Exploiting inertia of wind turbines in power network frequency control: A model predictive control approach

van Deelen, N.P.G. and Jokić, Andrej and Bosch, P.P.J. van den and Hermans, R.M. (2011) Exploiting inertia of wind turbines in power network frequency control: A model predictive control approach. = Exploiting inertia of wind turbines in power network frequency control: A model predictive control approach. In: IEEE International Conference on Control Applications 2011, 28.-30.09.2017., Denver, SAD.

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Official URL: https://doi.org/10.1109/CCA.2011.6044490

Abstract

With the expected increase in penetration level of wind turbine generators in the near future, it will be necessary for them to participate in power network frequency control. In this paper we exploit the inertia of wind turbine generators using model predictive control (MPC). In this way wind turbines can actively contribute to primary control. Safe operation is possible because MPC explicitly takes safety constraints into account. In a case study a nonlinear model of a power network is balanced by exploiting the inertial response of wind turbine generators. We have considered both centralized MPC and a decentralized MPC implementation, and have shown their efficiency in counteracting deviations in generation and demand introduced either by unpredictable exogenous disturbances, or by imbalanced transients during market rescheduling processes. The obtained results demonstrate the potential of wind turbine inertia exploitation in contributing to the challenging task of balancing future power networks.

Item Type: Conference or Workshop Item (Lecture)
Keywords (Croatian): wind turbine control; model predictive control; optimal control
Subjects: TECHNICAL SCIENCE > Mechanical Engineering
Divisions: 900 Department of Robotics and Production System Automation > 910 Chair of Engineering Automation
Indexed in Web of Science: No
Indexed in Current Contents: No
Date Deposited: 21 Apr 2017 13:19
Last Modified: 19 Nov 2018 16:06
URI: http://repozitorij.fsb.hr/id/eprint/7711

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