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

被引:17
作者
Balavignesh, S. [1 ]
Kumar, C. [2 ]
Ueda, Soichiro [3 ]
Senjyu, Tomonobu [3 ]
机构
[1] Bannari Amman Inst Technol, Elect & Elect Engn, Sathyamangalam 638401, India
[2] Karpagam Coll Engn, Elect & Elect Engn, Coimbatore 641032, Tamil Nadu, India
[3] Univ Ryukyus, Fac Engn, Nakagami, Okinawa 9030213, Japan
关键词
Demand side management; Smart grid; Microgrid; Smart home; Optimization; STORAGE; TECHNOLOGIES;
D O I
10.1016/j.egyr.2023.10.037
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页码:3733 / 3756
页数:24
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