نشریه تحقیقات نوین در سیستم های قدرت هوشمند
سال دهم شماره 4 (پیاپی 28، زمستان 1400)

Charging electric vehicles in the distribution network is one of the most basic solutions for technical and economic management of energy distribution. In many traditional charging methods, the condition of fully charging cars when leaving the parking lot has always been a problem. But in this article, each car is intelligently charged only based on the amount of energy required to travel its daily journeys. In order to implement this smart method, owners of electric vehicles provide information about the number of trips and the length of their route to the parking charge management, and based on the specifications of the vehicles and the initial energy level of their battery when entering the parking lot, the amount The charge required for them is determined. Then, the charging manager plans the charging based on the tariff of energy consumption time, limitation of distribution network transformers, type of charge level selected (normal or fast) so that charging costs are minimized by observing technical and economic constraints. . The result of intelligent car charging in normal and fast charging conditions and in different limitations of the distribution network and in the presence or absence of load response program is compared with each other. YALMIP and MOSEK software have been used to solve the mixed integer linear programming model.

In recent years, utilization of the renewable based power plants has become widespread in the power systems. One of the most widely used renewable based power plants is wind power plants. Due to the utilization of wind energy to generate electricity, wind turbines have not emitted any environmental pollution. Thus, in addition to economic benefits, utilization of these power plants is of great interest from the environmental view point. As the uncertainty in the generated power of these plants is due to the fluctuating nature of wind speed, some problems related to the power system operation including the optimal Power Flow (OPF) issue are revealed. In this paper, instead of using the predetermined probability distribution functions (such as Weibull distribution), the probability density functions extracted based on previous information of wind power plants are used to consider the generated power uncertainty of the wind power plants. The OPF issue has been modeled as an optimization problem aimed at minimizing the network operating cost. The direct power flow constraints are used to model the power grid. To evaluate the above method, the uncertainty scenarios are divided into two categories of sample scenarios and tests that the network performance in sample scenarios will be evaluated by test scenarios. To solve the above optimization problem, a new Random Walk Gray Wolf Optimization (RWGWO) has been used. The simulation of the above method has been performed using MATLAB software for two test networks of IEEE 30 buses and IEEE 118 buses. The simulation results indicate that the utilization of the extracted probability density function has been more consistent with the actual power network conditions and therefore has reduced the network costs as compared to the predetermined probability density function.

Energy carriers are addressed today as one of the main pillars of energy generation due to the increased growth of industries in developed and developing countries. According to the global view, increased consumption of energy carriers is causing an increase in the production of greenhouse gases, the global average temperature, and this, in turn, results in the quicker melting of ice in Antarctica. Moreover, increased pollution stemming from the consumption of fossil fuels for generating electricity is causing numerous problems for industrial cities, photochemical smog, and numerous lung diseases for humans and often living organisms. For the above reasons, using methods to minimize the consumption of energy carriers and applying renewable energy in order to reduce the extent of generated pollution is highly important. Using integrated energy systems (IES) may help manage energy resources in a proper manner and minimize the extent of pollution. This article has proposed a smart management system of an energy hub that includes an input (consisting of fossil fuels, renewable energy, and electricity grid) and output (consisting of consumers). Not only is the proposed system capable of managing energy carries and maximizing the use of renewable energies, but also capable of absorbing high levels of generated CO2 and transforming it to gas energy carries in addition to saving energy.  Such an approach has caused the reduction of CO2 level and its derivatives inside the energy hub and also, reduction of the total cost of the system and this is because a part of gas fuel needed by the system is supplied. This article has shown that using such a system reduces the cost stemming from system pollution generation by 9.08% and the total cost of the system by 13.57%.

Recently, researchers are increasingly paying attention to the continuous use of renewable systems (such as fuel cells, wind, or solar) due to increased energy consumption, energy supply, and prevention of environmental damage. Photovoltaic power generation (PV) systems are one of the most important low-cost ways among new energy sources. In this paper, a photovoltaic (PV) system is used as the main power generator, and also PEMFC is used as energy storage equipment. The advantages of the proposed system include environmental compatibility, reduction of system noise, and low cost. The proposed control strategy also ensures the system stability in various situations of connection of distributed generation sources, such as photovoltaic system to the grid and non-linear load, which brings the island mode of operation. In addition, a battery with a fuel cell is used to store energy, which has a control system similar to the control of the distributed generation system and does not need to change the control mode of the system when changing the mode from an island.

In this paper, a control system based on adaptive sliding mode theory is designed to track active and reactive powers in a wind turbine with a brushless doubly-fed induction generator. The dynamics of this type of turbine are nonlinear and include parametric uncertainties and perturbations such as wind speed changes. Therefore, to control this nonlinear and uncertain system, sliding model theory has been used. Due to the unknown of the bound of various uncertainties in this system, the adaptive type of sliding mode is used. In this method, the controller gain is considered as a variable with time and is designed to converge to the uncertainties upper bound. The proposed designed controller in a complete simulation in MATLAB software, on the model of a brushless doubly-fed wind turbine, is evaluated and the good performance of this proposed method in comparison with standard sliding model methods and the classical PID controller is shown.

This study examines the impact of renewable energy consumption on the sustainable economic growth index (As a measure of the welfare of developing countries in the current situation) in twenty developing countries; Using the fixed effects model and multivariate panel model from 1990 to 2018 by providing a basic index for sustainable economic growth that includes economic variables and an environmental component; Pays and then ranks. After static analysis of variables; Cao method for co-accumulation and Granger causality were used to determine the direction of the relationship. In addition to per capita energy consumption and renewable energy production, the variables; Gross per capita gross fixed capital formation (percentage of GDP); work force; Per capita emission index of pollutants as an index of environmental constraints; Capital leases; Degree of commercial openness; Is used. The results showed that the increase in per capita energy consumption, except for renewable energy; It has an impact on sustainable economic growth and there is a two-way relationship between energy consumption and pollution emissions. In the ranking discussion it was identified; The UAE has the highest and Nigeria the lowest sustainable economic growth.