Allocation and sizing of distribution transformers and feeders for optimal planning of MV/LV distribution networks using optimal integrated biogeography based optimization method

作者：Yosef Mohamed; Sayed M M^*; Youssef Hosam K M

来源：Electric Power Systems Research, 2015, 128: 100-112.

DOI：10.1016/j.epsr.2015.06.022

• Planning
• Distribution network
• Reliability
• Optimization
• BBO

Optimization techniques have got much attention for solving complex problems related to different fields. Most of the planning researches deal with primary and secondary distribution systems separately because of complexity of both. This may lead to a local minimum for each but not a global minimum for both. In this paper, we try to reach the global minimum of joined primary and secondary distribution systems planning problem, which is essentially more complicated than planning each of them separately. To overcome such complexity, biogeography-based optimization (BBO) is employed in this work. BBO is a new technique for problem solving, developed by Dan Simon and has attracted wide attention in the last years. BBO is not a reproductive technique and this makes it distinguished from other strategies. Besides, BBO solutions can last or "survive" forever and are modified directly via migration from other solutions, so that BBO solutions directly share their features with other solutions. All of those above mentioned features of BBO algorithm may prove that it can perform efficiently for solving optimization problems and that it might be able to provide better performance compared to other optimization algorithms. In this paper, BBO is employed for solving the problem of optimal planning of a distribution system (OPDS) including both medium voltage (MV) and low voltage (LV) networks and based on uniform or non-uniform load density, where a planning procedure is employed iteratively to find the optimal location and rating of distribution transformers and substations, as well as the type and route of MV and LV feeders. The results are compared with genetic algorithm (GA) and particle swarm optimization (PSO), which indicate that BBO provides better performance in all cases.

• 出版日期2015-11