Azarbaijan Shahid Madani University

About: Azarbaijan Shahid Madani University is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Graphene & Nanocomposite. The organization has 1477 authors who have published 3186 publications receiving 30278 citations. The organization is also known as: Azarbaijan University.

Optimal percolation on multiplex networks

Abstract: Optimal percolation is the problem of finding the minimal set of nodes such that if the members of this set are removed from a network, the network is fragmented into non-extensive disconnected clusters. The solution of the optimal percolation problem has direct applicability in strategies of immunization in disease spreading processes, and influence maximization for certain classes of opinion dynamical models. In this paper, we consider the problem of optimal percolation on multiplex networks. The multiplex scenario serves to realistically model various technological, biological, and social networks. We find that the multilayer nature of these systems, and more precisely multiplex characteristics such as edge overlap and interlayer degree-degree correlation, profoundly changes the properties of the set of nodes identified as the solution of the optimal percolation problem.

A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements

Abstract: Energy storage devices are very popular in systems with the presence of renewable-based power generation. However, inappropriate usage of them may lead to aging and also the early failure of such systems. The factors affecting the aging of the batteries as the main energy storage devices in electrical systems are frequent changes in charging and discharging status, deep discharge, and a large number of power transactions in a short time. Furthermore, the development of microgrids (MGs) in distribution networks (DNs), leads to more complicated structures known as multi-microgrid (MMG) systems. In this paper, the day-ahead scheduling for the MMG system considering the degradation cost of the energy storage systems has been proposed. To model the uncertainties, chance-constrained programming (CCP) approach has been employed. The CCP helps the system operator to make better decisions without jeopardizing the system security. The degradation cost has been designed in a way that in the energy management process, misuse of the batteries is avoided. A hierarchical three-stage energy management process is designed. First, deterministic equivalents for the CCP problem are made and using these equivalents, in the first stage, MGs perform local scheduling and in the second stage, again considering deterministic equivalents of the CCP, DN performs global management and finally, MGs perform a rescheduling. Simulations with four case studies on a modified version of the IEEE 33-bus test system have shown the effectiveness of the proposed scheduling framework. The results show that decreasing confidence level in CCP decreases the system operational costs, however, threatens the system stability. Also, considering the battery degradation cost prevents the system operator from misapplication of the battery energy storage systems.

A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty

Abstract: Dispersed application of Battery Energy Storages (BESs) can have many benefits in terms of voltage regulation and energy management in Active Distribution Networks (ADNs). The batteries are high-cost technologies, and they must be installed and managed optimally to benefit from their innumerable advantages. In addition, each battery technology has specific economic and technical attributes which can be appropriate or inappropriate in a particular condition and utilization. The purpose of this paper is that the optimal size and location of various battery technologies are specified in the distribution network to minimize total cost and maximize reliability index considering the uncertainty of load demand as well as the output power of the wind and solar. Also, multi-objective particle swarm optimization (MOPSO) algorithm is used to minimize two objective functions. Monte Carlo Simulation (MCS) is used to model the uncertainties of economic and technical characteristics of photovoltaic, wind power and load demand. The suggested planning scheme is tested on the modified IEEE 33-bus system. Finally, the different types of the battery technologies in one, five, ten, fifteen and twenty-year period are compared to present the optimum type.