Modeling and Optimal Control of Energy Storage Strategy For Battery Life Extension Via Model Predictive Control

Islanded microgrids face reliability risks due to renewable energy sources intermittent nature and the varying load demands that could lead to continuous risk of power mismatch in the system. Coupling renewable energy sources (RESs) with advanced energy storage systems, e.g. battery energy storage s...

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Bibliographic Details
Published in2021 European Control Conference (ECC) pp. 1981 - 1986
Main Authors Shehzad, Muhammad, Gueniat, Florimond
Format Conference Proceeding
LanguageEnglish
Published EUCA 29.06.2021
Subjects
battery management
Costs
Degradation
energy management
energy storage
MPC
optimization
Predictive models
Renewable energy sources
Switches
Urban areas
Wind farms
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Summary:Islanded microgrids face reliability risks due to renewable energy sources intermittent nature and the varying load demands that could lead to continuous risk of power mismatch in the system. Coupling renewable energy sources (RESs) with advanced energy storage systems, e.g. battery energy storage system (BESS), helps with maintaining the power balance in the system. Limitations persist, in particular regarding the BESS time autonomy, degradation issues and overall costs.The paper presents a novel battery aging conscious energy management strategy to control a renewable energy system powered by RESs (wind and solar) and an integrated battery bank for energy storage in the events of excess RESs hours with respect to the user requested electrical load. We design a model predictive controller which takes into account the proposed BESS operating and economical costs, the degradation issues, the local load demand, while respecting system physical and the dynamical constraints. The dynamics of the BESS have been modeled by adopting the mixed-logic dynamic framework which helps in capturing different behaviors according to its possible operating modes. Numerical simulations show the feasibility and the effectiveness of the proposed approach.
DOI:10.23919/ECC54610.2021.9654971