A stochastic optimization model for gas retail with temperature scenarios and oil price parameters

The paper deals with a new stochastic optimization model, named Optimization Modelling for Gas Seller–Second Stochastic Version (OMoGaS–2SV), to assist companies dealing with gas retail commercialization. We consider temperature as a source of stochasticity, but we take into account also information on energy-related indices. Temperature influences gas consumption of small consumers and is modelled by a mean reverting process. Oil prices and exchange rates influence the energy-related indices to which sell and purchase prices are related. Forward curves of these are analyzed by a vector autoregressive model while exchange rates are modelled by a generalized autoregressive conditional heteroskedasticity model. The profit function depends on the number of contracts with the final consumers, the typology of such consumers, the cost supported to meet the final demand and penalties for daily maximum consumption exceeding daily maximum capacity. Linear constraints related to a maximum daily gas consumption and binary constraints on indexation formulas are included.