Utilizing flexibility in Microgrids using Model Predictive Control | Zendy

Frederik Banis | Zendy; Daniela Guericke | Zendy; Henrik Madsen | Zendy; Niels Kjølstad Poulsen | Zendy

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Utilizing flexibility in Microgrids using Model Predictive Control

Author(s) -

Frederik Banis,

Daniela Guericke,

Henrik Madsen,

Niels Kjølstad Poulsen

Publication year - 2018

Publication title -

mediterranean conference on power generation, transmission, distribution and energy conversion (medpower 2016)

Language(s) - English

Resource type - Conference proceedings

ISBN - 978-1-83953-133-0

DOI - 10.1049/cp.2018.1856

Subject(s) - model predictive control , flexibility (engineering) , computer science , renewable energy , controller (irrigation) , energy management system , energy management , control engineering , control (management) , stochastic programming , focus (optics) , energy (signal processing) , engineering , mathematical optimization , artificial intelligence , statistics , mathematics , agronomy , physics , optics , electrical engineering , biology

We derive a control strategy for the operation of Microgrids (MGs) with high shares of Renewable Energy Sources involving Model Predictive Control (MPC). By combining the MPC with an Energy Management System (EMS) utilizing stochastic programming techniques and a sufficiently large temporal optimization window we improve the point of operation of the system regarding both short and long-term operational aspects. We aim for a system operation that allows for the utilization of the MG as a Virtual Power Plant. In this work we focus on the predictive controller design and the incorporation of information derived in the EMS layer.

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