Abstract
This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.
Highlights
Most of the middle voltage distribution networks have mashed topology in all or some parts of the network, they are usually kept in radial operation by opening sectionalizing and tie-switches [1,2,3,4]
This paper proposes a novel algorithm for the optimal reconfiguration of distribution networks, based on the combination of heuristic method and genetic algorithms, with specific adjustments due to the nature of the given problem
This paper is organized as follows: Section 2 gives a general overview of the proposed algorithm; Section 3 defines algorithms for the efficient initial population generation based on a successive branch-exchange algorithm and stochastic Kruskal’s algorithm; Section 4 describes and illustrates the main modifications introduced in the genetic algorithm process, adjusted for the distribution network reconfiguration problem; Section 5 provides results of the proposed method on different test case networks, as well as a comparison with other state-of-the-art approaches; Section 6 gives main paper conclusions
Summary
Most of the middle voltage distribution networks have mashed topology in all or some parts of the network, they are usually kept in radial operation by opening sectionalizing and tie-switches [1,2,3,4]. In order to solve these issues, different strategies are used: penalization of objective function for unfeasible topologies, repeating crossover process until radial topology is achieved, application of graph traversal algorithm and modification of candidate solution All these approaches significantly increase computational burden and usually don’t provide mechanisms necessary for the transfer of “good” genetic material to a new set of candidate solutions. This paper is organized as follows: Section 2 gives a general overview of the proposed algorithm; Section 3 defines algorithms for the efficient initial population generation based on a successive branch-exchange algorithm and stochastic Kruskal’s algorithm; Section 4 describes and illustrates the main modifications introduced in the genetic algorithm process (crossover, mutation), adjusted for the distribution network reconfiguration problem; Section 5 provides results of the proposed method on different test case networks, as well as a comparison with other state-of-the-art approaches; Section 6 gives main paper conclusions. Replacement of worst individuals obtained after crossover and mutation process with elite individuals k ≤ Niter k=k+1
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