1,721,101 research outputs found
V2G Potential in Italy: Status, Challenges, and Future Directions from a Grid and Consumer Perspective
No full textOne of the main contributors to global energy consumption and greenhouse gas emissions is the transportation industry. Starting the change to electric vehicles (EVs), Italy is aiming at complete energy sustainability. Vehicle-to-grid (V2G) technology, which lets EVs provide energy back to the grid, therefore enhancing grid stability and efficiency, marks a significant development in this shift. This study presents the infrastructure, implementation issues, and current status of V2G in Italy. It highlights smart meter deployment, pilot projects, and the expansion of charging infrastructure while addressing important technical and regulatory barriers like the limited availability of bidirectional chargers and uncertain standards and tariffs. Recommendations are provided to original equipment manufacturers (OEMs), grid operators, and legislators, emphasizing investment, standardization, and consumer education to mitigate these difficulties. The study indicates that while V2G offers significant prospects for energy resilience and decarbonization, its successful implementation relies on coordinated efforts across multiple sectors
Using Controlled Thermostatic Loads in Buildings as Auxiliary Services to the Power Grid: An Investigation With Thoroughly Simulated Case Study
Full text linkThe massive integration of renewable energy sources poses increased power generation fluctuations, necessitating enhanced regulation capacities to maintain a power balance in supply and demand. Beyond conventional generators, advanced information and communication technologies offer an alternative approach, utilizing flexible loads, mainly thermostatically controlled loads (TCLs) of buildings for grid regulation services. TCLs garner significant attention due to their unique thermal inertia capability in demand response (DR) programs. However, to manage the distributed TCLs effectively, it is vital to study the performance of the aggregator, which acts as a bridge between the grid operator and end-users and thus assumes a pivotal role in the management of TCLs. This paper extensively reviews and compares fundamental technologies inherent to TCLs within the context of DR applications, covering basic models, response modes, control techniques, dispatch models, and strategies. Further, the review explores these critical aspects and addresses current challenges and potential prospects. Moreover, as an illustration, the study has developed and analyzed a comprehensive model of the automatic generation control (AGC) system to integrate TCLs for power balancing services in large-scale wind energy-integrated power systems. A power dispatch strategy has been designed for the AGC model to integrate the loading capacities of TCLs in providing grid ancillary services. The designed dispatch strategy prioritizes utilizing TCL capacity over conventional generating units, reducing the overall operation cost, and decreasing the grid’s dependency on conventional power plans. DigSilent PowerFactory software was used to obtain the simulation results, demonstrating the significant efficacy of aggregated TCL response in actively contributing to power support during grid balancing services
Leveraging Ancillary Services from Building Thermostatic Loads in Power Grids
No full textThe integration of large-scale renewable energy sources and the incorporation of state-of-the-art technologies in contemporary power systems require advanced control techniques to maintain the stability of the power grids. One solution could be building thermostatic loads to provide ancillary services like operating reserves. The possibility of using buildings' thermostatic loads to contribute to grid services is facilitated by automatic generation control (AGC), which is thoroughly discussed in this paper. The building thermostatic load (BTL) can be flexibly controlled to provide the grid signal with an automatic short-term reserve capacity. To utilize the reserve provisioning capability of BTLs, a comprehensive power grid model with an AGC system is developed using the DIgSilent power factory software. The model integrates different power plants, including wind energy generation stations, thermal power stations, gas-fired power stations, and BTLs. A dynamic power allocation strategy is developed for the proposed power model and implemented using 12-hour simulations. The analysis results revealed that integration of BTLs into the power grid assists in balanced demand and supply of power systems and also enhances the efficiencies and reliability of the power system network
Securing Autonomous Vehicles Against GPS Spoofing Attacks: A Deep Learning Approach
Full text linkWith the rapid advancement of technology and multimedia systems, ensuring security has become a critical concern. Connected and Autonomous Vehicles (CAVs) are vulnerable to various hacking techniques, including jamming and spoofing. Global Positioning System (GPS) location spoofing poses a significant threat to CAVs, compromising their security and potentially endangering pedestrians and drivers. To address this issue, this research proposes a novel methodology that uses deep learning (DL) algorithms, such as Convolutional Neural Networks (CNN), and machine learning (ML) algorithms, such as Support Vector Machine (SVM), to protect CAVs from GPS location spoofing attacks. The proposed solution is validated using real-time simulations in the CARLA simulator, and extensive analysis of different learning algorithms is conducted to identify the most suitable approach across three distinct trajectories. Training and testing data include GPS coordinates, spoofed coordinates, and localization algorithm values. The proposed machine learning algorithm achieved 99% and 96% accuracy for the best and worst case scenarios, respectively. In case of deep learning, it achieved as high as 99% for best and 82% for the worst case scenario
Applications of Artificial Intelligence and Machine learning in smart cities
No full textSmart cities are aimed to efficiently manage growing urbanization, energy consumption, maintain a green environment, improve the economic and living standards of their citizens, and raise the people's capabilities to efficiently use and adopt the modern information and communication technology (ICT). In the smart cities concept, ICT is playing a vital role in policy design, decision, implementation, and ultimate productive services. The primary objective of this review is to explore the role of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) in the evolution of smart cities. The preceding techniques are efficiently used to design optimal policy regarding various smart city-oriented complex problems. In this survey, we present in-depth details of the applications of the prior techniques in intelligent transportation systems (ITSs), cyber-security, energy-efficient utilization of smart grids (SGs), effective use of unmanned aerial vehicles (UAVs) to assure the best services of 5G and beyond 5G (B5G) communications, and smart health care system in a smart city. Finally, we present various research challenges and future research directions where the aforementioned techniques can play an outstanding role to realize the concept of a smart city
Deep learning-based meta-learner strategy for electricity theft detection
Full text linkElectricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes a deep learning-based meta-learner model to distinguish between normal and malicious patterns in EC data. The proposed model consists of two stages. In Fold-0, the ML classifiers extract diverse knowledge and learns based on EC data. In Fold-1, a multilayer perceptron is used as a meta-learner, which takes the prediction results of Fold-0 classifiers as input, automatically learns non-linear relationships among them, and extracts hidden complicated features to classify normal and malicious behaviors. Therefore, the proposed model controls the overfitting problem and achieves high accuracy. Moreover, extensive experiments are conducted to compare its performance with boosting, bagging, standalone conventional ML classifiers, and baseline models published in top-tier outlets. The proposed model is evaluated using a real EC dataset, which is provided by the Energy Informatics Group in Pakistan. The model achieves 0.910 ROC-AUC and 0.988 PR-AUC values on the test dataset, which are higher than those of the compared models
Optimal utilization of frequency ancillary services in modern power systems
Full text linkThe widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants
RUHEED-Rotated Unequal Clustering Algorithm for Wireless Sensor Networks
No full textProlonging the network lifetime, scalability and balancing are very important requirements when implementing a wireless sensor network (WSN). Clustering is a technique that has been widely applied for achieving these goals. However, there exists the energy hole problem which causes an unbalanced energy consumption in equally formed clusters. More specifically, nodes near the base station (BS) die very quickly since they, not only transmit their own data, but also forwards the rest of the network data. In this article, we propose a rotated unequal clustering protocol RUHEED in order to mitigate the energy hole problem. Our experiments show that RUHEED improves the network lifetime when compared to other clustering protocols
A Lightweight Controller for Autonomous Following of a Target Platform for Drones
No full textDrones or Unmanned Aerial Vehicles (UAVs) are providing interminable opportunities to capture high-quality video feeds that were previously impossible and have transformed the digital era. Many applications today require computer vision (CV) and machine learning (ML) techniques to extract the useful information captured from the onboard camera, and send it to an embedded controller that can make independent decisions. For instance, maneuvering the drone to follow a target platform by using only the onboard camera feed is critical in target tracking. Therefore, in this paper, we exploit the applicability of a low-computational embedded tracking controller to follow a target platform e.g. a car or pedestrian, and thus, react in real-time, adjusting the drone steering angles and velocity. We developed a system that enables drones to follow a target platform autonomously without requiring continuous human intervention on an embedded state-of-the-art STM32 Nucleo board. The system includes a lightweight controller that controls the drone's movement and enables it to track and follow a target platform accurately. To validate the performance of our embedded controller, we performed a number of experiments in an open-source AirSim simulator. The tracking controller footprint and memory consumption was less than 3%, and was able to reliably track and trail the target platform in different environmental conditions
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