Estimation of Wind Power Production Through Short-Term Forecast

Agabus, Hannes; Tammoja, Heiki

doi:10.3176/oil.2009.3s.04

Cited by 10 publications

(6 citation statements)

References 3 publications

(2 reference statements)

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“…Using the statistical properties of the wind generation and the opportunistic user demand, reported in literature, we now further simplifyR l (ψ l , s, u, v). According to [14], the standard deviation of wind generation, is of the same order as the mean of wind generation. Typically, wind generation and the demand of opportunistic energy users are comparable.…”

Section: Approximate Solutionsmentioning

confidence: 99%

“…Proof: In practical power systems, according to [13], [14], D t and W usually have continuous, symmetrical and unimodal probability distributions. Since σ 2 Y ≫ σ 2 o , intuitively, there exists a finite constant c 0 ≥ 0, such that:…”

Section: Approximate Solutionsmentioning

confidence: 99%

“…When the nonpersistent opportunistic energy users are relatively inelastic, i.e., −1 ≤ γ o < 0, the real-time pricing policy is given by θs,u (ψ l ) = v cap . Proof: In practical power systems, according to [13], [14], D t and W usually have continuous, symmetrical and unimodal probability distributions. Since σ 2 Y ≫ σ 2 o , intuitively, there exists a finite constant c 0 ≥ 0, such that:…”

Section: Approximate Solutionsmentioning

confidence: 99%

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Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

Murugesan

Zhang

2011

2011 Proceedings IEEE INFOCOM

107

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Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that the load and generation in the system remain balanced all the time. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy userstraditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in real-time scheduling, with the realization of wind generation and the load of traditional energy users, we optimize realtime prices to manage the opportunistic energy users so as to achieve system-wide reliability. More specifically, when the opportunistic users are non-persistent, we obtain closed-form solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multi-timescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques.

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Section: Approximate Solutionsmentioning

confidence: 99%

Section: Approximate Solutionsmentioning

confidence: 99%

Section: Approximate Solutionsmentioning

confidence: 99%

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Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

Murugesan

Zhang

2011

2011 Proceedings IEEE INFOCOM

107

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“…The performance requirements for a forecasting service are dictated by the needs of both the grid owners and the wind generators' operators [16].…”

Section: The Wind Forecasting Problemmentioning

confidence: 99%

An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting

Vaccaro

Mercogliano²,

Schiano³

et al. 2011

Electric Power Systems Research

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“…If there are some smaller wind turbines and wind farms connected to the electricity system, the system is able to manage with this wind power variations. When the share of wind energy in electricity system grows, the production fluctuation starts to generate more problems to the power grid [2]. In Estonia the wind speed varies from zero to 48 m/s (maximum wind speed measured in Ruhnu in 1969) [3].…”

Section: Introductionmentioning

confidence: 99%

ESTONIAN WIND farms' NEED FOR FULL BALANCE POWER

Pertmann¹

2011

Oil Shale

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Estonia had set a target to increase the share of renewable energy sources in the energy production up to 5.1% of total consumption by the end of 2010. There are no large rivers and solar activity is also low in Estonia. Therefore one of the biggest possibilities to achieve that target is to use bio fuels and wind power. Approximately 90% of total electricity is generated from oil shale, hereby oil shale power plants have to cover wind energy deficits. Wind speed is very variable and therefore electric power production by wind turbines varies from zero to nominal power. This is a problem for the power grid. The current paper analyses whether wind turbines and wind farms need 100% balanced power. Wind data of four weather stations around Estonia were investigated. Wind speeds were calculated to 100 meters above the ground level and Weibull distributions were composed. Distribution and duration of zero energy periods were analysed. In 2008 zero wind production periods varied from 1 to 7 hours (63 times) and their sum was 137 hours. In these periods wind turbines need 100% balance power.

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Estimation of Wind Power Production Through Short-Term Forecast

Cited by 10 publications

References 3 publications

Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration

An adaptive framework based on multi-model data fusion for one-day-ahead wind power forecasting

ESTONIAN WIND farms' NEED FOR FULL BALANCE POWER

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