A non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems

Abstract

SUMMARY Distribution system reconfiguration is performed during normal or emergency operations by changing the status of switches. System reconfiguration can be used for system loss reduction or voltage profile improvement during normal operations. For emergency operations, de‐energized loads can be transferred to adjacent energized feeders through system reconfiguration. Several objectives, for example, system real power loss reduction and transformer load balancing improvement, are considered simultaneously during system reconfiguration. In order to consider the objectives simultaneously, weighted sum method was commonly used. Only one solution may be obtained when weighted sum method was applied. However, several compromise solutions were preferred for the distribution system operators. Multi‐objective approach was one of the methods that can be used to provide multiple compromise solutions. In this paper, the fitness function in non‐dominated sorting genetic algorithm and the reproduction procedure in evolutionary programming were integrated. The proposed non‐dominated sorting evolutionary programming was applied to the multi‐objective problems in distribution systems in order to provide system operators with different compromise solutions. The results of this study indicated that the proposed non‐dominated sorting evolutionary programming was capable of identifying better solutions than that of non‐dominated sorting genetic algorithm for distribution system reconfiguration problems. Copyright © 2011 John Wiley & Sons, Ltd.

A joint automatic and manual switch placement within distribution systems considering operational probabilities of control sequences

“…Load transfer constraint: Each feeder has a thermal capacity limit set by a maximum current that it can carry through its conductors. Hence, network feeders should not overload during the restoration process [1,[13][14][15]26] Voltage constraint: Distribution utilities are obliged to meet customers' load demand within the standard power quality requirements; as a result, the entire load points must be supplied within a maximum permissible voltage drop during the restoration process [13][14][15][16]26] Network topology constraint: The distribution network should be made up of a radial structure, based on an operational viewpoint; to such aim, in both normal and restoring states, the network should meet the radiality constraint [10][11][12][13][14][24][25][26][27] To meet the technical constraints, load flow analysis is performed for each feasible control sequence under contingencies. The developed load flow is conducted based upon the maximum load currents, which is usually the case in network planning studies.…”

Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems

“…Sedighizadeh et al [22] used an Efficient Hybrid Big Bang-Big Crunch Algorithm for multi-objective reconfiguration of balanced and unbalanced distribution systems in fuzzy framework. Hsu and Tsai [23] proposed a non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems.…”

Assessing the potential of MV automation for distribution network reliability improvement

“…Automation may be integrated in the process of fault detection isolation and service restoration (FDIR) to improve continuity of supply (CoS). In addition, automated network reconfiguration may be used for feeder load balancing to mitigate voltage problems and manage congestions, thus increasing the capacity of distribution networks to host distributed generation (DG) and other DER, as well as to reduce energy losses …”

“…In addition, automated network reconfiguration may be used for feeder load balancing to mitigate voltage problems and manage congestions, thus increasing the capacity of distribution networks to host distributed generation (DG) and other DER, as well as to reduce energy losses. [14][15][16][17] Continuity of supply is the most important quality factor for consumers and an explicit term of the allowed remuneration of the distribution activity under incentive-based regulation, which is the most common regulatory approach in Europe. 18 Therefore, CoS is the main driver for MV grid automation and the focus of this article.…”