Open AccessProbabilistic and multi‐objective approach for planning of microgrids under uncertainty: a distributed architecture proposal
Author(s) -
Machado Pedro,
Netto Roberto Silva,
Souza Luiz E.,
Maun JeanClaude
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6242
Subject(s) - computer science , probabilistic logic , architecture , mathematical optimization , resource (disambiguation) , contingency , operations research , reliability (semiconductor) , particle swarm optimization , reliability engineering , risk analysis (engineering) , power (physics) , engineering , artificial intelligence , machine learning , mathematics , medicine , art , computer network , linguistics , philosophy , physics , quantum mechanics , visual arts
This study aims to present an architecture for the planning of microgrids (MGs) in order to support system operator decision. In short, the proposed strategy is an iterative procedure that tries to find the optimal size of distributed energy resource (DER), which attends the necessities of stakeholders. The architecture has five distributed and correlated stages named MG coordination, MG operation optimisation, reliability assessment, contingency assessment, and searching mechanism. Since the DER selection involves multiple criteria and interests of different parts, it requires a multi‐attribute decision system providing a list of possible configurations based on their relative importance as denoted by the stakeholders. Owing to that, the particle swarm optimisation is used to create the multidimensional space of search in which the optimal solution will be selected by means of Pareto front decision criteria. As a result, the architecture provides a candidate solution to optimal size (optimal rated power) of each DER, that must be installed in the MG in order to have an optimal balance between technical, economical, social, and environmental aspects. To have realistic results, such a strategy is performed on a case study of a potential campus MG program.