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首页> 外文会议>2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies >Multi-objective kinetic-molecular theory optimization algorithm with application to automatic demand response
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Multi-objective kinetic-molecular theory optimization algorithm with application to automatic demand response

机译：多目标动分子理论优化算法及其在自动需求响应中的应用

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摘要

Intelligent household as an extension of smart grid in the user side highly integrates loads management and control. Home energy management system (HEMS) with automatic demand response (ADR) is a key part of intelligent household, which is able to fit their electricity demand without changing the residents' habits too much. Furthermore HEMS schedule their power consumption to save energy, reduce emission, shift peak load and reduce the financial burden. The characteristics of various electrical devices were analyzed in this paper, and a mathematical model of ADR was established. Multi-objective kinetic-molecular theory optimization algorithm was used to optimize the solution of the ADR model. Implementation results showed that the KMTOA was more accurate and reliable than other algorithms for the complexities of model and data size considered in this study. Compared with some similar algorithms, the multi-objective kinetic-molecular theory optimization algorithm shows more advantages.
机译：智能家庭作为用户端智能电网的扩展，高度集成了负载管理和控制。具有自动需求响应（ADR）的家庭能源管理系统（HEMS）是智能家居的关键部分，它能够满足他们的用电需求，而又不会过多改变居民的生活习惯。此外，HEMS还可以计划其功耗以节省能源，减少排放，转移峰值负荷并减轻财务负担。分析了各种电器的特性，建立了ADR的数学模型。采用多目标动分子理论优化算法对ADR模型求解进行了优化。实施结果表明，对于本研究中考虑的模型和数据大小的复杂性，KMTOA比其他算法更准确和可靠。与同类算法相比，多目标动分子理论优化算法具有更多的优势。

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