In this paper, a preliminary analysis of the impact of high wind power penetration in the planning and operation of the Spanish power system is presented. The problems studied in the paper are those related to the stability of the power system. This key subject could be deeply influenced by the installation of up to 15000 MW of wind power generation as it has been planned for the next six years. The analysis presented here is based on the results of dynamic simulations. Dynamic models of the induction generator (squirrel cage and doubly fed) and models of the wind farms have been developed to make possible the simulations. From the simulation results some conclusions and recommendations have been extracted, which would contribute to the appropriate integration of the new wind generation foreseen for the Spanish power system.

This paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology.

This paper proposes an area inertia estimator based on an iterative equation error (EE) system identification (SI) approach. The inertia value can be accurately extracted for areas of different compositions with different penetrations of converter-interfaced generators. Firstly, the internal frequency variation of the generator units is computed by means of a frequency divider-based estimator. Subsequently, these internal frequency variations are used to carry out a generator clustering, which provides groups of coherent generators to the proposed inertia estimator. Finally, the iterative EE SI approach provides a joint inertia estimation for each of these coherent groups. Numerical results on a properly modified version of both the IEEE 39-bus and the IEEE 118-bus test systems highlight the accuracy of the proposed method using both ambient measurements and ringdown signal measurements (power imbalance events). Furthermore, the proposed method presents a low computational burden that allows fast estimation updating times.

This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calculates the optimal operating point of the system, accommodating high shares of non-synchronous renewable generation while ensuring transient stability in the event of critical incidents. Synthetic inertia controllers are used to improve the dynamic stability of the system in cases of very high share of renewable generation. The proposed tool is tested in the North-West Spanish system, a network with a high penetration of wind energy that causes a reduction in the total system inertia. The results of the study show that 1) synthetic inertia in renewable power plants can diminish electromechanical oscillations after a severe contingency, reducing the cost of ensuring transient stability; 2) using synthetic inertia the system becomes more stable when conventional generation is decommissioned following de-carbonization and renewable promotion policies; and 3) the proposed model can be used to calculate the parameters of the synthetic inertia control.