This paper expresses the decentralized strategy based on multi-agent system (MAS) for distribution network restoration with considering Coupling neighboring microgrids (CNMGs) which is modelled renewable energy sources (RESs), electric vehicles (EVs), battery storage system (BSS) and load. The MAS approach can be obtained suitable restoration path with the different zone agents distributed along the network and it can be managed the overloading microgrids (MGs) due to unbalancing between of RES, BSS, EV and load using interconnecting these MGs to neighboring non-overloading MGs. This strategy is new method that is implemented in this paper, and it is based on expert system rules, and it also considers the system operational limits such as voltage and line flow limits. Finally, this technique applied to 12.66 kV and 33-bus radial distribution network, where agents and power flow calculation are simulated in MATLAB environment for decisions making and validate the correctness of the agents’ decisions, respectively.

In this paper, a new method for extending and relaxing the noise-Coupling (NC) technique is proposed to enhance the noise-shaping order without adding the number of integrators. The noise-shaping order of the introduced ∑∆ modulator whit applying a second-order noise-Coupling technique is enhanced and its performance with optimizing the noise transfer function (NTF) zeros is improved. Also, by removing the analog adder at feedforward path and transferring it to a new feedback branch before the last integrator and adding second-order NC path can be decreased the input voltage swing to the quantizer. Thus, by improving the modulator resolution, power consumption can be reduced. Mathematical analyses and behavioural simulation results confirm the effectiveness of the new NC method. To examine its performance, a 2nd-order single loop ΣΔ modulator was designed. The new noise-Coupling method is used to achieve the three-order noise shaping to increase the resolution with low complexity and low-power. The results show an outstanding improvement in signal-to-noise and distortion ratio (SNDR) compared to the conventional structure.

Dou to penetration of stochastic resources in the microgrids, the need for new control and scheduling method is essential. Reconfiguration strategy can be considered as a practical solution for network performance improvement. Therefore, in this paper the scheduling of reconfigurable microgrid (RMG) in the normal and emergency conditions are analyzed. It is assumed that microgrid consists of several small neighboring microgrids, so in addition to the microgrid scheduling in the normal condition, the problem of neighboring microgrid management will be solved in the emergency condition. In the first condition, microgrid total cost minimization is considered as an objective while in the emergency condition, the microgrid divided to several neighboring microgrids and the proposed method tries to find the optimal structure for neighboring connection and minimizes the total load shedding, until the return to the normal condition. The results show the effectiveness of proposed method from the cost and load shedding minimization point of view.

In this paper, the effect of a static synchronous compensator (STATCOM) influence on the frequency of islanded microgrids based on frequency control using fuzzy cooperative control is investigated. To achieve fast frequency control, instantaneous power balance between generation and consumption is inevitable, and it can be supplied through energy storage systems such as battery with a proper frequency control method. Besides, the frequency control of islanded microgrids could be studied under different circumstances, where one aspect analyzed is added to a flexible AC transmission system (FACTS) device, such as STATCOM, in the microgrids. Although STATCOM is charged with improving the voltage profile, it can affect frequency stability by adjusting the voltage very quickly. Due to the importance of refining frequency stability, two controller methods are compared: a classic PI controller and a fuzzy PID controller. Accordingly, the performance of STATCOM is evaluated via two scenarios. Based on simulation results, by applying the fuzzy PID controller to the microgrid, STATCOM can reach the nominal frequency. Moreover, with greater validation and investigation of this topic, this device could be an agreeable alternative to the battery energy storage system (BESS).

The stability analysis of islanded inverter-based microgrids (IBMGs) is increasingly an important and challenging topic due to the nonlinearity of IBMGs. In this paper, a new linear model for such microgrids as well as an iterative method to correct the linear model is proposed. Using the linear model makes it easy to analyze the eigenvalues and stability of IBMGs due to the fact that it derives the eigenvalues directly and the linearization around an operating point to study the small signal stability and also use the newton-raphson method or other load flow solutions to solve these systems are no longer needed. An effective eigenvalue calculator is developed which is able to calculate the eigenvalues of IBMGs in the first few iterations of the proposed method. The proposed method provides the superior performance considering the simulation time compared to the conventional methods. The validation and comparison of the results show the performance of the proposed method.

microgrids (MGs), which can incorporate renewable energies such as wind and solar, can be divided into several sub-MGs with multiple connection points (MCPs) to the grids. However, this configuration is not ideal for MG operation due to the lack of adequate protection and operation mechanisms that ensure the safe and reliable functioning of distributed generation. A key issue with these MGs is the identification of islanding, which is challenging due to the presence of a broad non-detection zone (NDZ). Passive islanding identification approaches primarily depend on over/under voltage protection (OVP/UVP), over/under frequency protection (OFP/UFP), and monitoring metrics, such as phase jump at the point of common Coupling (PCC). This study examines the power equations for real and reactive power in renewable-energy-based MGs (referred to as renewable MGs) with multiple connections to different grids and MGs, which are of significant size. The analysis focuses on the NDZ of OVP/UVP and OFP/UFP approaches. Passive approaches observe the changing system parameters that occur when the MG is isolated, while active methods depend on the system's reaction to a minor disturbance introduced to identify the isolation situation. Traditional passive islanding detection approaches exhibit a significant NDZ that may compromise the accuracy of islanding detection in these types of MGs. Even if one grid is disconnected, the MG remains connected to other grids, preventing islanding. Consequently, typical active islanding detection methods are unable to identify the off-grid status.

This paper addresses the stochastic optimal day-ahead microgrid (MG) energy resources scheduling, considering the uncertain load, price of electricity, and generated electrical power by wind and solar units. Moreover, the vehicle-to-grid (V2G) implementation, load curtailment cost, and spinning reserve requirements are modeled to make the results more practical and applicable. Furthermore, the price elasticity of supply is considered to explore the relation between V2G capability and the optimization process. The stochastic energy resources scheduling problem is formulated in a two-level optimization framework. The unit commitment of dispatchable resources is analyzed in the upper level, and the lower level is formulated as a scenario-based two-stage stochastic programming problem that minimizes the operation cost ofMGconsidering all the constraints. The risk of attaining unfavorable high costs ofMGscheduling is considered using the variance approach. The generated scenarios are reduced by using the backward reduction method for each uncertain variable at each hour, independently. The artificial intelligent-based methods, including differential evolution algorithm (DEA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES) are applied to solve the problem. The effectiveness of the proposed approaches is confirmed by simulations on a modified 13-bus IEEE test system, in two cases of neglecting the risk and including the managed risk by applying the real-world data. The results confirmed the better performance of CMAES for solving such optimization problems.