Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids (Artifact)

doi: 10.4121/14185139.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14185139
Datacite citation style:
Badings, Thom S. (2023): Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids (Artifact). Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
In our paper titled "Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids", presented at the 2021 NASA Formal Methods Symposium, we study a smart grid with wind power and battery storage. Traditionally, day-ahead planning aims to balance demand and wind power, yet actual wind conditions often deviate from forecasts. Short-term flexibility in storage and generation fills potential gaps, planned on a minutes time scale for 30-60 minute horizons. Finding the optimal flexibility deployment requires solving a semi-infinite non-convex stochastic program, which is generally intractable to do exactly. Previous approaches rely on sampling, yet such critical problems call for rigorous approaches with stronger guarantees. Our method employs probabilistic model checking techniques. First, we cast the problem as a continuous-space Markov decision process with discretized control, for which an optimal deployment strategy minimizes the expected grid frequency deviation. To mitigate state space explosion, we exploit specific structural properties of the model to implement an iterative exploration method that reuses pre-computed values as wind data is updated. This artifact contains all code and data needed to reproduce the results presented in the paper. Instructions on how to install and use the code are included in the ReadMe.txt file in the artifact.
  • 2023-03-08 first online, published, posted
*.xlsx *.py *.txt
  • NWO NWA.1160.18.238
  • Verification of Cyber-Physical Systems: Exploiting Uncertainty for Scalability (grant code 639.021.754) [more info...] Dutch Research Council
Radboud University Nijmegen, Department of Software Science
University of Twente, Formal Methods and Tools Group


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