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| Home > Publications database > Data-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models |
| Home > Publications database > Data-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models |
| Contribution to a conference proceedings | FZJ-2023-01339 |
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2022
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Please use a persistent id in citations: doi:10.1109/OSMSES54027.2022.9769104
Abstract: The scheduling of hybrid energy systems with battery storage systems (BSS) and hydrogen storage systems (HSS) for the storage of renewable energies is a non-trivial task due to the nonlinear nature of electrolyzers and fuel cells and the volatile electricity generation by renewable energies. Mathematical optimization of the scheduling increases the system efficiency and decreases the share of grid electricity required to cover the electrical demand. Hence, tailor-made models are required for each hydrogen component due to the uniqueness of each hydrogen system. Therefore, the time-consuming work of model generation and validation needs to be done for every system in order to ensure adequate accuracy of the mathematical models used for model predictive control. This work derives and utilizes a simulation model of a hybrid energy system as a substitute for a real-world system. We propose a framework that uses functional mock-up units of detailed simulation models to derive tailormade mixed-integer linear programming (MILP) formulations of the steady-state operational behavior. We combine the derived formulations for the operational behavior of each component into an optimization model of the whole hybrid energy system. The optimization model is then used for model predictive control of the simulation model. The results show that we can generate accuratemodels of the component behavior without detailed knowledge of the simulation model. The resulting optimization model of the whole energy system accurately reflects the simulation model and is, therefore, suitable for model predictive control.
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