Probabilistic Load-Flow Using Analysis Using DPL Scripting Language

F. González-Longatt, S. Alhejaj, A. Marano-Marcolini, José L. Rueda

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

1 Citation (Scopus)

Abstract

Load-flow analysis is an effective tool that is commonly used to capture the power system operational performance and its state at a certain point in time. Power grid operators use load-flow extensively on a daily basis to plan for day-ahead and dispatch scheduling among many other purposes. Also, it used to plan any grid expansion, alter or modernization. However, due to the deterministic nature and its applicability for only one set of operational data at a certain period, deterministic load-flow reduces the chances for predicting the uncertainty in power system. Researchers usually create a data model using probabilistic analyses techniques to produce a stochastic model that mimics the realistic system data. Combining this model with Monte Carlo methodology leads to form a probabilistic load-flow tool that is more powerful and potent to carry on many uncertainty tasks and other aspects of power system assessment. This chapter presents the DIgSILENT PowerFactory script language (DPL) implementation of a DPL script to perform probabilistic power flow (PLF) using Monte Carlo simulations (MCS) to consider the variability of the stochastic variables in the power system during the assessment of the steady-state performance. The developed PLF script takes input data from an external Microsoft Excel file, and then, the DPL can carry on a probabilistic load-flow and export the results using a Microsoft Excel file. The suitability of the implemented DPL is illustrated using the classical IEEE 14 buses.

Original languageEnglish
Title of host publicationAdvanced Smart Grid Functionalities Based on PowerFactory
Subtitle of host publicationGreen Energy and Technology
EditorsF. Gonzalez-Longatt, J. Torres
Place of PublicationCham
PublisherSpringer
Chapter5
Pages93-124
Number of pages32
ISBN (Electronic)978-3-319-50532-9
ISBN (Print)978-3-319-50531-2
DOIs
Publication statusPublished - 2018

Publication series

NameGreen Energy and Technology
PublisherSpringer
Number1
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537

Keywords

  • Distribution density function
  • Distribution function
  • DPL script
  • Monte Carlo
  • Probabilistic load-flow
  • Simulation
  • Stochastic model

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