Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Multi-rotor virtual machine for grid-forming converter to damp sub-synchronous resonances

Grid-forming power converters (GFMC) have been widely adopted in power systems as an attractive solution against the challenges imposed by the ever-increasing penetration of renewables. Despite its versatility, GFMC is employed only to provide islanded operation, grid regulations, and synthetic inertia. To further extend the use of GFMC in enhancing power system stability, this paper proposes a multi-rotor virtual machine (MRVM) controller to attenuate sub-synchronous oscillations. Driven by the formulation of a virtual synchronous machine (VSM), the proposed MRVM implements a VSM-based GFMC with several virtual rotors whose electromechanical characteristics can be individually adjusted to target specific oscillatory modes in the system. In this work, the MRVM’s working principle is described in detail and tuning guidelines are proposed to simplify the selection of control parameters by using frequency-domain techniques and the eigenvalue locus analyses. To validate the performance of the MRVM, an IEEE benchmark grid model is adopted namely, the three-machine-infinite-bus system. It is evident from the results that the MRVM (i) provides higher degrees of freedom when dealing with sub-synchronous oscillations, and (ii) outperforms conventional GFMC, especially in damping intra-area power oscillations.This work was supported by the European Commission under Project FLEXITRANSTORE-H2020-LCE-2016-2017-SGS-774407.Peer ReviewedPostprint (published version

Power quality services provided by virtually synchronous FACTS

The variable and unpredictable behavior of renewable energies impacts the performance of power systems negatively, threatening their stability and hindering their efficient operation. Flexible ac transmission systems (FACTS) devices are able to emulate the connection of parallel and series impedances in the transmission system, which improves the regulation of power systems with a high share of renewables, avoiding congestions, enhancing their response in front of contingencies and, in summary, increasing their utilization and reliability. Proper control of voltage and current under distorted and unbalanced transient grid conditions is one of the most critical issues in the control of FACTS devices to emulate such apparent impedances. This paper describes how the synchronous power controller (SPC) can be used to implement virtually synchronous FACTS. It presents the SPC functionalities, emphasizing in particular the importance of virtual admittance emulation by FACTS devices in order to control transient unbalanced currents during faults and attenuate harmonics. Finally, the results demonstrate the effectiveness of SPC-based FACTS devices in improving power quality of electrical networks. This is a result of their contribution to voltage balancing at point of connection during asymmetrical faults and the improvement of grid voltage quality by controlling harmonics flow.Postprint (published version

Monte Carlo Deep Neural Network Model for Spread and Peak Prediction of COVID-19

Just a few days before the beginning of this year a new virus, widely known as the COVID-19, was detected in Wuhan, capital of the province Hubei, China. Since then, COVID-19 has spread all across the globe infecting more than half a million people resulting to the passing of nearly 25000 patients. Beside the social pain that this new pandemic is causing, the measures put in force to halt the spreading of the virus are stressing the global economy indicating a domino efect that can last even longer than the probable eradication of COVID-19. Yet, these measures are necessary to prevent health system reach their capacity, an occasion where di cult decisions will need to be made such as prioritization of patients to be treated

Grid-forming power converters tuned through artificial intelligence to damp subsynchronous interactions in electrical grids

The integration of non-synchronous generation units and energy storage through power electronics is introducing new challenges in power system dynamics. Specifically, the rotor angle stability has been identified as one of the major obstacle with regards to power electronics dominated power systems. To date, conventional power system stabilizer (PSS) devices are used for damping electromechanical oscillations, which are only tuned sporadically leading to significant deterioration in performance against the ever-changing operating conditions. In this paper, an intelligent power oscillation damper (iPOD) is proposed for grid-forming converters to attenuate electromechanical inter-area power oscillation. In particular, the iPOD is applied to a synchronous power controller (SPC) based grid-forming power converter to increases gain of the active power control loop at the oscillatory frequency. Predictions regarding the mode frequency, corresponding to the current operating points, are given by an artificial intelligence ensemble model called Random Forests. The performance of the proposed controller is verified using the two area system considering symmetrical fault for random operating points. In addition, a comparison with PSS installed in each generator reveals the individual contribution with respect to the inter-area mode damping.This work was supported in part by the European Commission under Project FLEXITRANSTORE—-H2020-LCE-2016-2017-SGS774407, and in part by the Spanish Ministry of Science under Project ENE2017-88889-C2-1-R.Peer ReviewedPostprint (published version

External inertia emulation controller for grid-following power converter

The advent of renewable energy has posed difficulties in the operation of power systems whose net inertia is becoming critically low. To face such challenges, grid-forming power has been one of the potential solutions pursued by the industry and research community. Although promising, grid-forming power converters are still immature for mass deployment in power systems. In the meanwhile, an enormous amount of grid-following power converters has been underexploited when it comes to grid-supporting functionalities. Therefore, this article proposes an external inertia emulation controller (eIEC) for grid-following power converter to provide frequency support to the grid. For the purpose of minimizing installation efforts and resources, the controller is designed in such a way that it can be implemented in an external controller communicating with the grid-following power converter via an industrial communication link. This article also investigates the effect of communication delay on the stability performance of the proposed controller. In addition to the detailed analysis, hardware-in-the-loop experiments are also carried out to validate the proposed eIEC.This work was supported by the European Commission under Project FLEXITRANSTORE-H2020-LCE-2016-2017-SGS-774407Peer ReviewedPostprint (author's final draft

COVID-19 Impact Estimation on ICU Capacity at Andalusia, Spain, Using Artificial Intelligence

In early 2020, the world began to watch with caution the spread of an outbreak of pneumonia associated with a new coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in late December 2019 in Wuhan City, Hubei Province, China. The rapid spread of this outbreak meant that in just one month more than 33 countries were infected with more than 85,200 cases

Battery and hydrogen energy storage control in a smart energy network with flexible energy demand using deep reinforcement learning

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network