State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation - Abud, Tiago P.; Augusto, Andre A.; Fortes, Marcio Z.; Maciel, Renan S.; Borba, Bruno S. M. C. (Energies (Basel), 2023)
Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend.