小工具
智能电网
计算机科学
家庭自动化
需求响应
付款
能源管理
网格
钥匙(锁)
设施管理
平面图(考古学)
能源管理系统
风险分析(工程)
电
业务
计算机安全
能量(信号处理)
电信
工程类
营销
统计
几何学
数学
考古
算法
万维网
电气工程
历史
作者
S. Balavignesh,Kumar Chandrasekaran,Soichiro Ueda,Tomonobu Senjyu
出处
期刊:Energy Reports
[Elsevier]
日期:2023-10-18
卷期号:10: 3733-3756
被引量:11
标识
DOI:10.1016/j.egyr.2023.10.037
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 (∼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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