Stochastic MPC for energy hubs using data driven demand forecasting

• URL: http://arxiv.org/abs/2304.12438v2
• Date: Mon, 24 Jul 2023 06:19:17 GMT
• Title: Stochastic MPC for energy hubs using data driven demand forecasting
• Authors: Varsha Behrunani, Francesco Micheli, Jonas Mehr, Philipp Heer, John Lygeros
• Abstract summary: 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. In this paper, we propose a MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands.
• Score: 4.033600628443366
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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