Distributed Demand Side Management With Stochastic Wind Power Forecasting

Distributed Demand Side Management With Stochastic Wind Power Forecasting

Scarabaggio, P. (Polytechnic University of Bari)
Grammatico, S. (TU Delft Team Bart De Schutter)
Carli, Raffaele (Polytechnic University of Bari)
Dotoli, Mariagrazia (Polytechnic University of Bari)

2022

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.

Demand-side management (DSM)
model predictive control
Optimization
sample average approximation (SAA)
smart grid
Smart grids
stochastic optimization.
Stochastic processes
Uncertainty
Wind forecasting
Wind power generation
Wind speed

http://resolver.tudelft.nl/uuid:93e094ca-0bf7-4fe3-b4fa-fa3831dd4006

https://doi.org/10.1109/TCST.2021.3056751

1063-6536

IEEE Transactions on Control Systems Technology, 30 (1), 97-112

Accepted Author Manuscript

Institutional Repository

journal article

© 2022 P. Scarabaggio, S. Grammatico, Raffaele Carli, Mariagrazia Dotoli