On the MILP Modeling of Remote-Controlled Switch and Field Circuit Breaker Malfunctions in Distribution System Switch Placement

Abstract

Installing sectionalizing switches and field circuit breakers (FCBs) is vital for the fast restoration of customer electricity supply in distribution systems. However, the high capital costs of these protection devices, especially remote-controlled switches (RCSs) and FCBs, necessitate finding a trade-off between their costs and financial benefits. In this study, we propose a mixed-integer linear programming (MILP) model for optimizing switch planning in distribution systems. The proposed model determines the optimal allocation of manual switches, RCSs, and FCBs to minimize the costs of switches and the reliability-oriented expenses. While the former includes the costs of installing and operating the switches, the latter consists of the distribution company's lost revenue due to the undelivered energy and the regulatory incentives (or penalties) associated with service reliability indices. Two penalty-reward mechanisms are used to account for the financial benefits of increasing the service reliability through reducing the duration and frequency of interruptions. Proposing a novel reliability assessment model, we consider the possibility of malfunctions in both RCSs and FCBs in a highly efficient manner, which is a major contribution of this work. The proposed MILP model is applied to three test networks to validate its applicability and efficacy. The results show the importance of considering the possibility of switch malfunctions in distribution networks. INDEX TERMS Distribution system reliability, field circuit breaker, mixed-integer linear programming, optimal switch placement, remote-controlled switch.

Optimisation of the Distribution System Reliability with Shielding and Grounding Design Under Various Soil Resistivities

“…PSOGSA demonstrates superior convergence and robustness compared to other evolutionary methods like PSO (Sadati et al, 2009), genetic algorithm (Katic & Savic, 1998), ordinal optimisation (Orille-Fernández et al, 2004), and imperialist competition (Soroudi & Ehsan, 2012). Although mixed integer linear programming (MILP) Jooshaki et al, 2023) achieves the global optimum solution quickly, PSOGSA has advantages over MILP: it handles continuous and discrete problems without requiring linear programming formulation, has good global search capability, has a simpler implementation, handles non-linear objectives, and is naturally parallelisable for faster optimisation in distributed computing.…”