State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation

Tiago P. Abud (), Andre A. Augusto, Marcio Z. Fortes, Renan S. Maciel and Bruno S. M. C. Borba
Additional contact information
Tiago P. Abud: Graduate Program in Electrical and Telecommunications Engineering (PPGEET), Federal Fluminense University (UFF), Niterói 24210-240, RJ, Brazil
Andre A. Augusto: Graduate Program in Electrical and Telecommunications Engineering (PPGEET), Federal Fluminense University (UFF), Niterói 24210-240, RJ, Brazil
Marcio Z. Fortes: Graduate Program in Electrical and Telecommunications Engineering (PPGEET), Federal Fluminense University (UFF), Niterói 24210-240, RJ, Brazil
Renan S. Maciel: Electrical Engineering Department, Federal University of Technology—Parana (UTFPR), Apucarana 86812-460, PR, Brazil
Bruno S. M. C. Borba: Graduate Program in Electrical and Telecommunications Engineering (PPGEET), Federal Fluminense University (UFF), Niterói 24210-240, RJ, Brazil

Energies, 2022, vol. 16, issue 1, 1-24

Abstract: 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.

Keywords: Monte Carlo Method; electric power systems; smart grids; distributed generation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/394/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/394/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2022:i:1:p:394-:d:1019077

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().