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.

This research article describes the frequency regulation of an interconnected power system that includes wind energy systems and thermal non-reheat systems, with a proportional integral derivative (PID) controller optimized using metaheuristic algorithms such as Genetic-Algorithm (GA), Harmony-Search-Algorithm (HSA), Bat-Algorithm (BA), and Flower-Pollination-Algorithm (FPA). With the demand for precisely efficient energy systems growing, system engineers are increasingly looking for the finely optimized control solution that also has the benefit of faster convergence and avoids entrapment in local minimal. To minimize the fitness function which is based on ITAE (Integral of Time multiplied Absolute Error) criteria composed of frequency and tie-line power changes, we have obtained an optimum solution in terms of PID controller gain values using the metaheuristics optimizing techniques. Change in frequency in area 1, deviation in tie-line power, and change in frequency in area 2 obtained from different techniques are compared. The results obtained by simulating MATLAB/Simulink convey that PID controller gain values optimized using the HSA technique provide better dynamic performance compared to BA, FPA and GA techniques. The simulation results have been experimentally validated using hardware-in-loop (HIL) on a real-time simulator based on field-programmable gate arrays (FPGA). The HSA optimized PID controller is used to investigate the robustness of the system by Step-Load-Perturbation (SLP) and Random Step Load Pattern (RSLP). Results obtained by running simulation also show that the HSA optimized PID controller for the same optimized gain value can withstand the SLP and RSLP variation made in the system.

This study aims to introduce a new structure based on a nonlinear controller for controlling and analyzing the stability of the Microgrids. In the proposed model, AC and DC resources and loads are located on two different sides. In addition, an AC/DC bidirectional interface converter is applied to supply loads by AC/DC sources. There are AC/DC products on both sides of the converter and each side can supply the load of the other side via a bidirectional interface converter and its load. Alternatively, an energy storage system is used for the system stability on the DC side. The nonlinear microgrid controller is designed to adjust the AC bus side frequency and the DC bus side voltage properly. In this structure, the coordinated optimal power exchange and precise regulation of control signals lead to constant improvement. Thus, system performance is improved. The results show that the proposed model is efficient for both reduction of the fluctuations and improvement of the system stability

This paper deals with day-ahead programming under uncertainties in Microgrids (MGs). A two-stage stochastic programming with the fixed recourse approach was adopted. The studied MG was considered in the grid-connected mode with the capability of power exchange with the upstream network. Uncertain electricity market prices, unpredictable load demand, and uncertain wind and solar power values, due to intrinsically stochastic weather changes, were also considered in the proposed method. To cope with uncertainties, the scenario-based stochastic approach was utilized, and the reduction of the environmental emissions generated by the power resources was regarded as the second objective, besides the cost of units’ operation. The ɛ-constraint method was employed to deal with the presented multi-objective optimization problem, and the simulations were performed on a sample MG with one month of real data. The results demonstrated the applicability and effectiveness of the proposed techniques in real-world conditions.

Integration of Distributed Generations (DGs) in power grids is expected to play an essential role in the infrastructure and market of electrical power systems. Microgrids are small energy systems, capable of balancing captive supply and requesting resources to retain stable service within a specific boundary. Microgrids can operate in grid-connected or islanding modes. Effective islanding detection methods are essential for realizing the optimal operation of Microgrids. In this paper, a new passive islanding detection method is presented according to the change rate of DG’s voltage over active power index. This technique has been applied on inverter-based and synchronous-based Microgrids. The efficiency of the proposed method is verified through a comprehensive set of simulation studies carried out in Matlab/Simulink.