Enhancement of voltage stability and power losses for distribution system with distributed generation using genetic algorithm

• Main Author: Omar Khalaf, Omar Tahseen
• Format: Thesis
• Language: English
• Published: 2015
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/48762/25/OmarTahseenOmarMFKE2015.pdf; http://eprints.utm.my/id/eprint/48762/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:87866
• Institution: Universiti Teknologi Malaysia

Distributed generation (DG) is in rising attention in power systems as a solution to environmental and economic challenges caused by conventional power plants. The optimal location and capacity of DGs in power systems is very important for obtaining their maximum potential benefits. A lot of research studies have been carried out to propose different methods in terms of optimal placement and capacity for the distribution generator units to minimize and improve costs and efforts. In this project, genetic algorithm (GA) optimization method along with Newton Raphson (NR) load flow calculation method are used to obtain the optimum size and optimum location of the DGs in the standard IEEE 34-bus radial distribution network. The developed GA-NR algorithm is based on minimizing the power losses and maximizing the voltage profile in the primary radial distribution network. The developed algorithm is applied to determine the optimal sizes and locations for four different cases where each case includes a specific number of DGs. Results indicated that, case 3 where three DG units were installed is the optimal solution to enhance both of the voltage stability and the power losses for the IEEE 34-bus radial distribution system. Furthermore, if the three DGs are located at their suggested optimal locations and have the suggested optimal sizes which are proposed by GA, the total power losses in the IEEE 34-bus radial distribution network will be reduced by nearly 55% and 65% for active and reactive power respectively. Besides, the voltage profile will be improved by nearly 26% if the same condition was applied. Finally, the results have been verified and demonstrated their robustness through comparing with other optimization methods, such as CPF and NLP optimization methods, and by observing the buses voltage profiles and the power losses when relocating the DGs randomly. The comparison results proved that GA placement and sizing is superior to CPF placement and NLP placement and sizing when both of voltage stability and power losses are considered.