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Hybridizing renewable energy sources with electric vehicles is more important to maintain a clean and emission-less environment. Electric vehicles (EVs) are incorporated into the curtailed incentive-based demand response program (CIBDR) and microgrid system to minimize thermal and transaction costs. In this study, a novel choice-based decision-making mechanism (CBDMM) is proposed. In CBDMM, charging and discharging of EVs power are generated based on the availability of renewable, pricing and state of charge. The objective of the proposed work is to minimize the grid burden by minimizing the thermal, transaction cost and maximizing the customer benefits. To test the effectiveness of the proposed work, three case studies (three, six, and ten units) are considered. A modified whale optimization algorithm (MWOA) is proposed to satisfy the power balance constraints. When incorporating electric vehicles into the system, thermal and transaction cost is reduced by 2.108% and 13.5%, respectively. At the same customer, incentives are increased to 2.49%, and objectives are satisfied. Scheduling interval is used over a 24-h period. Ethereum and ganache used to write the smart contract and validate the transaction, respectively. Metamask is used to verify and view the status of complete transaction. The energy transaction between EVs and the grid is achieved using blockchain technology by hashing mechanism. [ABSTRACT FROM AUTHOR]

Identifying a person primarily relies on their facial features, which even distinguish identical twins. As a result, facial recognition and identification become crucial for distinguishing individuals. Biometric authentication technology, specifically facial recognition systems, are utilized to verify one’s identity. This technology has gained popularity in modern applications, such as phone unlock systems, criminal identification systems, and home security systems. Due to its reliance on a facial image rather than external factors like a card or key, this method is considered more secure. The process of recognizing a person involves two primary steps: face detection and face identification. This article delves into the concept of developing a face recognition system utilizing Python’s OpenCV library through deep learning. Due to its exceptional accuracy, deep learning is an ideal method for facial recognition. The proposed approach involves utilizing the Haar cascade techniques for face detection, followed by the following steps for face identification. To begin with, facial features are extracted through a combination of CNN methods and the linear binary pattern histogram (LBPH) algorithm. For attendance to be marked as “present,” the check-in and check-out times of the detected face must be legitimate. If not, the face will be displayed as “unknown.”

The Phasor Measurement Unit (PMU) is a modern metering device installed to monitor and control the power system network. However, the number of PMUs and their placements ought to be optimized to satisfy the techno-economic demand. In this paper, the PMU placement problem has been expressed as a multi-objective problem. Modified whale optimization algorithm (MWOA) is proposed to solve Optimal Placement of PMUs (OPP) problem formulation aiming at minimizing the required number of PMU installation subjected to complete network observability and maximizing the reliability for better operation of power systems. Here, the simultaneous optimization of the two conflicting objectives is performed using a fuzzy-based modified whale optimization algorithm (FMWOA). Besides, single objective optimization problems with and without considering the zero-injection bus constraint and multi-objective optimization problem with zero injection bus constraint have been investigated. The proposed method is validated on small and larger test systems, namely IEEE 14, 30, 57, 118 bus systems, and the polish 2383-bus system. Also, the proposed algorithm results are compared with those of previously published for better clarity. [ABSTRACT FROM AUTHOR]

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature. [ABSTRACT FROM AUTHOR]