- Power system operations and electricity markets are modeled by solving Security Constrained Unit Commitment and Economic Dispatch (SCUCED) optimization problems.
- Power flows are modeled using a DC approximation (based on the following three assumptions: 1) per unit voltages are the same across the network, 2) voltage angle differences between adjacent nodes are very small, and 3) the resistance of transmission elements is negligible compared to their reactance). Power flows can also be modeled using a transport model.
- Commitment and dispatch decisions of generators, storages, and flexible demands are optimized to meet both electricity demand and ancillary service requirements (e.g., operational reserves, such as upward and downward frequency regulation). Different types of ancillary services can be defined. Generators and storage can contribute to meeting ancillary service requirements.
- Thermal generators are modeled with varying heat rates and by considering minimum and maximum generation constraints, upward and downward ramping limits, and minimum up and down times. In addition to variable operation and maintenance costs, startup and shutdown costs can be defined for thermal generators. Different prices and constraints can be defined for the fuels consumed by thermal generators.
- In order to correctly model hydropower, detailed water networks can be modeled by considering reservoir volumes, natural inflows, man-made outflows, as well as spilling and turbination flows between them. Hydro generators are modeled by defining their electricity production rate and their electricity generation or water turbination constraints.
- The variability of wind and solar PV generators is modeled accurately thanks to SAInt’s ability to create high-resolution wind and solar PV generation profiles for any plant design and for any location for which resource data is available. The curtailment of variable renewable generators can be limited or modeled with different curtailment prices.
- Different types of storage technologies can be modeled. For any storage, the following constraints can be defined: minimum and maximum rate of charge and rate of discharge, charge and discharge efficiencies, as well as minimum, maximum, and fixed states of charge.
- Electricity demand can be modeled in different ways. A node may have any number of demands. Demands can be modeled as fixed, curtailable with different unserved energy costs, or as dispatchable.
- Customized multi-object constraints can be defined. For instance, the user may specify a minimum or maximum flow over several lines or the minimum number of online generators in a region.
- The user can define the temporal resolution (e.g., 1 sec., 5 min., 1 hour, etc.) and the temporal horizon of one or multiple consecutive optimizations. Additional look-ahead windows with customized resolutions can be added to each optimization.
- Consecutive and interleaved simulations can be flexibly set up to model the interactions between commitment and dispatch decisions of day-ahead, intra-day, and real-time markets.
- The user can choose between different LP and MIP optimization solvers.
- The user can access the optimization problem formulation, including the objective function, the constraints it is subject to, a list of the continuous variables and their bounds, and a list of the binary variables.

- Power flows are modeled by running an AC power flow (ACPF) simulation. The nonlinear power balance equations are solved using Newton-Raphson’s method.
- Optimal power flows are modeled by running an AC optimal power flow (ACOPF) simulation. Optimal dispatch decisions are based on the minimization of quadratic generation cost functions.