Solving MKP Applied to IoT in Smart Grid Using Meta-heuristics Algorithms: A Parallel Processing Perspective

Jandre Albertyn, Ling Cheng, A. Abu-Mahfouz
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Abstract

Increasing electricity prices in South Africa and the imminent threat of load shedding due to the overloaded power grid has led to a need for Demand Side Management (DSM) devices like smart grids. For smart grids to perform to their peak, their energy management controller (EMC) systems need to be optimized. Current solutions for DSM and optimization of the Multiple Knapsack Problem (MKP) have been investigated in this paper to discover the current state of common DSM models. Solutions from other NP-Hard problems in the form of the iterative Discrete Flower Pollination Algorithm (iDFPA) as well as possible future scalability options in the form of optimization through parallelization have also been suggested.
利用元启发式算法求解智能电网物联网中应用的MKP:并行处理视角
南非不断上涨的电价以及电网过载导致的迫在眉睫的减载威胁,导致了对智能电网等需求侧管理(DSM)设备的需求。为了使智能电网达到最佳运行状态,需要对其能源管理控制器(EMC)系统进行优化。本文研究了DSM的求解方法和多背包问题(MKP)的优化问题,了解了常见DSM模型的现状。本文还提出了以迭代离散授粉算法(iDFPA)的形式解决其他NP-Hard问题的方法,以及通过并行化优化的可能的未来可扩展性选项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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