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Social Spider Optimization Algorithm-Based Optimized Power Management Schemes

机译：基于社会蜘蛛优化算法的优化电源管理方案

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摘要

Energy management (EM) is the essential factors of a smart grid, among residential consumers. These also include the demand side response and cost analysis. The aim of this paper is to be ecological benefit analysis for better consumption and load management. By shifting the load demand, the housing electricity cost reduced by the house energy management system (HEMS)-based cost analysis technique. The optimization is developed to minimize the cost, reduce greenhouse gases emissions and curtail dump energy. The implementation results are obtained by MATLAB R2016a working platform and the results are compared with different kind of algorithms such as ant lion optimization (ALO), Shuffled frog leaping algorithm (SFLA), flower pollination algorithm (FPA), Bees algorithm (BA), crow search optimization (CSO) algorithm. The reduction of electricity cost in a smart home is the main aim of the objective constrained problem and provides the solution to the proposed system. In addition, it uses to discover the operation modes of different loads with production systems. The simulated results obtained with the lowest price in the proposed system, which is about 10, 716.12Rs. Thus, the efficiency of the proposed system makes 76% better than the existing methods.
机译：能源管理（EM）是智能电网的基本因素，住宅消费者。这些还包括需求副作用和成本分析。本文的目的是为更好的消费和负荷管理是生态效益分析。通过移位负载需求，房屋能量管理系统（HEMS）的成本分析技术降低了住房电力成本。开发了优化以最大限度地减少成本，减少温室气体排放和缩减能量。实施结果是通过MATLAB R2016A工作平台获得的，结果与不同种类的算法进行比较，如蚂蚁优化（ALO），随机蛙跨赛算法（SFLA），花授粉算法（FPA），蜜蜂算法（BA），乌鸦搜索优化（CSO）算法。智能家居的电力成本降低是客观受限问题的主要目的，并为提出的系统提供解决方案。此外，它用来发现使用生产系统的不同负载的操作模式。拟议系统中最低价格获得的模拟结果，约为10,716.12rs。因此，所提出的系统的效率优于现有方法76％。

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