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• W2587984046 abstract "Recent studies highlighted the model predictive control as a promising platform for complex systems management and energy efficiency improvement in a large number of applications, particularly prominent in building climate and smart grid control. Involvement of microgrids offered great possibilities of power consumption peak shaving, improved grid stability and decentralisation, by introducing buildings as active market participants. To this aim, the paper is focused on the development of a microgrid optimal control that acts as an intermediary method for integration of a building to the smart grid with dynamic pricing of electricity. A multilevel optimal control is applied on a building cooling system and energy flow optimization of a DC microgrid that consists of a photovoltaic array, batteries stack and fuel cells stack with electrolyser, all connected to the utility grid via a bidirectional power converter. Performance of the proposed approach is verified through 4-month simulations of a microgrid integrated with a building cooling system, in actual meteorological and electricity price data scenario. Microgrid energy storages and the proposed control method fully exploit possibilities of dynamic pricing and greatly reduce cooling system operation costs while ensuring the high level of user comfort." @default.
• W2587984046 created "2017-02-24" @default.
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• W2587984046 date "2016-10-01" @default.
• W2587984046 modified "2023-09-27" @default.
• W2587984046 title "Multi-level optimal control of a microgrid-supplied cooling system in a building" @default.
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• W2587984046 doi "https://doi.org/10.1109/isgteurope.2016.7856211" @default.
• W2587984046 hasPublicationYear "2016" @default.
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