Citation
Alberto Puggelli, Alberto Sangiovanni-Vincentelli, Sanjit Seshia. "Robust Strategy Synthesis for Probabilistic Systems Applied to Risk-Limiting Renewable-Energy Pricing". Proceedings of the International Conference on Embedded Software (EMSOFT), 12, October, 2014.

Abstract
We address the problem of synthesizing control strategies for Ellipsoidal Markov Decision Processes (EMDP), i.e., MDPs whose transition probabilities are expressed using ellipsoidal uncertainty sets. The synthesized strategy aims to maximize the total expected reward of the EMDP, constrained to a specification expressed in Probabilistic Computation Tree Logic (PCTL). We prove that the EMDP strategy synthesis problem for the fragment of PCTL disabling operators with a finite time bound is NP-complete and propose a novel sound and complete algorithm to solve it. We apply these results to the problem of synthesizing optimal energy pricing and dispatch strategies in smart grids that integrate renewable sources of energy. We use rewards to maximize the profit of the network operator and a PCTL specification to constrain the risk of power unbalance and guarantee quality-of-service for the users. The EMDP model used to represent the decision-making scenario was trained with measured data and quantitatively captures the uncertainty in the prediction of energy generation. An experimental comparison shows the effectiveness of our method with respect to previous approaches presented in the literature.

Electronic downloads

• https://www.src.org/library/publication/p069481/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1145/2656045.2656069

• HTML

  Alberto Puggelli, Alberto Sangiovanni-Vincentelli, Sanjit
  Seshia. <a
  href="http://www.terraswarm.org/pubs/344.html"
  >Robust Strategy Synthesis for Probabilistic Systems
  Applied to Risk-Limiting Renewable-Energy Pricing</a>,
  Proceedings of the International Conference on Embedded
  Software (EMSOFT), 12, October, 2014.

• Plain text

  Alberto Puggelli, Alberto Sangiovanni-Vincentelli, Sanjit
  Seshia. "Robust Strategy Synthesis for Probabilistic
  Systems Applied to Risk-Limiting Renewable-Energy
  Pricing". Proceedings of the International Conference
  on Embedded Software (EMSOFT), 12, October, 2014.

• BibTeX

  @inproceedings{PuggelliSangiovanniVincentelliSeshia14_RobustStrategySynthesisForProbabilisticSystemsApplied,
      author = {Alberto Puggelli and Alberto
                Sangiovanni-Vincentelli and Sanjit Seshia},
      title = {Robust Strategy Synthesis for Probabilistic
                Systems Applied to Risk-Limiting Renewable-Energy
                Pricing},
      booktitle = {Proceedings of the International Conference on
                Embedded Software (EMSOFT)},
      day = {12},
      month = {October},
      year = {2014},
      abstract = {We address the problem of synthesizing control
                strategies for Ellipsoidal Markov Decision
                Processes (EMDP), i.e., MDPs whose transition
                probabilities are expressed using ellipsoidal
                uncertainty sets. The synthesized strategy aims to
                maximize the total expected reward of the EMDP,
                constrained to a specification expressed in
                Probabilistic Computation Tree Logic (PCTL). We
                prove that the EMDP strategy synthesis problem for
                the fragment of PCTL disabling operators with a
                finite time bound is NP-complete and propose a
                novel sound and complete algorithm to solve it. We
                apply these results to the problem of synthesizing
                optimal energy pricing and dispatch strategies in
                smart grids that integrate renewable sources of
                energy. We use rewards to maximize the profit of
                the network operator and a PCTL specification to
                constrain the risk of power unbalance and
                guarantee quality-of-service for the users. The
                EMDP model used to represent the decision-making
                scenario was trained with measured data and
                quantitatively captures the uncertainty in the
                prediction of energy generation. An experimental
                comparison shows the effectiveness of our method
                with respect to previous approaches presented in
                the literature.},
      URL = {http://terraswarm.org/pubs/344.html}
  }
  

Posted by Alberto Puggelli on 12 Aug 2014.
Groups: tools