Using DC Micro-grid to connect more efficiently with DC output has increased. The sources are photovoltaic and energy storage systems. The conversion losses of the sources for supplying the loads are reduced compared to the AC Micro-grid. This method aims at combining the above methods to achieve maximum use of Renewable Energy and Energy Storage and enhancing the efficiency of the grid by using the Active power Filter and applying the DC to DC converter to boost the conversion efficiency and load management with having island inputs and the way to manage the inputs in the state of load disturbance and exit of one of the island inputs from the circuit is rapid injection of energy stored into the grid in the quickest time and the coordinated control between grid and photovoltaic system and battery have been studied in MATLAB/SIMULINK software. The amount of simulation time will be considered 0. 5 seconds.

In this paper, a new method is presented for solving the energy optimization problems. The goal is to find the optimal Energy Resources are Distributed (DER). so that minimizes the cost of the total operations. The methods we use in this paper is genetic algorithms, particle swarm algorithm and modified cuckoo optimization algorithm that genetic and particle swarm algorithms used to compare. Finally we use the proposed method which is modified cuckoo optimization Algorithm in the test Micro grid (MG). Results show that the proposed algorithm, in addition to providing electrical necessity of Micro grid, it can be excellent of economically, which is the goal of this paper.

The intermittent nature of energy has increased the tendency to use hybrid systems, and the optimal control of these systems in terms of cost and reliability has become very important with an increase in the use of hybrid renewable energy systems, especially in remote electrification. In this research, energy management has been done using dynamic decision-making algorithm and intelligent controller. The system automatically and dynamically adapts to changes in the output of the solar and wind power sources and batteries and is able to respond to external requests such as voltage signals, power system limitations, or operator’s requests. The results have showed that if the fraction of renewable energy is 23.8% with an average annual fossil fuel consumption of 407 liters, the production energy of the combined renewable energy conversion system will be equal to 1697 kWh/yr. Accordingly, the net present value will be equal to 553.68 US dollars, and the domestic return rate will equal 21.49% with a return period of 15.71 years.

In this paper, the short term optimal economic planning for a Microgrid is implemented at two levels with different horizons. The purpose of 24-hour scheduling is to determine the generation of Micro-turbines, the contribution of responsive loads, the status of charging and discharging energy storage resources, the exchange rate with the upstream network, the prediction of renewable resources generation, optimum cost for the system operator and the amount of pollutant emissions will be minimized. Planning in the second layer runs for 15 minutes ahead. At this stage, the goal is to reduce the forecast error and minimize the cost of first layer operation. The proposed Method is implemented and tested using MATLAB programming. The proper performance of this method is reduce operation costs and maximize responsive loads profit.

Energy management changes the electricity consumption pattern of customers. This change is done to achieve the optimal consumption curve. Using energy management, by reducing consumption in periods, besides the appropriate load curve, it reduces the cost of operation and planning. Electricity supply in traditional networks is done by large power plants that are concentrated in certain places. The generated energy must be transferred to consumption points by transmission and distribution networks. The above-mentioned power system has many problems, among which we can mention the decrease in reliability and availability due to the wear and tear of the electrical system infrastructures and the imposition of high costs of losses in the energy transmission to the load points. In other words, management includes a set of activities that affect the pattern and amount of consumer load. Basically, consumption management programs aim to achieve various goals, the most important of which are improving the efficiency of energy systems, increasing the load factor, reducing the need for investment to build and postponing the construction of new power plants, reducing the effects harming the environment, reducing the cost of electricity supply to customers, compensating for the shortage of supply and reducing the excess demand for electricity, improving the reliability and quality of power, promoting the development of energy economy, creating a culture of saving and effective support for customers pointed out.

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.

This paper presents an optimization method to optimize the parameters of the Microgrid controller in islanding mode. The controller optimal parameters have been obtained by using the particle swarm optimization (PSO). This is done based on minimization of the errors in the current and voltage controllers. Finally, simulation has been carried out to verify the effectiveness of the optimized controller. Stability analysis of the controller is verified using classical approach

This paper aims to establish an Arduino and IoT-based Hierarchical Multi-Agent System (HMAS) for management of loads’ side with incentive approach in a Micro-grid. In this study, the performance of the proposed algorithm in a Micro-grid has been verified. The Micro-grid contains a battery energy storage system (BESS) and different types of loads known as residential consumer (RC), commercial consumer (CC), and industrial consumer (IC). The user interface on a smartphone directly communicates with the load management system via an integrated Ethernet Shield server which uses Wi-Fi communication protocol. Also, the communication between the Ethernet Shield and the Arduino Microcontroller is based on Wi-Fi communication. A simulation model is developed in Java Agent Development Environment (JADE) for dynamic and effective energy administration, which takes an informed decision and chooses the most feasible action to stabilize, sustain, and enhance the Micro-grid. Further, the environment variable is sensed through the Arduino Microcontroller and sensors, and then given to the MAS agents in the IoT environment. The test results indicated that the system was able to effectively control and regulate the energy in the Micro-grid.

The expansion of renewable energy sources (RESs ( and the advances in power electronics have led to more attention being paid to DC Micro-grids (DCMGs). DCMGs enable the exploitation of all renewable energy potentials. Along with the advantages of RESs and DCMGs, the use of RESs is associated with the challenges of absence or lack of inherent inertia. Inertia in DCMGs plays an important role in reducing the voltage changes under the destructive events such as the load change and power change. Therefore, by applying energy storage systems (ESSs) in DCMGs and inertia emulation, the mentioned challenges can be overcome. The proposed control scheme is implemented based on the concept of the virtual supercapacitor in the inner control loop of the ESS interface dual-half-bridge (DHB) converter with DCMG to emulate the inertia. Due to the high efficiency, electrical insulation, inherent soft switching, and the requirement for a smaller filter, the DHB converter is used. Finally, a DCMG is simulated in MATLAB/Simulink. The simulation results obtained show the efficiency and flexibility of the proposed scheme in terms of inertia emulation.

The benefits of renewable energy have led to the increased penetration of Distributed Generators (DG) not only in the military sites، but have also increased the availability of continuous supply of electrical energy even in the critical conditions. During the national grid supply interruption، the stability of these systems (Micro grid) is essential. In order to maintain frequency stability of these isolated Micro grids، a balance between generation and consumption has to be always maintained. It is customary to use energy saving devices to maintain balance by increasing generation in the short runs، where large frequency deviation results in load shedding. In this paper use of interruptible/curtailable loads to maintain the balance is proposed. That is a specific load has been reduced to control the frequency. Simulation of the proposed method has been run on the model of a real network which has verified the results and its effectiveness