Flatness-Based Control Methodologies to Improve Frequency Regulation in Power Systems with High Penetration of Wind

To allow for high penetration of distributed generation and alternative energy units, it is critical to minimize the complexity of generator controls and to minimize the need for close coordination across regions. We propose that existing controls be replaced by a two-tier structure of local control operating within a global context of situational awareness. Flatness as an extension of controllability for non-linear systems is a key to enabling planning and optimization at various levels of the grid in this structure. In this study, flatness-based control for: one, Automatic Generation Control (AGC) of a multi-machine system including conventional generators; and two, Doubly fed Induction Machine (DFIG) is investigated. In the proposed approach applied to conventional generators, the local control tracks the reference phase, which is obtained through economic dispatch at the global control level. As a result of applying the flatness-based method, an $n$ machine system is decoupled into n linear controllable systems in canonical form. The control strategy results in a distributed AGC formulation which is significantly easier to design and implement relative to conventional AGC. Practical constraints such as generator ramping rates can be considered in designing the local controllers. The proposed strategy demonstrates promising performance in mitigating frequency deviations and the overall structure facilitates operation of other non-traditional generators. For DFIG, the rotor flux and rotational speed are controlled to follow the desired values for active and reactive power control. Different control objectives, such as maximum power point tracking (MPPT), voltage support or curtailing wind to contribute in secondary frequency regulation, can be achieved in this two-level control structure

Control and estimation techniques applied to smart microgrids : a review

DATA AVAILABILITY : No data was used for the research described in the article.The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network.http://www.elsevier.com/locate/rserhj2023Electrical, Electronic and Computer Engineerin