Paper Info
Title
A genetic algorithm approach for the identification of microgrids partitioning into distribution networks
Abstract
In this paper a Genetic Algorithm (GA) is used to partition a distribution network with the aim to minimize the energy exchange among the microgrids (i.e. maximize self-consumption) in presence of distributed generation. The proposed GA is tested on the IEEE prototypical network PG & E 69-bus. The microgrid partitioning is tested over a period of one year with hourly sampled data of real household consumption and real distributed generation data. The proposed GA approach is compared with a Tabu Search (TS) method already presented in the scientific literature. Results show that both GA and TS lead to the identification of equivalent microgrids. However, the GA based approach achieves better convergence results allowing for a reliable network partitioning with less CPU effort. Moreover, the histograms of the power unbalances of the microgrids show unimodal and skewed distributions offering an interesting starting point for the appropriate deployment of storage and control systems.
Year
DOI
Venue
2017
10.1109/IECON.2017.8216008
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Keywords
Field
DocType
genetic algorithm approach,distribution network,energy exchange,hourly sampled data,household consumption,distributed generation data,GA approach,Tabu Search method,TS,equivalent microgrids,GA based approach,reliable network partitioning,Genetic Algorithm,microgrid identification,IEEE prototypical network PG & E 69-bus
Convergence (routing),Histogram,Mathematical optimization,Control theory,Distributed generation,Control system,Engineering,Cluster analysis,Genetic algorithm,Tabu search,Microgrid
Conference
ISSN
ISBN
Citations 
1553-572X
978-1-5386-1128-9
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Saman Korjani100.34
Angelo Facchini200.34
Mario Mureddu311.64
Alfonso Damiano4347.94