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The operation of the electrical systems is a major problem for electrical companies’ subject to uncertainties threatening. In this study, the optimal management of the energy demand in the electrical distribution grid is done by interval optimization approach under electrical price uncertainty. The management of the energy demand is implemented via incentive-based modelling of the demand response programs (DRPs). The incentive-based modelling as reserve, and based on bid price for reduction of the electrical demand at peak hours is proposed. The interval optimization approach is used for the minimization of the electrical price uncertainty effects. The main objective in the proposed approach is minimizing operation cost; epsilon-constraint method is utilized to solve the problem. Finally, an electrical distribution grid has been used at various case studies to numerical simulation results and positive effects of the proposed modelling under uncertainties.

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]

Renewable energy sources, such as wind and solar, are becoming increasingly popular due to their environmentally friendly and sustainable nature. However, one major challenge associated with these systems is their intermittent nature, which makes it difficult to rely on them as a consistent source of energy. To address this challenge, researchers have developed a combined system that incorporates wind and solar resources with a battery as a storage device, which can provide a sample load pattern independent of the grid. The primary objective of this system is to determine the optimal economic combination of these resources, which can ensure a reliable and consistent supply of electricity. To achieve this objective, the Cuckoo Optimization Algorithm (COA), a metaheuristic optimization algorithm, has been used to optimize the system. The objective function has been implemented in accordance with the constraints, and the results provide insight into the optimal combination of resources. This paper provides a comprehensive analysis of the design and optimization of a wind-solar hybrid energy system with battery storage, using the COA, as well as the results of this analysis. The outcomes indicated that the optimal hybrid system model may be able to reduce system costs by 10–25%. This research's findings can be used to inform the design of sustainable and dependable renewable energy systems. [ABSTRACT FROM AUTHOR]

In this study, the combination of Gray Wolf Optimization and Artificial neural networks (GWO-ANN) algorithm was applied to predict the long-term electricity demand in Iraq, considering the nonlinear trend and uncertainties in the variables affecting it. The results indicate that the population and gross domestic product are significant explanatory variables for long-term energy demand, consistent with previous studies. Compared to other intelligent methods, the GWO-ANN algorithm requires less data for modeling and optimally designs the ANN structure. The modeling and forecasting model outperform the ANN in simulating and predicting the long-term energy demand. Based on the most likely scenario, the predicted electricity demand in Iraq will reach approximately 415 GWh. Electricity is a critical factor in the development of societies and is utilized in various economic sectors. [ABSTRACT FROM AUTHOR]