A novel practical method for simultaneous placement of switching and protective devices considering load uncertainty

Abstract

Summary A smart distribution system should be able to restore interrupted customers as quickly as possible after outages. By optimal allocation of switching and protective devices, it is possible to enhance the reliability and increase the restoration capacity of the loads after an outage. In this paper, by taking into account uncertainty in the load data, a novel practical method for the simultaneous planning of optimum location of switching devices including tie‐lines and remote‐control switches (RCSs), fault indicators, and cut‐out fuses is proposed. In this paper, a method for minimization of the costs associated with equipment investment and interruption cost has been proposed. Practical parameters such as geographical position and conditions of the understudy network and feeders, and the configuration and possible constraints of the real network from the experts' viewpoint, have been considered in this study. The actual characteristics of the network are extracted by utilizing the analytical hierarchical process (AHP), the geographic information system (GIS) data, and the event recording software. To optimize the problem, the multi‐objective nondominated sorting genetic algorithm II (NSGAII) is employed. The efficacy of the proposed method is proved through simulation of a real medium‐voltage (MV) feeder.

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

“…The following computational intelligence based optimisation methods (also called metaheuristic optimisation methods) have been used for the solution of the OAPCD problem: � Alliance algorithm [62]; � Ant colony system (ACS) [33,[46][47][48]; � Artificial bee colony (ABC) [76,81,93]; � Differential evolution (DE) [84]; � Differential search [80]; � Genetic algorithm (GA) [24,25,35,36,39,43,53,56,57,61,78,86,88,89,91,95,99,100,108,110,116,117,119]; � Greedy randomized adaptive search procedure [82]; � Immune algorithm [42,51]; � Memetic algorithm [71,74]; [26]; � Shuffled frog leaping algorithm [60]; � Tabu search [45,50]. These optimisation algorithms are generally nature-inspired methods.…”

“…In case an overcurrent passes through the fuse, it is heated and, depending on time, it may melt. The allocation of fuse is considered in the following reviewed works [27,29,31,35,41,45,[48][49][50]63,64,77,85,89,94,[98][99][100]104,108,110,113], and [117].…”

“…When a fault takes place downstream the fault indicator, it sends a signal to the control centre. The allocation of fault indicator is considered in the following reviewed works [51,53,54,61,70,[87][88][89][90][91]97,101,105,106,[108][109][110][118][119][120].…”

“…Out of 98 papers, 37 works consider the power flow constraints at their optimisation models [26, 28, 32–34, 39, 44, 45, 47, 48, 50, 52, 54‐57, 59, 62, 65–68, 71, 76, 79, 81, 82, 90, 92, 93, 96, 99, 100, 107, 108, 110, 115], and [117].…”

A MILP optimization model for siting isolation devices in distribution systems with DERs to enhance reliability

“…The integration of DERs is further incorporated in the optimization model for optimal isolation device placements in Reference 20, which is solved by genetic algorithm. In References 21,22, load uncertainties are considered to determine isolation device placements in distribution networks with substantial uncertainties. A common drawback of these publications is that the coordinative behaviors of isolation devices to perform primary and backup protection are not formulated coherently.…”