A Wind Power Forecasting Method and Its Confidence Interval Estimation

Iizaka, Tatsuya; Jintsugawa, Ryo; Kondo, Hideyuki; Nakanishi, Yousuke; Fukuyama, Yoshikazu; Mori, H.

doi:10.1541/ieejeiss.131.1672

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…6) The weather "stability," i.e., stable weather conditions have more reliable forecasts. Iizaka et al [29] shows that uncertainty is strongly related to the wind speed value and the forecasting horizon. These relations, together with the uncertainty related to a specific weather prediction model and the wind farm location and geography, can be estimated using historical data.…”

Section: Forecast Distributionmentioning

confidence: 99%

“…The formulation in ( 29) defines a generalized Nash equilibrium (GNE) problem that we can indicate in compact form as G = (X , J), where X is the collective global feasible set as in (28) and J = ( Ji (x i , x −i , ω)) |N | i=1 is the collection of the active users' cost function. In the game theory solving the GNE problem in (29) means the computation of a GNE, which is a collective strategy profile x * ∈ X with the property that no single player can benefit from a unilateral deviation from x * i , if all the other players act according [32]. More formally, we have…”

Section: A Game Modelmentioning

confidence: 99%

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Distributed Demand Side Management With Stochastic Wind Power Forecasting

Scarabaggio

Grammatico

Carli

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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In this article, we propose a distributed demandside management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.

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Section: Forecast Distributionmentioning

confidence: 99%

Section: A Game Modelmentioning

confidence: 99%

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Scarabaggio

Grammatico

Carli

et al. 2022

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…This relation is not linear, especially with high-speed value; • the forecasting horizon, because a long term forecast has a higher uncertainty; • the weather "stability", i.e., stable weather conditions have more reliable forecasts. Reference [29] shows that uncertainty is strongly related to the wind speed value and the forecasting horizon. These relations, together with the uncertainty related to a specific weather prediction model and the wind farm location and geography, can be estimated using historical data.…”

Section: Forecast Distributionmentioning

confidence: 99%

“…The formulation in (30) defines a generalized Nash equilibrium (GNE) problem that we can indicate in compact form as G = (X ,J), where X is the collective global feasible set as in (29)…”

Section: A Game Modelmentioning

confidence: 99%

Distributed Demand Side Management with Stochastic Wind Power Forecasting

Scarabaggio¹,

Grammatico²,

Carli³

et al. 2022

Preprint

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In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

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“…The formulation in (30) defines a generalized Nash equilibrium (GNE) problem that we can indicate in compact form as G = (X , J), where X is the collective global feasible set as in (29) and…”

Section: A Game Modelmentioning

confidence: 99%

Distributed Demand Side Management with Stochastic Wind Power Forecasting

Scarabaggio¹,

Grammatico²,

Carli³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br> We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br> The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability.<br> We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br> In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br> </p> <p>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</p> <p> </p> <p>Postprint accepted for publication in <em>IEEE Transactions on Control Systems Technology</em></p> <p><br></p> <p><strong>How to cite: </strong>P. Scarabaggio, S. Grammatico, R. Carli and M. Dotoli, (2021) "Distributed Demand Side Management With Stochastic Wind Power Forecasting, "<em>IEEE Transactions on Control Systems Technology</em>, 2022. <strong>DOI</strong>: <a href="http://doi.org/10.1109/TCST.2021.3056751" target="_blank">http://doi.org/10.1109/TCST.2021.3056751</a></p> <h4><strong>© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</strong><br> </h4>

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A Wind Power Forecasting Method and Its Confidence Interval Estimation

Abstract: The results show the effectiveness of the proposed methods.

Cited by 6 publications

References 7 publications

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Distributed Demand Side Management with Stochastic Wind Power Forecasting

Distributed Demand Side Management with Stochastic Wind Power Forecasting

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