Open AccessPlacement of minimum distributed generation units observing power losses and voltage stability with network constraints
Author(s) -
Esmaili Masoud
Publication year - 2013
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.2013.0140
Subject(s) - stability (learning theory) , distributed generation , voltage , power (physics) , computer science , control theory (sociology) , power network , mathematical optimization , reliability engineering , electric power system , mathematics , electrical engineering , engineering , renewable energy , physics , control (management) , quantum mechanics , machine learning , artificial intelligence
Distributed generations (DGs) are recently in growing attention as a solution to environmental and economical challenges caused by conventional power plants. In this study, a multi‐objective framework as a nonlinear programming (NLP) is proposed for optimal placement and sizing of DG units. Objective functions include minimising the number of DGs and power losses as well as maximising voltage stability margin formulated as a function of decision variables. The objective functions are combined into one objective function. To avoid problems with choosing appropriate weighting factors, fuzzification is applied to objective functions to bring them into the same scale. DG units are placed at more efficient buses rather than end buses of radial links as usually determined by previous methods for improving voltage stability. Also, power system constraints including branch and voltage limits are observed in the problem. The proposed method not only is able to model all types of DG technologies but also it employs adaptive reactive limits for DGs rather than fixed limits. In addition, a three‐stage procedure is proposed to gradually solve the multi‐objective problem in order to prevent infeasible solutions. Also, a new technique is proposed to formulate the number of DGs without converting the NLP problem into mixed‐integer NLP. Results of testing the proposed method show its efficiency.