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Distributed Demand Side Management with Stochastic Wind Power Forecasting

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posted on 22.12.2020, 11:49 by Paolo Scarabaggio, Sergio Grammatico, Raffaele Carli, Mariagrazia Dotoli
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.
We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.
The core challenge in this paper 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 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 dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.

Preprint of paper submitted to IEEE Transactions on Control Systems Technology


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Politecnico di Bari

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