Distributed Demand Side Management With Stochastic Wind Power Forecasting

Journal Article (2022)
Author(s)

Paolo Scarabaggio (Polytechnic University of Bari)

S. Grammatico (TU Delft - Team Bart De Schutter)

Raffaele Carli (Polytechnic University of Bari)

M. Dotoli (Polytechnic University of Bari)

Research Group
Team Bart De Schutter
Copyright
© 2022 P. Scarabaggio, S. Grammatico, Raffaele Carli, Mariagrazia Dotoli
DOI related publication
https://doi.org/10.1109/TCST.2021.3056751
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 P. Scarabaggio, S. Grammatico, Raffaele Carli, Mariagrazia Dotoli
Research Group
Team Bart De Schutter
Issue number
1
Volume number
30
Pages (from-to)
97-112
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Abstract

In this article, 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. 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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