In this paper, the authors develop a stochastic optimization model, named Optimization Modelling for Gas Seller (OMoGas), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumption on temperature uncertainty. Nonlinearities are present in both the objective function and the constraints. The model can be classified as a non-linear programming (NLP) mixed integer model, with the profit function depending on the number of contracts with the final consumers, the characteristics of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, yearly maximum and minimum consumption in order to avoid penalties and consumption profiles are included. The model is implemented in the General Algebraic Modeling System (GAMS) environment and the results obtained by the stochastic version, based on consumption scenarios, are compared with the deterministic solution.

A stochastic optimization model for a gas sale company

ALLEVI, Elisabetta;
2008-01-01

Abstract

In this paper, the authors develop a stochastic optimization model, named Optimization Modelling for Gas Seller (OMoGas), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumption on temperature uncertainty. Nonlinearities are present in both the objective function and the constraints. The model can be classified as a non-linear programming (NLP) mixed integer model, with the profit function depending on the number of contracts with the final consumers, the characteristics of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, yearly maximum and minimum consumption in order to avoid penalties and consumption profiles are included. The model is implemented in the General Algebraic Modeling System (GAMS) environment and the results obtained by the stochastic version, based on consumption scenarios, are compared with the deterministic solution.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/21656
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