Simulated annealing and tabu search applied on network reconfiguration in distribution systems

Andrade, Breno Avelar Rodrigues de; Ferreira, Niraldo Roberto

doi:10.1109/sbse.2018.8395757

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…This drawback is overridden by metaheuristic methods (MM), but they are more complicated in formulation with larger execution time requirements than HM. Therefore, many MM have been developed using ideas of nature behavior [9], which could be based on genetic algorithms [2], particle swarm optimization [3,10,11], tabu search [12,13], simulated annealing [13][14][15], variable scaling hybrid differential algorithm [16], ant colony [17,18], plant growth simulation [19,20], bacterial foraging [21], gray wolf [22], salp swarm [23], symbiotic organism search, hybrid cuckoo search [24], harmony search [25], and binary gravitational search [26], among others. On the other hand, mathematical optimization algorithms solve the reconfiguration problem by using conventional optimization techniques, for example, OPF by Bender Decomposition [8], mixedinteger convex programming [27,28], convex models [29], mixed-integer linear programming [30], and mixed-integer second-order cone programming [31].…”

Section: • Metaheuristic Algorithmsmentioning

confidence: 99%

Network Reconfiguration and Reactive Power Compensation Dispatch in Smart Distribution Systems

Ramírez¹,

Ramírez²,

Gutiérrez-Alcaraz³

2023

Smart Grids Technology and Applications

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A significant challenge is to design strategies to minimize electrical losses in smart distribution systems while observing voltage and feeder loading constraints. Unfortunately, few studies have solved the problem of simultaneously coordinating already installed capacitors banks with network reconfiguration problems. This book chapter presents two methodologies for solving the reconfiguration and reactive power compensation dispatch. Both methodologies are formulated as two-stage solve reconfiguration and reactive power compensation problems with the characteristic of having acceptable computational efficiency and loss reduction close to the optimal solution. In the first stage, network reconfiguration is carried out to discriminate radial configurations that do not satisfy voltage and overload restrictions. In the second stage, a reactive power compensation dispatch is applied to each feasible network configuration by connecting capacitor blocks successively until all available reactive capacity has been used or until a specified loss tolerance has been reached. Finally, switching each capacitor block is carried out using voltage linear sensitivities related to shunt reactive compensation to make a relatively low computational work during the process.

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Section: • Metaheuristic Algorithmsmentioning

confidence: 99%

Network Reconfiguration and Reactive Power Compensation Dispatch in Smart Distribution Systems

Ramírez¹,

Ramírez²,

Gutiérrez-Alcaraz³

2023

Smart Grids Technology and Applications

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“…The article referenced as [10] presents a comparison between two well-known metaheuristic algorithms employed for solving combinatorial optimization problems, specifically focusing on network reconfiguration problems. The methods under investigation in this study are simulated annealing and tabu search.…”

Section: Introductionmentioning

confidence: 99%

Network Reconfiguration for Loss Reduction Using Tabu Search and a Voltage Drop

Ñaupari Huatuco,

Filho,

Pucuhuayla

et al. 2024

Energies

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This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm.

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“…generations and microgrid resilience enhancement and control, which are predicated on renewable power sources [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In a radial distribution network, some switches are normally open (NO) and some others are normally closed (NC).…”

mentioning

confidence: 99%

“…One of the deficiencies found in previous research and studies was that they were focusing on only one or two objectives in reconfiguration, such as reducing the power losses [1] or increasing load balance [2,3]. In [4], as a part of a new compilation, a method based on the simulated annealing and tabu search algorithms was presented.…”

mentioning

confidence: 99%

Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

Mousavi¹,

Atazadegan²,

Mousavi³

2021

ECTI-EEC

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Optimization of power distribution system reconfiguration is addressed as a multi-objective problem, which considers the system losses along with other objectives, and provides a viable solution for improvement of technical and economic aspects of distribution systems. A multi-objective chaotic fractional particle swarm optimization customized for power distribution network reconfiguration has been applied to reduce active power loss, improve the voltage profile, and increase the load balance in the system through deterministic and stochastic structures. In order to consider the prediction error of active and reactive loads in the network, it is assumed that the load behaviour follows the normal distribution function. An attempt is made to consider the load forecasting error on the network to reach the optimal point for the network in accordance with the reality. The efficiency and feasibility of the proposed method is studied through standard IEEE 33-bus and 69-bus systems. In comparison with other methods, the proposed method demonstrated superior performance by reducing the voltage deviation and power losses. It also achieved better load balancing.

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Simulated annealing and tabu search applied on network reconfiguration in distribution systems

Cited by 5 publications

References 8 publications

Network Reconfiguration and Reactive Power Compensation Dispatch in Smart Distribution Systems

Network Reconfiguration and Reactive Power Compensation Dispatch in Smart Distribution Systems

Network Reconfiguration for Loss Reduction Using Tabu Search and a Voltage Drop

Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

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