Minimal Loss Reconfiguration Using Genetic Algorithms With Restricted Population and Addressed Operators: Real Application

Mendoza, Jorge E.; Lopez, Rigoberto; Morales, Dario; López, E.; Dessante, Philippe; Moraga, Rubén

doi:10.1109/tpwrs.2006.873124

Cited by 222 publications

(120 citation statements)

References 24 publications

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“…Modification to (10) needs to be made in order to prevent the constraint from being binding for any inactive path corresponding to bus j (an inactive path is a path from substation to the node in which there is one or more open branches). Therefore, an additional term is added to the right hand side of (10) as follows:…”

Section: A Mip-based Dsrmentioning

confidence: 99%

Robust Reconfiguration of A Distribution System

Moradzadeh

Liu

Tomsovic

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in net loads, i.e. load minus renewable generation, due to lack of sufficient measurements and high utilization of demand response programs and renewable resources, creates challenges for effective reconfiguration. Deterministic optimization of reconfiguration may no lead to optimal/feasible results. Two methods to address these uncertainties are introduced in this paper: one, based on a stochastic MIP (SMIP) formulation and two, based on a fuzzy MIP (FMIP) formulation. Case studies demonstrate the robustness and efficiency of the proposed reconfiguration methods.

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Section: A Mip-based Dsrmentioning

confidence: 99%

Robust Reconfiguration of A Distribution System

Moradzadeh

Liu

Tomsovic

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…In this work we used the scheme detailed in [12] to code the candidate topologies that are identified along the solution algorithm. This scheme uses integer numbers to indicate the branches of the system that are opened/disconnected.…”

Section: A Coding the Candidate Solutionsmentioning

confidence: 99%

“…Step I: The initial population is generated using the heuristic described in [12]. This heuristic uses the fundamental loops to randomly generate candidate topologies.…”

Section: B Sgacb Applied To the Dsr With Different Demand Levelsmentioning

confidence: 99%

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Specialized genetic algorithm of Chu-Beasley applied to the Distribution System Reconfiguration problem considering several demand scenarios

Souza

Romero

Pereira

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

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“…It can make the distribution system, power system and power transmission system more closely related with the distribution and supply of power to the user. In order to safeguard the power quality and supply capacity, the analysis and optimization of distribution network which is the most important link in the power distribution system have gradually been a research focus [1]. Distribution network configuration is a complicated large-scale combinatorial optimization problem.…”

mentioning

confidence: 99%

The Research on the Distribution Network Reconfiguration and the Application of Immune Particle Swarm Optimization

Huang¹,

Chen²,

Chen³

et al. 2016

IJGDC

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With the rapid development of national economy, the demand for electricity is more intense. There is a new demand for the security, reliability and applicability of the power distribution system. The 1.IntroductionThe power system consists of four parts: electricity generation, electricity transmission, power distribution and load. Among them, power distribution system is the last link of power system. It is the infrastructure which directly connects with users. It can make the distribution system, power system and power transmission system more closely related with the distribution and supply of power to the user. In order to safeguard the power quality and supply capacity, the analysis and optimization of distribution network which is the most important link in the power distribution system have gradually been a research focus [1]. Distribution network configuration is a complicated large-scale combinatorial optimization problem. Whereas the distribution network configuration studies study from the perspective of the topology, mathematical model, flow calculation method and immune particle swarm optimization method. Through analyzing the topology of distribution network, the paper simplifies the distribution network model distribution network model by using the characteristics of flow calculation method. Through deeply studying particle swarm calculation method and the principle of immune calculation method, the paper finally form a new hybrid algorithm by effectively combining two calculation methods [2]. This is immune particle swarm calculation method. This method mixes respective characteristics of particle swarm calculation method and immune calculation method and can searches and guarantees individual diversity in group, which can improve search ability and accelerate the convergence speed. Minimize the network loss by building distribution network model. And conduct simulation of dynamic configuration and static configuration to distribution network. The result shows that the inherited particle swarm calculation method is effective in the application of distribution network configuration [3].

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Minimal Loss Reconfiguration Using Genetic Algorithms With Restricted Population and Addressed Operators: Real Application

Cited by 222 publications

References 24 publications

Robust Reconfiguration of A Distribution System

Robust Reconfiguration of A Distribution System

Specialized genetic algorithm of Chu-Beasley applied to the Distribution System Reconfiguration problem considering several demand scenarios

The Research on the Distribution Network Reconfiguration and the Application of Immune Particle Swarm Optimization

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