Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks

Vita, Vasiliki

doi:10.3390/en10091433

Cited by 143 publications

(63 citation statements)

References 13 publications

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“…It proposes a hybrid method, based on data clustering and contingency enumeration, by clustering the input data, including the output power of the WFs and systems' loads, to a finite set, to reduce the complexity of the transfer capability problem. In [24], a decision-making algorithm is presented for the optimal placement and sizing of distributed generation (DG) units in distribution grids that can define the optimal location and estimate the optimum size for a DG unit to be connected. This process is performed for improvement of voltage profiles and reduction of power losses.…”

Section: Introductionmentioning

confidence: 99%

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

et al. 2018

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Abstract:The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.Keywords: direct probabilistic load flow; wind-integrated radial distribution systems; K-means based data clustering; Monte Carlo simulation; indirect probabilistic forward/backward sweep load flow

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Section: Introductionmentioning

confidence: 99%

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

et al. 2018

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“…In the optimum value approach, the most appropriate sizing and/or siting is determined according to the criteria considered in the study. The most important optimization studies can be given as grouped according to their objectives on which they are based; minimization of power losses [20,[23][24][25][26], minimization of energy losses [27], increasing system reliability based on various reliability indices [28,29] and minimization of costs [29,30]. There are also approaches to define the maximum permissible capacity value based on a single system criterion other than the hosting capacity and optimum values approaches [31][32][33][34][35][36][37].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

et al. 2018

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Abstract:Increasing demand for electricity, as well as rising environmental and economic concerns have resulted in renewable energy sources being a center of attraction. Integration of these renewable energy resources into power systems is usually achieved through distributed generation (DG) techniques, and the number of such applications increases daily. As conventional power systems do not have an infrastructure that is compatible with these energy sources and generation systems, such integration applications may cause various problems in power systems. Therefore, planning is an essential part of DG integration, especially for power systems with intermittent renewable energy sources with the objective of minimizing problems and maximizing benefits. In this study, a mathematical model is proposed to calculate the maximum permissible DG integration capacity without causing overvoltage problems in the power systems. In the proposed mathematical model, both the minimum loading condition and maximum generation condition are taken into consideration. In order to prove the effectiveness and the consistency of the proposed mathematical model, it is applied to a test system with different case studies, and the results are compared with the results obtained from other models in the literature.

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“…With the increasing usage of WTGSs, multiple feeding points are emerging on the distribution system. This can cause power flow in both directions (towards source or load), unlike the conventional system operation [6]. For example, in a case where the distribution network is dominated by wind energy resources, this distribution network can behave as an active network and can give feedback to the high voltage bus.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a case where the distribution network is dominated by wind energy resources, this distribution network can behave as an active network and can give feedback to the high voltage bus. As a distributed generation system, not only WTGSs, but also other renewable or non-renewable generation systems have an impact on the conventional distribution network's voltage profile and energy losses [6,7]. It should be acknowledged that these kinds of operations can cause a number of technical difficulties and operational issues [8].…”

Section: Introductionmentioning

confidence: 99%

A New Wind Turbine Generating System Model for Balanced and Unbalanced Distribution Systems Load Flow Analysis

et al. 2018

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Abstract:Wind turbine generating systems (WTGSs), which are conventionally connected to high voltage transmission networks, have frequently been employed as distributed generation units in today's distribution networks. In practice, the distribution networks always have unbalanced bus voltages and line currents due to uneven distribution of single or double phase loads over three phases and asymmetry of the lines, etc. Accordingly, in this study, for the load flow analysis of the distribution networks, Conventional Fixed speed Induction Generator (CFIG) based WTGS, one of the most widely used WTGS types, is modelled under unbalanced voltage conditions. The Developed model has active and reactive power expressions in terms of induction machine impedance parameters, terminal voltages and input power. The validity of the Developed model is confirmed with the experimental results obtained in a test system. The results of the slip calculation based phase-domain model (SCP Model), which was previously proposed in the literature for CFIG based WTGSs under unbalanced voltages, are also given for the comparison. Finally, the Developed model and the SCP model are implemented in the load flow analysis of the IEEE 34 bus test system with the CFIG based WTGSs and unbalanced loads. Thus, it is clearly pointed out that the results of the load flow analysis implemented with both models are very close to each other, and the Developed model is computationally more efficient than the SCP model.

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Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks

Cited by 143 publications

References 13 publications

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

A New Wind Turbine Generating System Model for Balanced and Unbalanced Distribution Systems Load Flow Analysis

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