Planning and Monitoring of EV Fast-Charging Stations Including DG in Distribution System Using Particle Swarm Optimization

2021 ◽  
pp. 269-302
Author(s):  
Dhiraj Kumar Singh ◽  
Aashish Kumar Bohre
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Fast Charging ◽  
Charging Stations

Optimal restoration of power distribution system through particle swarm optimization

2015 ◽  
Author(s):  
Leonardo W. Oliveira ◽  
Edimar J. Oliveira ◽  
Ivo C. Silva ◽  
Flavio V. Gomes ◽  
Thiago T. Borges ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Power Distribution System

scholarly journals Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distribution System ◽  
Urban Areas ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Rural And Urban Areas ◽  
Enhanced Particle Swarm Optimization

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

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