GAP is a software dedicated to analysis and planning of hydro-thermal, WIND and SOLAR power generation systems.

A stochastic production simulation model calculates the technical and economical results of several production scenarios for a medium to long-term study period. A user-friendly graphical interface and a powerful scenario manager allow the user to easily analyze several generation configurations and to determine the optimum system expansion plan.

GAP1Graphical User Interface
Most data and results are represented in a graphical form to better interpret them. The user interface is especially designed to offer very simple, friendly and efficient analysis and planning procedures.


Calculation modules
Capabilities, such as multi-tasking and threads have been used to build real multi-processing applications, allowing to make complex but extremely fast calculations.


Study and Scenario managers
The study and scenario manager allows to easily create and maintain data and results of several system scenarios (hypothesis, or cases study), using a hierarchical scenario architecture and the “inheritance” principle. Inheritance means that, if some data is not defined in a scenario, its value equals the value defined in its parent scenario, making the management of data easy in studies concerning a large number of hypotheses.


Stochastic simulation of the productionGAP2
The core of the GAP is a stochastic Production Simulation programme designed for computing the yearly production cost and system reliability of a power generation system. This calculation module is called PROSIM.
The method is stochastic because it conceptualizes the random aspect of the energy demand and considers the reliability of the units park. It uses direct calculations based on probability distributions to produce the expected values of the main variables of interest: the energy produced by each unit, the operating cost, reliability of the generation system, and marginal production costs.
Individual loading probabilities for each unit can also be obtained. The uncertainty on hydro generation capabilities (natural water inflows) and on the fuel costs is modelled by repeated runs of PROSIM and sensitivity analysis using study scenarios.