• Energy Research
• 2016-2025close
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Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.

Recently, the use of electrical vehicles has abruptly increased due to environmental crises. The high energy density of lithium-ion batteries is their main advantage for use in electric vehicles (EVs). However, the thermal management of Li-ion batteries is a challenge due to the poor heat resistance of Lithium ions. The performance and lifetime of lithium ion batteries are strongly affected by the internal operating temperature. Thermal characterization of battery cells is very important to ensure the consistent operation of a Li-ion battery for its application. In the present study, the OHP (Oscillating Heat Pipe) system is proposed as a battery cooling module, and experimental verification was carried out. OHP is characterized by a long evaporator section, an extremely short condenser section, and almost no adiabatic section. Experimental investigations were conducted using various parameters such as the filling ratio, orientation, coolant temperature, and heat flux. Average temperature of the heater’s surface was maintained at 56.4 °C using 14 W with 25 °C coolant water. The experimental results show that the present cooling technology basically meets the design goal of consistent operation.

This paper presents a comprehensive review of current trends in battery energy storage systems, focusing on electrochemical storage technologies for Smart Grid applications. Some of the batteries that are in focus for improvement include Lithium-ion, metal-air, Sodium-based batteries and flow batteries. A descriptive review of these batteries and their sub-types are explained along with their suitable applications. An overview of different types and classification of storage systems has been presented in this paper. It also presents an extensive review on different electrochemical batteries, such as lead-acid battery, lithium-based, nickel-based batteries and sodium-based and flow batteries for the purpose of using in electric vehicles in future trends. This paper is going to explore each of the available storage techniques out there based on various characteristics including cost, impact, maintenance, advantages, disadvantages, and protection and potentially make a recommendation regarding an optimal storage technique.

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

The future smart grids (SGs) consist of considerable amount of renewable energy sources (RESs), electrical vehicles (EVs), and energy storage systems (ESSs). The uncertainties associated with EVs and uncontrollable nature of RESs have magnified voltage stability challenges and the importance of an effective energy management system (EMS) in SGs as a practical solution. This study presents a multi‐agent transactive energy management system (TEMS) to control demand and supply in the presence of high levels of RESs and EVs, and maximises profit of each participant in addition to satisfying voltage regulation constraints. For this purpose, a real‐time pricing is considered based on Cournot oligopoly competition model for demand and merit order effect for production to compensate RESs’ fluctuations in real time by an indirect control method. Simulations are conducted in the modified IEEE 37‐bus test system with 1141 customers, 670 EVs, two solar plants, four wind turbines, and one ESS. The results show that the proposed multi‐agent TEMS can indirectly control EVs, elastic loads, and ESSs to balance the RESs oscillation, minimise customers cost, and regulate voltage in a real‐time manner.

Generally, new and renewable energy systems generate electricity by installing and operating multiple modules simultaneously. In the Republic of Korea, recent studies and developments have focused on asymmetric wave energy converters (hereafter referred to as rotors) suitable for marine environments off the western coast of Jeju. These rotors are arranged on a large floating truss-structure platform and designed to harness electricity from the rotors’ pitch motion. However, when multiple rotors operate on a platform, their behavior diverges from that of a single module due to hydrodynamic interactions between them. Moreover, because the rotors are connected to the floating platform, their motion is influenced by the platform’s dynamics. In this study, a time-domain multibody motion equation was established to analyze changes in the behavioral characteristics of the rotors, both with and without a floating platform. The hydrostatic and hydrodynamic coefficients were derived in the frequency domain using WAMIT, a commercial code based on linear potential flow theory for three-dimensional diffraction/radiation analyses. The motion equation was then applied under regular and irregular wave conditions using OrcaFlex version 11.3, a marine systems design and analysis program. The resulting behaviors were compared to elucidate the influence of the platform and hydrodynamic interactions on the rotors’ performance.

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.