Répartitions De Charges Possibiliste Et Probabiliste Pour L’analyse Des Incertitudes

Répartitions De Charges Possibiliste Et Probabiliste Pour L’analyse Des Incertitudes

Wendy Carolina Briceno Vicente Raphaël Caire Nouredine Hadjsaid

G2ELAB, UMR 5529 INPG/UJF-CNRS 11, rue des Mathématiques BP 46, 38402 St Martin d’Hères Cedex, France

Corresponding Author Email: 
wendy.briceno-vicente@g2elab.grenoble-inp.fr; raphael.caire@g2elab.grenoble-inp.fr; nouredine.hadjsaid@g2elab.grenoble-inp.fr
Page: 
233-266
|
DOI: 
https://doi.org/10.3166/EJEE.17.233-266
Received: 
20 mai 2013
| |
Accepted: 
25 juin 2015
| | Citation

OPEN ACCESS

Abstract: 

The Power Flow studies in short and long term are essential in planning, dispatching, and analysis of electrical networks. The consideration of renewable energy sources in the power flow computation has become indispensable to determine: the optimal point of power generation of electrical machines, network topology, as well as load management plan to minimize operational costs, respecting the network constraints and maintaining the reliability of the power network. The aim of this paper is to develop a power flow strategy considering the influence of variations of distributed generation power injection and load consumption. Two methods are proposed and compared: the probabilistic method of Monte-Carlo and the possibilistic method of the fuzzy arithmetic.

Keywords: 

power flow, Monte-Carlo method, fuzzy arithmetic, random variable, fuzzy number, interval arithmetic.

1. Introduction
2. La modélisation des sources d’incertitude du réseau électrique
3. La modélisation probabiliste
4. La modélisation possibiliste de l’incertitude
5. La transformation possibiliste-probabiliste
6. Le calcul de répartition de charges
7. Description du réseau de distribution de test
8. Résultats obtenus
9. Conclusion
  References

Alberta Electric Operator System (AESO). 10 Minute Historical Data for Total Wind Power and Alberta Internal Load. Wind Power/AIL Data, 11 Oct. 2011.

Allan R., Al-Shakarchi M. (1976). Probabilistic ac load flow. Electrical Engineers, Proceedings of the Institution of, vol. 123, n˚ 6, p. 531-536.

Allan R., Al-Shakarchi M. ( 1977). Linear dependence between nodal powers in probabilistic ac load flow, Electrical Engineers, Proceedings of the Institution of, vol. 124, n˚ 6, p. 529-534.

Allan R., Borkowska B., Grigg C. (1974). Probabilistic analysis of power flows. Electrical Engineers, Proceedings of the Institution of, vol. 121, n˚ 12, p. 1551-1556.

Allan R., Leite da Silva A., Burchett R. (1981). Evaluation methods and accuracy in probabilistic load flow solutions. Power Apparatus and Systems, IEEE transactions on, n˚ 5, p. 2539-2546.

Alvarado F., Hu Y., et Adapa R. (1992). Uncertainty in power system modeling and computation, IEEE International Conference on Systems, Man and Cybernetics, vol. 1, p. 745-760.

Anders G. J. (1989). Probability concepts in electric power systems, John Wiley and Sons Inc. 

Bloom J. (1985). Probabilistic production costing with dependent generating sources. Power Apparatus and Systems, IEEE Transactions on, n˚ 8, p. 2064-2071.

Borkowska B. (1974). Probabilistic load flow. Power Apparatus and Systems, IEEE Transactions on, n˚ 3, p. 752-759.

Briceño Vicente, W. C. (2012). Modélisation des réseaux de distribution sous incertitudes (Doctoral dissertation, Université de Grenoble, Grenoble). Septembre 20.

Caramanis M., Tabors R., Nochur K., Schweppe F. (1982). The Introduction of NonDIispatchable Technologies a Decision Variables in Long-Term Generation Expansion Models. Power Apparatus and Systems, IEEE Transactions on, n° 8, p. 2658-2667.

Civanlar M. R., Trussell H. J. (1986). Constructing Membership Functions Using Statistical Data, Fuzzy Sets and Systems., vol. 18, p. 1-13.

Council G. (2006). Perpectivas Globales de la Energía Eólica.

Dopazo J., Klitin O., Sasson A. (1975). Stochastic load flows. Power Apparatus and Systems, IEEE Transactions on, vol. 94, n° 2, p. 299-309.

Dubois D., Prade H. (1993). Fuzzy Sets and Probability: Misunderstandings, Bridges and Gaps. Second IEEE International Conference on Fuzzy Systems, vol. 2, p. 1059-1068.

Dubois D., Prade H., Sandri S. (1993). On Possibility/Probability Transformations. Fuzzy Logic, Kluwer Academic Publishers, p. 103-112.

Ellard D., Ellard P. (2003). SQ Course Book.

Hanss M. (2005). Applied fuzzy arithmetic, Springer.

Khodr H., Ocque L., Yusta J., Rosa M. (2006). New Load Flow Method SE Oriented For Large Radial Distribution Networks. Transmission & Distribution Conference and Exposition: Latin America, 2006. IEEE/PES, IEEE, p. 1-6.

Klir G., Yuan B. (1995). Fuzzy sets and fuzzy logic : Theory and Applications, Possibility Theory versus Probability Theory, Prentice Hallp. 200-207.

Kundur, Prabha (1994). Power system stability and control. Eds. Neal J. Balu, and Mark G. Lauby. vol. 7, New York, McGraw-hill.

Leite da Silva A., Arienti V. (1990). Probabilistic load flow by a multilinear simulation algorithm, Generation, Transmission and Distribution, IEE Proceedings C, vol. 137, IET, p. 276-282. 

Leite da Silva A., Allan R., Soares S., Arienti V. (1985). Probabilistic load flow considering network outages, Generation, Transmission and Distribution, IEE Proceedings C, vol. 132, IET, p. 139-145.

Leite da Silva A., Arienti V., Allan R. (1984). Probabilistic load flow considering dependence between input nodal powers. power apparatus and systems, IEEE transactions on, n° 6, p. 1524-1530.

Sauer P. (1977). Generalized stochastic power flow algorithm, Technical report, Purdue Univ., Lafayette, IN (USA). School of Electrical Engineering. 

Teng J. (2003). A direct approach for distribution system load flow solutions. Power Delivery, IEEE Transactions on, vol. 18, n° 3, p. 882-887.

Zadeh L. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, vol. 100, p. 9-34.