Large-scale integration of renewable generation, usually interfaced to the network through power electronics, has led to drastic changes in power system dynamics. This paper presents novel insights into stability properties of such systems. For that purpose, a high-fidelity dynamic model of a generic low-inertia power system has been developed. The full-order, state-of-the-art control schemes of both synchronous and converter-based generators are included, with the latter differentiating between grid-forming and grid-following mode of operation. Furthermore, the dynamics of transmission lines and loads are captured in the model. Using modal analysis techniques such as participation factors and parameter sensitivity, the most vulnerable segments of the system are determined and the adverse effects of timescale coupling and control interference are investigated. More precisely, this work characterizes the maximum permissible penetration levels of inverter-based generation as well as the nature of the associated unstable modes and the underlying dynamics. Finally, potential directions for improving the system stability margin under different generation portfolios are proposed for several benchmark systems.

In this paper, the overvoltage problems that might arise from the integration of photovoltaic (PV) panels into low-voltage (LV) distribution networks is addressed. A distributed scheme is proposed that adjusts the reactive and active power output of inverters to prevent or alleviate such problems. The proposed scheme is model-free and makes use of limited communication between the controllers in the form of a distress signal only during emergency conditions. It prioritizes the use of reactive power, while active power curtailment is performed only as a last resort. The behavior of the scheme is studied using dynamic simulations on a single LV feeder and on a larger network composed of 14 LV feeders. Its performance is compared with a centralized scheme based on the solution of an optimal power flow (OPF) problem, whose objective function is to minimize the active power curtailment. The proposed scheme successfully mitigates overvoltage situations due to high PV penetration and performs almost as well as the OPF-based solution with significantly less information and communication requirements.

In this paper, the Unit Commitment (UC) problem in a power network with low levels of rotational inertia is studied. Frequency-related constraints, namely the limitation on Rate-of-Change-of-Frequency (RoCoF), frequency nadir and steady-state frequency error, are derived from a uniform system frequency response model that incorporates dynamics and controls of both synchronous generators and grid-forming inverters. These constraints are then included into a stochastic UC formulation that accounts for wind power and equipment contingency uncertainties using a scenario-tree approach. In contrast to the linear RoCoF and steady-state frequency error constraints, the nadir constraint is highly nonlinear. To preserve the mixed-integer linear formulation of the stochastic UC model, we propose a computationally efficient approach that allows to recast the nadir constraint by introducing appropriate bounds on relevant decision variables of the UC model. This method is shown to be generally more accurate and computationally more efficient for medium-sized networks than a piece-wise linearization method adapted from the literature. Simulation results for a modified IEEE RTS-96 system revealed that the inclusion of inertia-related constraints significantly influences the UC decisions and increases total costs, as more synchronous machines are forced to be online to provide inertial response.

This paper presents a novel virtual synchronous machine controller for converters in power systems with a high share of renewable resources. Using a linear quadratic regulator-based optimization technique, the optimal state feedback gain is determined to adaptively adjust the emulated inertia and damping constants according to the frequency disturbance in the system, while simultaneously preserving a tradeoff between the critical frequency limits and the required control effort. Two control designs are presented and compared against the open-loop model. The proposed controllers are integrated into a state-of-the-art converter control scheme and verified through electromagnetic transient (EMT) simulations.

The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on sub-optimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.

The increasing penetration of power-electronic-interfaced devices is expected to have a significant effect on the overall system inertia and a crucial impact on the system dynamics. In the future, the reduction of inertia will have drastic consequences on protection and real-time control and will play a crucial role in the system operation. Therefore, in a highly deregulated and uncertain environment, it is necessary for Transmission System Operators to be able to monitor the system inertia in real time. We address this problem by developing and validating an online inertia estimation algorithm. The estimator is derived using the recently proposed dynamic regressor extension and mixing procedure. The performance of the estimator is demonstrated via several test cases using the 1013-machine ENTSO-E dynamic model.