A stochastic framework for gas retailer based on temperature and oil prices evolution

We consider a new two–stage stochastic optimization model, named OMoGaS–2SV (Optimization Modelling for Gas Seller-Second Stochastic version), to assist companies dealing with gas retail commercialization. Due to nonlinearities present in the objective function, the model can be classified as an NLP mixed integer model, with the profit function depending on the number of contracts with the final consumers, the typology of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, to yearly maximum and minimum consumption in order to avoid penalties are included. Consumers consumption profiles are also considered. Temperature influences gas consumption of small consumers and is modelled by a mean reverting process. Oil prices influence the energetic indices to which sell and purchase prices are related. Forward curves of energetic indices have been analyzed by econometric models while exchange rates are modelled by a GARCH model. The results obtained by the stochastic version give clear indication of the amount of losses that may appear in the gas seller’s budget.