Today, energy supplies and transportation are not traditionally affordable. So Smart Grids mean intelligent energy management and lower energy costs for consumers. Significant features of Smart Grids, scalability, resilient network distribution capability, lower network cost distribution, network flexibility and fault tolerance. All of these features appear in the concept of a micro-grid. The use of scattered products in micro energy networks has many environmental, economic and technical benefits. Optimization of the location and magnitude of generators in micro-Grids plays an important role in reducing energy losses. Optimal clustering of micro-networks is also a major challenge in this regard. In this paper, we present a framework for clustering of small networks using Apollonius circular algorithm. Apollonius circles increase the precision of clustering with the help of geometric structure and high precision. Performance is equal to 5. 38% for distance criterion and 16. 15% for the system energy losses, and finally, 14. 79% of the total annual cost is better than the past work done. Then, we have used the simulated annealing algorithm to compensate for the lack of power micro-Grids and the stability of the entire network, which this action led to reducing energy losses in the amount of 26. 54 kw in study grid.

Weather based power curtailments have a huge share in total customers outage. Hence, reliable-affordable exploitation of networks during the adverse weather condition is one grid operator’s main issue. This article addresses an approach for optimal operational by considering dynamic line outages rate during extreme weather condition. In this paper, resiliency modification is accomplished by probing influences of weather condition on line outages using embedded sources, power storages and feeder topology reconfiguration. This work addresses objectives associated with resiliency issue in order to minimize total operation cost from distribution Company’s viewpoint, reduce amount of outages and maximize private sector’s benefits by probing weather changes during operational time interval. In this regard, a multi-objective optimization problem including both economic and resiliency targets is proposed to model the behavior of distribution company and private sector. Also, a benefit sharing mechanism is applied to increase synergistic integration between these players. A hybrid genetic- ɛ constraint strategy employing fuzzy decision maker is applied to achieve optimum Pareto-front solution based on fair profit sharing. Results proves that the proposed method increase profits for all players due to reduction in energy not supplied penalty cost as well as it enhance resiliency during adverse weather conditions.

As a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the Smart grid (SG). This process is changing continuously and needs advanced methods to process big data produced by different segments. Artificial intelligence methods can offer data-driven services by extracting valuable information which is produced by meter devices and sensors in Smart Grids. To this end, machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) can be applied. These methods are able to process huge amounts of data and propose an appropriate solution to solve power industry complex problems. In this paper, the state-of-the-art approaches based on artificial intelligence used by Smart power Grids for applications and data sources are investigated. Also, the role of big data in Smart power Grids, and its features such life cycle, and efficient services such as forecast, predictive maintenance, and fault detection are discussed.

Smart Grids are complex, nonlinear and uncertain systems. Smart Grids may consist of one or more cost functions, in which their optimal values have to be obtained. Moreover. In these systems the optimal condition has to be find with small computation burden, high accuracy, and relatively fast in real time. In this paper, a novel approach is proposed to find the minimum value of the total cost for the energy consumption in Smart Grids, in which the total cost is considered as uncertain objective function. The advantages of this technique are its stability, fast convergence, insensitivity with respect to initial condition and real time implementation. Some simulation results are provided to show the effectiveness of the proposed approach.

Optimal energy management in multi area Smart Grids will increase social welfare, reduce economic costs and environmental pollution. Power management solutions for Smart Grids include issues such as economical distribution of load, suitable load management, optimized charging and discharging of energy storages, and the availability of renewable resources considering limitation of power exchange in different area, all of which are issues in an intelligent grid, that in this paper has been considered. This paper presents a bi-level mixed integer quadratic programming (MIQP) model for energy management in multi-are Smart Grids with the aim of reducing economic costs and environmental pollution and increasing social welfare by considering energy storage systems, load management and Renewable resources are presented. In this paper presents a bi-level approach that the upper level is formulated to minimization economic cost and pollution of resource and lower level is presented to maximization social welfare in the form of Karush– Kuhn– Tucker (KKT) conditions. The simulation is implemented in MATLAB with Gurobi solver that the results show that the proposed bi-level model is also an efficient way to optimize energy in multi-area Smart Grids compared to Pareto front and Weight methods.

The problem of unit commitment (UC) or in other words, the issue of placing the units in orbit is a very important optimization problem, the exact solution of which can lead to a significant reduction in operation costs. In general, UC's goal is to minimize the total cost of production units by considering the constraints of the units and the system during the intended period. In fact, UC is divided into two issues. First, there is the issue of determining the on or off mode of production units for each hour, which is usually 24 hours, and the second is the distribution of economic dispatch between production units. Considering both problems at the same time will complicate the solution. It should also be noted that the difficulty of the problem also increases in proportion to the number of production units as well as the constraints considered. As mentioned, unit cost is typically the primary goal of minimizing UC issues. This paper considers Smart Grids, one of whose goals is reducing environmental pollution in addition to reducing costs. Therefore, this paper considers UC problem-solving in Smart Grids by considering the pollution of production units, so a multi-objective function is selected for minimization. On the other hand, with the introduction of Smart Grids, the existence of energy storage systems (ESS) in the network is also considered. This paper suggests optimal charging and discharging of energy storage systems, too. Another issue modeled in this paper is the issue of demand response in Smart Grids. Demand response program modeling allows grid demands to be shifted within the specified range at different times of the day. The proposed model is a mixed integer linear programming model (MILP) that has been tested to validate the performance of the proposed model, i. e., 4-unit and 10-unit systems with storage resources. To solve the proposed model, a powerful Gurobi solver is used. This solver also guarantees optimal global solutions and can get optimal solutions in the shortest time. In the end, the simulation results showed that modeling the problem of optimizing the charge and discharge of the energy storage system, along with the problem of demand-response management in the issue of unit participation, is very effective in reducing operating costs and reducing pollution. On the other hand, due to the linear nature of the problem, the proposed model can be solved in larger dimensions of the power systems.

This paper presents a methodology for detecting and classifying the errors occurring on Smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current angles are obtained. These angles are analyzed by fast fourier transform (FFT) and used to detect of transmission line errors. Detection of the transmission line error is performed using the nerve-fuzzy inference system methodology, and the diagnostic error classification is performed using support vector machine (SVM). Validation of the proposed method for the IEEE 14 system is also tested in the MATLAB software environment.

In this paper, different methods of wavelet coefficients have been used to detect power quality in Smart power Grids and have investigated the advantages and disadvantages of continuous and discrete wavelet transforms in detection and performance of different mother wavelets in detecting different types. The perturbations are investigated by discrete wavelet transform. Also, in analyzing the power quality of Smart Grids, the type of disturbance in voltage or current signals is discussed.