Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

Tolabi, Hajar Bagheri; Ali, Mohd. Hasan; Ayob, Shahrin Md.; Rizwan, Mohammad

doi:10.1016/j.energy.2014.04.099

Cited by 36 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, IEEE 33‐bus radial distribution test system is used. This system has a rated load of 3.7 MW and 2.3 MVAr with its diagram shown in Figure .…”

Section: Case Study and Discussionmentioning

confidence: 99%

A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant

Nosratabadi

Hooshmand

Gholipour

et al. 2015

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

SUMMARYVirtual power plant (VPP) is combining different types of generations and interruptible loads to be able to contribute to the market as a power plant with a significant output. In other words, different power generations are combined in a complementary manner to form a defined generation and a demand profile. Because the most important elements of these operators are distributed generation (DG) units and demand response loads (DRLs), the determination of optimal location, capacity, type, as well as the installation time of DGs, DRLs participation size, and place in the distribution system are the most important challenges of operators. In this paper, the formulation of optimal placement of DG resources and DRLs is presented and solved simultaneously in a distribution system to determine the optimal VPP. For this purpose, the concepts of commercial virtual power plant and technical virtual power plant are introduced first, and then the optimal VPP, which can send its energy bids as a short-term and long-term power scheduling to the power market, is determined. Here, commercial virtual power plant and technical virtual power plant will act jointly as commercial-technical virtual power plant to extract the results of the proposed optimization procedure. In this paper, the binary particle swarm optimization algorithm is used to solve the optimization problem in the distribution system. The proposed method is applied to the IEEE 33-bus distribution test network, and the results confirm the effectiveness of the proposed method.

show abstract

“…In this paper, IEEE 33‐bus radial distribution test system is used. This system has a rated load of 3.7 MW and 2.3 MVAr with its diagram shown in Figure .…”

Section: Case Study and Discussionmentioning

confidence: 99%

A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant

Nosratabadi

Hooshmand

Gholipour

et al. 2015

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…Literature [6] takes the combination of distribution network switch and photovoltaic cell sequence arrangement as the optimization variable, and the change of photovoltaic cell sequence affects the reconstruction effect, so as to achieve the optimal network loss from both the distribution network reconstruction and the adjustment of photovoltaic cells. Literature [7] uses the power flow characteristics after the reconfiguration of the distribution network to optimize the large DG access location and its output, uses random power flow and Monte Carlo to simulate uncontrollable DG, and combines DG to achieve power flow optimization and reduce lines power consumption and improve reliability.…”

Section: Introductionmentioning

confidence: 99%

Research on optimization algorithm of distribution network reconstruction based on cuckoo search-particle swarm optimization

Jin¹,

Li²,

Miao³

et al. 2020

E3S Web Conf.

View full text Add to dashboard Cite

As the distribution network environment becomes more complex, improving the optimization effect and running time is the key to distribution network reconfiguration. In this paper, firstly, the minimum branch power loss is used as the evaluation index of the result, and the hybrid algorithm of PSO algorithm and CS algorithm is selected as the optimization method of the reconstruction problem. Secondly, the mathematical model of the distribution network introduced by DG is simplified, and the static distribution network weight is used. It is a simplified way of power flow solving by introducing DG into the distribution network model. Based on the CS-PSO algorithm, the optimization calculation of the distribution network reconfiguration with DG is carried out. Finally, a calculation example proves the rationality and effectiveness of the method.

show abstract

“…The appreciable growing trend of on-site generation units in distribution systems over the years, it is essential to carry out the reconfiguration and DG sizing simultaneously to reach the optimal or near optimal operating condition of the distribution system [36]. Many researchers [37][38][39][40][41][42][43][44][45][46][47] tried the same, most of these methods are a combination of meta-heuristic approaches for DG sizing and heuristic/metaheuristic approaches for reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

“…Imran et al [39] presented a novel integration technique based on Fireworks algorithm. Tolabi et al [40] used Bees algorithm approach for reconfiguration and improved the analytical method for multiple DG sizing and siting. Guan et al [41] proposed a quantum particle swarm optimisationbased approach to solve a reconfiguration problem along with different types of DG modelling.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous distribution system reconfiguration and DG sizing algorithm without load flow solution

Jasthi

Das

2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

Cited by 36 publications

References 25 publications

A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant

A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant

Research on optimization algorithm of distribution network reconstruction based on cuckoo search-particle swarm optimization

Simultaneous distribution system reconfiguration and DG sizing algorithm without load flow solution

Contact Info

Product

Resources

About