Abstract 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.

中文翻译：

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基于社交蜘蛛优化算法的优化电源管理方案

摘要 能源管理 (EM) 是住宅消费者中智能电网的基本要素。这些还包括需求侧响应和成本分析。本文的目的是进行生态效益分析，以更好地进行消费和负荷管理。通过转移负载需求，基于房屋能源管理系统（HEMS）的成本分析技术降低了房屋电力成本。该优化旨在最大限度地降低成本、减少温室气体排放并减少垃圾倾倒能源。实现结果是在MATLAB R2016a工作平台上得到的，并与蚁狮优化（ALO）、混杂蛙跳算法（SFLA）、花授粉算法（FPA）、蜜蜂算法（BA）等不同类型的算法进行了比较，乌鸦搜索优化（CSO）算法。智能家居中电力成本的降低是客观约束问题的主要目标，并为所提出的系统提供了解决方案。此外，它还用于发现生产系统不同负载的运行模式。在建议系统中以最低价格获得的模拟结果约为 10, 716.12Rs。因此，所提出系统的效率比现有方法高 76%。