  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.
