Reduced search space combined with particle swarm optimization for distribution system reconfiguration

Silva, Lindenberg I.; Belati, Edmarcio Antônio; Gerez, Cassio; Silva, Ivo Chaves da

doi:10.1007/s00202-020-01150-z

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…Noted: the random initialization of PSO might influence the final performance, but it can be solved by improvement of PSOA, which is not the key in this paper. And Other PSOA parameters are employed from the reference [16].…”

Section: B Initializationmentioning

confidence: 99%

“…In response, flexible network topologies have been optimized by the reinforcement learning algorithm [14] and particle swarm optimization algorithms (PSOA) [15][16] to improve the adequacy of power supply. Because these methods consider all circuit breakers (CBs) as switches when optimizing network topology, a large number of variables are involved in the optimization process and this prolongs simulation time.…”

Section: Introductionmentioning

confidence: 99%

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Flexible Topology for Mitigating the Congestion of Urban Power Grids with Dynamic Thermal Rating

Teh

2023

Preprint

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Urban power grid (UPG) combines transmission and distribution networks. Past studies on UPG congestion mitigation have focused on relieving local congestion while ignoring large-scale energy transfer with safety margins load balancing. This situation is expected to worsen amid the proliferations of renewable energies and electric vehicles. In this paper, a two-layer congestion mitigation framework is proposed, which considers the congestions of UPG with flexible topologies. In the upper-layer, the PSO algorithm is employed to optimize the power supply distribution (PSD) of substation transformers, known as upper-layer PSD. The lower-layer model recalculates the new PSD, known as the lower-layer PSD, based on the topology candidates. A candidate topology is optimum when the Euclidean distance mismatch between the upper- and lower-layer PSDs are the smallest. This optimum topology is tested by the standard power flow to ascertain its feasibility. The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize the voltage deviation and line overloading of UPG considering dynamic thermal rating. The proposed framework is tested on a 56-node test system. Results show that the proposed framework can significantly reduce congestion, maintain safety margins, and determine the optimum transitioning sequence.

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Section: B Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flexible Topology for Mitigating the Congestion of Urban Power Grids with Dynamic Thermal Rating

Teh

2023

Preprint

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“…Methods that have been used to enforce radiality constraints can be categorized into three groups. In first group, the search space is limited to the feasible and radial (FR) space [35] in pre-optimization. This approach is not useful for large networks with big search space due to the complexity of reducing the search space.…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration of harmonic polluted distribution network using modified discrete particle swarm optimization equipped with smart radial method

Behbahani

Jalilian

2023

IET Generation Trans & Dist

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Distribution network reconfiguration (DNR) is capable to improve indices of power systems by changing the distribution network (DN) topology under normal conditions. Here, DNR is implemented to improve the power quality (PQ) and power losses of DN. Furthermore, to achieve better optimum in DNR, discrete particle swarm optimization (DPSO) algorithm is equipped with a smart radial method. The proposed algorithm is faster than other metaheuristic methods due to the prevention of regeneration non-radial configurations. In addition, the algorithm increases the probability of finding the optimal configurations using the mutation function. The results clearly demonstrated the positive effect of DNR in improving harmonic losses, total harmonic distortion (THD), and power losses. Furthermore, the proposed method is compared with other metaheuristic algorithms in IEEE 33 bus DN, IEEE 69 bus DN, and real 95-bus DN in presence of photovoltaic (PV). The results show that the developed algorithm leads to the better or same accuracy and speed in all comparisons. In addition, results indicated that considering the summation of fundamental and harmonic losses compared to only fundamental losses in DNR can lead to further energy saving in DNs.

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“…Throughout 2000s and 2020s, metaheuristics, evolutionary algorithms, and artificial intelligence started to come in pre‐eminence, becoming widely used to solve the DNR problem. Various methods, such as ant colony search (ACS), 7 particle swarm optimization (PSO), 8–10 bacterial foraging algorithm (BFOA), 11 flower pollination algorithm (FPA), 12 runner‐root algorithm (RRA), 13 cuckoo search (CS), 14 firefly algorithm (FA), 15 wild goats algorithm (WGA), 16 beetle search algorithm (BSA), 17 bat algorithm (BA), 18 whale optimization (WHO), 19 vector immune systems, 20 and deep learning, 21,22 among others, were applied.…”

Section: Introductionmentioning

confidence: 99%

Behavior analysis of new bio‐inspired metaheuristics to solve distribution network reconfiguration problem under different radiality constraints representation

Gerez

Costa

Filho

2021

Int J Numerical Modelling

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As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics.

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Reduced search space combined with particle swarm optimization for distribution system reconfiguration

Cited by 12 publications

References 42 publications

Flexible Topology for Mitigating the Congestion of Urban Power Grids with Dynamic Thermal Rating

Flexible Topology for Mitigating the Congestion of Urban Power Grids with Dynamic Thermal Rating

Reconfiguration of harmonic polluted distribution network using modified discrete particle swarm optimization equipped with smart radial method

Behavior analysis of new bio‐inspired metaheuristics to solve distribution network reconfiguration problem under different radiality constraints representation

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