Article
Full entry |
PDF
(1.2 MB)
Feedback
PDF
(1.2 MB)
Feedback
Keywords:
gas sale company; mean reverting process; stochastic programming
gas sale company; mean reverting process; stochastic programming
Summary:
The paper deals with a new stochastic optimization model, named OMoGaS–SV (Optimization Modelling for Gas Seller–Stochastic Version), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumptions on temperature uncertainty. 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 and to consumption profiles are included. The results obtained by the stochastic version give clear indication of the amount of losses that may appear in the gas seller’s budget and are compared with the results obtained by the deterministic version (see Allevi et al. [ABIV]).
References:
[1] Alaton P., Djehiche, B., Stillberger D.: On modelling and pricing weather derivatives. Appl. Math. Finance 9 (2002), 1, 1–20 Zbl 1013.91036
[2] Allevi E., Bertocchi M. I., Innorta, M., Vespucci M. T.: A mixed integer nonlinear optimization model for gas sale company. Optim. Lett. 1 (2007), 1, 61–69 MR 2357608 | Zbl 1122.90107
[3] Allevi E., Bertocchi M. I., Innorta, M., Vespucci M. T.: A stochastic optimization model for gas sales companies. IMA J. Management Math. (2007), 1–14
[4] Basawa I. V., Rao B. L. S. Prakasa: Statistical Inference for Stochastic Processes. Academic Press, London 1980 MR 0586053
[5] Bibby B. M., Sorensen M.: Martingale estimation functions for discretely observed diffusion processes. Bernoulli 1 (1995), 1/2, 17–39 MR 1354454
[6] Brockwell P. J., Davis R. A.: Time Series: Theory and Methods. Second edition. Springer, Berlin 1996 MR 1093459 | Zbl 1169.62074
[7] Brooks R. E.: Using generalized networks to forecast natural gas distribution and allocation during periods of shortage. Math. Programming Stud. 15 (1981), 23–42
[8] Davidson J.: Econometric Theory. Blackwell Publishing, 2000 MR 1190175
[9] (2003), Deliberazione 138: Criteri per la determinazione delle condizioni economiche di fornitura del gas naturale ai clienti finali e disposizioni in materia di tariffe per l’attività di distribuzion.
[10] Dornier F., Queruel M.: Pricing weather derivatives by marginal value. Quantitative Finance 1, Institute of Physics Publishing 2000
[11] Eydeland A., Wolyniec K.: Energy and Power Risk Management. Wiley, New York 2003
[12] Ermoliev Y., Wets J.-B.: Numerical Techniques for Stochastic Optimization. Springer-Verlag, Berlin 1988 MR 0957304 | Zbl 0658.00020
[13] Ruszczynski A., Shapiro A.: Stochastic Programming. Elsevier, Amsterdam 2003 MR 2052755 | Zbl 1183.90005
Search
Partner of
