Distributed Demand Side Management With Stochastic Wind Power Forecasting

Paolo Scarabaggio, Sergio Grammatico, Raffaele Carli, Mariagrazia Dotoli

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Number of pages16
JournalIEEE Transactions on Control Systems Technology
DOIs
Publication statusAccepted/In press - 15 Feb 2021

Keywords

  • 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

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