Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Nuhanovic, Amir; Hivziefendić, Jasna; Hadžimehmedović, Amir

doi:10.2478/jee-2013-0039

Cited by 13 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of them is NSGA II, which belongs to the Pareto‐based optimisation approaches. One of the main advantages of NSGA II, when compared with the original GA, is the ability to preserve population diversity, using the crowding distance approach and therefore achieve uniform distribution of solutions within the Pareto front and overcome the convergence problem . Non‐dominated sorting genetic algorithm II is widely used because it is robust, efficient, and relatively simple.…”

Section: Theoretical Fundamentalsmentioning

confidence: 99%

Distributed generation allocation considering uncertainties

Šarić

Hivziefendić

Konjić

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a method for distributed generation (DG) allocation planning and investigates the extent to which system load and generation output uncertainties influence the final optimisation results. The problem is presented as a multiobjective constrained optimization problem in which the objective functions are power loss reduction and voltage profile improvements, while the constraints are the voltage, current, and short‐circuit power limits. The optimization is performed by using a multiobjective genetic algorithm. The best trade‐off among candidate solutions from Pareto front is achieved by using Bellman‐Zadeh method. The system load and DG power output uncertainties are addressed by using the possibilistic, α‐cut method. The proposed method is applied to a real 35 kV distribution system. It was demonstrated that appropriately planned DG allocation has potential to positively influence network losses and voltage profile. Further, it was determined that load and generation uncertainty, inherently present in the DG allocation problem, have significant impact on optimization results. This paper makes a contribution to the existing knowledge by applying, to a realistic test power system, a DG allocation method and by determining the extent to which load and DG output uncertainties influence the final optimization results.

show abstract

Section: Theoretical Fundamentalsmentioning

confidence: 99%

Distributed generation allocation considering uncertainties

Šarić

Hivziefendić

Konjić

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The result obtained showed the possibility of finding optimal state of the switches which will in turn produce smaller total losses and smaller total customer interruption costs. The limitation of the work was that it only encompassed active losses in calculating network losses while other losses like those due to insulation off-lines and capacitors were not accommodated [7] Tomoiaga et al (2013) addressed DFR using GA based on graph theory with a view to minimizing active power losses. Backward/forward sweep load flow techniques were used to determine steady state parameters of the network algorithms.…”

Section: 0mentioning

confidence: 99%

“…This in no doubt affected the fidelity of distribution system in faithfully delivering the energy meant for sale to customers.Ideally, distribution network is expected to supply electrical power to all connected customers in a manner to avoid overloading, feeder thermal overload and abnormal voltage across the line, as well as minimization of active power loss andmaintenance of radial topologysimultaneously [4]. Previous researches had shown that the reverse is the case, it was estimated thatout of totally generated electrical power, 13% of powerloss occurs at distribution system alone [3,[5][6] which is a wide margin deviation from permissible power loss of 3-6 % in an ideal RDS [7]. Researcher [8] identified the major problems of radial distribution network (RDN) to include low reliability, low voltage and high-power loss.…”

Section: Introductionmentioning

confidence: 99%

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Ajenikoko¹,

Ajenikoko²,

Adedoyin³

et al. 2018

IJASTR

View full text Add to dashboard Cite

Distribution system being the final link in the hierarchical structure of power system is expanding daily due to exponential growth in man's activities that require power supply for its function. The fidelity of distribution system to faithfully accounts for energy obtained is highly hampered by the losses occurring at this end. It is reported that distribution system alone account for 13% of the total loss in power system. In view of this, power system engineers are faced with the challenges of minimizing the loss, reducing voltage deviation and maintaining system topology. One of the most cost effective means of doing this is network feeder reconfiguration which harnessed the feature of distribution system. Network reconfiguration re-positions the status of sectionalizing the tie-switches with a view to minimize real power losses, reduce voltage deviation and maintain network topology. This paper presents application of genetic algorithm to network feeder reconfiguration in radial distribution system. The motivation of picking GA is not unconnected with its ability to handle ill-structured optimization problems effectively. Samples of relevant papers were reviewed from 2010 till date, it was discovered that several variants of GA has emerged, alongside with hybridized version of GA. The modification(s) made and improvement on the original GA has been carefully presented. In all, the summary of improvement on GA on this concept revolved around codification methods, adaptive operators, and changes in fitness functions.It is hoped that this piece of work will be found useful to those working or intend working on distribution network reconfiguration using GA.

show abstract

“…DSR is the act of opening and closing the switching devices of power systems to reach a topology that optimizes the desired objectives [1]. Variety of objectives such as power losses and total interruptions [2], Load Balancing [3], Voltage Profile Improvement [4], Service Restoration [5], reliability and power quality improvement [6] has been considered to solve DSR problem. A rigorous classification of the DSR solution methods is difficult.…”

Section: Introductionmentioning

confidence: 99%

Optimal network reconfiguration in distribution system for loss reduction and voltage-profile improvement using hybrid algorithm of PSO and ACO

Heidari¹

2017

CIRED - Open Access Proceedings Journal

View full text Add to dashboard Cite

Considering time varying nature of load in conventional distribution system, network reconfiguration is combinatorial complex optimization problem. In this paper, a hybrid configuration of particle swarm optimization (PSO) method with ant colony optimization (ACO) algorithm called hybrid PSO-ACO algorithm is presented for optimal network reconfiguration in distribution network in presence of distributed generation (DG) resources. The objectives consist of minimizing power losses and improving the voltage profile. MATLAB based simulation conducted on 33-bus IEEE test feeder to verify the effectiveness of proposed method. Results demonstrate accuracy and efficiency of PSO-ACO algorithm.

show abstract

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Cited by 13 publications

References 12 publications

Distributed generation allocation considering uncertainties

Distributed generation allocation considering uncertainties

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Optimal network reconfiguration in distribution system for loss reduction and voltage-profile improvement using hybrid algorithm of PSO and ACO

Contact Info

Product

Resources

About