Search Results (4)

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios. Full article

Recently, electric vehicles (EVs) that use energy storage have attracted much attention due to their many advantages, such as environmental compatibility and lower operating costs compared to conventional vehicles (which use fossil fuels). In a microgrid, an EV that works through the energy stored in its battery can be used as a load or energy source; therefore, the optimal utilization of EV clusters in power systems has been intensively studied. This paper aims to present an application of an intelligent control method to a bidirectional DC fast charging station with a new control structure to solve the problems of voltage drops and rises. In this switching strategy, the power converter is modeled as a DC fast charging station, which controls the fast charging of vehicles with a new constant current or reduced constant current method and considers the microgrid voltage stability. The proposed method is not complicated because simple direct voltage control realizes the reactive power compensation, which can provide sufficient injected reactive power to the network. As a result, the test is presented on a fast charging system of electrical outlets with a proposed two-way reactive power compensation control strategy, in which AC/DC converters are used to exchange two-way reactive power to maintain the DC link voltage as well as the network bus voltage in the range of the basis. This charging strategy is carried out through the simulation of fast charge control, DC link voltage control, and reactive power compensation control to adjust the voltage and modify the power factor in the MATLAB software environment and is then verified. Finally, the results indicate that the proposed method can charge with high safety without increasing the battery’s maximum voltage. It can also significantly reduce the charging time compared to the common CV mode. Full article

Photovoltaic charging stations (PVCSs) are one of the most important pieces of charging equipment for electric vehicles (EVs). Recently, the process of designing solar charging stations as flexible sources has been growing and developing. This paper presents a relatively complete design of a solar charging station as a flexible economic resource in a 10-year planning horizon based on a genetic algorithm in two scenarios. PVCSs are not considered in the first scenario. This scenario is only to confirm the results, and the proposed method is proposed. However, in the second scenario, the effects of PVCSs and the demand response strategy (DR) on this development are considered. Copula probability distribution functions are used to create appropriate scenarios for vehicles during different planning years. The proposed energy management system shows a stable performance in terms of the annual load growth index and electricity price of each level of demand over the time horizon along with minimizing power losses and costs required, which makes PVCS efficiency higher and gives them a suitable structure and stability. The modeling results in terms of uncertainties in the system indicate that the use of load management along with PVCS design and flexible electric vehicle charge control strategies improves power quality parameters and optimizes system cost over a period of 10 years. Compared to the obtained results with the traditional case, it is observed that long-term planning in terms of DR and PVCSs and the technical specifications of the network have been improved. As a result of this proposed long-term planning, PVCSs are more flexible. Full article

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method. Full article