Abstract
Distribution network reconfiguration (DNR) is the optimized change in the topological structure of distribution systems without violating its radial configuration. DNR has been of interest in applied mathematics and engineering because of its importance in modern power systems. In literature, various optimization techniques that constitute a large area of applied mathematics were proposed to obtain optimized radial configurations; however, most of them were tested in small distribution systems. In this paper, a novel graphically-based DNR is proposed to obtain the optimized radial configurations for power loss minimization. The proposed DNR is based on the graphical representation of the distribution system without any need for a radiality check. Case studies were conducted on 16-, 33-, 70-, 83-, 136-, 415-, 880-, 1760-, and 4400-node distribution systems in order to minimize the total power loss. Results have proven the ability of the proposed graphical DNR for power loss minimization by obtaining fast radial configurations in comparison with previous studies and also its ability to deal with large distribution systems efficiently. The proposed DNR succeeded in minimizing the total losses for large distribution systems as the 880-, 1760-, and 4400-node distribution systems by 69.45%, 72.51%, and 74.35%, respectively.
Highlights
Distribution systems are the final stage to deliver power from the transmission system to the connected distributed loads
The proposed Distribution network reconfiguration (DNR) mathematical approach is tested on 16, 33, 70, 83, 136, 415, 880, 1760, and 4400-node distribution systems [21]
A novel DNR mathematical approach is proposed based on the graphical structure of the distribution network under investigation
Summary
Distribution systems are the final stage to deliver power from the transmission system to the connected distributed loads. The main concern linked to the heuristic and metaheuristic approaches is the long computational time of these approaches resulting from the random search To overcome this concern, a parallel genetic algorithm was implemented in a graphics processing unit (GPU) in order to minimize power loss for large distribution systems [14] which has proven its ability to provide an optimal/near-optimal solution for the DNR problem for large distribution systems. A parallel genetic algorithm was implemented in a graphics processing unit (GPU) in order to minimize power loss for large distribution systems [14] which has proven its ability to provide an optimal/near-optimal solution for the DNR problem for large distribution systems Another optimization approach was proposed in [15] using a discrete-continuous hyper-spherical search algorithm (DC-HSS) which is able to provide radial configurations directly and decrease the computational burden, but still the main problem was the time consumed to check radiality.
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