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Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution. [ABSTRACT FROM AUTHOR]

Globally, and especially in developing nations, the increasing demand for energy, coupled with transmission and consumption inefficiencies, poses significant challenges. As the proliferation of household appliances and electric vehicles (EVs) rises, dependency on electricity surges, further straining the existing power infrastructure. While renewable energy resources (RERs) such as solar and wind gain traction, the reliance on conventional utilities persists, especially in homes. This reliance, especially on fossil fuels, intensifies environmental concerns due to greenhouse gas emissions. This paper presents the smart household energy management system (SHEMS), designed to optimize domestic energy consumption. Capitalizing on the Internet of Things (IoT), SHEMS offers real-time energy monitoring and management, providing homeowners an adaptable architectural framework to regulate their energy use. Fundamental component of SHEMS is a controller interfacing with all household loads and the main meter, ensuring timely data on power consumption. Notably, during peak times, the system can autonomously adjust appliance settings, balancing energy savings without compromising user convenience. Paired with a user-friendly application, SHEMS provides individuals comprehensive oversight of their consumption patterns, fostering more sustainable energy use in an increasingly electrified world. [ABSTRACT FROM AUTHOR]

The krill herd (KH) approach is proposed in this article for tackling the dynamic economic dispatch (DED) problem with the integration of wind power generation, including constraints such as valve-point loadings and prohibited operating zones (PoZs). The purpose of resolving the DED problem is to determine the optimum feasible combination of power outputs for all allocated generating units within a given period while simultaneously meeting dynamic operational constraints and power demand. Due to the addition of valve points and PoZs to the cost characteristics of the generating unit, the DED issue becomes a highly nonlinear and non-convex optimization issue. When transmission losses are taken into account, the DED problem becomes significantly more complex to solve. KH is a swarm-inspired algorithm based on krill individual herding behavior. KH employs two global and two local tactics, and these work in parallel, making KH a powerful algorithm. The effectiveness of the KH method is investigated and validated through extensive testing on various test systems, including 5-, 6-, 10-, 16-, and 30-unit test systems. Extensive studies are conducted to evaluate the efficacy of the proposed KH algorithm; the present research work compares the convergence properties of the suggested approach with those of the other recently published methods. In addition to reduced generation costs compared to other results published in recent literature, the numerical findings of the KH approach reveal that it has strong convergence qualities. [ABSTRACT FROM AUTHOR]

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network's resilience during critical periods is investigated, as well as improvements in the network's technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem's objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA-SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]