Paper_TCST_v2.pdf (1.67 MB)
Download fileDistributed Demand Side Management with Stochastic Wind Power Forecasting
preprint
posted on 2020-12-22, 11:49 authored by Paolo ScarabaggioPaolo Scarabaggio, Sergio Grammatico, Raffaele CarliRaffaele Carli, Mariagrazia DotoliIn 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.
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
https://doi.org/10.1109/TCST.2021.3056751
History
Email Address of Submitting Author
paolo.scarabaggio@poliba.itORCID of Submitting Author
0000-0002-4009-3534Submitting Author's Institution
Politecnico di BariSubmitting Author's Country
- Italy