article · IEEE Access
Controlling active and reactive power is essential for improving the performance of electrical distribution systems. An enhanced genetic algorithm combines genetic algorithm techniques with a local search mechanism to identify the optimal location and capacity for distributed generators and shunt capacitors simultaneously within radial networks. The inclusion of the local search component enhances exploration across the search space to secure global solutions. The optimisation process focuses on reducing total real power losses alongside overall voltage deviation. Tested against standard benchmark networks, specifically the IEEE thirty-three, sixty-nine, and one hundred and nineteen bus distribution systems, simulation findings demonstrate superior performance compared to existing optimisation methods. Furthermore, an economic cost assessment across light, shoulder, and heavy loading scenarios shows that the approach yields notable technical and financial benefits for network operation.
Electrical distribution networks often suffer from energy losses and voltage instability during power delivery. Using an advanced computational method to place generators and capacitors strategically helps utilities maintain consistent voltage levels and reduce wasted electricity. This leads to more reliable power grids and lower operating costs under varying consumer demand profiles.
The optimisation procedure can assist electrical utilities, distribution system operators, and grid planning software providers seeking to integrate decentralised power generation and capacitors efficiently. Because the findings are based on simulation studies on standard benchmark test networks rather than physical utility deployment, the technology represents early-stage to applied simulation research requiring validation on real-world networks before commercial adoption.
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Controlling active/reactive power in distribution systems has a great impact on its performance. The placement of distributed generators (DGs) and shunt capacitors (SCs) are the most popular mechanisms to improve the distribution system performance. In this line, this paper proposes an enhanced genetic algorithm (EGA) that combines the merits of genetic algorithm and local search to find the optimal placement and capacity of the simultaneous allocation of DGs/SCs in the radial systems. Incorporating local search scheme enhances the search space capability and increases the exploration rate for finding the global solution. The proposed procedure aims at minimizing both total real power losses and the total voltage deviation in order to enhance the distribution system performance. To prove the proposed algorithm ability and scalability, three standard test systems, IEEE 33 bus, 69 bus, and 119-bus test distribution networks, are considered. The simulation results show that the proposed EGA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literature. Moreover, an economic based cost analysis is provided for light, shoulder and heavy loading levels. It was proven, the proposed EGA leads to significant improvements in the technical and economic points of view.
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DOI: 10.1109/access.2020.2981406
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