Comprehensive Review on Static and Dynamic Distribution Network Reconfiguration Methodologies

Abstract

Reconfiguration of a distribution network is one of the main approaches to control and enhance distribution network indices, such as voltage profile and power losses. Distribution network operators perform reconfiguration for long-term or short-term periods based on network equipment and intended objectives. Long-term or static reconfiguration is suitable for traditional and modern networks with conventional switches. On the other hand, modern distribution networks that are equipped with one or more remote control switches can perform reconfigurations within short-term periods, to maximize predefined objectives. This paper presents a comprehensive review of recent literature on network reconfiguration. Reconfiguration methodologies are classified into five groups: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. The paper provides a general definition and comparison of the categories and discusses their application in dynamic and static reconfiguration. The paper introduces dynamic reconfiguration as the future challenges in smart and modern distribution networks and for the first time categorizes various methodologies in dynamic reconfiguration. The paper serves as a guide to assist engineers and researchers in selecting the most suitable methodology based on their system equipment and objectives.

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

“…The simplification of Constraint (11), detailed in the Appendix A, yields topological decisions consistent with the best outcomes reported in the literature for the 14, 33, and 84 case studies. However, in larger systems, such as the 133 bus system, the error from assuming a constant voltage of 1 p.u.…”

“…This reduction is more pronounced in test systems with a larger number of nodes (84 and 136). Further reductions in computation time are achieved by simplifying Constraint ( 6), based on the assumption that voltage variation at the nodes is negligible, and by applying convex relaxation (11). The model that incorporates these simplifications approaches an optimality gap of 0.0%.…”

“…Recent solutions have managed to reduce computational time and enhance solution accuracy, even considering the presence and strategic placement of distributed generation in the network. For a more detailed review of the historical evolution of techniques to solve DPSR you can refer to [8,10,11].…”

“…Note that (6) contains nonlinear terms [4,8], and is also a non-convex constraint that can be convexified as described in (10). Based on the limited range within which nodal voltage magnitudes lie in practice, the product V 2 i I 2 k,i in (10) can be linearized as shown in (11). This approach is simpler than the one commonly used to linearize the term V 2 i I 2 k,i [2,4], and as shown in the results section, it is very accurate, allowing us to reduce computational effort.…”

“…Model with a simplification of the term V 2 i I 2 k,i : in this case, Constraint ( 11) is included instead of Constraint (6); the model then consists of Constraints ( 2)-( 5), ( 7), ( 8) and ( 11)- (13) • Model with a double power triangle constraint: in this case, the set of Constraints (10) and (11) is included instead of Constraint (6); the model then consists of Constraints ( 2)-( 5), ( 7), ( 8) and ( 10)-( 13)…”