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Synchronized Optimization of Smart Home Appliance Scheduling and Cost-Effective Energy Management Using Harris-Hawks Algorithm in Smart Grids

A. Sundar RajE.G.S. Pillay Engineering College,Biomedical Engineering,Nagapattinam,Tamil Nadu,IndiaSaloni BansalGLA University,Computer Engineering and Applications,Mathura,IndiaHassan M. Al-JawahrThe Islamic University,Najaf,IraqT. SheebaR.M.K. Engineering College,Electronics and Communication Engineering,Kavaraipettai,IndiaM. SasikumarVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Computer Science and Engineering,Chennai,Tamil Nadu,IndiaJ.B. SapaevNational Research University,Tashkent Institute of Irrigation and Agricultural Mechanization Engineers,Tashkent,AzerbaijanBalappagari. Indu Priya
2023en
ABI

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This work utilizes the Harris Hawks Optimization Algorithm (HHOA) to optimize Smart Home Appliance scheduling and construct an independent smart grid. The aim is to showcase that the HHOA is used to create smart grids that are both cost-effective and dependable. A smart grid has been constructed explicitly for the Ankara Gazi University Technology Park structure. The structure's energy use and weather data have been observed and documented over a year. The suggested smart grid configuration incorporates Photovoltaics (PV), Wind Turbines (WT), Battery Energy Storing Systems (BESS), and Diesel Generators (DG), according to the available data. Determining these components' optimal capacity is essential to accomplish the desired goals of cost-effectiveness and dependability while maintaining full autonomous capabilities. Random evaluations without scheduling optimization lead to oversizing, increasing investment, functioning, and repair expenses. Inadequate capacity scheduling resulting from hasty judgments leads to difficulties in ensuring an uninterrupted energy supply. Hence, optimizing the schedule is crucial for the design of a smart grid to be efficient. After resolving the scheduling issue using the HHOA method, the outcomes are used to develop the suggested smart grid. The MATLAB modeling of the planned smart grid has shown a good performance, resulting in savings varying from 1.28% to 19.53% in the Total Net Present Cost (TNPC) value compared to alternative methods.

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