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A Smart Grid Energy Management Problem for Data-driven Design with Probabilistic Reachability Guarantees

18 pagesPublished: June 27, 2017

Abstract

In this paper we describe an energy management benchmark problem for a smart grid where electrical energy is supplied to a load via local power production from a solar PhotoVoltaic (PV) installation. The smart grid is connected with the main grid, which can eventually provide the energy needed for balancing demand and generation. The goal is to set the battery energy flow so as to keep the energy exchange with the main grid as close as possible to a nominal profile, within certified bounds, avoiding the fluctuations caused by the local PV energy production. Some energy production profiles of the PV installation and environmental data on irradiation and temperature are available for the design of the energy management strategy, together with a hybrid model for the battery and the electrical load profile. We describe a data-driven solution, pointing out its limits and providing some hint on possible direction for improvement.

Keyphrases: Data-driven design, Probabilistic Reachability, Smart grid energy management

In: Goran Frehse and Matthias Althoff (editors). ARCH17. 4th International Workshop on Applied Verification of Continuous and Hybrid Systems, vol 48, pages 2--19

Links:
BibTeX entry
@inproceedings{ARCH17:Smart_Grid_Energy_Management,
  author    = {Daniele Ioli and Alessandro Falsone and Marianne Hartung and Axel Busboom and Maria Prandini},
  title     = {A Smart Grid Energy Management Problem for Data-driven Design with Probabilistic Reachability Guarantees},
  booktitle = {ARCH17. 4th International Workshop on Applied Verification of Continuous and Hybrid Systems},
  editor    = {Goran Frehse and Matthias Althoff},
  series    = {EPiC Series in Computing},
  volume    = {48},
  pages     = {2--19},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/RN8},
  doi       = {10.29007/5qvt}}
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