Optimal distributed generation output and battery switching station placement via ranked evolutionary particle swarm optimization

• Main Author: Jamian, Jasrul Jamani
• Format: Thesis
• Language: English
• Published: 2013
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/36872/1/JasrulJamaniJamianPFKE2013.pdf; http://eprints.utm.my/id/eprint/36872/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:77785?queryType=vitalDismax&query=Optimal+distributed+generation+output+and+battery+switching+station+placement+via+ranked+evolutionary+particle+swarm+optimization&public=true
• Institution: Universiti Teknologi Malaysia

Improvements in DC electrical motor and battery technologies have stimulated interest in Electrical Vehicle (EV) among industrial and personal users. To support the growth of EV, multiple types of Charging Station (CS) have been introduced. The three available types of CS units are Levels 1, 2 and 3. In the charging process, Levels 1 and 2 use the AC/DC charging approach whereas Level 3 uses the DC/DC. However, there are some drawbacks in these CS types, either in terms of charging time (for Levels 1 and 2) or the impact to the system performance (Level 3). This research used the concept of Battery Switching Station (BSS) to solve these problems and introduced analytical and optimization methods to identify appropriate locations of BSS that would have a significant impact on the distribution network even with the existence of Distributed Generation (DG). Besides that, a new meta-heuristic optimization known as Ranked Evolutionary Particle Swarm Optimization (REPSO) and Multi-Objectives REPSO (MOREPSO) which are superior and simple algorithms were employed to find the optimum results for DG output and BSS placement. The analysis started by validating the REPSO performance with three other existing PSOs to solve the 10 benchmark mathematical functions and find the optimal DG output. REPSO had produced optimal results with faster computing time requiring less iterations. In the optimal BSS placement analysis, REPSO gave the best location and had lower power loss in the system for BSS as compared to the analytical approach and randomization of BSS placement. For further improvement to the distribution network, REPSO was employed to compute the optimal output of DG and BSS placement simultaneously where this technique produced the lowest power loss and flexible locations. Another contribution of this research is performing MOREPSO would achieve balanced results between power losses and line capacity increment that are caused by DG output and BSS placement in the distribution network.