جستجوی مقالات مرتبط با کلیدواژه « انرژی باد » در نشریات گروه « برق »

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.

By performing cooperative spectrum sensing in a cognitive radio network, although the network throughput increases with the increase in the number of secondary users, but at the same time, it also causes an increase in energy consumption. This makes it necessary to provide a system that is able to create a tradeoff between throughput and energy consumption. In contrast to the conventional method of spectrum sensing based on one detection threshold, spectrum sensing with double thresholds avoids reporting unreliable data to the fusion center, thus potentially leading to greater energy saving. In this paper, a double threshold spectrum sensing cognitive radio network with a non-ideal reporting channel is optimized. The values of the threshold and the sensing time are jointly optimized to maximize the throughput of the network, provided that the network energy consumption and the amount of interference with the primary users are limited. The optimization problem is formulated and a numerical method is presented to solve it. The simulation results show a flexible system that can simultaneously provide higher throughput and lower energy consumption than the conventional sensing method. These results, while confirming the higher tolerance against the error of the reporting channel, show a significant energy saving of up to 70% by guaranteeing the throughput efficiency greater than 1.

Today, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper introduces a new multi-objective optimization model, which in the first objective, minimizes the total operation cost of the microgrid, and in the second objective, improves the reliability index by reducing the amount of energy not supplied. Due to these two objectives, a multi-objective evolutionary seagull optimization algorithm is used to find the optimal global solutions. In this regard, hybrid electric vehicles and demand response programs are used to smooth out distribution nodal prices and reduce CO2 emissions. The 69-bus distribution network has been used to evaluate the efficiency of the proposed method.

The practical efficiency of commercial m-Si and CIS PV modules measured in reality by our group was less than 15% due to the higher temperature of the PV modules, and the rest is more than 85% of the exhaust solar energy. Environmental Heating It was previously reported in 2020 that the principle of an environmentally friendly PV/T (photovoltaic/thermal) solar panel using m-Si PV module to use 71.3% of solar energy for electricity and 40°C water This panel was designed to confirm the principle, that is, it was an experimental PV/T solar panel. In this paper, a new environmentally friendly PV/T solar panel for use in BIPVT (photovoltaic) systems is presented. /integrated heating in the building) is proposed. The new panel uses a CIS PV module and all functions, including the heat exchanger using aluminum flat tubes, are housed in a panel box that is approximately the size of a simple CIS PV panel. PV/T Solar Panel Proposed 73.5 % of solar energy with 13.0% power generation efficiency and 60.5% heat collection efficiency in a 40°C hot water source in Yokohama, Japan. The efficiency is higher than the previous test panel. The proposed panel can also suppress heat radiation around 50°C even in the case of hot water of 60°C. The proposed PV/T solar panel can meet all residential thermal needs such as domestic hot water (DHW) and space heating or cooling using solar heat with less environmental heat load.

This research proposes a novel method for utilizing renewable energies in electric vehicle charging stations. In the proposed method, the required energy for charging the electric vehicles, the needed energy for supplying the cryptocurrency mining farm as well as the energy for supplying the critical loads of the upstream grid are afforded by 100% renewable resources, and the electric vehicle parking station (i.e., charging station) receives no energy from the utility grid. In this model, the uncertainty of renewable energies and the mismatch between generation and demand are resolved by optimal charging-discharging of electric vehicles. While the utility grid is available, the electric vehicles are fully charged, the cryptocurrency mining farm continues operation at full capacity, and excess renewable energy is sent to the upstream grid to supply the critical loads. The critical loads are supplied from two directions of the grid and parking station. During an outage or blackout of the utility grid, the critical loads are fully supplied by an electric vehicle parking station. In this situation, the cryptocurrency miners are modeled as a responsive load. In the off-grid operation, the proposed planning manages the energy of cryptocurrency miners as well as the charging-discharging pattern of electric vehicles for reaching two objectives of fully charging all the electric vehicles and fully supplying the critical loads. If there is any surplus of energy, it is used to run the cryptocurrency miners. In the proposed model, since no energy is received from the utility grid, the issues related to cryptocurrency mining are inconsequential. The objective function of the proposed method is to minimize the number of discharge cycles on electric vehicles to avoid battery degradation. The problem is formulated as mixed integer linear programming and solved by GAMS software. The wind and solar energy uncertainty are incorporated in the model and an optimal charging-discharging pattern is designed for electric vehicles to confirm feasible operation under all energy variations.

In the optimization problems of intelligent distribution systems where there are many variables and parameters, providing an efficient algorithm that has the ability to converge and solve in large networks is one of the main challenges of researchers. In this paper, a compound integer quadratic optimization model for improving the performance of large-scale distribution network using demand-side management problem, energy storage system, battery-to-metro system, optimal control of OLTC and SVR tap-trans and distributed generation resources. Fossil and renewable are offered alongside capacitor and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy sources. According to the considered problems and also the model of large networks of 118 and 874 buses, most of the algorithms are not able to converge due to the existence of many variables. In this article, the optimal performance of the proposed hybrid evolutionary algorithm is shown on large distribution networks, which is able to reach global optimal solutions compared to similar algorithms.

Deep learning method is used to predict the future value of load demand. Based on obtained results, a new model based on the forward-backward load shifting and unnecessary load shedding is presented. As well, to increase energy efficiency, excess renewable energy has been used to produce green hydrogen. For this purpose, GAMS optimization software has been used for optimal operation of the microgrid in the presence of renewable energy sources, battery, diesel generator, aqua electrolyzer, and fuel cell considering demand side management (DSM) restrictions. The obtained results from the proposed model of the considered microgrid show that the huge amount of excess electricity can be saved to enhance energy efficiency. This issue increases green hydrogen production that can be used for fuel cell consumption. As well, the proposed model provides lower cost of operation cost.  In addition, the diesel generator consumes lower diesel fuel.

Hub Energy systems meet the needs of customers in critical situations by coordinating the use of different infrastructures, resulting in improved system resilience, reduced losses, and reduced operational costs. Due to the increase in the construction of energy hub systems in distribution networks, the issue of the optimal design of energy hubs has received much attention. Considering this issue, this paper presents a new two-stage optimization model to improve the resilience of distribution systems against storms. We formulate the proposed framework as a two-level planning process, taking readiness decisions in the first stage and optimizing operational variables in the second stage to enhance system resilience. We investigate the mutual interactions between electricity and thermal energy using an integrated electricity and gas model, and propose a computational method to model the worst-case scenarios of gas and electricity flow on an integrated IEEE 69-bus network.

In Iran and the world, the energy sector of the building is on average about one-third of the total energy consumption, and this has caused the increasing focus of countries to plan the management of production, distribution and energy consumption. In Iran, national building regulations are one of the main legal capacities for building energy management, and very low-energy buildings are among the goals listed in it. This article tries to design a sustainable transition to a very low-energy building by using the policy roadmapping tool. For this purpose, by determining the mission statement and the purpose of creating a roadmap; Identifying the current situation and the challenges facing the transition; identifying drivers; Determining time horizons and determining continuous policies in different time frames for the transition to a very low energy building, a map was drawn. The current methodology is a mixed research method (quantitative-qualitative) and the nature of the research is applied. The time frame of the research is single-section and its purpose is prescriptive. The results of this process are drawn in the form of a transition policy map that has three-time frames (short-term, medium-term and long-term) corresponding to the three operational, tactical and strategic levels of transition management. At the same time, the drivers have also been identified and the two factors "lack of a common image of the future of energy, industry, and urban life" and "divergence of policies affecting the field of building energy in the dimensions of economy, industry, urbanization and urbanization, cultural and social" were recognized as the main drivers. . Based on the drivers and the existing situation, the policies are designed in three layers national policies, sectoral programs and enablers. In this article, an attempt was made to adapt the features of the transition management to the policy mapping of the content of the transition mapping to the very low energy building, and the developed mapping would be able to respond to long-term changes and meet the requirements of the transition management.

In this paper an off-grid microgrid, based on 100% renewable energy, integrated with an electric vehicle charging station, an electric vehicle parking station, and a demand-response program was modeled. The electric vehicle charging station operated based on a battery swapping model in order to charge the electric vehicles in the shortest time possible. The electric vehicle parking station was used to park vehicles during various hours of the 24-hour period. The vehicles inside the parking had to be fully charged by the microgrid when leaving the parking station. Since these vehicles were parked for several hours, their charging time was not limited, and they are charged by direct chargers rather than battery swapping. The loads of the microgrid were under a demand-response program, and they were curtailable, non-curtailable, shiftable, and interruptible loads. The only energy source of microgrid was solar PV systems; the solar-energy related issues such as zero energy during night, output power variations, and the possibility of losing the whole or a part of energy due to shade should be dealt with. In the proposed method, an optimal programming was applied to the charging-discharging of the swapping batteries in charging station, to the charging-discharging of electric vehicles in the parking station, and to the energy management of loads (i.e., curtailable, non-curtailable, shiftable, and interruptible loads). The mismatch of energy and the lack of solar energy were compensated by the discharging power from charging and parking stations as well as by the management of the power of loads. Simulation results demonstrated that the unavailability of solar energy during the night resulted in paying 50% of the daily revenue as penalty cost. During hours such as 7 to 19, when the solar energy was available, the plan used the solar power as much as possible and limited the extracted energy from other energy resources. The energy of shiftable loads was supplied mostly from 13 to 15 hours, when solar energy was at the maximum level.

Iran is one of the countries with a radiation potential that is very suitable for the construction of solar carports. Considering the high initial cost of constructing these parking lots, the importance of their feasibility and optimal design is very high. In the present study, the design and simulation of a solar carport connected to the power grid that was implemented at the Faculty of Agriculture of Tarbiat Modares University (TMU) in Tehran was carried out. For this purpose, the photovoltaic system was calculated in PVsyst software considering the geographical location and weather conditions of Tehran. In this case, the optimal side angle for installing the photovoltaic modules on the parking lot was set at 35 degrees to maximize the annual electricity production. Then, in determining the type and model of the photovoltaic modules and the solar inverter, their optimal electrical arrangement was designed. In addition, the losses of the various parts of the system were calculated, including the losses caused by shading, temperature rise, connecting wires, and so on; besides, the optimal spacing and arrangement of the modules were presented. Based on the final results of the simulation, the amount of electricity generated by the parking lot was 48.2 MWh/year; the annual power factor was 0.791; the reduction rate of CO2 production was 499.89 tons in a total of 30 years, and the sensitivity analysis and lower return on investment were calculated from 5 years onwards. The results of this study showed that the implementation of the project at the investigated site was economically justified in addition to the benefit of providing renewable electricity.

The challenge of energy and environmental pollution has forced researchers to make more use of solar energy. In the present study, a new solar air heater collector system with a synthetic combination of arc-shaped wire roughness and external recycle of airflow has been presented, and its performance has been evaluated in different conditions. Modeling is performed using energy conservation equations for different parts of the collector and is solved by a semi-analytical method. The validation of the present results indicates the accuracy of the method used. The results showed that the new hybrid system effectively increased energy and exergy efficiencies at mass flows less than about 0.04 kg/s. Also, in a constant roughness condition and at low mass flow rates, increasing the flow recycle ratio to about 3, increased the collector performance.  However, in high flow rates and recycle ratios, exergy efficiency was reduced sharply due to high pressure drop, despite an increase in energy efficiency. Thus, it was less than the simple collector system. Increasing the intensity of solar radiation increased energy and exergy efficiencies in the new system. Examination of the geometric parameters of artificial roughness showed that an increase in the diameter of the curved wire improved the performance of the new system while changing the curvature angle of the wire had little effect.

There are different ways to produce electricity in the world, which each country will produce according to its resources, technology, and geographical location, and producing electricity from non-renewable and renewable sources has advantages and disadvantages that developed countries, despite having huge resources. non-renewable are looking for a way to produce electricity from renewable sources, in this article we will first examine the history and advantages and disadvantages of producing electricity from five renewable and nonrenewable sources that have the largest share of production. In the second part, we will examine the quantity of electricity production in Iran and the world from these sources in 2022, and finally, the geographical location of Iran and the ways to produce electricity from renewable sources.

In this paper, a new method for detection and fault location and classification in MTDC solar microgrid is presented. Some issues such as expanding renewable energy sources and DC loads and efforts to increase power quality and reduce the environmental impact of electricity generation have led to the expansion of solar networks. Identifying the types and locations of faults is important to ensure service continues and to prevent further breakdowns and the increasing the protection’s selectivity characteristic. In this method, an orbital kit is connected to the network. In the fault occurrence time in the network, the fault is detected by passing a current through the connected kits and measuring the traveling waves derived from the fault current, and applying it to a mathematical morphological filter The location of the error is determined using orbital equations and flow calculations. Mathematical morphology filter output and signal energy analysis were used to determine the type of faults. The method presented in an MTDC microgrid connected to energy storage and renewable sources was tested with many faults. The results indicate the accuracy of the proposed method. This method is resistant to changes in arcs resistance (up to 100 ohms), and has a very good performance in high impedance faults conditions(up to 1000 ohms).

One of the biggest problems facing the human being is energy supply due to reduced resource and cost. The largest share of energy consumption in the world has been allocated to the construction sector. The main sources of energy supply are coal, natural gas and oil, all of which are non-renewable and will be completed in the near future. Major energy consumers can be referred to household, industrial, agricultural, general, commercial, and street lighting. Among the energy consumers, the share of domestic and office sectors is higher than other consumers, and attention to reducing energy consumption and energy losses in the construction sector is an unavoidable necessity. In this paper, a new method of building energy management is proposed, which, with the help of internet networks of objects, controls the energy consumption of buildings. An administrative building with six areas is considered. The proposed method consists of two phases: the first phase, which is the prediction stage, is performed using artificial neural network and six parameters: outside temperature of the building, set point temperature, sun radiation, occupancy, previous temperature and the hour of the day are given as inputs to the perceptron neural network and the output of this phase is inside temperature of building and the energy consumption, which is given as input to the next phase. The second phase uses the gray wolf algorithm to determine the optimal temperature for each part of the building at any hour of the day. The energy consumption and cost of the building are calculated using the software of MATLAB, which results in a significant reduction in energy consumption and energy cost optimization in the office. The proposed method shows a reduction in energy consumption of 22 Kw/h in the early morning hours.

Simultaneous consumption of electrical and thermal energy, together with economic and technical advantages of close-to-consumption feeding of consumers, has demonstrated the importance of multi-carrier energy systems planning in recent years. Therefore, in this paper, the planning of an islanded hybrid system (IHS) with renewable energy sources (RESs), combined heat and power system (CHP), boiler, electric and thermal energy storage systems (ESSs) to provide clean energy for electric and thermal consumers is presented. The proposed design is responsible for minimizing the total weighted annual operating costs and pollution consumption of the system. The problem is then subjected to the planning-operation constraints and modeling the RESs and ESSs. The proposed problem is in the form of mixed-integer nonlinear programming. Therefore, in order to obtain the optimal solution with a low standard deviation error, the Grey Wolf Optimizer (GWO) is developed in this paper. The novelties of this paper include the extraction of the IHS with electric and thermal sources and ESSs, and provision of clean energy to the electrical and thermal consumers. Finally, by implementing the proposed design on the actual data of Rafsanjan city in Iran and investigating the numerical results, the ability of design to obtain the optimal hybrid combination of RESs and ESSs to supply electric and thermal consumers from an economic and environmental point of view is confirmed.

Energy security is one of human society's most important topics, affecting many tensions and relations between different countries. Considering the breadth of the concept of energy security and its mutual effects in the economic, political, social and environmental fields, it has become an essential concern of governments and thinkers. The energy security risk index examines a country’s level of security risk e factors affecting energy security in geopolitical, economic, political and environmental fields by measuring 29 determining components. The main goal of the current research is to investigate the impact of renewable and non-renewable energy consumption on the energy security risk in Iran from 1980 to 2020 using the structural vector autoregression (SVAR) method. The obtained results show that the consumption of renewable energy has a negative and decreasing effect, and the consumption of non-renewable energy has a positive and increasing impact on Iran’s energy security risk. Also, the added value variables of the industry sector and government size have a positive and increasing effect on the energy security risk in Iran. On the other hand, the results of this research show that one of the reasons for the increasing trend of the energy risk index for Iran is the rising consumption of non-renewable energies. Following that, the amount of environmental pollution has also been increasing; Therefore, to prevent this, policies should be implemented to reduce the consumption of this type of energy and to replace it with other energy, including renewable energy.

Solar energy is the world's most unique and affordable renewable energy source and can be converted into many other forms. In this article, it will be discussed in a long-term perspective the technical and economic feasibility of installing stand-alone solar power plant units with battery support to supply part of Baghdad's electricity. The objective function of this problem includes the cost of installation and maintenance of solar panels, batteries and inverter, which is solved with a certain interest rate in a 20-year perspective using IPSO and ALPSO methods. Furthermore, the load loss supplied and the charging/discharging limit are among the constraints. This article is unique in that it is implemented in the context of Baghdad city, and it also investigates the possible profit from selling power to main grid. Other features and innovations include the implementation of the new ALPSO algorithm. In this algorithm, the constraints of the problem are respected through a three-step adaptive search process. The results show that the proposed methods significantly reduce the lost load (especially in the ALPSO method), reduce the cost of maintenance and installation, and generally improve the performance of the system.