Radial Power Flow Tolerance Analysis by Interval Constraint Propagation

Vaccaro, Alfredo; Villacci, D.

doi:10.1109/tpwrs.2008.2009383

Cited by 49 publications

(16 citation statements)

References 27 publications

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“…Transmission line analysis using interval mathematics for inductance calculation purpose was presented in the literature [14]. Modelling and optimization of power flows in electric power transmission and distribution networks using interval analysis was described by earlier researchers [15][16][17][18][19]. Application of uncertainty analysis for various applications in distribution systems such as compensation studies, feeder sizing and loss reduction were discussed in the literature [20][21][22][23].…”

Section: Modelling Uncertainty Using Interval Mathematicsmentioning

confidence: 99%

Economic Analysis of Power Transmission Lines using Interval Mathematics

Kishore¹,

Singal²

2015

Journal of Electrical Engineering and Technology

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-A major portion of the capital costs in the present day power transmission systems are due to the cost of equipment and construction process. Transmission utilities in the recent years are drawing greater attention towards performing life cycle costing studies for cost management and decision making. However, the data involved in these studies are highly uncertain and the effect of these uncertainties cannot be directly included in the study process, resulting in inaccurate solutions. Interval mathematics provides a method for including these uncertainties throughout the cost analysis and provides final solution range in the form of intervals. In this regard, it is essential and extremely important that significant research has to be carried out in understanding the principles of life cycle costing methodology and its applicability to cost analysis of transmission lines along with uncertainties involved in the cost assessment process. In this paper, economic analysis of power transmission lines using interval mathematics has been studied. Life cycle costing studies are performed using net present value analysis on a range transmission lines used in India and the results are analyzed. A cost break even analysis considering right of way costs was carried out to determine the point of economy indifference.

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Section: Modelling Uncertainty Using Interval Mathematicsmentioning

confidence: 99%

Economic Analysis of Power Transmission Lines using Interval Mathematics

Kishore¹,

Singal²

2015

Journal of Electrical Engineering and Technology

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“…Due to the importance of these uncertainties regardless their nature in the reconfiguration problem, seen in Section 1, it is noteworthy to mention the works that have addressed power flow methods under uncertain environments. Some authors have proposed versions of power flow analysis to cope with uncertainties [6,12,32,34,35]. Load variations were modeled for network planning purposes by Chaturvedi et al [6] and later by Borges and Martins [4].…”

Section: Related Workmentioning

confidence: 99%

“…In [6], a proposal was devised based on interval arithmetic approach that represents loads through Gaussian probability density functions. Vaccaro presented an alternative to the samplingbased technique applied to radial power flow analysis supported by interval mathematics [32] or affine arithmetic [33]. He was the first to introduce an affine arithmetic approach [33] to find the distribution network's operating point, while considering variation in load and generation levels.…”

Section: Related Workmentioning

confidence: 99%

“…In our approach, robust solutions are those solutions that are less susceptible to uncertainties and they are able to maintain a system normally operating even in the worst case scenario. In this work, we restricted uncertainties to PQ loads, because they are among the most relevant factors in feeder reconfiguration decisions, mainly if we consider power generation in those buses [12,[32][33][34]. Because feeder reconfiguration depends on some power flow method [2], uncertainties must be accounted for using a mathematical model capable of reflecting all immediate effects on the power flow [11,12,32,33].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust feeder reconfiguration in radial distribution networks

Barbosa

Mendes

Vasconcelos

2014

International Journal of Electrical Power & Energy Systems

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a b s t r a c tDistribution feeder reconfiguration has been an active field of research for many years. Some recent theoretical studies have highlighted the importance of smart reconfiguration for the operating conditions of such radial networks. In general, this problem has been tackled using a multi-objective formulation with simplified assumptions, in which the uncertainties related to network components have been neglected by both mathematical models and solution techniques. These simplifications guide searches to apparent optima that may not perform optimally under realistic conditions. To circumvent this problem, we propose a method capable of performing interval computations and consider seasonal variability in load demands to identify robust configurations, which are those that have the best performance in the worst case scenario. Our proposal, named the Interval Multi-objective Evolutionary Algorithm for Distribution Feeder Reconfiguration (IMOEA-DFR), uses interval analysis to perform configuration assessment by considering the uncertainties in the power demanded by customers. Simulations performed in three cases on a 70-busbar system demonstrated the effectiveness of the IMOEA-DFR, which obtained robust configurations that are capable to keep such system working under significant load variations. Moreover, our approach achieved stable configurations that remained feasible over long periods of time not requiring additional reconfigurations. Our results reinforce the need to include load uncertainties when analyzing DFR under realistic conditions.

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“…Many indeterministic methods have been developed to study the uncertainty of power flow introduced by DGs, mainly including probabilistic power flow methods [9,10] and interval power flow methods [11][12][13]. When calculating probabilistic power flow, the probability distribution function (PDF) of each DG output power is estimated firstly, then probability distribution of the system power flow is acquired via convolution.…”

Section: Introductionmentioning

confidence: 99%