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Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy. [ABSTRACT FROM AUTHOR]

The world is endeavoring to transition to clean, renewable resource-based power generation systems such as wind and solar due to decreasing fossil fuel availability, a severe increase in power demand, environmental pollution, and global warming caused by unwanted gas emissions from current fossil fuel-based conventional power generation systems. An emerging technology, Microgrid (MG), has the capability to interface power generated from multiple distributed generation energy resources (DGER) such as wind, solar, fuel cells, biomass, geothermal, diesel engine generators, electric vehicles (EVs), and energy storage systems like batteries, compressed air storage, supercapacitors, and flywheels. This complexity in control and protection renders traditional power grids outdated. MGs are designed to operate in two modes: grid-connected mode (GCM) and islanded mode (IM), addressing power demand increases, transmission issues, and electricity storage challenges. MGs offer numerous advantages for power systems, yet their integration into distribution networks presents various challenges in protection systems, including fault current (FC) under different operating conditions, bidirectional fault current flow, unexpected relay tripping, disconnections due to sudden accidents, and cybersecurity issues. This paper provides a comprehensive review of various protection issues associated with MG and its possible solutions as suggested by various researchers. © 2024 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

In this paper, an improved Reinforcement Learning (RL) based approach for adaptive Virtual Synchronous Generator (VSG) control of grid-forming inverters is proposed. High penetration of inverter-based resources causes recurrent frequency stabilization issues. For this matter, a novel model-free Actor–Critic policy optimization technique is proposed to improve frequency transients metrics such as frequency nadir and Rate of Change of Frequency (RoCoF). Stability analysis of the VSG controlled system is conducted through small signal modeling to determine the stability margins of both the virtual inertia parameter J and damping coefficient D p. The control problem is formulated in RL terms using a new technique for state, action and reward construction that improves the learning process of the agents. The proposed approach's efficiency is tested for three RL algorithms, namely, Deep Deterministic Policy Gradient (DDPG), Soft Actor–Critic (SAC), and Twin-Delayed DDPG (TD3) with special attention given to the latter. Various performance metrics such as training time and maximum cumulative episodic reward are used to compare the three algorithms. The resulting agent based VSG adaptive controllers are simulated and then compared for power quality and frequency stabilization capabilities. Finally, a comparison between non-adaptive VSG and TD3 based VSG is presented to conclude on the efficiency improvements achieved by the proposed approach. • Conventional VSG Controllers necessitate recurrent parameter adjustments. • Adaptive VSG parameter adjustment is mandatory during power disturbance events. • Model-free RL-based Adaptive VSG Controllers provide more frequency stabilization. • The TD3 Algorithm is better than DDPG and SAC for Virtual Inertia Optimization. • RL Based Adaptive VSG Methods can be improved by using a novel state formulation. [ABSTRACT FROM AUTHOR]

The widespread integration of renewable energy sources to the main electrical grids has led to the increased adoption of AC microgrids. However, the protection of AC microgrids is a challenging task due to inverter interfaces, bidirectional power flow, multiple modes of operation and the requirement for selective phase tripping. This paper presents an innovative artificial neural network (ANN) based approach for fast and accurate identification and localization of symmetrical and asymmetrical faults occurring in the distribution networks of AC microgrids. In the proposed methodology, the three phase and the neutral currents which are sampled at either ends of the distribution lines, undergo discrete wavelet transform to extract the features exhibited during faults in the network. These features are used by two neural networks for classification and localization of the fault. To achieve high accuracy and computational efficiency, the network architectures of the ANNs are optimized, and the extracted features contain the detailed information required for ANNs to clearly distinguish different fault types and locations. A comprehensive evaluation and validation reveal that the proposed scheme accurately and efficiently classifies and localizes faults in AC microgrids. The existing research gap of fault localization in AC microgrids is also addressed through this approach.

Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy.

The usage of distributed generation sources and the segmentation of power systems into microgrids brings several advantageous features, such as energy independence, lower transmission losses, and improved energy management flexibility. Still, the expected steady abolition of traditional centralized networks and their centralized control schemes introduces several operational difficulties, that can compromise (micro)grid's stability and reliability. Consequently, several approaches to how microgrids should be operated have been proposed, albeit with several salient deficiencies. Precise power exchange, control of frequency and voltage excursions, applicability on different grid topologies, and the possibility to integrate grid codes, which are very important control aspects, are not jointly enabled in a general case. This paper proposes a novel approach, based on a specific goal function and corresponding decentralized control that addresses the mentioned problems and guarantees the microgrid's stability by constraining the frequency and node voltage deviations, simultaneously supporting the desired active power exchange between prosumers. Unlike droop-based and droop-derived control schemes, the proposed approach aims to inherently preserve the frequency and voltage levels within the limits defined by the grid operator or future microgrid codes, thus preventing the generation unit's unwanted disconnection during transients and disturbances. In addition, the control algorithm is independent of network topology, can be applied in both islanded and non-islanded microgrids, and secures system scalability. To confirm the effectiveness of the proposed control strategy, a number of simulation results are presented and thoroughly discussed. • A novel seamlessly scalable self-stabilizing microgrid control framework is proposed. • Guaranteed upper bounds on deviations of node frequencies and voltages are provided. • Precise active power exchange between nodes is enabled. • Priorities of the proposed scheme can be adjusted according to user preferences. • Comprehensive tests in relevant operational scenarios are performed. [ABSTRACT FROM AUTHOR]

Hierarchical control of AC microgrids, despite providing stable voltage, frequency, and optimal power distribution, is susceptible to false data injection (FDI) attacks on its secondary control. Electric power systems in countries such as the United States and Venezuela have suffered cyberattacks. This paper proposes a redundant secondary control method to resist FDI attacks. This method uses distributed generators (DGs) online as redundant units on standby, generating dynamic corrections to the attacked DGs, thus eliminating the attack impact. It effectively addresses the numerical tampering and addition brought by FDI to the frequency and voltage setting values of the secondary control, ensuring the microgrid can still output stable frequency and voltage. The principle of this control method is direct and practical; it does not require complex controllers and can respond quickly to FDI attacks. Then, focusing on a microgrid system containing six DGs, the setting values of the microgrid system are tampered with by multiple values in different time periods, the simulation results confirm that the proposed method can effectively resist FDI attacks under various scenarios. Finally, field programmable gate array (FPGA)-in-the-loop experiments further verify the effectiveness of the proposed method.