GAP - Generation Analysis and Planning
GAP is a software for the analysis and planning of energy power systems for a given territory or at the level of a country. It will make it possible to optimize investments related to energy generation and the development of new power plants and to control the maintenance of networks and the risks of supplying energy on the network following environmental and / or technical hazards. At the heart of the GAP lies a simulation model of generation scenarios, calculating the technical and economic results of different hypotheses of expansion of the generation park. Various scenarios can be studied and compared in order to identify, through these sensitivity studies, the most technically and financially optimized options. The proposed reports thus allow a detailed analysis of the technical and economic results and the follow-up of indicators of strategy development for power generation expansion planning activities.
1/ User interface
A user-friendly, modern user interface makes it easy to enter data and create scenarios in a variety of ways. Most of the data and results are displayed graphically for better interpretation but also as spreadsheet exportable tables. The user interface has been specifically designed to enable fast and efficient analysis of data and results.
- Manager of Studies and Scenarios
The Study and Scenario Manager makes it easy to manage the data and results of multiple assumptions and case studies through a functional scenario organization.
- Stochastic generation simulation
At the heart of the GAP program is a stochastic production simulation model called PROSIM. This model calculates the annual production costs and evaluates the reliability of supply of the production system. The method is stochastic because it models the randomness of the unplanned outages of the machine park. It is analytic because it provides a unique mathematical expectation of the estimated values resulting from a calculation based on probability distribution functions.
The main results are: the energy produced for each machine, the operating costs, the reliability of the load supply, and the generation marginal costs.
Uncertainty about hydraulic production and fuel costs is addressed by a parameterization of these factors. A powerful 'scenario' management system makes it easy to assess the sensitivity of the production fleet to these variables.
2/ Settings and database
The GAP software considers up to 9 groups of data according to the scenarios and technologies:
- General setting
The aim is to define cross-sectional data for all scenarios and in particular demand profiles and sunshine profiles, as well as the list of fuels, interconnections and wind turbines available for the territory.
- Load forecast
The evolution of the load during the study period can be described either in a very compact or detailled way. An annual peak is defined for each year, an annual load profile giving the weekly peaks, weekly profiles defining the daily peaks, and daily profiles defining the hourly load values. All these profiles are displayed in a graphic way allowing a visual verification of their evolution and an easy comparison of the considered hypotheses. Annual hourly profil can also be capture or imported from a DAP load forecast analysis.
- Fuels data
For each fuel used, the calorific value and the evolution of the cost over time are defined. This evolution is displayed in a graphical way allowing an immediate plausibility check.
- Wind resources
It involves editing the characteristic of wind resources as well as the performance of wind turbines.
- Solar resources
This is to define the characteristics of sunshine and the description of the panels used.
This module allows to define during the planning period the exchanges with the countries or border areas in terms of import and / or export of energy
- Generation and storage Powerplants
The generation data define the production capacities of the plants, the export and import capacities as well as the characteristics of the storage stations. The years of commissioning and dismantling, investment and operating costs, power, reliability, specific consumption, etc., will be defined for each unit. User can also define Battery Energy Storage System (BESS)
- Hydraulic Data
For each hydroelectric station, for each week of the study, the minimum and maximum power levels and the energy produced will be defined. Different alternatives can be defined to represent the hazards of the water supply and the different modes of operation of the hydraulic system. This data can be graphically displayed for immediate plausibility check
User defined maintenance data determines, for each thermal generation unit, the weeks of the year during which this unit will be unavailable due to scheduled maintenance. Different (alternative) hypotheses can be defined for each unit.
3/ Data compilation
GAP uses a stochastic algorithm, classified as Monte Carlo methods, which leads to the identification of the annual energy produced by each unit taking into account that the units of highest merit have been prioritised, following the order of merit. The stochastic generation simulation model - PROSIM - thus uses the weekly load monotones to determine the optimal use of thermal, hydro, solar and wind units and pumping stations.
GAP provides economic results (investment and operating costs), technical results (probability of failure, total energy produced, expectation of energy not supplied, etc.) and key indicators (Co2 emission, renewable share...).
These results are obtained at the level of the generating power system and for each individual unit, for each year of the study and for the entire period. They are presented in the form of tables whose columns are to be defined by the user. The corresponding reports can be displayed and printed. In addition, weekly results can be displayed as graphs.
The results corresponding to the different expansion scenarios can be displayed simultaneously in different windows, allowing an easy comparison of the considered hypotheses.
For each of these groups, the user will define several evolution alternatives in the study period. A particular combination of these alternatives constitutes a Study Scenario.