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Microgrid agent-based modelling and optimization under uncertainty

Abstract : This thesis concerns the energy management of electricity microgrids. The scientific contribution follows two directions: (i) modelling individual intelligence in energy management under uncertainty and (ii) microgrid energy management integrating diverse actors with conflicting objectives. Agent-Based Modelling (ABM) is used to describe the dynamics of microgrid actors operating under limited access to information, and operational and environmental uncertainties. The approaches considered to model individual intelligence in this thesis, Reinforcement Learning and Robust Optimization, provide each agent with the capability of making decision, adapting to the stochastic environment and interacting with other agents. The modelling frameworks developed have been tested on urban microgrids integrating different energy consumers, sources of renewable energy and storage facilities, for optimal energy management in terms of reliability and economic indicators under operational and environmental uncertainty, and components failures.
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Submitted on : Tuesday, January 27, 2015 - 11:57:12 AM
Last modification on : Saturday, May 8, 2021 - 3:26:54 AM
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  • HAL Id : tel-01109990, version 1


Elizaveta Kuznetsova. Microgrid agent-based modelling and optimization under uncertainty. Electric power. Université de Versailles Saint-Quentin-en-Yvelines, 2014. English. ⟨tel-01109990⟩



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