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A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

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hdl:2117/130420

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Guerra Sánchez, Luis Gerardo
Martínez Velasco, Juan AntonioMés informació
Document typeArticle
Defense date2018-01-01
Rights accessOpen Access
Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain
Abstract
Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.
CitationGuerra, L.; Martinez, J. A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing. "AIMS Energy", 1 Gener 2018, vol. 6, p. 764-800. 
URIhttp://hdl.handle.net/2117/130420
DOI10.3934/ENERGY.2018.5.764
ISSN2333-8326
Publisher versionhttp://www.aimspress.com/article/10.3934/energy.2018.5.764
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