Electricity is an essential good, which can hardly be replaced. It can be produced thanks to a wide rangeof sources, from coal to nuclear, not to mention renewables such as wind and solar. In order to meetdemand at the lowest cost, an optimisation is made on electricity markets between the differentproduction plants. This optimisation mainly relies on the electricity production cost of each technology.In order to include long-term constraints in the short-term optimisation, a so-called use value (oropportunity cost) can be computed and added to the production cost. One long-term constraint thatEDF, the main French electricity producer, is facing is that its gas plants cannot exceed a given numberof operation hours and starts between two maintenances. A specific software, DiMOI, computes usevalues for this double constraint but its parameters needs to be tested in order to improve thecomputation, as it is not thought to work properly.DiMOI relies on dynamic programming and more particularly on an algorithm called Bellman algorithm.The software has been tested with EDF R&D department in order to propose some modellingimprovements. Electricity and gas market prices, together with real plant parameters such as startingcosts, operating costs and yields, were used as inputs for this work, and the results were checkedagainst reality.This study gave some results but they appeared to be invalid. Indeed, an optimisation problem wasdiscovered in DiMOI computing core: on a deterministic context, a study with little degrees of freedomwas giving better profits than a study with more degrees of freedom. This problem origin was notfound precisely with a first investigation, and the R&D team expected the fixing time to be very long.The adaptation of a simpler tool (MaStock) was proposed and made in order to replace DiMOI. Thisproject has thus led to DiMOI giving up and its replacement by MaStock. Time was missing to testcorrectly this tool, and the first study which was made was not completely positive. Further studiesshould be carried out, for instance deterministic ones (using real past data) whose results could becompared to reality.Some complementary studies were made from a fictitious system, in order to study the impact of someparameters when computing use values and operations schedules. The conclusions of these studiesare the little impacts that changes in gas prices and start-up costs parameters have on the global resultsand the importance of an accurate choice in the time periods durations used for the computations.Unfortunately these conclusions might be too specific as they were made on short study periods.Further case studies should be done in order to reach more general conclusions.