باتری

The concerns arising from fossil fuels have drawn significant attention to the development of microgrids with the presence of energy storage batteries. Microgrids, utilizing batteries, can store energy during peak production times and inject it into the grid during peak consumption periods, thereby enhancing the stability and reliability of the system. Additionally, batteries assist in load management and reduce energy losses, enabling optimal use of renewable resources. To ensure the economic performance of microgrids, optimizing the measurement of battery energy storage is essential. Factors significantly impacting the accuracy and efficiency of battery energy storage measurement results are often overlooked. These factors include a range of characteristics for different technologies, the effects of depth of discharge, the number of charge/discharge cycles on the degradation of battery energy storage quality, and the coordination of microgrid operating modes.  This paper presents a model for measuring battery energy storage in microgrid applications that considers key factors such as size, technology type, number, and optimal maximum depth of discharge in the presence of uncertainties related to renewable resources and electrical load. Monte Carlo simulation is employed to analyze these uncertainties. The model used is a mixed-integer linear programming model. Furthermore, the nonlinear relationship between the depth of discharge of battery energy storage and its cycle life is modeled using a piecewise linearization approach. The results indicate that the proposed model can effectively determine the size, technology type, number, and optimal maximum depth of discharge for battery energy storage.

This article examines the challenges of energy management in microgrids, considering the uncertainties associated with renewable energy sources, dynamic demand, and the presence of various devices such as batteries, distributed generation sources, and electric vehicles. The article introduces a complex optimization model designed for microgrid operations. This model focuses on mitigating the challenges of integrating power electronic generation units, managing demand within microgrids, and incorporating small-scale renewable energy sources. The goal of this model is to minimize various costs associated with energy losses, electricity purchases, load reduction, distributed generation operations, and battery costs over a 24-hour period. Simulations conducted on a test system demonstrate that the proposed model is effective, achieving up to a 20% reduction in microgrid operational costs. This approach provides an effective framework for enhancing the flexibility and efficiency of microgrid energy management, and the findings indicate that it outperforms comparative methods by a margin of at least 8%, demonstrating its effectiveness in improving critical indices in the microgrid system.

In this paper, an optimization model based on stochastic quadratic mixed integer programming to provide an integrated energy management of electricity and heat in electrical and thermal microgrids, taking into account the uncertainty of renewable energy sources, location of electricity generation sources and combined heat and power (CHP) along with energy and thermal storage systems and demand side management are provided in island operation and connected to the grid. A multi-objective function including minimization of energy loss, voltage deviation, cost of resource utilization, as well as reduction of renewable energy sources and reduction of installation cost is considered. The IEEE 69 bus distribution network was selected for analysis and coding was done in MATLAB software and CVX optimization package. The proposed model is also solved by the most powerful existing solver called Gurobi. The obtained results show the performance and accuracy of the proposed model.

Energy management between renewable energy sources and energy storage systems is challenging especially in different operating modes in stand-alone DC microgrid. In this paper, a new energy management system strategy based on law and fuzzy logic is proposed for a stand-alone DC microgrid with hybrid energy storage system (HESS) consisting of battery and supercapacitor (SC). The design of the proposed system is simple and does not require the mathematical model of the system. The main advantages of the proposed strategy are: high DC link voltage reference tracking in the presence of source and load power disturbances, keeping the battery and supercapacitor charge level within the permissible range, preventing battery and supercapacitor from overcharge/discharge and controlling the battery charging/discharging rate. Moreover, the proposed strategy increases battery life span while decreasing its current stress by using high charge/discharge cycles in SC and providing the majority of the output current through it. The proposed technique has been simulated in MATLAB and results show the priority of this method from the abovementioned points of view.

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.

This article presents a new energy management strategy (EMS) for optimal power allocation in the battery/supercapacitor hybrid energy storage (HESS) in DC microgrid applications. The proposed system is composed of a semiactive structure where the supercapacitor is directly connected to DC bus. In this structure, since the main purpose of the HESS is to maintain the DC bus voltage at the desired value, controlling the supercapacitor becomes more important. While, in the semi-active structure, there is no control on the supercapacitor. In this paper, by considering the supercapacitor current as a cost function in the optimization problem, this challenge has been solved. Also, in the case of microgrid excess power mode, a new cost function has been considered to charge the battery with a constant current to reduce the charging time and improve its lifespan. Another objective is to increase battery life by responding the supercapacitor to the sudden power changes and drawing a smooth current from the battery. In order to online determination of the optimal global solution, this multi-objective problem has been converted into a single-objective problem by use of the weighted method and then solved using KKT conditions. Therefore, real-time implementation is the main advantage of the proposed method. Finally, using simulation, the performance of the proposed method is compared with an adaptive cutoff frequency filter-based method and fuzzy logic method, in three 24 seconds different scenarios, in terms of the DC bus voltage regulation, the battery peak current, and the battery state of charge (SoC).

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.

Due to the high penetration of renewable energy resources and the direct impact on the power system, the issue of energy management has received more attention than researchers. Power-to-gas (P2G) system causes the surplus electricity generated from renewable energy resources in the network to be converted to gas and sold to the gas network, so energy management and profitability are a matter of particular importance, considering the two grids as a joint optimization of integrated energy systems. This paper presents a scenario-based stochastic mixed-integer linear programming (MILP) model to optimize integrated gas and electricity integrated systems considering natural gas distributed generation resources, P2G systems, energy storage systems, and electric vehicles. It aims to reduce the cost of purchasing energy and cut off the power of renewable energy resources. The 33-bus power distribution network and the 7-node natural gas network are considered for the analysis of the proposed model, and the proposed model is solved using the powerful Gurobi solver, considering various cases. The results of different cases show the performance of the proposed model.

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.

The combined heat and power (CHP) generation is an energy-saving method in which power and heat are simultaneously generated. The important features of heat and power units are the interdependence between the amount of power and heat generated, which increases the complexity of the problem and can be solved by powerful optimization methods. In this paper, planning and optimizing the combined heat and power generation, including the supply of electrical and thermal power required by the network, are performed. This goal is achieved with two main approaches, including the valve point effect and examining this effect while modeling load peak in the presence of energy storage systems of the battery type, which is the novelty of this paper. This paper offers the hyper-spherical search (HSS) algorithm and particle swarm optimization method to optimize and solve the problem of CHPED in large units and, then, compares and validates the results. The simulation results show that the proposed methods can be used as a reliable method to solve the problem of CHPED by considering different constraints.

Renewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an affordable system based on storing the solar and wind energy. In this paper, optimization of a solar-wind hybrid system is presented with a saving battery system for supplying a specific hourly load annually to minimize annual system expenses and the probability of Loss of Power Supply Probability (LPSP). Annual expenses of the system include initial investment, maintenance, and replacement costs. The purpose of optimization is to determine the numbers of solar panels, wind turbines, batteries, the height of the wind tower, and the angle of the solar panel toward solar radiation. For this issue, a new method named Grasshopper Optimization Algorithm (GOA) is employed. Also, the effects of changes in inverter efficiency, load demand, and of maximum probability of LPSP on system designing are evaluated. Simulation results show that the efficiency reduction, load increase, and increasing the load and maximum reliability in the system in the form of reducing of LPSP lead to an increase in annual energy costs of systems. Furthermore, the results indicate the superiority of the GOA method toward particle swarm optimization (PSO) in reaching better target function and less cost.

One of the most useable parts and, in fact, the heart of a Hybrid vehicle is its Lithium Bromide battery. In these batteries, a large quantity of heat is generated, and if this heat is not managed properly, the useful cycle life of the battery will be reduced progressively. Thus, the vital issue of these batteries is their cooling. Since the Phase Change Material (PCM) absorbs a great deal of energy due to its Latent heat capacity during its melting process, PCM can wonderfully be effective for quick and great heat dissipations. Hence, in this paper, it is tried to investigate the simultaneous effects of the application of PCM and fins on the cooling process of these hybrid batteries; in other words, it is tried to present the optimum fin sizes to minimize the battery temperature rise during its recharge process. The results indicate that 6-fin batteries show the highest performance when the fin length is 11.67mm.

The microgrid is a set of local energy producers and consumers that can be utilized with low cost and high reliability. In this paper, a robust multi-objective model is proposed to reduce operating costs and carbon emissions in which a smart grid utilizes a wind turbine and micro-turbine to feed its connected loads. The microgrid also uses a battery to store electrical energy in off-peak hours and to deliver energy in on-peak hours. On the other hand, it is connected to the main grid and can exchange energy with it. Consumers connected to this microgrid are divided into two groups. The first group is uncontrollable loads with certain load pattern and the second group is controllable loads that have certain energy consumption but can be controlled by the operating time. The proposed model is a mixed-integer linear programming problem and is simulated with the CPLEX solver in GAMS software. The results show that when the price of electricity is low, the loads are often supplied by grid electricity, and when the price of electricity is high, they are often fed by micro-turbines, batteries, and wind turbines.

Due to the energy storage cells low voltage, such as batteries and super-capacitors, it is often necessary to connect them in series to form a string. Generally, these components are sensitive to over-voltage. Also, due to the inevitable differences in these devices, their charging and discharging processes cause their voltages to be different. This problem leads to inappropriate use of capacity, short life time, and even explosion. To overcome these problems and also to achieve safe and economic operation of these storage devices, employing voltage equalizer converters is necessary, in practice. The proposed converter can overcome the above-mentioned problems and it has less components than the conventional voltage equalizers, smaller dimensions, and lower cost. It operates at a fixed switching frequency under soft switching conditions without any feedback circuit and transformer. Therefore, it is more economic than the conventional voltage equalizers. Here, simulations and analysis of the different operating modes of the converter are given for four series connected lithium-ion batteries. To verify the given analysis and simulations, its prototype has been tested under different conditions. The experimental results are in good agreement with the analysis and simulation results, as well. The batteries voltages maximum differences in simulations and in practice are less than 5 and 10 mV, respectively, which indicate proper circuit operation.

In recent years, hybrid energy storage (HES) to increase lifetime and reliability extremely are used in renewable systems and microgrids. In this paper, a new method for determining battery and supercapacitor capacity in an isolated microgrid is presented. Difference between Generation and load power using discrete fourier transform is transferred to the frequency domain and by determining the cut-off frequency, the optimized cost function is obtained; so that high frequencies are provided by supercapacitor and low frequencies by the battery. In the presented method, determining the capacity of the storages is done in such a way that in addition to the good response to loads, the lifetime of storages is maximized. The use of the proposed method due to the hybridization of storages improve the dynamic performance of the system by reducing the number of battery charging and discharging, decreases storage cost. Due to necessity and importance of storages in the isolated microgrid, the considered microgrid is isolated from the main grid that is consisted of different distributed generations. The proposed algorithm is tested on an isolated microgrid and the results confirm method effectiveness.

In this study, we will investigate the possibility of using renewable energy resources in area where electrical energy is not supported. We will consider it as a micro-grid and we will try to supply its electrical energy by decreasing cost and having reliability in system. Generally, the main reason of designing and developing of distribution grid is responsible to growth of electrical consumption with maximum economic efficiency which the restriction of system is not made violation. Existing more elements in the distribution and sub-transmission networks have faced the designing and developing with problem where there are many decision variables. Advert of the distributed generation in distribution systems furthermore the changing operation of these systems provide this possibility to companies that can design systems with low cost. In this study, we aim to minimize the cost of the hybrid system of hydroelectric, wind, solar and battery and we will calculate LPSP index by GSO algorithm. It is designed and implemented for long-term network over 20-year.

Nowadays the combined power generation system has become one of the most promising solutions to address the power needs of different regions. Given the discontinuity of energy produced by renewable sources a proven practice of combining these renewable energies can be a good solution for generating electricity in different atmospheric conditions, and with an appropriate combination of these sources an affordable production system can be achieved. In this research the village of Souqand was surveyed in the central part of Neyshabour city. Demand for power consumption has been provided by 20.63 kWd since 1381 .Which feeds the scattered loads of the area. In this research considering the proper position of the village in terms of climate conditions and also the efforts to improve the economic and environmental conditions of the region in the future we will examine the role of the addition of distributed generation resources to the network, and we conclude that using these resources will reduce the cost to 57% and reduces the amount of pollutants to 70%. This paper was reviewed in two scenarios and the results of selecting an optimal system have been developed.

In this paper, a hybrid photovoltaic-battery-diesel generator system is designed and optimized for a remote islanded electric network in the northwest regions of the country. Considering the hardship of transferring fuel to the region where the system is installed, this study aims to minimize the generator fuel consumption. To evaluate the effectiveness of the proposed system economically, the HOMER software is used to find appropriate sizes for the photovoltaic and battery systems in order to minimize the fuel cost of generator. After finding the power sources’ optimized sizes ,a power management system for reducing the generator work hours during a day is introduced using MATLAB software. In the proposed management method, the predicted photovoltaic power production and anticipated load demand profiles are used. As it is concluded in this paper, taking advantage of the proposed method, the generator work hours and consequently the generator fuel consumption are decreased to its minimum value. Moreover, in the proposed method, the required battery capacity is much lower than one presented by the HOMER, in turn, resulting a lower system installation and fuel costs. Furthermore, it is confirmed that, in the middle of the day, when the photovoltaic power generation and load consumption are at their maximum and minimum values respectively, there is no need to waste the surplus of photovoltaic power in dump loads. As in the proposed method no dumping loads are needed, the generator average working time per day is decreased by 26% compared to the method used in HOMER software.

Many countries have paid attention to the electric vehicles during the past decade. This kind of vehicle is equipped with an energy-saving system which is usually composed of a set of batteries. This system also suffers from several problems including excessive warming, low energy-saving capacity and low efficiency; hence, development of electric vehicles has been influenced by costs caused by such problems. A possible and proper way to solve some of these problems and to improve function of the energy- saving system is installing a supercapacitor as an accessory energy reservoir. Aiming at reducing both size and wastes of energy-saving system and also integrating the system's output current, this paper has not only used a three port bidirectional converter, as an interface among battery, supercapacitor and electric motor, but also has considered the way by which power of both reservoirs (i.e. battery and supercapacitor) are controlled directly though turning this convertor on. It has been implemented through two controlling layers including a fuzzy controller and an adjusting block. To analyze the result, an electric vehicle equipped with the suggested saving- energy system, as a sample, was manufactured by the authors and then the practical results were studied. Moreover, the suggested energy-saving system, in comparison to the common energy-saving systems, was modeled in MATLAB/SIMULINK. Modeling and practical results demonstrate that the efficiency of the suggested strategy is higher than that in other strategies.