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1. Frequency Regulation using Sparse Learned Controllers in Power Grids with Variable Inertia due to Renewable Energy

   https://doi.org/10.1109/CDC40024.2019.9029534  

   Hidalgo-Gonzalez, Patricia ; Henriquez-Auba, Rodrigo ; Callaway, Duncan S. ; Tomlin, Claire J. ( December 2019 , 2019 IEEE 58th Conference on Decision and Control (CDC))

   Inertia from rotating masses of generators in power systems influence the instantaneous frequency change when an imbalance between electrical and mechanical power occurs. Renewable energy sources (RES), such as solar and wind power, are connected to the grid via electronic converters. RES connected through converters affect the system's inertia by decreasing it and making it time-varying. This new setting challenges the ability of current control schemes to maintain frequency stability. Proposing adequate controllers for this new paradigm is key for the performance and stability of future power grids. The contribution of this paper is a framework to learn sparse time-invariant frequency controllers in a power system network with a time-varying evolution of rotational inertia. We model power dynamics using a Switched-Affine hybrid system to consider different modes corresponding to different inertia coefficients. We design a controller that uses as features, i.e. input, the systems states. In other words, we design a control proportional to the angles and frequencies. We include virtual inertia in the controllers to ensure stability. One of our findings is that it is possible to restrict communication between the nodes by reducing the number of features in the controller (from 22 to 10 in our case study) without disrupting performance and stability. Furthermore, once communication between nodes has reached a threshold, increasing it beyond this threshold does not improve performance or stability. We find a correlation between optimal feature selection in sparse controllers and the topology of the network. 

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2. Frequency Regulation using Data-Driven Controllers in Power Grids with Variable Inertia due to Renewable Energy

   https://doi.org/10.1109/PESGM40551.2019.8973437  

   Hidalgo-Gonzalez, Patricia ; Henriquez-Auba, Rodrigo ; Callaway, Duncan S. ; Tomlin, Claire J. ( August 2019 , 2019 IEEE Power & Energy Society General Meeting (PESGM))

   With the increasing penetration of non-synchronous variable renewable energy sources (RES) in power grids, the system's inertia decreases and varies over time, affecting the capability of current control schemes to handle frequency regulation. Providing virtual inertia to power systems has become an interesting topic of research, since it may provide a reasonable solution to address this new issue. However, power dynamics are usually modeled as time-invariant, without including the effect of varying inertia due to the presence of RES. This paper presents a framework to design a fixed learned controller based on datasets of optimal time-varying LQR controllers. In our scheme, we model power dynamics as a hybrid system with discrete modes representing different rotational inertia regimes of the grid. We test the performance of our controller in a twelve-bus system using different fixed inertia modes. We also study our learned controller as the inertia changes over time. By adding virtual inertia we can guarantee stability of high-renewable (low-inertia) modes. The novelty of our work is to propose a design framework for a stable controller with fixed gains for time-varying power dynamics. This is relevant because it would be simpler to implement a proportional controller with fixed gains compared to a time-varying control. 

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3. Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications

   https://doi.org/10.3390/app8122695  

   Tamrakar, Ujjwol ; Shrestha, Dipesh ; Malla, Naresh ; Ni, Zhen ; Hansen, Timothy ; Tamrakar, Indraman ; Tonkoski, Reinaldo ( December 2018 , Applied Sciences)

   The rapid transition towards an inverter-dominated power system has reduced the inertial response capability of modern power systems. As a solution, inverters are equipped with control strategies, which can emulate inertia by exchanging power with the grid based on frequency changes. This paper discusses the various current control techniques for application in these systems, known as virtual inertia systems. Some classic control techniques like the proportional-integral, the proportional-resonant, and the hysteresis control are presented first, followed by the design and discussion of two more advanced control techniques based on model prediction and machine learning, respectively. MATLAB/Simulink-based simulations are performed, and results are presented to compare these control techniques in terms of harmonic performance, switching frequency, and transient response. 

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4. Frequency Regulation in Hybrid Power Dynamics with Variable and Low Inertia due to Renewable Energy

   https://doi.org/10.1109/CDC.2018.8619580  

   Hidalgo-Gonzalez, Patricia ; Callaway, Duncan S. ; Dobbe, Roel ; Henriquez-Auba, Rodrigo ; Tomlin, Claire J. ( December 2018 , Frequency Regulation in Hybrid Power Dynamics with Variable and Low Inertia due to Renewable Energy)

   As more non-synchronous renewable energy sources (RES) participate in power systems, the system's inertia decreases and becomes time dependent, challenging the ability of existing control schemes to maintain frequency stability. System operators, research laboratories, and academic institutes have expressed the importance to adapt to this new power system paradigm. As one of the potential solutions, virtual inertia has become an active research area. However, power dynamics have been modeled as time-invariant, by not modeling the variability in the system's inertia. To address this, we propose a new modeling framework for power system dynamics to simulate a time-varying evolution of rotational inertia coefficients in a network. We model power dynamics as a hybrid system with discrete modes representing different rotational inertia regimes of the network. We test the performance of two classical controllers from the literature in this new hybrid modeling framework: optimal closed-loop Model Predictive Control (MPC) and virtual inertia placement. Results show that the optimal closed-loop MPC controller (Linear MPC) performs the best in terms of cost; it is 82 percent less expensive than virtual inertia placement. It is also more efficient in terms of energy injected/absorbed to control frequency. To address the lower performance of virtual inertia placement, we then propose a new Dynamic Inertia Placement scheme and we find that it is more efficient in terms of cost (74 percent cheaper) and energy usage, compared to classical inertia placement schemes from the literature. 

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5. Frequency Response Improvement of PMSG Wind Turbines Using a Washout Filter

   https://doi.org/10.3390/en13184797  

   Shabestari, Parisa M. ; Mehrizi-Sani, Ali ( September 2020 , Energies)

   null (Ed.)

   High integration of renewable energy resources, such as wind turbines, to the power grid decreases the power system inertia. To improve the frequency response of a low-inertia system, virtual inertia approach can be used. This letter proposes a control method to decrease the frequency transients and restore frequency to its nominal value. A wind turbine usually works based on maximum power point tracking (MPPT) curves to achieve the maximum power. In this letter, the proposed controller uses a non-MPPT method to leave power for frequency regulation during transients. Moreover, it uses a washout filter-based method to remove the steady-state error in the frequency. Simulation results in the PSCAD environment validate the improved performance of the proposed method during load changes by comparing it with the MPPT and non-MPPT methods. 

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