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The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers’ discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

At present, the use of smart systems to reduce energy consumption has been developing more and more, and the need to use these systems plays a significant role in energy systems. Smart systems can play a significant role when there are many energy production sources and there are various loads in the system. In this research, the smart system of the sports stadium is evaluated considering the renewable energy resources, and the electric vehicles are also one of the demanded loads. The studied system uses a solar cell to supply the electric load, which is used to model the equipment using GAMS software. Different scenarios have been evaluated considering the uncertainty in solar radiation intensity. Furthermore, the conditional value at risk measure is considered to manage and reduce the risk of smart sports stadium energy supply cost. Finally, the effects of the battery capacity of electric vehicles on the installed solar cell power and the cost of the produced energy have been evaluated. The results indicate that in a scenario based on a system consisting of an electric vehicle under a photovoltaic cell and a battery, the energy cost will be equal to around 111.2 cents, which represents a 34% reduction compared to a different scenario. This can be an important achievement in the design of sports stadiums under smart energy systems. Furthermore, the developed strategy in this article can manage the load shifting and the operation of the electric vehicle and the storage system during the charging and discharging periods. [ABSTRACT FROM AUTHOR]

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The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system. [ABSTRACT FROM AUTHOR]

A radial flux permanent magnet generator can be effectively used in generating wind power at low speeds. This generator uses a combination of permanent magnets and a stator to produce electricity from the wind's kinetic energy. The permanent magnets are arranged in a radial pattern, which reduces the amount of heat lost and therefore increases the generator's efficiency. This design allows for a low rotational speed, which makes it suitable for applications such as wind turbines. The air gap between the rotor and the stator affects the output voltage and power due to the decrease in magnetic flux. This study aims to increase the generator's output voltage and output power by fitting stator teeth widths to the stator teeth. The stator teeth width fitting design method adjusts the air gap between the stator and the rotor to a uniform size. This helps to reduce the pulsating torque and ensure that the output voltage and power are maximized. The three variables are the stator slot width, the rotor slot width, and the air gap width. By adjusting these variables, the stator teeth width fitting design method can adjust the air gap between the stator and rotor. [ABSTRACT FROM AUTHOR]

Recently, many cases of missing children between the ages of 14 and 17 years have been reported. Parents are always worried about the possibility of their children being kidnapped or lost. This work proposes an Androidbased solution to aid parents in tracking their children in real-time. Nowadays, most mobile phones are equipped with internet service capabilities, allowing them to connect with the world. The system's architecture is built on three main components: GPS satellite, IoT technology, and NodeMCU microcontroller board. The most important features of this work are its low cost, lightweight design, and small size. The Blynk app is installed on the mobile phone, and the GPS module is attached to the NodeMCU microcontroller to obtain the child's location. Then, the location information is accessed through the Blynk app with the help of map services. [ABSTRACT FROM AUTHOR]

During peak demand hours, hydroelectric energy is one of the most significant sources of energy. Power sector restructuring has increased competition among the country's electricity providers. Estimating the future price of energy is critical for producers in order to enhance investment profit and make better use of resources. One of the most significant technologies of artificial intelligence, Artificial Neural Networks (ANN), has various applications in estimating and forecasting phenomena. Combining artificial intelligence models with optimization models (e.g. Artificial Bee Colonoy [ABC]) has recently become quite popular for improving the performance of artificial intelligence models. The goal of this study is to look at the effectiveness of ANN and ABC-ANN models in forecasting the dispersed and sinusoidal data of Angola's daily peak power price. The findings reveal that in this case study, the employment of the ABC-ANN model is not superior to the ANN model and has not resulted in enhanced performance and forecasting of power market data. As a result, the R2 of the ANN and ABC-ANN models is 0.88 and 0.85, respectively. [ABSTRACT FROM AUTHOR]