Short-circuit analytical model for modular multilevel converters considering DC cable capacitance

Developing analytical short-circuit models for Modular Multilevel Converters (MMC) is not straightforward due to their switching and blocking characteristics. Short-circuit models for MMCs have been developed previously in the literature. However, there is a lack of understanding regarding the dynamics in the short-circuit model when the DC cable capacitance is taken into account. Therefore, this work proposes an analytical pole-To-pole short-circuit model for half-bridge MMCs that considers the cable capacitance and terminal capacitors and accounts their contribution to fault dynamics. An approximated analytical model has been derived separating the system solutions in different natural frequencies. The proposed model provides an excellent approximation for a vast range of realistic system parameters. The analytical model reproduced the behaviour of the variables in the time domain and provided a clear basis for interpreting the dynamics of the voltages and currents involved

Deep reinforcement learning-based secondary control for microgrids in islanded mode

Microgrids are generally low-inertia systems with a high penetration of renewable energy sources. The design of advanced control structures is required to keep these grids’ electrical variables within an acceptable range. In this context, the present article proposes an intelligent secondary controller for islanded microgrids using the Deep Deterministic Policy Gradient (DDPG). The DDPG controller changes the output power of the storage elements to secure the voltage and frequency stability. This work tested the designed controller for a microgrid that comprises a synchronous generator, two battery energy storage systems and one photovoltaic generator. The controller performance was compared to droop controllers, considering a short-circuit event, feeder and load disconnections. Results showed a consistent reduction of the microgrid’s voltage and frequency deviations with the DDPG algorithm.Peer ReviewedPostprint (author's final draft

Definition of Scenarios for Modern Power Systems with a High Renewable Energy Share

Abstract Recent environmental policies have led academic, industrial, and governmental stakeholders to plan scenarios with a high share of renewable energy sources (RES), to ensure that future energy systems, composed mostly of RES, can remain stable, match the demand during seasonal variations and are economically feasible. This article considers different energy scenarios to obtain various options in terms of size, generation technologies, and grid configuration. The scenarios are studied in the POSYTYF project and are quantified through an optimization‐based algorithm, where the test grids topologies are based on specific locations in Europe, and real data related to the availability of RES, as well as the demand. Different RES technologies are considered to meet requirements of grid integration of renewables at different horizons of time, up to 100% in the most futuristic case. The optimization algorithm is applied to three scenarios. It is shown that solar photovoltaic (PV) and wind can provide the renewable backbone, but they lack flexibility to achieve a very high share in the energy mix. Solar thermal and pumped hydro become important to cover the last range of integration, as they provide high flexibility, which is crucial for high share, but they are expensive for low share