Open AccessProbabilistic modelling of renewable generation to account for uncertainties in interconnection studies
Author(s) -
Garry Aurel,
AlvarezHerault MarieCecile,
Cadoux Florent,
Hadjsaid Nouredine
Publication year - 2019
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12091
Subject(s) - interconnection , computer science , probabilistic logic , renewable energy , overcurrent , reliability engineering , generator (circuit theory) , electricity generation , statistical model , electric power system , industrial engineering , operations research , power (physics) , engineering , telecommunications , electrical engineering , artificial intelligence , physics , quantum mechanics , voltage
Summary The development of renewable generation connected to the distribution network can create overvoltage or overcurrent situations leading to expensive upgrades of the network. Solutions such as production curtailment could avoid these expenditures, but in order to take full advantage of these new solutions, one must first appropriately model the statistical behaviour of loads and generation over several years with a high level of uncertainties. This paper performs a statistical analysis on 4254 real‐world generation time series that run over about 2 years, with a 10‐minute time step, for four different types of generation technologies. This large dataset was made available thanks to a partnership with the major French distribution system operator (DSO), Enedis. The objective is to develop probabilistic models of the power output for each type of generator and also of the statistical dependency between two generators and between generation and load and to apply them on an interconnection case study.