4.6 Article

Optimization-based optimal energy management system for smart home in smart grid

Journal

ENERGY REPORTS
Volume 10, Issue -, Pages 3733-3756

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2023.10.037

Keywords

Demand side management; Smart grid; Microgrid; Smart home; Optimization

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The introduction of advanced technologies has led to a significant increase in automated appliances in the housing sector. Building new administrative structures to meet electrical needs has become crucial for ensuring the safety of residential devices. Demand Side Management (DSM), a key component of micro-grid and Smart Grid technology, is one approach to achieve this. DSM involves carefully controlling requirements while maintaining client trust. Most of the research in DS management focuses on helping households manage their power plans.
The introduction of advanced technologies has led to an unprecedented rise in automated appliances in the housing sector. Building new administrative structures to satisfy electrical needs has grown more crucial to ensure the safety of residential devices. One of the approaches to achieve this is Demand Side Management (DSM), a key component of both micro-grid and Smart Grid technology. DSM can be accomplished by carefully controlling requirements while upholding the trust of clients. Most of the DS Management which has been covered in the research is aimed at helping households manage their power plan. The innovative HBA+DMO technique inherits Honey Badger Optimization (HBA) and Dwarf Mongoose Optimization (DMO) for executing the DSM program. The groundwork for the proposed framework implemented in this investigation is provided by the Critical-Peak-Price (CPP) and Real-Time-Price (RTP) payment processes. Two operational instances (60 min and 12 min) are being taken into consideration to evaluate client requirements and behavior over the suggested strategy. In accordance with the results from simulations, the suggested strategy arranges the devices in the best possible way, leading to fewer energy expenses while maintaining user comfort (UC). Customers sometimes pay a premium as a result of gadget waiting periods in order to gain the most comfort. As equipment is turned on in response to user comfort, the amount of time spent waiting during an unscheduled situation is close to zero. Tools for lowering energy expenditures and consumption for buildings, communities, or enterprises are frequently provided through energy management software. The three main uses of the energy data that EMS collects are reporting, monitoring, and engagement. The computational time of the proposed approach is (similar to 213.42). Future testing involving different conditions and control methods for study into HRES microgrid infrastructure may be done on the medium of the experiment bench.

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