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Energy management strategies for smart grids

Abstract : Electricity grids are currently undergoing a profound transformation away from a centralized towards a decentralized power management paradigm. The two main drivers are the emergence of renewable energy sources and the rapid development of information systems. The latter enables the deployment of advanced control strategies, able to respond to the numerous challenges which arise for the reliable operation of the evolving electricity grids.This thesis is dedicated to the development and assessment of such advanced control strategies at distribution grid level. More precisely, energy management systems using Distributed Model Predictive Control (DMPC) and Stochastic Optimization are proposed. In order to optimally coordinate the operation of a large number of assets in a distribution grid, one challenge is to deal with the large-scale nature of the system. For this purpose, two hierarchical DMPC frameworks for resource sharing problems are proposed and compared with each other. Both of them rely on dividing a large-scale MPC problem into several local MPC problems and one coordinator problem. The two frameworks which are based on a primal- and on a dual decomposition of the initial centralized optimization problem are shown to be computationally tractable despite the large-scale nature of the system. Moreover they come along with a better modularity, safety and data privacy compared to a centralized MPC solution.Another important challenge stems from the increasing amount of uncertainties in the electricity grid. This is mainly due to the high intermittency of renewable energy sources and due to the foreseeable vehicle electrification which comes along with highly fluctuating charging needs. Dealing with those uncertainties requires innovative technical solutions in order to maintain the balance of power production and consumption at all times. In order to address this issue, two energy management systems, one for PV power plants and another one for electric vehicle charging stations, are proposed in this thesis. They explicitly take into account the uncertainties in the control strategy, using randomized algorithms. This way a robust and more predictable behavior of the systems is achieved.This Ph.D. thesis was prepared within the Gipsa-lab in partnership with Schneider-Electric in the scope of the AMBASSADOR project (www.ambassador-fp7.eu).
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Submitted on : Tuesday, October 19, 2021 - 12:08:22 PM
Last modification on : Wednesday, April 20, 2022 - 3:08:43 AM


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  • HAL Id : tel-01472704, version 2



Peter Pflaum. Energy management strategies for smart grids. Electric power. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAT006⟩. ⟨tel-01472704v2⟩



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