The electric power system is undergoing considerable changes in operation, maintenance, and planning as a result of the integration of Renewable Energy Resources (RERs). The transition to a smart grid (SG), which employs advanced automation and control techniques, brings with it new difficulties and possibilities. This paper provides an overview of next-generation smart grids by presenting the most current and cutting-edge developments in the SG sector. This paper discusses the benefits, drawbacks, and prospects of smart grids. The difficulties of integrating RERs into the grid, as well as alternative energy storage solutions, are discussed. The unpredictable nature of resources has an impact on RER output. The energy storage system is critical in dealing with RERs’ unpredictable nature and ensuring a smooth and reliable supply to load demand. Smart energy systems provide a number of problems and possibilities in terms of developing, integrating, and implementing electrical grids that incorporate network and communication technologies, as well as important privacy and security concerns for various components within the grid. This paper also shows the influence of SGs on distributed energy generation, as well as a comparative analysis on electric cars (EVs), including classification, i.e., battery, and hybrid electric vehicles, as well as current difficulties and challenges in EV technology. A discussion of SG protection concerns and their resolution is also included.
Advanced Metering Infrastructure (AMI) plays a crucial role in enabling the efficient functioning of Smart Electrical Grids, but its successful implementation hinges on robust cybersecurity measures. To uphold data confidentiality and integrity, the deployment of an effective key management scheme (KMS) for multiple Smart Meters (SMs) and devices is imperative. The AMI exhibits unique characteristics, including storage and computation constraints in SMs, hybrid message transmission techniques, and varying participation levels in Demand Response (DR) projects, necessitating a tailored approach to security compared to other systems. In this research, we propose a KMS that is designed to address the specific security concerns of the AMI. The scheme comprises three key management procedures catering to the unicast, broadcast, and multicast modes of hybrid transmission. Given the resource limitations of SMs, we adopted simple cryptographic techniques for key creation and refreshing policies, ensuring efficiency without compromising on security. Furthermore, considering the variability of participants in DR projects, we established key refreshing policies that adapted to changing involvement. The effectiveness and security of the proposed KMS were rigorously evaluated, demonstrating its practical applicability and ability to safeguard the AMI ecosystem. The results of the evaluation indicate that our approach provides a viable and robust solution to the security challenges faced by AMI systems. By employing the proposed KMS, stakeholders can confidently deploy and manage AMI, ensuring the protection of sensitive data and maintaining the integrity of the Smart Electrical Grid.
This paper is about enhancing the smart grid by proposing a new hybrid feature-selection method called feature selection-based ranking (FSBR). In general, feature selection is to exclude non-promising features out from the collected data at Fog. This could be achieved using filter methods, wrapper methods, or a hybrid. Our proposed method consists of two phases: filter and wrapper phases. In the filter phase, the whole data go through different ranking techniques (i.e., relative weight ranking, effectiveness ranking, and information gain ranking) The results of these ranks are sent to a fuzzy inference engine to generate the final ranks. In the wrapper phase, data is being selected based on the final ranks and passed on three different classifiers (i.e., Naive Bayes, Support Vector Machine, and neural network) to select the best set of the features based on the performance of the classifiers. This process can enhance the smart grid by reducing the amount of data being sent to the cloud, decreasing computation time, and decreasing data complexity. Thus, the FSBR methodology enables the user load forecasting (ULF) to take a fast decision, the fast reaction in short-term load forecasting, and to provide a high prediction accuracy. The authors explain the suggested approach via numerical examples. Two datasets are used in the applied experiments. The first dataset reported that the proposed method was compared with six other methods, and the proposed method was represented the best accuracy of 91%. The second data set, the generalization data set, reported 90% accuracy of the proposed method compared to fourteen different methods.
Traffic jam is becoming a headache in the big cities all over the world, which causes a significant delay for drivers and passengers. A smart on/off traffic signal optimization based on a motion IR Sensor (Infrared) is now a necessity to overcome this problem. This work is a design and implementation of a smart traffic signal (STS) that controls the time of the traffic signals (Red–Yellow–Green) according to the traffic congestion on the road. The STS is designed to imitate a side road (with a low traffic move) with a highway road (with a high traffic move). A motion IR sensor along with an Arduino PIC were installed to automatically control the traffic signals on/off delay times based on the existence of the vehicles on the side road. When the side road is empty, the highway traffic signal is always green (highway–always–on mode). However, when a vehicle reaches the traffic signal in the side road, the motion IR sensor sends a signal to the Arduino card, so that the highway traffic signal turns red, while the side road traffic signal turns green letting the vehicle to pass the intersection. The system will then automatically set back to the highway–always–on mode. The entire system is designed and simulated using Proteus workbench.
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