform decisions that influence the network’s state beyond the end of the optimization window. The resulting mathematical model is a linear, mixed-integer optimization model (MIP) solved with Gurobi’s MIP-Solver using a rolling time horizon with look-ahead method.
Electric	`CEM`	Optimization	Capacity Expansion Model Optimization Long-term planning tool to model the installation of generation, storage, and transmission capacities in an energy system. CEM seeks to find optimal values for the capacities of these generation, storage, and transmission assets to meet forecasted demand for one or several future years. It can incorporate policy restrictions, such as emission goals, prices, or governmental subsidies, to study their effects in a perfect market environment. The resulting mathematical model is a linear optimization model (LP), which is solved with Gurobi’s LP-Solver considering discrete investment years and multiple representative periods for each investment year.
Gas	`SteadyGas`	Simulation	Steady State Hydraulic Gas Network Simulation Steady-state, single-time-step, hydraulic gas network simulation with gas quality, composition, and temperature tracking and considering: pressure drop equations across the from- and to-node of pipelines; nodal continuity equations; control modes, set points, and constraints of compressors, control valves, valves, entry and exit stations, underground gas storages, and LNG Terminals. The resulting mathematical model is a set of non-linear equations solved using a Newton-Raphson linearization algorithm.
Gas	`DynamicGas`	Simulation	Dynamic Hydraulic Gas Network Simulation Dynamic hydraulic gas network simulation. Simulates the operation of a gas network under time-varying demand profiles, control settings, and set points considering: pressure drop equations across the from- and to-node of pipelines; nodal continuity equations; control modes, set points, and constraints of compressors, control valves, valves, entry and exit stations, underground gas storages, and LNG Terminals. The resulting mathematical model is a set of nonlinear, implicit finite difference equations solved using a Newton-Raphson linearization algorithm.
Thermal	`SteadyThermal` and `QuasiDynamicThermal`	Simulation	Thermal-Hydraulic District Heating and Cooling Pipeline Network Simulation Single- or multi-time step (quasi-dynamic), steady-state thermal-hydraulic simulation of district heating and cooling pipeline networks considering: temperature drop, pressure drop, and heat losses in the supply and return pipeline system; mixing of the temperatures of incoming flows at the hot and cold sides of a node; distributed heat balance compensation for multi-source systems. The resulting mathematical model is a set of non-linear equations solved using a Newton-Raphson linearization algorithm.
Hub	`SteadyGas` - `SteadyACPF`	Simulation	Steady State Hydraulic Gas Network Simulation combined with Balanced AC-Power Flow Simulation Steady (single-time step) combined simulation of alternating current power flow and a steady hydraulic gas network simulation. The resulting mathematical model is a set of non-linear equations solved using a Newton-Raphson linearization algorithm.
Hub	`DynamicGas` - `DynamicACPF`	Simulation	Dynamic Hydraulic Gas Network Simulation combined with a Balanced AC-Power Flow Simulation Combined simulation of a succession of (steady AC-power flow) simulations of power flows in an electric network using AC power flow equations and a transient hydraulic gas network simulation under time-varying demand profiles, control settings, and set points. The resulting mathematical model is a set of non-linear equations solved using a Newton-Raphson linearization algorithm.

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.