Management of the Power Distribution Network Reconstruction Process Using Fuzzy Logic

Šarić, Mirza; Hivziefendić, Jasna

doi:10.1007/978-3-319-47295-9_13

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Since the determination of the exact Pareto optimal front value is computationally prohibitive, the objective is reduced to the determination of an approximate Pareto front such that the distance to the Pareto front is minimised, diversity of the Pareto optimal set is maximised, and existing non‐dominated solutions are maintained . The best trade‐off among candidate solutions, in this paper, is obtained by using the fuzzy multicriteria decision‐making method, described by Bellman and Zadeh and applied in Saric and Hivziefendic . The following equation shows the value of the membership function (MF) for each value from the Pereto optimal set of solution for the case of the objective F j that needs to be minimised:

μ_{A_{j}} ((), x) = \frac{{min}_{x \in D_{x}} F_{j} ((), x)}{F_{j} ((), x)},

where

μ_{A_{j}}

respesents the MF of a candidate solution x , which belongs to the decision set D x .…”

Section: Theoretical Fundamentalsmentioning

confidence: 99%

Distributed generation allocation considering uncertainties

Šarić

Hivziefendić

Konjić

et al. 2018

Int Trans Electr Energ Syst

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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.

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μ_{A_{j}} ((), x) = \frac{{min}_{x \in D_{x}} F_{j} ((), x)}{F_{j} ((), x)},

where

μ_{A_{j}}

respesents the MF of a candidate solution x , which belongs to the decision set D x .…”

Section: Theoretical Fundamentalsmentioning

confidence: 99%

Distributed generation allocation considering uncertainties

Šarić

Hivziefendić

Konjić

et al. 2018

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…A probabilistic approach is difficult to apply to this problem because of the lack of significant data and because uncertainty is not random. In practice, it is not possible to model wide range of practical engineering problems using the exclusive domain of Aristotelian binary logics in which particular element x, either belongs to a set A (A=1) or it does not belong to a set A (A=0), because such a rigid approach to boundary definition between two sets reduces natural process to discrete ones [10]. Probabilistic and deterministic methods model coefficients as crisp values which is not realistic scenario [1] and might lead towards misallocation of resources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is therefore justified to use new methods and tools, such as fuzzy systems, to create a logical framework, which will use realistic data to model EPDS planning criteria. Some of the examples of fuzzy approach to EPDS and Distributed Generation (DG) planning and development applications are presented in [10], [14][15][16][17][18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

New algorithm for distribution system reconstruction planning based on fuzzy inference and multicriteria decision making

Šarić¹,

Hivziefendić

Kevrić

2018

Eng. rev. (Online)

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This paper presents a new algorithm for distribution system reconstruction planning based on Mamdani type fuzzy inference and BellmanZadeh multi criteria decision making method. The proposed algorithm takes system attributes as inputs (number of customers served by renewed infrastructure, energy losses, power demand and cost of investment) and returns crisp output values which are used as planning criteria. The aim of this paper is to provide a logical decision making framework which can be used to model, evaluate, and rank projects according to required criteria. The proposed model is flexible and can be extended to include additional planning criteria. The proposed method is tested on a realistic distribution system to demonstrate its relevance. It is expected that this paper will make a contribution toward more effective management of power distribution network planning process and that it will be used by planning engineers in practical problems.

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