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Microgrid is a system of generating and distributing electrical energy. Microgrids include various power systems, each of which has different power capacity and produces different oscillations. By producing reliable and flexible power, microgrids can ensure the safety and electrical reliability of the network and reduce energy costs. A microgrid is a small power system with distributed energy sources and loads, which can work in two modes connected to the grid and island. The controller in a microgrid must provide a seamless transition between different modes of operation. By reviewing the studies, the application of microgrids has expanded to respond to the growing energy demand and solve environmental pollution problems. In this paper, a brief overview of the effectiveness of direct current microgrids in the power system has been provided. Direct current microgrids do not need frequency synchronization and reactive power management, and in terms of stability, flexibility, and complexity, they are superior to alternating current microgrids. The source voltages are the variables that control the direct current microgrid power, so they must be well managed to achieve the desired voltage regulation. Various classifications have been stated for microgrids based on control methods, structures, performance, and other parameters. This review can be considered as an initial platform for research study in the development of direct current microgrids based on centralized and intelligent control. [ABSTRACT FROM AUTHOR]

In this paper, online controller scheme is proposed for voltage control of inverter-based distributed generations in islanded microgrid (MG). The MG has a completely nonlinear structure and its dynamics is constantly changing. As a result, linear controllers with constant and non-flexible coefficients are unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses an optimal online and nonlinear controller based on fuzzy logic. To improve the performance of the proposed controller, the differential evolution optimization algorithm is used to optimal tune its coefficient for the different MG operating scenarios. The goal of the proposed scheme is to control the voltage of the islanded MG at the standard level with the least transient state after the island's occurrence, as well as during load changes in island MG. In the proposed control scheme, proportional-resonant controller is used due to the advantages in order to voltage and current control of distributed generation units in the static frame space. The simulation results indicate the changing of the voltage controller coefficients, resulting in a better transient response, the elimination of the steadystate error and less harmonic distortion. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for islanding detection based on fast STransform, in distribution networks with distributed generators. In this method, at first, S-Transform of voltage and current at point of common coupling are calculated; then, features of normal and islanding operation are extracted using S matrix and frequency contours. The features are extracted using several simulations; considering load switching, motor load switching, transient faults, and islanding conditions with different power mismatches for local load. Finally, support vector machine has been proposed for classifying the extracted features to detect the islanding. For more studies, a distribution system is simulated using PSCAD/EMTDC and feature vectors corresponding to different situations of islanding and normal operation are extracted and used for training and test of support vector machine classifier. To evaluate the performance of proposed scheme, the obtained results are compared with results of other methods. Comparison of the results shows that the proposed method has higher accuracy and speed in islanding detection. Also, the proposed approach is effective, fast and would not be affected by noise during classifying of conditions. [ABSTRACT FROM AUTHOR]

The microgrid inertia as a result of tiny structure and barely tolerance variations, is fairly low. Thus, the maintenance of voltage stability and frequency specifically in islanded mode is extremely demanding. Even if these products have efficient control system, they can’t retain microgrid stability due to the low speed of response in primary sources of energy and communication delays of the links between outer unit and control system in distributed generation. Introducing a structure of fuzzy control arranged with neural network to balance between generation part and consumption part in micro grid is the main purpose of this paper. Using the fuzzy logic, this controller enables flexible operation of microgrids in both network and islanded modes. In the proposed control system, a trainable neural network in different operating conditions is responsible for fine tuning of the fuzzy logic system. Because of the sensitivity of the loads in the microgrid the proposed structure is designed to interact with the storage source in order to increase the response speed to the imbalance between production and consumption. This might prevent excessive voltage and frequency deviation, especially in the severe situations. With this controller, fluctuations in the production of renewable resources quickly compensated without a negative impact on resilience and instability of the microgrid, especially while disconnecting from the main network

The challenge of electricity distribution’s upgrade to incorporate new technologies is big, and electric utilities are mandated to work diligently on this agenda, thus making investments to ensure that current networks maintain their electricity supply commitments secure and reliable in face of disruptions and adverse environmental conditions from a variety of sources. The paper presents a new model based on energy homeostasis for power control and energy management using tariffs differentiation as incentive, considered by ENEL, the largest electric utility in Chile. The model optimizes grid-tied distributed generation (DG) systems with energy storage, in line with the utility’s green energy program, part of its Smart Grid Transformation, aimed at installing grid-tied DG systems with solar generation and energy storage in Santiago, Chile. Results present different tariff options, system’s capacity and energy storage alternatives, in order to compare proposed strategies with the actual case, where no green energy is present. The results show the advantage of the proposed tariffs scheme and power-energy management model based on different scenarios, providing a good and safe option for installing DG solutions to the grid.

Voltage and reactive power control in distribution networks is one of the most basic tasks of a distribution network operator. This is usually controlled by on-load tap-changer (OLTC), substation capacitors, and feeder capacitors. This paper first investigates a local voltage and reactive power control in a distribution system and then how the presence of induction machine based DG affects it. In the following a proper coordination among OLTC and capacitors to minimize losses in the distribution system, with and without DG, is formulated. The results showed that in the presence of induction generator, local control was not optimal, and sometimes operating constraints are violated. Finally three methods for controlling voltage and reactive power in the presence of induction generator to reduce losses and comply with the limitations operation during the day are proposed. Induction generator can be both fixed and variable be evaluated. Simulation studies on a distribution network of 10 bus with 70/10kV voltage has been made.

This paper presents a comprehensive model for dynamic expansion planning of distribution grids (DDGEP) considering distributed generation technologies. The proposed model determines the optimal location, capacity and dynamics (i.e. timing) of DG investment as well as optimal time schedule of reinforcement of distribution feeders. The objective function of this model encompasses both investment and operation costs of distribution grids and DG units along a specified planning horizon. To solve the suggested model, a new two-level solution method composed of Binary Enhanced Imperialist Competition Algorithm (BEICA) and Improved Particle Swarm Optimization (IPSO) is introduced. BEICA optimizes the location, capacity and timing of DG investment and also timing of existing feeders' reinforcement while IPSO optimizes the operation point of distributed generator-integrated distribution system. In order to demonstrate the effectiveness of proposed two-level solution approach (BEICA+IPSO), it is applied on a radial distribution test system and the obtained results are compared with several other solution methods.

This paper presents a new hybrid method for optimal multi-objective reconfiguration simultaneous determining the optimal size and location of Distributed Generation (DG) in a distribution feeder. The purposes of this research are reducing the losses, improving the voltage profile and equalizing the feeder load balancing in a distribution system. Invasive Weed Optimization (IWO) is used to simultaneously reconfigure and identify the optimal capacity and location for installation of DG units in the distribution network. In order to facilitate the algorithm for multi-objective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. The multi-objective optimization problem is transformed into a fuzzy inference system (FIS), where each objective function is quantified into a set of fuzzy objectives selected by fuzzy membership functions. The proposed method is validated using the IEEE 33 bus test system at nominal load. The obtained results prove this combined technique is more accurate and has an efficient convergence property compared to other intelligent search algorithms. Also, the obtained results lead to the conclusion that multi-objective reconfiguration along with placement of DGs can be more beneficial than separate single-objective optimization.