Statistical evaluation of voltages in distribution systems with embedded wind generation

Masters, Clare Louise; Mutale, Joseph; Štrbac, Goran; Curcic, Srdjan; Jenkins, Nicholas

doi:10.1049/ip-gtd:20000410

Cited by 40 publications

(30 citation statements)

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“…In particular, the "point-forecast" methods considered here are the Persistence Method (PM), the Generalized Persistence Method (GPM), and the Nielsen Method (NM) [7,8]. The "pdf-forecast" methods considered here were the Bayesian wind-speed prediction method (BM) proposed in [9,10] and an improved version of the Markov Method (MM) proposed in [11]. [5] All the considered "point-forecast" methods are based on the use of the hourly on-line measurements of wind power.…”

Section: Wind Power Forecast Methodsmentioning

confidence: 99%

“…This stochastic process is modeled approximately by using the discrete Markov process (Markov's chains) [11][12][13][14]; the use of the discrete Markov process to model wind speed (and then the wind farm) was proposed in the relevant literature both for probabilistic power flow analysis and for reliability analysis of the distribution networks that have wind farms.…”

Section: B Markov Methodsmentioning

confidence: 99%

“…The approach is based on the method proposed in [11] that we have modified in order to obtain a hourly-based wind power forecast method. In particular, in [11], the authors proposed a Markov chain model that is able to generate a wind speed time series; this series is used as input to the power curve to convert wind speed to a generated power time series.…”

Section: B Markov Methodsmentioning

confidence: 99%

See 2 more Smart Citations

New Approaches for Very Short-term Steady-State Analysis of An Electrical Distribution System with Wind Farms

et al. 2010

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Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation. Dispersed generation systems require particular attention due to their incorporation of uncertain energy sources, such as wind farms, and due to the impacts that such sources have on the planning and operation of distribution networks. In particular, the foreseeable, extensive use of wind turbine generator units in the future requires that distribution system engineers properly account for their impacts on the system. Many new technical considerations must be addressed, including protection coordination, steady-state analysis, and power quality issues. This paper deals with the very short-term, steady-state analysis of a distribution system with wind farms, for which the time horizon of interest ranges from one hour to a few hours ahead. Several wind-forecasting methods are presented in order to obtain reliable input data for the steady-state analysis. Both deterministic and probabilistic methods were considered and used in performing deterministic and probabilistic load-flow analyses. Numerical applications on a 17-bus, medium-voltage, electrical distribution system with various wind farms connected at different busbars are presented and discussed.

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Section: Wind Power Forecast Methodsmentioning

confidence: 99%

Section: B Markov Methodsmentioning

confidence: 99%

Section: B Markov Methodsmentioning

confidence: 99%

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New Approaches for Very Short-term Steady-State Analysis of An Electrical Distribution System with Wind Farms

et al. 2010

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“…The ramp rates of these generators are assumed to be sufficiently small to become neglected. The predicting time series for the fluctuating wind power generation are derived here by using a simple Markov chain model as described in [24]- [26], with the formalism given in Appendix B. However, owing to the generality of the proposed line temperature assessment methodology, alternative probabilistic models for short-term wind power forecasting could be readily used as well, such as the Markov-switching autoregressive model [27] or the ARIMA technique [17], [18], also depending on the specific meteorological conditions and data availability.…”

Section: B Example B: Line Temperatures Within a Transmission Networmentioning

confidence: 99%

Probabilistic Modeling and Simulation of Transmission Line Temperatures Under Fluctuating Power Flows

Schläpfer

Mancarella

2011

IEEE Trans. Power Delivery

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Abstract-Increasing shares of fluctuating renewable energy sources induce higher and higher power flow variability at the transmission level. The question arises as to what extent existing networks can absorb additional fluctuating power injection without exceeding thermal limits. At the same time, the resulting power flow characteristics call for revisiting classical approaches to line temperature prediction. This paper presents a probabilistic modeling and simulation methodology for estimating the occurrence of critical line temperatures in the presence of fluctuating power flows. Cumbersome integration of the dynamic thermal equations at each Monte Carlo simulation trial is sped up by a specific algorithm that makes use of a variance reduction technique adapted from the telecommunications field. The substantial reduction in computational time allows estimations closer to real time, relevant to short-term operational assessments. A case study performed on a single line model provides fundamental insights into the probability of hitting critical line temperatures under given power flow fluctuations. A transmission system application shows how the proposed method can be used for a fast yet accurate operational assessment.

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“…(1) * 305-8568 1-1-1 Energy Technology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1, Umezono, Tsukuba 305-8568 (2) BTB Back-to-Back (3) FRIENDS (4) 2. …”

mentioning

confidence: 99%

Study on Voltage Regulation Methods for Distribution Systems with Dispersed Generators

et al. 2004

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Connection of a large number of the dispersed generators to distribution networks is not easy due to various technical considerations. Thus we have been trying to devise a concept for future electrical distribution systems with a lot of dispersed generators. In this work, it has been considered that each customer's load and each generator's active and reactive power should be controlled in order to stabilize and optimize the networks. Under this consideration, two control methods for future distribution systems are proposed, a cooperative control and an independent control. We have confirmed experimentally that the voltage regulation ability is higher with the cooperative control than with the independent control, especially in the cases of an eccentric load profile in a feeder and a heavy load. 2.2

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Statistical evaluation of voltages in distribution systems with embedded wind generation

Cited by 40 publications

References 1 publication

New Approaches for Very Short-term Steady-State Analysis of An Electrical Distribution System with Wind Farms

New Approaches for Very Short-term Steady-State Analysis of An Electrical Distribution System with Wind Farms

Probabilistic Modeling and Simulation of Transmission Line Temperatures Under Fluctuating Power Flows

Study on Voltage Regulation Methods for Distribution Systems with Dispersed Generators

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