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A data-driven stochastic optimization approach to the seasonal storage energy management
Publikationstyp
Journal Article
Date Issued
2017-06-09
Sprache
English
Journal
Volume
1
Issue
2
Start Page
394
End Page
399
Citation
IEEE Control Systems Letters 1 (2): 394-399 (2017-10-01)
Publisher DOI
Scopus ID
Publisher
IEEE
Several studies in the literature have shown the potential energy savings emerging from the cooperative management of the aggregated building energy demands. Sophisticated predictive control schemes have recently been developed that achieve these gains by exploiting the energy generation, conversion, and storage equipment shared by the building community. A common difficulty with all these methods is integrating knowledge about the long term evolution of the disturbances affecting the system dynamics (e.g., ambient temperature and solar radiation). In this context, the seasonal storage capabilities of the system are difficult to be optimally managed. This letter addresses this issue by exploiting available historical data to: (i) construct bounds that confine with high probability the optimal charging trajectory of the seasonal storage and (ii) generate a piece-wise affine approximation of the value function of the energy stored in the seasonal storage at each time step. Using these bounds and value functions, we formulate a multistage stochastic optimization problem to minimize the total energy consumption of the system. In a numerical study based on a realistic system configuration, the proposed method is shown to operate the system close to global optimality.
Subjects
Smart cities/houses
Stochastic optimal control
DDC Class
004: Informatik
More Funding Information
This work was supported in part by CTI within the SCCER FEEB&D, in part by the Swiss National Science Foundation under the Project IMES, and in part by ETH Zurich Foundation.