Robust Strategy Synthesis for Probabilistic Systems Applied to Risk-Limiting Renewable-Energy Pricing

Alberto Puggelli, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia. Robust Strategy Synthesis for Probabilistic Systems Applied to Risk-Limiting Renewable-Energy Pricing. In Proceedings of the 14th International Conference on Embedded Software (EMSOFT), October 2014.

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Abstract

The use of economic incentives has been proposed to manage user demand in smart grids that integrate renewable sources of energy to compensate for the intrinsic uncertainty in the prediction of the supply generation. We address the problem of synthesizing optimal energy pricing strategies, while quantitatively constraining the risk due to uncertainty for the network operator and guaranteeing quality-of-service for the users. We use Ellipsoidal Markov Decision Processes (EMDP) to model the decision-making scenario. These models are trained with measured data and allow to quantitatively capture the uncertainty in the prediction of energy generation. We then cast the constrained optimization problem as a strategy synthesis problem for EMDPs, with the goal to maximize the total expected reward constrained to properties expressed using the Probabilistic Computation Tree Logic (PCTL), and propose a novel sound and complete synthesis algorithm. An experimental comparison shows the effectiveness of our method with respect to previous approaches presented in the literature.

BibTeX

@inproceedings{puggelli-emsoft14,
  author    = {Alberto Puggelli and Alberto Sangiovanni-Vincentelli and Sanjit A. Seshia},
  title     = {Robust Strategy Synthesis for Probabilistic Systems
           Applied to Risk-Limiting Renewable-Energy Pricing},
 booktitle = {Proceedings of the 14th International Conference on Embedded Software (EMSOFT)},
 month = "October",
 year = {2014},
 abstract = {The use of economic incentives has been proposed to manage user 
demand in smart grids that integrate renewable sources of energy 
to compensate for the intrinsic uncertainty in the prediction of the 
supply generation. We address the problem of synthesizing optimal 
energy pricing strategies, while quantitatively constraining the 
risk due to uncertainty for the network operator and guaranteeing 
quality-of-service for the users. We use Ellipsoidal Markov Decision 
Processes (EMDP) to model the decision-making scenario. 
These models are trained with measured data and allow to quantitatively 
capture the uncertainty in the prediction of energy generation. 
We then cast the constrained optimization problem as a 
strategy synthesis problem for EMDPs, with the goal to maximize 
the total expected reward constrained to properties expressed using 
the Probabilistic Computation Tree Logic (PCTL), and propose a 
novel sound and complete synthesis algorithm. An experimental 
comparison shows the effectiveness of our method with respect to 
previous approaches presented in the literature.},
}