Abstract
In this paper, an effective method to determine an initial searching point (ISP) of the network reconfiguration (NR) problem for power loss reduction is proposed for improving the efficiency of the continuous genetic algorithm (CGA) to the NR problem. The idea of the method is to close each initial open switch in turn and solve power flow for the distribution system with the presence of a closed loop to choose a switch with the smallest current in the closed loop for opening. If the radial topology constraint of the distribution system is satisfied, the switch opened is considered as a control variable of the ISP. Then, ISP is attached to the initial population of CGA. The calculated results from the different distribution systems show that the proposed CGA using ISP could reach the optimal radial topology with better successful rate and obtained solution quality than the method based on CGA using the initial population generated randomly and the method based on CGA using the initial radial configuration attached to the initial population. As a result, CGA using ISP can be a favorable method for finding a more effective radial topology in operating distribution systems.
Highlights
IntroductionNetwork reconfiguration (NR) is a method of changing the state of the switches on the distribution system in order to obtain the best radial structure to meet the goals such as reducing power loss, improving the load balance between branches or feeders, improving voltage quality, and improving power supply reliability. is is a nonlinear problem with constraints and has been solved by many different methods consisting of mathematical programming techniques such as linear, nonlinear, and dynamic programming [1,2,3,4,5,6,7,8], heuristic methods such as a discrete branch-andbound and branch exchange techniques [9,10,11,12], and metaheuristic methods such as firework algorithm (FW) [13], genetic algorithm (GA) [14, 15], random-key GA [16], runner root algorithm [17, 18], cuckoo search algorithm (CSA) [19,20,21], harmony search algorithm (HSA) [22], particle swarm optimization (PSO) [23, 24], backtracking search algorithm (BSA) [25], symbiotic organisms search (SOS) [26], binary PSO [27, 28], ant colony optimization [29], and flower pollination algorithm [30], combination of the wild goats and exchange market algorithms [31], and grey wolf optimizer (GWO) [32].For using the methods in the first method group, the NR problem is usually described in a rather complicated way.ey are generally ineffective for solving the NR problem. e best evidence for this is the limited number of studies that uses this method to solve the NR problem. e second group of methods approaches the NR problem based on technical criteria to find good solutions. e advantage of this method group is the use of knowledge related to the power system, so the NR problem is described relatively simple
Ey are generally ineffective for solving the Network reconfiguration (NR) problem. e best evidence for this is the limited number of studies that uses this method to solve the NR problem. e second group of methods approaches the NR problem based on technical criteria to find good solutions. e advantage of this method group is the use of knowledge related to the power system, so the NR problem is described relatively simple
To evaluate the effectiveness of the proposed method, the method of determining initial searching point (ISP) and the method of continuous genetic algorithm (CGA) using ISP is built on Matlab platform and run on personal computers. ree distribution systems including 33 nodes, 69 nodes, and 119 nodes are used to reconfigure for power loss reduction
Summary
Network reconfiguration (NR) is a method of changing the state of the switches on the distribution system in order to obtain the best radial structure to meet the goals such as reducing power loss, improving the load balance between branches or feeders, improving voltage quality, and improving power supply reliability. is is a nonlinear problem with constraints and has been solved by many different methods consisting of mathematical programming techniques such as linear, nonlinear, and dynamic programming [1,2,3,4,5,6,7,8], heuristic methods such as a discrete branch-andbound and branch exchange techniques [9,10,11,12], and metaheuristic methods such as firework algorithm (FW) [13], genetic algorithm (GA) [14, 15], random-key GA [16], runner root algorithm [17, 18], cuckoo search algorithm (CSA) [19,20,21], harmony search algorithm (HSA) [22], particle swarm optimization (PSO) [23, 24], backtracking search algorithm (BSA) [25], symbiotic organisms search (SOS) [26], binary PSO [27, 28], ant colony optimization [29], and flower pollination algorithm [30], combination of the wild goats and exchange market algorithms [31], and grey wolf optimizer (GWO) [32].For using the methods in the first method group, the NR problem is usually described in a rather complicated way.ey are generally ineffective for solving the NR problem. e best evidence for this is the limited number of studies that uses this method to solve the NR problem. e second group of methods approaches the NR problem based on technical criteria to find good solutions. e advantage of this method group is the use of knowledge related to the power system, so the NR problem is described relatively simple. E best evidence for this is the limited number of studies that uses this method to solve the NR problem. E advantage of this method group is the use of knowledge related to the power system, so the NR problem is described relatively simple. As changing constraint conditions and objective functions, the use of this method group for the NR problem will face many limitations. E third group of methods is based on the general knowledge to solve the NR problem. In [39], the authors have pointed out the limitations of some methods such as PSO and ant colony optimization (ACO) for the problem of determining the control approach for nonlinear system, but in [40], ACO has better performance than simulated annealing. In [41], PSO and GA have shown that their performance was worse than ant lion optimizer
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