Improved LSF method for loss estimation and its application in DG allocation

Fu, Xueqian; Chen, Haoyong; Cai, Runqing; Xuan, Peizheng

doi:10.1049/iet-gtd.2015.1471

Cited by 26 publications

(15 citation statements)

References 14 publications

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“…It should be emphasized that in some situations, the optimal values obtained in Equations 10 and 11 fall out of the limits as provided in the general limitations of DG defined in Equation 15. As a result, selecting optimal maximum limits in such situations becomes significant in minimizing active power loss.…”

Section: -Optimal Limits Of Dg Power Profilesmentioning

confidence: 99%

“…Step 4: Find the optimal profiles (P opt DGmi and Q opt DGmi ) and size of DG at each bus by using Equations 10 and 11, in case these optimal values are located out of the limits in Equation 15, use the maximum values {case 1 or case 2} as optimal values.…”

Section: Computational Proceduresmentioning

confidence: 99%

“…14 Many analytical methods have been proposed in determining optimal locations and size of a DG unit where the active loss is minimum. These methods include Loss Sensitivity Factor (LSF), which is used to estimate optimal bus number, 14,15 analytical expression methods, [15][16][17] improvement analytical (IA) method, 18 and efficient analytical (EA) method. 19 Also, other methods have been proposed for siting multiple DG units in distribution networks to minimize active loss.…”

Section: Introductionmentioning

confidence: 99%

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A time‐varying load‐based analytical approach for DG optimization in the distribution network

Iqteit

Arsoy

Çakır

2018

Int Trans Electr Energ Syst

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Summary This paper presents a novel analytical approach to estimate the optimal generation profiles/size and location of a distributed generation (DG) unit that can be integrated into distribution network to attain the lowest level of active loss. This approach considered the impact of significant parameters including daily load profiles, best power limits of a DG unit, and varying injected power profiles of the substation on results of minimum active loss of the distribution network and optimal calculations of DG. Additionally, the DG placement was speedily identified in this approach by using a new active loss expression along with four different scenarios. These scenarios considered a number of assumptions on the load profiles and on the state variables (voltage and angle). The obtained optimal active generation profile was also used to manage the uncertainty in the energy of the wind and photovoltaic distribution generators for achieving the highest loss reduction. Additionally, the exhaustive load flow method was used to check the validity of the analytical approach by using 13‐bus and 69‐bus distribution networks. The results obtained suggest the effectiveness of this approach in estimating the optimal location and profile/size of DG.

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Section: -Optimal Limits Of Dg Power Profilesmentioning

confidence: 99%

Section: Computational Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A time‐varying load‐based analytical approach for DG optimization in the distribution network

Iqteit

Arsoy

Çakır

2018

Int Trans Electr Energ Syst

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“…In this reference, electric power load variation has been presented in the form of a linear combination of some independent random factors and to determine energy losses for a certain time-period. Two formulas of the loss factor for calculating energy losses were improved, based on the Minimum Load Factor (MLF) and the load factor by Fu et al (2016). Due to the difficulties of estimating losses using maximum demands and the loss factor, Queiroz et al (2012) have proposed an alternative loss estimation approach that relies on the loss coefficient as the fundamental parameter for describing load variations in loss estimation.…”

Section: Introductionmentioning

confidence: 99%

Management of Effective Factors in Components’ Supply Chain on Losses of Electric Distribution System

Honarmand¹,

Ghazizadeh²,

Kermanshah³

2018

American Journal of Applied Sciences

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Restructuring in the electrical distribution utilities, the loss management is one of the critical activities to enhance network efficiency. Accordingly, management of technical loss would be the main target of the asset managers. For loss management, the usual methods of loss calculation and estimation need numerous data which is not used to real networks. In this research, a practical method for loss management is introduced from the viewpoint of components' supply chain to the network. For this purpose, sub-indictors are extracted using focus group for each process of this chain and their probability distribution functions are calculated. Then, permissible values of subindicators are determined by the Delphi method and to calculate new probability functions, the deviation of each process is calculated. Finally, a real network with historical data is used here to study the proposed method. The results illustrate that the most deviation is in the sub-indicator design process about 91% in the viewpoint losses.

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“…Reference [6] proposed loss coefficient (LSC) and equivalent hours of losses (EHL) to replace the LSF and the equivalent hours (EH) for improving the accuracy of energy loss estimation and the applicability of loss patterns. Two formulas of the LSF for calculating energy losses were improved, based on the minimum load factor (MLF) and the load factor (LF) in [7]. Reference [8] proposed an estimation method of technical losses that was based on the reference feeder (RF) of the medium voltage distribution network characterized by LSF, load distribution (LD), feeder peak power demand (PPD), and its length.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost

2017

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Abstract:The estimation of losses of distribution feeders plays a crucial guiding role for the planning, design, and operation of a distribution system. This paper proposes a novel estimation method of statistical line loss of distribution feeders using the feeder cluster technique and modified eXtreme Gradient Boosting (XGBoost) algorithm that is based on the characteristic data of feeders that are collected in the smart power distribution and utilization system. In order to enhance the applicability and accuracy of the estimation model, k-medoids algorithm with weighting distance for clustering distribution feeders is proposed. Meanwhile, a variable selection method for clustering distribution feeders is discussed, considering the correlation and validity of variables. This paper next modifies the XGBoost algorithm by adding a penalty function in consideration of the effect of the theoretical value to the loss function for the estimation of statistical line loss of distribution feeders. The validity of the proposed methodology is verified by 762 distribution feeders in the Shanghai distribution system. The results show that the XGBoost method has higher accuracy than decision tree, neural network, and random forests by comparison of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Absolute Percentage Error (APE) indexes. In particular, the theoretical value can significantly improve the reasonability of estimated results.

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Improved LSF method for loss estimation and its application in DG allocation

Cited by 26 publications

References 14 publications

A time‐varying load‐based analytical approach for DG optimization in the distribution network

A time‐varying load‐based analytical approach for DG optimization in the distribution network

Management of Effective Factors in Components’ Supply Chain on Losses of Electric Distribution System

A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost

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