Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

In this paper, a multi-objective framework is proposed for simultaneous network reconfiguration and power allocation of DGs (Distributed Generations) in distribution networks. The optimization problem has objective functions of minimizing power losses, operation cost, and pollutant gas emissions as well as maximizing the voltage stability index subject to different power system constraints. The uncertainty of loads is modeled using the TFN (Triangular Fuzzy Number) technique. A novel solution method called MOHBB-BC (Multi-objective Hybrid Big Bang-Big Crunch) is implemented to solve the optimization problem. The MOHBB-BC derives a set of non-dominated Pareto solutions and accumulates them in a retention called Archive. The diversity of Pareto solutions conserved by applying a crowding distance operator and afterwards, the 'best compromised' Pareto solution is selected using a fuzzy decision maker. The proposed method is tested on two test systems of 33-bus and 25-bus in different cases including unbalanced three-phase loads. Results obtained from test cases elaborate that the MOHBB-BC results in more diversified Pareto solutions implying a better exploration capability even with a higher fitness. In addition, considering load uncertainty leads to a more realistic solution than deterministic loads but with higher level of power losses.