Biblio

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.

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.