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

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 ch...

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Published inEnergies (Basel) Vol. 16; no. 1; p. 394
Main Authors Abud, Tiago P., Augusto, Andre A., Fortes, Marcio Z., Maciel, Renan S., Borba, Bruno S. M. C.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2023
Subjects
Alternative energy sources
Brazil
Confidence intervals
Decision theory
distributed generation
Distributed generation (Electric power)
Electric power systems
Electricity
Electricity distribution
Literature reviews
Markov analysis
Mathematical optimization
Methods
Monte Carlo Method
Optimization
Probability
Random variables
Sample variance
smart grids
Brazil
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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.
AbstractList 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.
Audience Academic
Author Maciel, Renan S.
Borba, Bruno S. M. C.
Augusto, Andre A.
Fortes, Marcio Z.
Abud, Tiago P.
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– volume: 16
  start-page: 641
  year: 2022
  ident: ref_40
  article-title: Mixed-integer Stochastic Evaluation of Battery Energy Storage System Integration Strategies in Distribution Systems
  publication-title: IET Gener. Transm. Distrib.
  doi: 10.1049/gtd2.12316
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Snippet Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of...
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SubjectTerms Alternative energy sources
Brazil
Confidence intervals
Decision theory
distributed generation
Distributed generation (Electric power)
Electric power systems
Electricity
Electricity distribution
Literature reviews
Markov analysis
Mathematical optimization
Methods
Monte Carlo Method
Optimization
Probability
Random variables
Sample variance
smart grids
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Title State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation
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