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Decentralized Model Predictive Control of Plug-in Electric Vehicles Charging based on the Alternating Direction Method of Multipliers

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Sep 2020 Italy Publisher:IEEEJournal:2020 28th Mediterranean Conference on Control and Automation (MED)Funded by:EC | MOBINCITY, EC | 5G-SOLUTIONS

Authors: Germana, Roberto; Liberati, Francesco; Di Giorgio, Alessandro;

doi: 10.1109/med48518.2020.9183137

handle: 11573/1438073

Decentralized Model Predictive Control of Plug-in Electric Vehicles Charging based on the Alternating Direction Method of Multipliers

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Abstract

This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system.

Country

Italy

Related Organizations

Sapienza University of Rome
Italy
Roma Tre University
Italy

Keywords

convex functions, load modeling, state of charge, optimization;optimal control;real-time systems;convex functions;state of charge;charging stations;load modeling, optimal control, real-time systems, charging stations, optimization, MAG: Real-time charging, MAG: Optimization problem, MAG: Computer science, MAG: Open-loop controller, MAG: Optimal control, MAG: Model predictive control, MAG: State of charge, MAG: Control theory, MAG: Control system, MAG: Convex function

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