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1 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec LGI - Laboratoire Génie Industriel - EA 2606, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec 2 Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec LGI - Laboratoire Génie Industriel - EA 2606, SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF-ECP-Supélec, Dipartimento di Energia

Abstract : The current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology e.g. wind or solar, etc. whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation DG. The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation-failure behavior of the generation systems. The universal generating function UGF technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices e.g. loss of load expectation LOLE, expected energy not supplied EENS are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder.

Keywords : Multi-state modeling Universal generating function Reliability assessment Distributed generation





Autor: Yan-Fu Li - Enrico Zio -

Fuente: https://hal.archives-ouvertes.fr/



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