Reduction in system inertia and maintaining the frequency at the nominal value is a staple of today's and future power systems since their operation, stability, and resiliency are degraded by frequency oscillation and cascading failures. Consequently, designing a stable, scalable, and robust virtual inertia control system is highly relevant to skillfully diminishing the deviations during major contingencies. Therefore, considering the potential problems in predesigned nonflexible control systems with offline tuning techniques, we propose a variable fractional-order PID controller for virtual inertia control applications, which is tuned online using a modified neural network-based algorithm. The new proposed tuner algorithm is trained using a deep reinforcement learning strategy with a simplified deep deterministic policy gradient, which considers microgrid uncertainties. Compared with existing methods, all the tuning knobs of the discrete type and fully tunable variable FOPID controller (for both gain and order) can be captured based on the proposed hybrid algorithm, which inherits features from both classical and advanced techniques. To demonstrate the effectiveness of the training of the proposed controller, a comparative analysis with the standard FOPID and PID controllers is given under three different scenarios with a smooth (dis)connection of renewable energy sources and loads.

Although the deployment and integration of isolated microgrids is gaining widespread support, regulation of microgrid frequency under high penetration levels of renewable sources is still being researched. Among the numerous studies on frequency stability, one key approach is based on integrating an additional loop with virtual inertia control, designed to mimic the behavior of traditional synchronous machines. In this survey, recent works related to virtual inertia control methods in islanded microgrids are reviewed. Based on a contextual analysis of recent papers from the last decade, we attempt to better understand why certain control methods are suitable for different scenarios, the currently open theoretical and numerical challenges, and which control strategies will predominate in the following years. Some of the reviewed methods are the coefficient diagram method, H-infinity-based methods, reinforcement-learning-based methods, practical-swarm-based methods, fuzzy-logic-based methods, and model-predictive controllers.

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios.