Efficient forecast system for distributed generators with uncertainties in the primary energy source

A high degree of variability reduces the available capability of Distributed Generators (DGs) based on renewable energy sources because their power output is uncertain. To determine the true available capability of this kind of DG, this uncertainty must be reduced so that these DGs can be regarded as a reliable alternative. In this work, an efficient forecast system for DGs with uncertainties in the primary energy source is proposed. The power generation uncertainty of these DGs is reduced by running a multiobjective optimization algorithm in multiple probabilistic scenarios combining the Monte Carlo method and the Markov models.