<span lang="EN-US">The integration of Distributed Generation (DG) in a distribution network may significantly affect distribution performance. With the penetration of DG, voltage security is no longer an issue in the transmission network. This paper presents a study of Distributed Generation on the IEEE 26-Bus Reliability Test System (RTS) with the use of Fast Voltage Stability Index (FVSI) for determining its location and incorporated with Grasshopper Optimization Algorithm (GOA) to optimize the sizing of the DG. The study emphasizes the power loss of the system in which a comparison between Evolutionary Programming (EP) and Grasshopper Optimization Algorithm is done to determine which optimization technique gives an optimal result for the DG solution. The results show that the proposed algorithm is able to provide a slightly better result compared to EP.</span>

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

Economic dispatch (ED) is an optimisation strategy to ensure power systems operate in an economic manner. This paper proposes a multi-objective optimisation method to minimise the total generation cost and total system loss simultaneously and find the best adjustment for this economic dispatch problem. This study focused on solving the multi-objective economic dispatch problem using a Heuristic Optimisation (HO) method, namely Multi-Objective Evolutionary Programming (MOEP). The Weighted Sum Method (WSM) is integrated with EP to find a trade-off solution between two objectives: total generation cost minimisation and total system loss minimisation. The practicable proposed method was tested on the IEEE 30-Bus Reliability Test System (RTS) for three different scenarios. MATLAB programming language was used to run the designated algorithm of MOEP. The performance of MOEP to solve the multi-objective ED problem was then compared with another method; the Multi-Objective Artificial Immune System (MOAIS). The experimental results show that MOEP dominates in all cases that have been tested, proving that MOEP is superior than MOAIS in providing high-quality solution to economic dispatch problem with multiple objectives in terms of cheap total generation cost and low total system loss.

This paper presents the application of compensating capacitors for optimal power flow in transmission system. The compensating capacitors are placed at suitable locations in the transmission system and the size of reactive power to be injected by the capacitors to the system is obtained using artificial immune system (AIS) optimization technique. AIS will find the best value of reactive power of the compensating capacitors so that the total system loss is minimum. It is found that applying the AIS optimization technique is prospective approach for finding optimal reactive power output of the installed compensating capacitors in controlling reactive power of transmission system.

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