Optimal use of microgrids for efficient and economic management of energy resources is of special importance. At the residential level, energy costs can be reduced by using the concepts of price-based intelligent control (response to demand). In this paper, an energy management problem that proposed to decrease the cost of operating a residential microgrid with Micro-CHP equipped by a smart meter. The problem objective function formulation in the form of short-term production planning is based on the total cost of Micro-CHP gas consumption and auxiliary burner and the cost of electricity purchased from the network or sell to it in 24 hours. The aim is to present the ideal energy management strategy of the resources available based on smart control, and due to the non-linear problem, GAMS software by Mixed Integer Non-Linear Programming (MINLP) was used to solve the problem. Finally, the proposed method is implemented on the sample microgrid and the results are compared with Heat-Led Control, the effect of battery on microgrid participation in load response programs was demonstrated. Also, the effect of different tariffs of the market price on the cost of microgrid operation and how to exchange with the grid in different cases are evaluated that results showed the proper performance of the proposed method.

An efficient power control technique for inverter-based distributed generation (DG) in an islanded microgrid is investigated in this work. The objective is to raise the caliber of the electricity pumped from network-connected DGs. The characteristics that are taken into consideration include voltage and frequency control, dynamic response, and steady-state response, particularly when the microgrid is operating in island mode or when there is a load change. The control method consists of an internal current control loop and an external power control loop based on a synchronous reference frame and a conventional PI controller. The power controller is designed based on voltage-frequency (VF) control. In addition, an intelligent search technique that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is utilized to automatically modify power controller parameters. The control technique in this research is that the DG modifies its control mode to modify the system voltage and frequency when the microgrid is islanded or load conditions change. The simulation results in MATLAB/SIMULINK software show that the proposed control system has been able to improve the power quality well.

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.

In this paper, the amount of microgrid frequency deviation in the dynamic state can be reduced by improving the frequency controller and implementing a new method. The proposed controller is designed for a microgrid including renewable resources, and the proposed control strategy is such that the controller coefficients are adjusted and optimised at all times by the model predictive control (MPC). The weight parameters of the MPC controller have been optimised by the particle swarm optimisation (PSO) algorithm. The proposed controller is located in the secondary frequency control loop, and by applying a control signal to the sources, the frequency perturbations following the power changes in the microgrid are reduced. The simulation results show that the proposed controller performs better than the Ziegler–Nichols PI controller (PI‐ZN) method, PI‐based controllers that rely on fuzzy logic (PI‐Fuzzy), the fractional‐order proportional‐integral‐derivative (FOPID) controller that is based on chaos particle swarm optimisation (FOPID‐CPSO) algorithm and the PID controllers based on CPSO algorithm (PID‐CPSO). It has been able to effectively reduce the frequency fluctuations in terms of amplitude and number of oscillations is also more resistant to the uncertainty of microgrid parameters and shows better performance when changing parameters than other methods.