Stochastic MPC for energy hubs using data driven demand forecasting
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Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.
Permanent link
Publication status
published
External links
Book title
22nd IFAC World Congress
Journal / series
Volume
56 (2)
Pages / Article No.
11026 - 11031
Publisher
Elsevier
Event
22nd IFAC World Congress
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Dynamic resource allocation; Predictive control; Data-driven control
Organisational unit
02650 - Institut für Automatik / Automatic Control Laboratory