Use of Interval Arithmetic to Incorporate the Uncertainty of Load Demand for Radial Distribution System Analysis

Chaturvedi, Ashvini; Prasad, K.; Ranjan, Rakesh

doi:10.1109/tpwrd.2005.848436

Cited by 36 publications

(20 citation statements)

References 4 publications

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“…Bisection method was used by PEI to solve the problems of early convergence, slow convergence and no convergence [7]. Interval algorithm was applied by Chaturvedi to solve three phase unbalanced radial distribution networks [8]. Affine arithmetic was added to interval algorithm by DING Tao to shorten the gap between the upper and lower boundaries which aims to the inner problem of interval algorithm-a large overestimation of boundaries [9].…”

Section: Introductionmentioning

confidence: 99%

Interval Power Flow Analysis Based on Type-II Fuzzy Logic Forecasting

Bi¹,

Li²,

Xu³

et al. 2016

Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering

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Abstract-In the traditional power flow analysis, the output of generator is controllable and certain. While due to the intermittence and randomness of new energy resources, the output of new energy generator is uncertain, traditional power flow calculation cannot deal with this kind of uncertain power flow problem, thus type-II fuzzy logic forecasting and interval arithmetic are applied to solve this uncertain problem in the paper. Type-II fuzzy logic forecasting is used to get the output of generator in a mathematical sense that the uncertain variables can be express as an interval form, then interval arithmetic is applied to calculate power flow. As one of interval arithmetic, Krawczyk-Moore method is applied in power flow calculation in this paper, which convergence does not need to get inverse of interval matrix, and the initial interval obtained by type-II fuzzy logic can improve the problems of early convergence, slow convergence and no convergence which are drawbacks of interval arithmetic. The Algorithm is tested on IEEE 30-bus system to validate the applicability.

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Section: Introductionmentioning

confidence: 99%

Interval Power Flow Analysis Based on Type-II Fuzzy Logic Forecasting

Bi¹,

Li²,

Xu³

et al. 2016

Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering

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“…Under normal conditions, seasonal variations in power demand require utility companies to rely on demand estimation models to properly meet demand within safety margins and quality indexes [4,6,16]. In these models, consumers are statistically classified according to their average demand [6].…”

Section: Introductionmentioning

confidence: 99%

“…In these models, consumers are statistically classified according to their average demand [6]. Loading forecasts contain inherent errors because these approaches are based entirely on estimations.…”

Section: Introductionmentioning

confidence: 99%

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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“…Widespread applications of interval arithmetic (IA) have appeared in recent years [9][10][11][12][13][14][15]. The uncertainty is an acute problem in power systems because of the complexity of power systems, so several interval arithmetic-based methods have been proposed for solving power system problems [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The uncertainty is an acute problem in power systems because of the complexity of power systems, so several interval arithmetic-based methods have been proposed for solving power system problems [12][13][14][15]. However, the naive use of IA is prone to pessimistic conclusions, which at times limits the industrial practicality of such methods.…”

Section: Introductionmentioning

confidence: 99%

Power system transient stability simulation under uncertainty based on Taylor model arithmetic

Wang

Zheng

Wang

2009

Front. Electr. Electron. Eng. China

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The Taylor model arithmetic is introduced to deal with uncertainty. The uncertainty of model parameters is described by Taylor models and each variable in functions is replaced with the Taylor model (TM). Thus, time domain simulation under uncertainty is transformed to the integration of TM-based differential equations. In this paper, the Taylor series method is employed to compute differential equations; moreover, power system time domain simulation under uncertainty based on Taylor model method is presented. This method allows a rigorous estimation of the influence of either form of uncertainty and only needs one simulation. It is computationally fast compared with the Monte Carlo method, which is another technique for uncertainty analysis. The proposed method has been tested on the 39-bus New England system. The test results illustrate the effectiveness and practical value of the approach by comparing with the results of Monte Carlo simulation and traditional time domain simulation.

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Use of Interval Arithmetic to Incorporate the Uncertainty of Load Demand for Radial Distribution System Analysis

Cited by 36 publications

References 4 publications

Interval Power Flow Analysis Based on Type-II Fuzzy Logic Forecasting

Interval Power Flow Analysis Based on Type-II Fuzzy Logic Forecasting

Robust feeder reconfiguration in radial distribution networks

Power system transient stability simulation under uncertainty based on Taylor model arithmetic

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