Robust Strategy Synthesis for Probabilistic Systems Applied to Risk-Limiting Renewable-Energy Pricing
Alberto Puggelli, Alberto Sangiovanni-Vincentelli, Sanjit Seshia

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.

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  • 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.
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