A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals

Chen, Qian; Wang, Weiqing; Wang, Haiyun; Wu, Jiahui; Li, Xiaozhu; Lan, Jiongfeng

doi:10.1109/access.2020.3036898

Cited by 18 publications

(10 citation statements)

References 37 publications

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“…A parallel slime mould algorithm for solving the NR problem with the allocation of DG is proposed in [15]. The problem of ONR is solved in [16] with various objectives such as minimization of voltage deviation, power losses, and load balance as a multi-objective problem of the RDS by satisfying various network constraints. An approach for the mitigation of voltage volatility in the RDS of using the ONR has been proposed in [17].…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

2022

IJATEE

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This work solves the optimal allocation problem of distribution static compensator (D-STATCOM) and distributed generation (DG) simultaneously. The network reconfiguration (NR) is also applied in this work for better utilization of the existing distribution network infrastructure. This work proposes a robust and efficient NR algorithm to address the NR problem, including DG and D-STATCOM allocation by considering techno-economic objectives. Power loss minimization is selected as an objective and a gravitational search algorithm (GSA) is used to solve this efficient allocation problem. This approach has been executed on IEEE 33 bus radial distribution system (RDS). The results demonstrate that the application of simultaneous NR, D-STATCOM, and DG allocation has resulted in a reduction in network losses as well as enhancement in the voltage profile of entire RDS. By this simultaneous approach, the power loss has reduced by 77.5% and minimum voltage in the RDShas increased from 0.9131 p.u. to 0.9735 p.u.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

2022

IJATEE

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“…For k time interval, assuming that φ is a variable of power angle, the relational expression of active power P k RDG and reactive power Q k RDG of RDG models is shown as follows [41].…”

Section: (2) Pv Rdg Modelmentioning

confidence: 99%

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

Wen

Lin

et al. 2022

International Transactions on Electrical Energy Systems

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Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.

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“…Additionally, due to the nature of renewable DER units, concurrent changes in the generation of DER units for supplying the demand load may result in under or overvoltage [21], [22]. The effects of such phenomena may vary according to the location of DER units and weather conditions [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Soesanti

Syahputra

2022

IEEE Access

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Multi-objective ant lion optimization (MALO) is a technique developed by imitating the behavior of the ant king foraging. This method has many advantages: straightforward, scalable, flexible, good balance, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has attracted the attention of many researchers and has been used successfully to find optimal solutions for power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is due to the integration of distributed energy resources (DER). DER is a renewable energy power plant with up to 10 MW, gaining popularity in recent times. In its application, the integration of DER into the distribution network can cause new problems, namely, load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, sound planning is needed to place DER. This research proposes a multi-objective optimization technique based on MALO to determine the optimal DER location and capacity. The MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases have been carried out for the IEEE 33-bus distribution network. Four scenarios have been carried out, namely integrating DER type-I, type-II, type-III, and type-IV. In each design, the placement of 1 DER, 2 DERs, and 3 DERs is modelled to optimize the location and capacity. The results of multi-objective optimization show that the MALO technique can improve the distribution network performance, characterized by a significant reduction in power losses, an increase in the bus voltage profile, and a balanced load on each feeder.

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A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals

Cited by 18 publications

References 37 publications

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

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