Integrated database approach in multi‐objective network reconfiguration for distribution system using discrete optimisation techniques

Abstract

Reconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non-radiality network solutions during the optimisation process. To address this issue, this work proposes to store predetermined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi-objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33-bus and 118-bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions.

Distribution network reconfiguration based on artificial network reconfiguration for variable load profile

“…In order to verify the proposed ANN technique, a comparison with other published studies is provided in Table 5. The optimal configurations obtained from references that used fireworks algorithm (FWA) [19], discrete evolutionary (DEP) [20], harmony search algorithm (HAS) [24],cuckoo search algorithm (CSA) [32] were reevaluated at 100% loading to determine the power loss using the same load flow program (from MATPOWER) as in this work. The results are presented in Table 5.…”

“…This task can be achieved by using any optimization technique. In this work, evolutionary programming (EP) is chosen due to it simplicity, reasonable convergence time and because it has been proven to work well for network reconfiguration application [20,30]. The application of EP for network reconfiguration can be summarized in the following steps:…”

“…Due to the increase of switching options and size of distribution systems, the second category of approaches, which are metaheuristic techniques, were used. Such techniques are genetic algorithm [17], particle swarm optimization [18], firefly [19], discrete evolutionary programming [20], discrete artificial bee colony [21], tabu search [22], runner root [23], and harmonic search [24]. Metaheuristic techniques have the ability to obtain global optimum solution; however, the computational time is very high.…”

“…Once a change occurs in the load, recalculation of DNR process is required. The second reason is inconsistency in providing optimal solution due to the random search process [20]. On the other hand, the application of ANN techniques can significantly reduce the time required for DNR process.…”

Loss allocation in distribution networks with distributed generators undergoing network reconfiguration

“…Moreover, Mishra et al [14] presented a comprehensive review of various NR methods up to 2016. Heuristic-based NR methods converge very quickly as compared to the meta-heuristic search based NR methods [15]. However, heuristic methods do not always guarantee a global minimum configuration.…”