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• An algorithm for optimal energy management in smart home devices is developed. • This preserves customer satisfaction and is more effective than earlier methods. • It also lowers energy costs, increases the peak average ratio, and user comfort. • It is also compared with other intelligent tools for energy optimisation. • The proposed method results in 80 % savings in Iraqi dinar (ID). Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396 % per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437 % per day. From the obtained results the proposed algorithm gives superior performance. [ABSTRACT FROM AUTHOR]

Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings. • A energy transition AI model for sustainable electricity is designed. • The model is to schedule microgrid appliances and battery charging/discharging. • A new optimization technique is also introduced to reduce the cost of supplying the load. • Different AI techniques are also compared to find the more efficient one. • An optimized real time middle range grid connected energy systems is obtained. [ABSTRACT FROM AUTHOR]

In this work, novel channel plasmon polariton (CPPs) based on symmetric double trenched structures are proposed and analyzed numerically. The dispersion characteristics over a special spectral range (0.7–1.7 μm) are studied in detail. The selected band of wavelength is chosen to ensure the existence of the CPP fundamental mode. The real effective index and losses (LdB) are used to evaluate the performance of the proposed structure. Also, a comparative study of the metal type (including gold and silver) is also performed. The results show that the gold–based CPPs structures are overcome the silver-based structures in terms of losses. A 24.71 dB/um at 0.925 μm wavelength is obtained which provides a good candidate for a small size photonic logic gate allowing to increase the integration density in photonic integrated logic gates. [ABSTRACT FROM AUTHOR]