Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
The current energy crisis has led to the increasing demand of environmental-friendly and high efficient energy. On top of all the solutions, distributed generation (DG) is one of the solutions that is capable to overcome this problem. The impact of DG towards the distribution system is significan...
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| Format: | Thesis |
| Language: | English |
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| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/2/Full%20text.pdf |
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| Summary: | The current energy crisis has led to the increasing demand of environmental-friendly
and high efficient energy. On top of all the solutions, distributed generation (DG) is one
of the solutions that is capable to overcome this problem. The impact of DG towards the
distribution system is significant where it can be used to improve the system reliability
and efficiency such as improving the voltage profile, reducing the total power losses, etc.
The optimal location and size of DG is very important in order to obtain the maximum
output from the DG allocation. Many researchers found out that solutions using
metaheuristic methods yield a better result compared to the conventional analytical
method. In this thesis, the Particle Swarm Optimization (PSO) combined with the
mutation strategy (PSO-MS) method is proposed in solving the DG allocation problem
with the purpose of minimizing the total real power loss and improving the voltage
profile of the system. This is to prevent the stagnancy of the particles’ population that
usually happens in PSO algorithm. A set of comprehensive simulations have been
carried out to validate the performance of the proposed method where they are
categorized into small system (24-bus distribution system), medium system (33-bus
distribution system), and large system (69-bus distribution system) for single DG and 2
DGs installation. The simulation results of the PSO-MS method are then compared with
PSO and Genetic Algorithm (GA) method in order to validate the performance of the
proposed method. From the results, it is shown that the proposed method has
successfully obtained the optimal DG location and size. As for the comparative study
with PSO and GA, the PSO-MS method also yields a better performance in terms of
total real power loss, voltage profile and simulation time. |
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