New Multi Objective Approach for Optimal Network Reconfiguration in Electrical Distribution Systems Using Modified Ant Colony Algorithm

Abstract

The losses in networks of Beninese Electrical Energy Company (SBEE) are very high and therefore constitute a concern for the operators. This work consisted in finding an optimal topology of a 41 nodes real network of SBEE by Modified Ant Colony Algorithms (MACA) in order to reduce the losses and ensure a continuous power supply to the customers in case of occurrence disturbances on any branch of this network. With technological breakthrough of Automation and Supervision Systems (SCADA), the operation of distribution networks can be ensured remotely in real time with the aim of minimizing losses, eliminating equipment overload and improving reliability. The criteria of technical performance improvement formulated under operating constraints are solved by Modified Ant Colony Algorithm (MACA) which is association of ant system and fuzzy logic on the Matlab platform. The best results obtained show the effectiveness and efficiency of this method. The SBEE's HVA networks can then be reconfigured automatically to significantly improve their continuity of supply and reliability. The improved results obtained after tests on a standard 33-nodes and a real 41 nodes networks show the robustness and accuracy of this MACA algorithm.

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

“…However, the PSO technique has some disadvantages, such as difficulty in initializing the design parameters and inapplicability to scattering problems. Tolabi et al and Oloulade et al [31,32] introduced the ant colony optimization (ACO) technique to tackle the allocation and size problem of renewable energy source-based DGs in radial distribution networks with the goal of minimizing overall system losses. Their analysis showed that ACO gives a better solution, and computational time is less than GA.…”